application of multihazard drought risk management...

Application of multihazard drought risk management tools

IMPREX has received funding from the European Union Horizon 2020 Research and Innovation

Programme under Grant agreement N° 641811 2

Report - Application of multihazard drought risk

management tools

3

Deliverable n°11.3

Deliverable Application of multihazard drought risk management tools

Related Work Package: 11

Deliverable lead: S. Groot

Author(s): Chapter 1: all

Chapter 2: Joaquín Andreu, Abel Solera, Sara Suárez-Almiñana (UPV)

Chapter 3: Susanne Groot, Carolien Wegman (HKV)

Chapter 4: Femke Schasfoort (Deltares)

Chapter 5: and 6: Femke Schasfoort and Susanne Groot

Contact for queries [email protected]

Grant Agreement Number: n° 641811

Instrument: HORIZON 2020

Start date of the project: 01.10.2015

Duration of the project: 48 months

Website: www.imprex.eu

Abstract This deliverable reports on the application of a multihazard drought risk

management tool in three pilot studies. One of the pilots is located in

Spain and two of the pilots are located in The Netherlands. The methods

used in the two countries differ. A description of the applied methods

and the results are given. The risk profiles and conclusions that can be

drawn from them are described. A comparison of the pilot studies is

made, in which the differences and the implications of the differences

are described.

The drought risk approach has not yet proven its use in management or

operational advice. Based on the results of the three case studies, the

method is promising.

mailto:[email protected]

http://www.powerstep.eu/



Dissemination level of this document

X PU Public

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the European Commission

Services)

CO Confidential, only for members of the consortium (including the European Commission

Services)

Versioning and Contribution History

Version Date Modified by Modification reasons

v.01 16-07-2018 S. Groot and other

authors

Full draft for first review by J Hunink (FW)


authors

Adjusted after review

v.03 14-09-2018 B vd Hurk review


authors

Adjusted after review by B vd Hurk (KNMI)

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Deliverable n°11.3

Table of Contents

Introduction ................................................................................................................................................... 7 1

Aim ........................................................................................................................................................ 7 1.1

Method ................................................................................................................................................. 8 1.2

Pilot areas ............................................................................................................................................ 10 1.3

Júcar basin ................................................................................................................................................... 17 2

Introduction ......................................................................................................................................... 17 2.1

Method ................................................................................................................................................ 18 2.2

Results ................................................................................................................................................ 24 2.3

From concept to user ........................................................................................................................... 38 2.4

Discussion and conclusions .................................................................................................................. 39 2.5

Next steps .......................................................................................................................................... 44 2.6

Berkel .......................................................................................................................................................... 45 3

Introduction ......................................................................................................................................... 45 3.1

Method ............................................................................................................................................... 48 3.2

Results ................................................................................................................................................. 50 3.3

Risk approach ...................................................................................................................................... 54 3.4


From concept to user .......................................................................................................................... 60 3.6

Next steps ........................................................................................................................................... 61 3.7

ARC-NSC ..................................................................................................................................................... 63 4

Introduction ......................................................................................................................................... 63 4.1

Method ............................................................................................................................................... 66 4.2

Results ................................................................................................................................................ 69 4.3

Risk profile ........................................................................................................................................... 79 4.4


From concept to user .......................................................................................................................... 80 4.6

Next steps .......................................................................................................................................... 80 4.7

Comparing the case study results ................................................................................................................. 81 5

Implications ................................................................................................................................................. 85 6

References ........................................................................................................................................................... 87

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Deliverable n°11.3

Introduction 1

Aim 1.1

Already in the present situation, delta regions world-wide encounter problems resulting from fresh water scarcity.

Climate change and socio-economic developments make delta societies even more susceptible to consequences

arising from drought events. The need for fresh water will increase, whereas more and longer periods of extreme

drought are expected. The resulting risks, for present day and for future situations, exhibit a wide range of

uncertainty. Management decisions on water resources are therefore difficult to make. Decision making in water

resources management and especially making appointments for fresh water assignment rules is becoming

increasingly more complex. There is a strong wish to make uncertainty in water shortage and drought related

risks more explicit. The inters

In WP5.3, multihazard drought risk management instruments are developed. These instruments combine

probability of occurrence of droughts with the effect of this drought on different land-uses, amongst which

agriculture. In other words, drought risk is determined as a probabilistic assessment of economic drought

impacts. In WP11.3 these instruments are applied in several case studies. The aim of application of a drought risk

management approach is to facilitate proactive drought risk management. A systematic process to prevent,

mitigate and prepare for drought-induced disaster is suggested by UNISDR (2009) as a more sustainable solution

than reactive emergency management. This will allow analysis of the effects of climate change, prediction of

drought damage under certain conditions and assessment of the effectiveness of management options and

interventions.

By means of the drought risk management instruments, the impact of climate variability on drought related risks

for the agricultural sector is evaluated. The cost-efficiency of measures to better cope with water scarcity is

assessed. The resulting risk profiles serve as a starting point for new management strategies for risk adaptation

and mitigation (explored in WP13).

This deliverable reports on the application of the instruments in case studies. The results so far are described, as

well as the findings in bringing the concept to (future) users. Ongoing work and future steps to improve the

application of the tools are illustrated.

To test different implementations of a drought risk management approach, three case studies are chosen. One is

the Júcar River Basin in Spain, the second is the Berkel catchment area in The Netherlands and the third is the

Amsterdam-Rhine Canal-North Sea Canal system in The Netherlands. Both pilot areas in The Netherlands are

part of the Rhine-Meuse estuary. All three areas are introduced in paragraph 1.3. The methods used for the

drought risk management approach also differ per area and are described in paragraph 1.2. In these case studies,

stakeholders are explicitly involved. Although economic optimization would often lead to other risk management

practices, legal and psycho-sociological aspects are often dominant in the real world's decision making. The input

of stakeholders is required to define what the demands are for the risk method to be adopted for policy and

management decisions.



Example of the need for a risk based approach in The Netherlands

In 2010, the Netherlands initiated a national Delta Programme that was designed to protect the Netherlands against flooding

and to secure freshwater supplies under a changing climate. As temperatures are rising, summers are expected to get hotter

and drier, which could lead to a lack of freshwater for farming, industry and nature. To mitigate these potential impacts, the

Delta Programme presented in 2016 a set of ‘fresh water’ measures and regulations, such as the enlargement of water supply

channels. The plans were supported by a cost-benefit analysis, which used characteristic drought years (return period of 1/10

and 1/100) to assess the benefits of measures (Stratelligence, 2014). Since the benefits of these measures highly depend on the

progression of a drought event over the year, and could be substantially influenced by extreme drought events, there is a

distinctive need to develop a drought risk approach, in which risk is based on multiple drought events with different return

periods (CPB, 2015). This approach could support the ability to make a well-founded decision on the implementation of new

measures in the next phase of the Delta Programme.

Method 1.2

The approach includes a conceptual framework for drought risk analysis. The drought risk-management

instruments (developed in WP5.3) consider:

1. the probabilities of the joint occurrence of low river discharges, salt-water intrusion and limited water

storage in the surface and ground water system,

2. the impact on water shortage and the consequences for the agricultural sector (using damage functions,

often depending on the frequency, duration and season in which shortage occurs),

3. the consequences of water shortage for other relevant functions.

Figure 1 A schematic overview of a drought risk assessment framework.

Different drought risk-management instruments are developed for both regions, the Rhine-Meuse estuary and

the Júcar River Basin. For the three pilot studies, different methods are applied. These methods are shortly

introduced here, and described in more detail in the following chapters per pilot study.

Method developed for Júcar basin in Spain

The tool used in the Júcar basin is a risk management model that allows for the risk assessment for different

decision alternatives. The tool used is SIMRISK (Andreu and Solera, 2006), which is one of the modules of the

Aquatool software (Andreu et al., 1996). Aquatool is the decision support toolbox that is used by the water

resources authorities to analyse the management of the Júcar River Basin.

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Deliverable n°11.3

SIMRISK assesses the risk of reduced water availability in the future and allows evaluating how this risk is

modified when a given set of measures are applied. SIMRISK requires a high number of streamflow series and a

stochastic approach to generate enough synthetic series to carry out the analysis. The current method used for

the risk assessment in the JRB begins with the use of a Multivariate Autoregressive-moving-average (ARMA)

model (Salas et al., 1980), or Autoregressive-moving average with exogenous terms (in this case precipitation)

model: ARMAX, for the generation of synthetic series of stream flows.

Results from various simulations with ARMA/ARMAX model can be inserted in the SIMRISK module to simulate

the management of the system and assess the risk in a certain period. Afterwards, any given set of management

measures may be incorporated and the risk modifications can be assessed. This can lead to advice on water

management to reduce the risk of drought.

It has to be clarified that risk assessment is performed in terms of probabilities of hydrological variables and

deficits, since these types of results are the ones expected by decision-makers and users because they are easy to

understand, and they relate them quite well with any other consequences (including their economic interests),

and the modification of risks due to the implementation of any set of mitigation measures is also easy to perceive.

So far, economic analysis, which can be produced with the drought risk management tools available in JRB, has

not contributed to practical decision making. The main reasons may be that participatory water allocation process

in JRB responds more to water rights, priorities, and equity, rather than to economic objectives, and also that the

economic results have at least one order of magnitude higher of uncertainty than the above-mentioned

indicators, and therefore, are not perceived as robust basis for decision making.

Method developed for Rhine-Meuse estuary in The Netherlands

The proposed framework, which has been developed in cooperation with regional water authorities, The

Directorate General for Public Works and Water Management, the Delta Programme, HKV and other research

partners, translates hydrological time series into the drought risk. Figure 2 illustrates the general steps of the

framework, in which hydrological impacts are converted into physical impacts on drought prone sectors, the

impact of droughts on national welfare and subsequently into risk, which is defined as the probability of having a

certain impact on welfare due to droughts.

Figure 2 Four graphs that are inter-connected conceptually show the drought risk assessment framework

(derived from Deltares et al, 2015).



The observed meteorological and hydrological time series are typically limited to 50-100 years, and thus these

series do not include information on extreme events with return periods of more than 50-100 years. Therefore,

also the use of synthetic time series is considered alike the Jucar case study, as these can be more representative

for the longer return periods than observed series. As such they can assess the impacts of more intense or

extreme events and support the assessment of the robustness of the existing system and potential benefits of

mitigating measures. Time series from RACMO and ARMA model are considered. The quality of these time series

is verified by using measured data of precipitation, evaporation and Rhine discharge as well as results from other

models. This work is still on-going and will be reported in the final deliverable of WP 5.

Input for the drought risk management tool are time series of drought damage per sector. In the case studies,

these are derived from the combination of a hydrological model (covering the surface water, groundwater and its

interactions), a dose-impact relation (relationship between water shortage and a physical effect) and a model

translating the physical effect of water shortage into a probabilistic welfare effect (or in other words the drought

risk). The drought risk is defined as the probability of occurrence of the drought, multiplied by the welfare effect it

causes. By collecting many realisations within a range of probabilities and magnitudes of welfare effect, a drought

risk curve can be derived. From this curve, an average drought risk per year, or for example per 10 years, can be

calculated.

Pilot areas 1.3

Júcar Basin

In the Deliverable 11.1 an exhaustive description of the Júcar River Basin (JRB) and its exploitation system was

made, including the current problems with water scarcity and long drought periods, as well as the management

options at short, medium and long term, depending on the users’ needs and the demands of stakeholders. A short

introduction to this basin and its main characteristics, problems and management strategies are stated below.

Figure 3 Iberian Peninsula and the Júcar River Basin District (JRBD).

The Júcar River Basin is the main exploitation system of the Júcar River Basin District (JRBD), which is located in

the east of the Iberian Peninsula (Figure 3). It has an extension of 22,186 km2 and an average volume of water

resources of around 1,605 hm3/year. The irrigated agriculture accounts for nearly 80% of water demand, while

other sectors (including urban supply) account for 20%. To cope with these demands, the water exploitation

system has several reservoirs, the more important ones are Alarcón (1,118 hm3), Contreras (852 hm

3) and Tous

(378 hm3), as can be seen in the next figure. In addition, based on the position of these reservoirs and the

hydrological characteristics of the area, this basin is divided in five sub-basins (Figure 4).

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Deliverable n°11.3

Figure 4 Júcar River Basin and the sub-basins division.

The upper (inland) part of the JRB is a mountainous area, and then, the middle basin is a relatively flat area that

supports about 100,000 hectares of relatively recently installed irrigated agriculture. The lower basin lies in the

coastal plain, which support 35,000 hectares of traditionally irrigated agricultural areas and around 25,000

hectares of relatively recent irrigated areas. In these areas are permeable materials that allow the infiltration of

the rainfall to the aquifers of La Mancha Oriental (middle part of the basin) and La Plana de Valencia (lower basin),

which at the same time allow the water abstraction for irrigation purposes. In addition, in the coastal area there is

an important wetland called La Albufera de Valencia that has an extension of 21,120 hectares including a vast

extension of rice crops.

Due to the influence of the Mediterranean climate, this basin is characterised by the semi-aridity of the climate

and the high intra-annual and inter-annual hydrological variability (low flows in summer, autumn floods, etc.) that

lead to recurrent multiannual droughts. These hydrological features forced to adaptation by different

management strategies (water storage infrastructures, conjunctive use of surface and ground waters, institutional

and legal developments, etcetera to cope with drought periods. The most considerable drought events over the

last 50 years were the periods 1981-1986, 1992-1995, and 2005/2006-2008, with less extreme droughts in

between. Currently, this system has been in drought state since 2013.

To cope with these extreme situations, a proactive approach was introduced in all Spanish basins, developing a

Special Drought Management Plan (PES in Spanish) approved in 2007 (Estrela and Vargas, 2012), which includes

drought monitoring by means of a compounded drought operative index (CDOI) (Ortega et al, 2015), used to

define drought scenarios. These scenarios are normality, pre-alert, alert and emergency, which are related to

specific measures as water saving practices, reclaimed wastewater direct reuse, conjunctive use (surface and

groundwater) and water rights purchase for environmental protection, among others. In addition, CDOI maps are

updated monthly (Figure 3) and displayed in the web page of the Júcar River Basin Partnership (CHJ in Spanish),

so these maps serve as a first step early warning system to trigger predefined anticipation and mitigation

measures attached to each scenario in order to decrease vulnerability and increase resiliency.

The CHJ is the public-private participatory institution that manages water resources since 1936, and allocates

them among urban, agricultural, hydropower and industrial users, taking into account environmental protection,

and predefined water rights and priorities. Water allocation decisions are taken at different time scales and under

different scenarios. In the long term, water allocation is made in the framework of JRBD Water Plans, designed by

the CHJ since 1998, and updated regularly (last update in 2015). At the seasonal and short term, water allocation



is decided at the Water Allocation Boards of every exploitation system in JRBD taking into account the actual

situation of water storages in reservoirs and aquifers. But, in alert and emergency scenarios, water allocation

decisions are taken in the sessions of the Permanent Drought Committee, taking into account the actual situation

and following the guidelines about mitigation measures included in the PES in order to find an equitable

allocation among users minimizing environmental impacts..

This institution uses a Decision Support System (DSS) to simulate the water resources system performance and

extract the CDOI, based on historical values. It is also possible to assess the risk and the effectiveness of the

selected measures to mitigate the effects of the drought on both the established uses and on the environment by

means of simulations with the DSS. More details about this procedure are described later.

Figure 5 Drought scenario by exploitation system in the Júcar River Basin District. Modified from the Monitoring

of Drought Indicators Report in the area of the CHJ (May, 2018).

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Deliverable n°11.3

Berkel

The Berkel catchment is located in the eastern part of The Netherlands, above mean sea level. Most of the

catchment drains freely. In the downstream part it is possible to let in water from the Twenthe Canal.

The choice for the Berkel catchment as a pilot area is made in consultation with the cooperation of stakeholders in

drought water management ZON (‘Zoetwatervoorziening Oost-Nederland’). This catchment is representative for

free draining catchments in the higher areas with sandy subsoils. Also, due to the different use functions for water

and the range of possible measures to prevent drought, such as measures relating to inlets, the restructuring of

waterways and water retention in the subsurface, makes this a suitable pilot case for application of the drought

risk management tool.

Figure 6 shows the location of the Berkel catchment. Figure 7 depicts the water uses.

Figure 6 Location of the Berkel catchment.



Figure 7 Water consumers in the Berkel catchment.

ARC-NSC

Also the case studie Amsterdam-Rhine Canal and the North Sea Canal (ARC-NSC) illustrates the applicability and

the added value of the risk approach. This completely managed polder system is expected to give specific insight

in the applicability of the risk method, related to the high level of management of the canals and importance of

the context specific management decisions during droughts. The frequency of occurrence of drought and the

welfare effects are depending on management decisions on water distribution. The question is if and how a risk

approach is applicable in such a managed system. Since large parts of The Netherlands are completely managed,

and many more systems also in other countries are managed up to a certain degree, this is a relevant question to

answer.

A specific reason to choose the ARC-NSC region as a pilot, is the other work that is carried out in the region. The

national water authority, Rijkswaterstaat, is improving the management of the canals by means of ‘Smart water

management’. Furthermore, a new sea-sluice is built, which will influence the salt intrusion and thereby the water

demand. There is a demand for knowledge on drought situations, their frequency of occurrence, effects on the

use functions and expected welfare effects of shortages. The canal system is part of the main water system in The

Netherlands, of which management decisions have an effect on other parts of the country as well.

The ARC-NSC is a controlled canal system in the western part of The Netherlands. The canals have a discharge,

water supply and navigation function. Water is let in from the rivers Lek and, during droughts, the river Waal. A

main use for water inlet in the canals is reducing salt water intrusion at the sea-sluice in IJmuiden. Besides, the

water from the canals is used for different functions. The operational water management in the area is dedicated

to maintaining surface water levels in and around the polder areas, and to prevent salinization of the surface

water. One source of salinization is the sea-sluice, others are more diffuse sources as saline seepage. A draft of

the system and use-functions is shown in Figure 8. All the water authorities surrounding the canals have a

dependency on water supply from the canals, albeit relatively small in the northernmost water board.

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Deliverable n°11.3

Figure 8 Location of the ARK-NZK and water demands.

There are several water use functions:

- Agriculture (flushing for water quality and irrigation)

- Navigation

- Drinking water

- Nature (flushing to reduce salinity)

- Safety (stability of dikes)

- Recreation, fishing

- Infrastructure (effects of soil subsidence in case of low groundwater levels)

- Industry.

All these functions experience potential negative effects of droughts, or water shortages.

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Deliverable n°11.3

Júcar basin 2

Introduction 2.1

As it was said above, in the Júcar River Basin the irrigated agriculture accounts for about 80% of water demand,

but agriculture is a second rank priority use in the operation rules of this water exploitation system. This means

that it is the most affected sector in periods of water scarcity, as it has happened in many past drought events.

Consequences were, for example, restrictions in water supply, reduced yields, loss of annual crops, and persistent

damage to permanent crops during the long drought event of 1992-1995. On the other hand, the impacts were

low-ranked in the 2005/2006-2008 drought event, due to the improvements in drought preparedness and

management in the JRB (Andreu et al, 2013). In this case, there also were water restrictions but they resulted in

minor reductions of yields. Despite this, it was complicated in many areas to meet full irrigation water

requirements and the management turned out to be more expensive.

Despite the improvements and measures taken, reduced yields resulted into huge economic losses. In the last

period, investments in emergency measures accounted for more than 75 million €. Besides, there was a reduction

in rain fed agriculture yields, which means a huge reduction in the GDP of this sector. The biggest reduction was in

2006 (respect to 2004) which was 411 million € in total.

These experiences demonstrate the importance of having a proactive drought planning and management

approach in order to cope with extreme droughts. Here is where the IMPREX project can help with forecasts,

projections and effective tools to improve resiliency and decrease vulnerability in front of droughts in the JRB.

In this work package (WP) devoted to agriculture, several lines of work related to WP5 are being developed. In

WP5, tasks 5.3 and 5.4, the tools are fitted to use them in sectorial work packages, and also in WP13. Thus, an

improvement of the risk assessment methodology is presented in this deliverable. This tool allows knowing the

deficits of water supply in all agricultural demands by the estimation of the risk of drought at short and long term,

and also analyses different scenarios derived from the application of measures in order to avoid damages and

economic loses for a certain period. With this improvement, it is expected that the methodology will allow a

better and quicker response to drought impacts, integrating climate and seasonal forecasts from different

institutions in their decision making. In the case of seasonal forecasts, these will produce better early warning

systems, and in the case of climate change predictions, these will produce better anticipation to water problems

related to droughts in the future.

In order to achieve this improvements in the early warning systems, the use of an Autoregressive-moving average

with exogenous terms (ARMAX) model was proposed. Outcomes from this model (river flows) will be used to run

the management model of the JRB to estimate the probabilistic risk of droughts at short or long term, depending

on the situation. In this case, short term is related to management at seasonal scale to know the potential deficits

of the water allocation system and also the evolution of these deficits when implementing measures. Long term is

related to planning in order to know the overall vulnerability of the system, from 6-12 to 24 years (planning

horizons in JRB). The tool allows the implementation of measures and several changes in the management of the

system and then estimates its response in terms of probabilities. Thus, the effectiveness of proposed measures

can be assessed, and changes in reliability, vulnerability and resilience. All this means that it can predict the

probabilities of having a better or worse scenario depending on the measures applied at each time scale.



The ARMAX results will be compared with results from the current methodology used in the JRB, which is based

on the application of an autoregressive model (AR(1)) based on the historical river flows in natural regime. In the

following section both are explained in more detail.

An alternative method previously mentioned, which will be employed for the long term risk assessment, is the use

of a hydrological model (HBV) that is able to process and extract river flows from provided precipitation and

temperature. Also these streamflows will be integrated in the risk management model and will be compared with

the other methods to know their predictive capabilities.

Also, once the effects of precipitation projections will be known, the idea is to include the change in temperatures

in the analysis in order to have a multihazard approach. The effect of the temperature increase will be noted in the

increment of demands, which may aggravate the lack of water availability. Thus, the decrease of precipitations

and the increase on temperatures will be jointly analysed in a second part of the analysis. These lines of work are

ongoing and will be reported in deliverables 13.4 and/or 11.4, as results are available.

The information of this deliverable is based on the progress so far with the drought risk management tool using

river flows data from the ARMAX model, which is forced with seasonal meteorological forecasts. Then, this tool is

run with river flows from the model currently used in the basin. At the end, probabilistic results from both

procedures are compared in order to assess their drought predictive capacity. In addition, it should be noted that

this tool is suitable for all uses of the system, whether urban, agricultural, hydropower or environmental since

they are related, but agriculture and environment uses are the most affected in extreme situations, so the

assessment of results will be related to them, as it is the objective of this WP.

Method 2.2

The tool proposed is a risk management model that allows the integration of different decision alternatives to

know how the system evolves in front of different situations. It is able to statistically process the management

results from various scenarios and to express them in form of probabilities, related to deficits and reservoir

storage, among others. In order to use it, it is necessary to generate multiple scenarios, or ensemble members, of

river flows from some hydrological or stochastic model, which needs to be properly calibrated in order to

represent the characteristics of the basin in an appropriate way. At the same time, these models need local

historical data of river flows, temperature and precipitation in order to be calibrated and validated. Within

IMPREX there are some providers of seasonal forecasts that are very helpful in order to increase or improve the

predictive capacity of the tools used in this basin, the main purpose of this analysis. The source of predictability is

partly coming from these climate model outputs. A first step therefore is to comparing these with local data, as

some ability is required to integrate them into the models and minimize uncertainty at the end of the process.

Some of these comparisons were presented in previous deliverables (D4.2, D5.2 and D11.1).

In this section the above process is explained step by step. First of all, the tool is introduced, it is called SIMRISK

(Andreu and Solera, 2006) and it is one of the modules of Aquatool software (Andreu et al., 1996), which has been

already mentioned and depicted in several deliverables (D5.2, D11.1) and in more detail in D13.3. The software

Aquatool is the DSS currently used in order to simulate the management of the JRB. This software is used in most

Spanish basins and also in other countries due to its user-friendly interface for water resources planning and

management, and its design oriented to assess reliability and vulnerability in drought prone areas. In addition, it

has been evolving over time, incrementing the number of modules associated in order to cover the major

problems related to water management, and also in order to facilitate the related work in the same tool. In Figure

9 the different modules of this software are shown, which are related between them and depend on each other in

some way. This assures the robustness of the modelling system and its flexibility to model a wide variety of water

resource systems. The relations are shown by the lines in Figure 9: solid lines mean that modules are inside the

same software or program, while dotted lines indicate that outputs of one module can be used as the inputs of the

other despite not being integrated in the same “window”.

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Deliverable n°11.3

Figure 9 Scheme of AQUATOOL modules where EH is EVALHID (hydrological module), OG is OPTIGES

(optimisation module), SG is SIMGES (water allocation module), SR is SIMRISK (risk management

module), GC is GESCAL (water quality module), CE is CAUDECO (module for environmental flows), and

MW is MASHWIN (stochastic module to generate synthetic series). Reference:

https://aquatool.webs.upv.es

The modules used to perform the work reported in this deliverable are:

- SIMGES: is a general water allocation model for river basin or complex water resources systems

management where there are storage elements both superficial and subterranean, and elements for collection,

transport, use and/or consumption, and artificial recharge of water. In this interface is where all the elements of

the exploitation system are located schematically in their place, providing a graphic overview of the system. The

simulation is done at a monthly scale and can reproduce the water flow through the system at any spatial scale. It

also considers the return flows to the surface system, the infiltration, the relations between river and aquifer, the

evaporation and infiltration loses from reservoirs and the relationship between surface water and groundwater.

The water resources management is simulated using reservoir operation rules which aim to maintain a similar

filling level in pre-defined reservoir zone curves. It is allowed to define environmental flows as well as different

water use priorities. The simulation and management of the surface system are done at the same time using a

network flows optimization algorithm. This algorithm determines the flows through the system trying to satisfy

the multiple objectives of deficit minimization, and maximum adaptation to the reservoir objective volume

curves. Moreover, this optimization is improved with an iterative process for the solution of the network, which

allows the simulation of non-linear processes such as infiltrations, evaporations and surface and groundwater

relationships.

- SIMRISK: a risk assessment module designed for its use in river basin management in the short, medium

and long terms to evaluate risks, mainly related to drought. It runs a simulation of the management of the system

from an initial condition of levels in reservoirs and aquifers, and uses as data multiple future hydrological inflows

scenarios of one or more years. Probability calculations are done for each month of the simulated period from the

results of all the scenarios. These can be used as probable estimation of the final situation of the system at the

end of the present campaign, or after two or more hydrological years. This module was designed to be used in

water resources system models calibrated with SIMGES, so each simulation is made in the same way than in

SIMGES.

- MASHWIN: is used to analyse historical hydrological inflow series and to facilitate the formulation of

stochastic models for generating synthetic series. Its principal utility is as complement of the SIMRISK module,

generating the different series of future scenarios for hydrological inflows.



As stated previously, the process of this analysis is focused on the use of SIMRISK, which anticipates the risk of

having problems with future resources availability and allows evaluating the risk modification if a given set of

measures were applied. This helps to identifying the most appropriate mitigation measures (Haro, 2014). It is

based on the Monte-Carlo analysis approach, which consists of generating multiple, future and equiprobable

natural streamflow scenarios, and simulating the management of the system according to the criteria defined

previously for each scenario. Each simulation will provide different management results, which are statistically

treated in order to obtain the probable estimation or the risk level at which the system will be at the selected

period.

As SIMRISK requires a high number of streamflow series, it is necessary to use some kind of stochastic model

from which is possible to generate as many synthetic series as necessary to carry out the analysis.

The current method used for the risk assessment in the JRB begins with the use of a Multivariate Autoregressive-

moving-average (ARMA) model (Salas et al., 1980). Using MASHWIN, a multivariate ARMA (1,0) monthly model,

also called AR(1), is calibrated using the historical monthly streamflows to generate multiple equiprobable time

series in order to know the risk of droughts in terms of probabilities within SIMRISK.

The basic equation for multivariate ARMA (1,0) models is:

(1)

Where Xt and Xt-1 are the variables, ϕ1 is an autocorrelation matrix, θ0 is an matrix of coefficients that multiplies

the random N(0,1) values vector represented by ε.

With this type of modelling, the generated series will reproduce not only the basic statistics of the historic

streamflow values, but also the cross-correlation between the 5 catchment sites over the basin, and the time

auto-correlations. The preservation of the historical drought characteristics is not explicit in the ARMA

formulation, but it is always checked in order to ensure that the main drought characteristics are reproduced.

On the other hand, the methodology proposed for this deliverable has the same process structure of the current

methodology but replacing the AR(1) model by the new development and the implementation of an

Autoregressive-moving average with exogenous terms (ARMAX) model (Salas et. Al, 1980), which takes into

account the influence of an exogenous time series on the process, as its name suggests. In this case, the external

variable is precipitation. In this way, the results from ARMAX model will be fit into SIMRISK module for risk

management instead of those coming from the AR(1).

The basic equation of the ARMAX model is:

∑ ∑ ∑

(2)

where Yt is the random variable "output" (or the variable to be modeled) at time t, Xt is an exogenous random

variable, whose information is used to explain the variable Yt, t is a white gaussian noise (WGN) and i, j and k

are the parameters of the model. p, q and r are called model orders. The parameter p represents the number of

self-regression terms to be considered for the output series (i.e. their dependence on their past values), q

represents the number of exogenous terms to be entered as information, and r the number of moving average

terms.

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Deliverable n°11.3

In this case, as we work with 5 sub-basins (Figure 4), this approach was extended to a multivariate case,

considering Yt and Xt, vectors of random variables and i, j, k parameter matrices. In this way, the spatial

dependence between sub-basins is considered and preserved in the analysis.

In order to have a correct representation of the basin features, the calibration is the most important process to be

carried out. In addition, it is a complicated procedure that takes a lot of time and many analysis to get the most

appropriate orders of the model. The specifications about ARMAX’s calibration will be explained in the

corresponding deliverable of WP5 at the end of the project, but a short introduction is written below and also in

the results section.

First of all, the characteristics of the historical series have to be known in order to reproduce them with the new

model. In this case, the interest is focused in the biased periodicity of the series, the temporal and spatial

dependence of the flows and the dependence between the variables flow and precipitation. To study these

properties, it is important to perform a statistical analysis.

In order to facilitate the process, a first step is necessary to obtain normalized and standardized series from the

historical series, both in terms of flow rates and rainfall. After that, it is important to test and fit the model

parameters taking into account the fit rates, as the lowest values of the Akaike’s Information Criterion (AIC) and

the Bayesian Information Criteria (BIC) (Salas et. Al., 1980). Once an initial set of best models is selected, the

generation of series and the extraction of its statistical properties is performed in order to compare them with the

historical ones and make the final selection of the best model for this basin.

Once the calibration of the model is ready, it is possible to generate new series of river flows at short or long term

using other series of precipitation (seasonal forecasts, RCMs…) as exogenous variables using the module

MASHWIN. In this way, results from various simulations with ARMAX model can be inserted in the SIMRISK

module to perform the management of the system and assess the risk in a certain period. Afterwards, any given

set of management measures may be incorporated and the risk modifications can be assessed using SIMRISK.

In conclusion, the comparison of the different model chain options could be decisive for the methodological

approach to be used in practice for future planning and management of this basin in case that the option with the

ARMAX model results to be better in terms of drought predictive capacity than the one currently employed.

In Figure 10 the different model chain options related to seasonal forecasts can be seen, where the processes

developed for this deliverable are highlighted in blue. In red is the data that was discarded in this process due to

the low skill in the JRB area, or due to the finding of a better option. The first case is related to the river flows

provided from the E-HYPE model (SMHI), and in the second case, meteorological data from ECMWF are replaced

by bias corrected data provided from SMHI, which seems to be more adequate for this area. In the line of using

the seasonal forecast provided from MetOffice, a preliminary analysis of data was performed and, as a result,

their use was discarded. The main reasons for this were the short lag time provided (up to 3 months) and the fairly

experimental rather than (pre-) operational state of results. This means that these data are too short for the

purpose of this study, which considers all seasons of the year. In addition, it was very hard to download the heavy

files and manage the huge number of variables provided, which resulted in significant time consumption. On the

other hand, the hydrological model HBV within the module EVALHID of Aquatool DSS will be further included in

the process, as already commented in the introduction section. Thus, all these models fed with different data will

be used to generate series for multiple scenarios and the risk management model (SIMRISK) will treat the

multiple management results in a statistical way in order to compare all possible options and choose the method

with the best predictive capacity. In this deliverable, results from the model chain highlighted in blue are

developed in the next section.



Figure 10 Working lines related to seasonal forecasts where elements in red were discarded, elements in grey will

be tested and elements in blue were performed in this analysis.

Indicators for drought risk assessment

UPV has a fluid relationship with the main stakeholder of the basin (CHJ) and all users that this institution

represents. As a consequence, many meetings and workshops happen in order to exchange information and

discuss about how problems have been solved, and, in addition, to how improve water resources management.

Moreover, in D11.1 the results of the survey related to the needs of the users of the JRB were presented, with

many questions related to climate services, forecasts and projections, time scales windows, etc.

As an example of this relationship, some meetings already reported in the progress reports of the project, can be

mentioned:

- Multilateral: 11/11/2015 (Júcar Basin Drought Seminars); 14/03/2016 (EDgE project); 15/06/2016 (JBDS);

07/10/2016 (EDgE project) and 21/03/2017 (Climateurope preparation). 29/01/2018 (Workshop about

update of JRB Drought Plan).

- Bilateral with EMIVASA (water provider of the city of Valencia): 29/10/2015; 04/02/2016; 11/04/2016;

21/10/2016.

- Bilateral with CHJ: 11/01/2016; 20/05/2016; 20/10/2016; 15/03/2017; 30/03/2017; 19/02/2018; 03/05/2018;

20/06/2018; 12/07/2018.

- Bilateral with USUJ (Jucar’s irrigation association): 26/01/2018; 23/03/2018; 26/04/2018; 15/06/2018;

19/07/2018 and 21/09/2018.

Thus, the above mentioned methodology was developed based on the stakeholders needs, the proposed

improvements for risk tools (by stakeholders) and the problems to be faced in this basin. In particular, it is

important to highlight that, besides any results that the risk management tools can provide, risk perception in JRB

in times of drought management is accomplished in two different levels:

- Collective risk perception, as a general idea of the situation of the basin.

- Sectoral and/or individual risk perception.

Sectoral and/or individual risk perception is attained by means of probabilistic results about expected deficits for

the sector/association/individual user in every month of the remaining of the campaign (i.e. time horizon

analyzed, generally, from the moment of the analysis until the end of next September), as it will be shown later.

23

Deliverable n°11.3

Collective perception of risk is gained through probabilistic results about the storage level of the reservoirs during

the time horizon analyzed, and particularly, at the end of the campaign (end of September).

These types of results are the ones expected by decision-makers and users because they are easy to understand,

and they relate them quite well with any other consequences (including their economic interests), and the

modification of risks due to the implementation of any set of mitigation measures is also easy to perceive. So far,

economic analysis, which can be produced with the drought risk management tools available in JRB, has not

contributed to practical decision making. The main reasons may be that participatory water allocation process in

JRB responds more to water rights, priorities, and equity, rather than to economic objectives, and also that the

economic results have at least one order of magnitude higher of uncertainty than the above-mentioned

indicators, and therefore, are not perceived as robust basis for decision making.

Moreover, the Drought Index (Figure 11) of the Drought Special Plan (PES) used for drought monitoring, early

warning, and guidance for mitigation, is largely influenced by the volume of water stored in the reservoirs. Instead

of using a complex model, users can relate the probabilities of the storage level with the Drought Index and each

user can relate these results to the measures stipulated for each level of risk.

Figure 11 Evolution of the JRB compounded drought index the period October 2001 to October 2009 (green colour

is for normal situation, yellow is for pre-alert, orange for alert and red for emergency) (source: self-

elaboration with data provided by CHJ).

For all the above-mentioned reasons, in this deliverable, results related to drought risk assessment and

management will be shown by means of reservoirs storage forecasts, and sector/group/individual deficit

forecasts. Nevertheless, if time allows, economic assessments of risks will be produced in a later phase, and

reported in following deliverables in WP11 and WP13.



Results 2.3

The JRB water exploitation system was developed, calibrated and tested in the SIMGES interface with all

elements of the system, reservoirs, demands, operation and priority rules. The schematic diagram is shown in

Figure 12 (for more information see D13.3).

Figure 12 Júcar River exploitation system in SIMGES.

As explained before, this program needs as input series of river flows in order to simulate the management of the

system. In this case, these inputs will come from the ARMAX model, which is already calibrated. All details from

calibration will be shown at the end of the project in the corresponding deliverable of WP5, but a summary of it is

informed in this deliverable.

ARMAX calibration

In the process of calibration, the general statistics of the streamflow historical series (1971-2006) in natural regime

were obtained (Figure 13) in order to take them into account when assembling the ARMAX model. As exogenous

variable, the series of historical precipitation were selected for the same period of 37 years (1971-2006). Both river

flows and precipitation series need to be treated and changed in order to work better with them, so the

normalisation and the standardisation of the series were carried out.

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Deliverable n°11.3

Figure 13 Statistics of historical series (1971-2007) from the five sub-basins of the JRB: the annual mean of

streamflows (top left), the standard deviation (top right), t he variation coefficient (bottom left) and the

correlation coefficient (bottom right).

Once all inputs of the model were selected and treated, the parameters of the model and the dependency orders

come into play. The dependency orders are 4 and there are stated the self-regression dependence of the flow

variable, the dependence of the external variable precipitation, the moving average order and the time lag

between the external variable (precipitation) and the simulated variable (flows). The possible combinations for

these orders are huge, so it was decided to not use dependency orders bigger than 4. It is known that the higher

the order, the less parsimonious is the model, which is an inconvenience. Thus, all possible combinations of the 4

dependency orders resulted in 180 ARMAX models. In order to choose the best fitted models, the AIC and BIC

indexes from all models were obtained and those with the lower rates were selected (40 models).

Then, streamflow series for 37 years were generated with each selected model and its statistical properties were

extracted. In addition, fit indicators as the RMSE were obtained from the comparison between the mean values of

historical and generated series, in order to choose the best-fitted model. Then, after visual and error analysis, the

best-fitted model was chosen.

Once the ARMAX model is calibrated and ready to use, the next step is to choose the precipitation data that will

act as an exogenous variable. The idea was to use seasonal forecast precipitation from ECMWF and/or MetOffice

institutions for short term, and then use projections from different Regional Climate Models (RCMs) for long term.

These projections can come from the CORDEX dataset or other projects with more adapted and processed data

as it is SWICCA. In addition, other variables related to streamflows may be considered as exogenous, as could be

the temperature.



Data used as exogenous variable

At this stage of the analysis, precipitation seasonal forecasts from System 4 (ECMWF) and its bias corrected

version provided by the SMHI were compared with regional data (Spain02) in order to know which one represents

better the characteristics of the basin. The SMHI corrected the data with the WDEI dataset, which statistics

characteristics are similar to those of Spain02. In the following figure can be seen the case of Alarcón sub-basin as

an example of the better fit from corrected data. However, there is a mismatch in Molinar and Tous sub-basins,

but the tendency is the same, so tests are being conducted to know which is the best and simplest method is to

correct them.

Figure 14 Mean year average for seven month accumulated precipitation of 15 scenarios (E1 -E15) from bias

corrected System 4 seasonal data (ECMWF) compared to Spain02 historical data in the Alarcón sub -

basin.

Thus, precipitation data from bias corrected seasonal forecasts were used to generate series of river flows with

the ARMAX (2,2,0,1) model.

Once series are simulated with the ARMAX model, they are integrated in the management model (Figure 12) and

SIMRISK is run to treat conjointly and statistically the results of the different management scenarios simulated

(Monte-Carlo method). The aggregation of these scenarios provides shortages at consumptive demands, aquifers

extractions, probability distributions for reservoirs storage and the status of drought indicators.

Results from the Risk management tool SIMRISK

In order to illustrate de methodology, the analysis will be performed for two forecast periods of 7 months for two

different years: a normal year (2003/2004) and for a dry year (2005/2006). For both years, the first forecast is done

in October, and corresponds to a 7-month from the start of the hydrological year (October) until the beginning of

the irrigation season (June). The second forecast is done in March, and corresponds to 7-month period which

includes the entire irrigation season.

27

Deliverable n°11.3

Forecasts are produced by means of SIMRISK module, as previously mentioned, using 15 inflow series, one for

each one of the 15 ensembles of precipitation provided in the ECMWF forecasts. Then, the results are treated

statistically in order to obtain expected values, probabilities, etc.

For the purpose of this deliverable, we will focus on two types of results:

- The probabilistic evolution of water storage in reservoirs (collective risk perception)

- The value of the deficit with exceedance probability of 10% of all agricultural demands in the irrigation season

(sectoral/association/individual risk perception).

Results are produced using the inflows obtained by means of the ARMAX model forecast (IMPREX proposed

methodology) and also using the inflows obtained by means of the AR model forecast (methodology currently in

use in JRB, which does not include precipitation forecast information). Then, the results are compared and

discussed.

Probabilistic evolution of water storage in reservoirs

a) using ARMAX model for inflow generation

In Figure 15, the October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous

reservoirs of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1)

model can be seen. For result displaying purposes, the total capacity of each reservoir has been divided into 10

equal intervals, and the probability of being in each interval is displayed. Alarcón and Contreras are located in the

headwaters, and Tous in the middle course of the river. Due to the operation rules, water from Tous reservoir is

discharged in the first months of this period in order to achieve a low storage to prevent damages from the typical

flash floods of this season; in other words, for safety. Then, water from headwater reservoirs is released to store it

in Tous in order to meet the demands of the irrigation season. That is the reason for the increase in probability of

lower storage from January to April in Alarcón and Contreras and the contrary in the case of Tous, which

probability of storing more than 72 hm3 is around 80% in these months.

Similarly, the March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous

reservoirs of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1)

model can be seen in Figure 16.

On the other hand, results corresponding to the October forecast and the March forecast for the dry year

2005/2006 (which corresponds with the starting of a 4-years drought period) can be seen in Figure 17 and Figure

18, respectively. The figures show higher probabilities of having lower level of storage in the three reservoirs.

Alarcón is the reservoir with higher storage capacity and the probability of being in the first (lower) zone is about

100% during all the period. In Contreras, the season starts with an 80% of probability of being in the second zone,

while probabilities of being in a higher zone are increasing towards the end of the season until 80%. In the case of

Tous, the probabilities of being in the third zone or higher are high during almost the entire period.



Figure 15 October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs

of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1)

model.

Figure 16 March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs of

the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1) model.

29

Deliverable n°11.3


of the (dry) year 2005/2006, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1) model.


the (dry) year 2005/2006, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1) model.



In order to get a more aggregated figure, and as it is currently done in the management of JRB, we can consider

the sum of the storages in these three reservoirs, providing an indicator of the evolution of the total storage of the

system. This results in a unique figure that represents the entire system in an easy and understandable way to

facilitate the decision making. Figure 19 shows the October 7-month forecast for the evolution of this indicator for

the normal and dry year, where differences are quite clear. In 2003/2004 the probabilities of being in the third

storage zone is about 100% for the first two months, then in December and January there are the same

probabilities (50%) of being in second and third zone. For the last months, the probabilities of being in zones

higher than the third is about 70%, showing a recovery tendency. On the other hand, for the year 2005/2006 the

situation is more complicated, and the probabilities of being in the first zone is around 70% for almost all period.

Figure 19 October 7-months forecast for the evolution of the indicator of total storage in the JRB system of the

(normal) year 2003/2004 and the (dry) year 2005/2006, given by SIMRISK using inflows provided by the

ARMAX (2,2,0,1) model.

Similarly, Figure 20 shows the March 7-months forecast for the evolution of this indicator for the normal and dry

year, where differences are quite clear. In both cases, the evolution is towards higher probabilities for lower

reservoir zones, as expected in the irrigation season. Obviously, for the dry year 2005/2006 the situation is more

complicated, and the probabilities of being in a zone lower than the third zone is around 90% for the last three

months.

31

Deliverable n°11.3

Figure 20 March 7-months forecast for the evolution of the indicator of total storage in the JRB system of the

(normal) year 2003/2004 and the(dry) year 2005/2006, given by SIMRISK using inflows provided by the

ARMAX (2,2,0,1) model.

b) using AR model for inflow generation

As was said before, once previous results were extracted, they were compared with those using the current

methodology (AR(1) for river flows forecast in order to know if the added value of the forecasts is meaningful to

predict drought events improving the tool that is implemented in the JRB for this purpose.

In this case, an AR(1) model was implemented and calibrated using MASHWIN module and generating 15 series of

river flows based on the historical ones. Figures 21 to 24 show the results for October and March forecasts, and for

the normal and dry years, and for each of the three reservoirs, corresponding to the same cases already

commented previously when using the ARMAX approach. In general, we can say that the results obtained with

the AR approach are more optimistic than the ones obtained with the ARMAX approach. No more comments will

be made, since it is preferable to focus on the indicator of the total storage in the system, rather than on the

individual reservoir storages.




of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the AR(1) model .


the (normal) year 2003/2004, given by SIMRISK using inflows provided by the AR(1) model .

33

Deliverable n°11.3


of the (dry) year 2005/2006, given by SIMRISK using inflows provided by the AR(1) model .


the (dry) year 2005/2006, given by SIMRISK using inflows provided by the AR(1) model .



Figure 25 and Figure 26 show the results for October and March forecasts, respectively, of the indicator of total

storage in the JRB system for the normal and dry years, corresponding to the same cases already commented

previously when using the ARMAX approach. Again, and in general, we can say that the results obtained with the

AR approach are more optimistic than the ones obtained with the ARMAX approach. However, more research

with the ARMAX model is needed to define whether ARMAX is better than the AR approach. Both indicators have

the same tendency of recovering at the end of the season. But, in the dry year the probabilities of having a lower

volume of water resources are higher using the ARMAX approach.


(normal) year 2003/2004 and the (dry) year 2005/2006, given by SIMRISK using inflows provided by the

AR(1) model..

35

Deliverable n°11.3

Figure 26 March 7-months forecast for the evolution of the indicator of total storage in the JRB system from

March to September of the (normal) year 2003/2004 and the (dry) year 2005/2006, given by SIMRISK

using inflows provided by the AR(1) model.

In the context of WP11, the attention is focussed on the probability of experiencing deficits by the agricultural

sector during the irrigation season. As expected, the results obtained with SIMRISK show notable differences

between a normal and a dry year. In the graphs of Figure 27, the deficits with a 10% probability of exceedance are

depicted for each agricultural demand, and also for the entire agricultural sector. Figures are given for every

month, and also as cumulative values. Even though 10% may seem a low probability, in this way the maximum

values that can be reached in a normal and a dry year are more differentiated and the worst situation can be

observed in both cases. The results presented in Figure 27 correspond to the use of the ARMAX approach.



Figure 27 March forecast of deficits with 10% probability of exceedance for each agricultural demand in the

normal (2003/2004) and dry (2005/2006) years coming from the ARMAX modelling approach.

In the normal year, the month with higher probability of having deficits is August (Figure 27 top left) and the most

affected crops are the citrus trees and orchards (grey colour). In the dry year (top right), the higher deficits are

brought forward by one month and remain high until September but with less intensity, in this case, the rice is

also affected (orange colour) to a large extent. These changes are due to the operation rules of the management

system regarding the priorities of the different agricultural demands in these situations. It is also evident that the

cumulative deficit at the end of the irrigation season (September) is much higher in a dry year (Figure 27, bottom

right), with a difference of about 120 hm3.

In Figure 28 the same type of results are presented corresponding to the use of the AR approach. If we compare

previously mentioned results with those coming from the AR(1) modelling approach (Figure 28, upper part),

deficits during the normal year are very small in the AR case compared with the ARMAX case. In addition, they are

more spread among the demands and reaching the highest deficits in July and August, while in the dry year, the

highest deficits are reached in August and the most affected crop seems to be citrus and orchards (grey colour). In

this case, the differences between cumulative deficits (Figure 28, bottom part) at the end of the irrigation season

(September) are lower, about 67 hm3.

37

Deliverable n°11.3

Figure 28 March forecast of deficits with 10% probability of exceedance for each agricultural demand in the

normal (2003/2004) and dry (2005/2006) years coming from the AR modelling approach .

In Figure 29 the differences between both modelling approaches can be seen, for normal and dry years. In both

cases, the cumulative deficits during the irrigation season are represented. In both years, the estimated deficit

with the ARMAX option results to be greater than those from AR(1) model.

Figure 29 March forecasts of cumulative deficits with 10% probability of exceedance for all agricultural demands

in the normal (2003/2004) and dry (2005/2006) years, coming from the ARMAX and the AR(1) modelling

approaches.



From concept to user 2.4

The main stakeholder interested in this analysis is the CHJ, which manages and allocates water resources of the

basin since 1936. In Figure 30 is showed clearly what the importance of SIMRISK process is in this study and where

stakeholders and decision makers are involved.

Figure 30 Scheme of the SIMRISK process within the decision making process.

Currently, water managers responsible for this system use AR(1) to generate multiple series of streamflows, and

to assess the risk of the basin to be in a drought situation given the current management and also to evaluate

impacts. This helps the decision makers drawing the different alternatives of management to minimize possible

impacts. With this methodology, it is also possible to analyse new management alternatives or mitigation

measures to select the most effective ones at reducing the risk. The methodology is an interesting tool to use with

and by stakeholders during public participation processes addressed to find the best management practices for

drought risk minimization as seen in Andreu et al (2009).

In this way, SIMRISK can show these results in graphical form, highlighting the evolution of probabilities and

percentiles for water demands and for reservoir storages. Cumulative distribution functions of any state or quality

variable at any time can be obtained. If the estimated risks are acceptably low, then there is no need to undertake

measures. However, if the estimated risks are unacceptably high, then some measures must be applied. In that

case, alternatives with sets of measures are formulated, and the modification of risks and the efficiency of

measures are assessed again with this module. This iterative procedure can be continued until an acceptable value

of risk is reached and the process ends. The approach provides a complete vision of the consequences of

decisions, either concerning management or infrastructure (Andreu et al, 2013).

As an example of this process, a reduction of the 30% in water delivery to agriculture demands (e.g. by growing

different crops and accepting lower yields) was imposed for the dry year 2005/2006 (Figure 31, bottom) in order to

see the differences in storage level evolution and judge if this could be a good measure for this dry situation. In

Figure 31 it can be observed how the probabilities of being in reservoir zone 2 increase for the entire forecast

period. The situation improves slightly, and consequently, additional measures will have to be taken in order to

maintain the system operative.

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Deliverable n°11.3


(normal) year 2003/2004 and the(dry) year 2005/2006, given by SIMRISK using inflows provided by the

ARMAX (2,2,0,1) model and modified probabilities after a reduction of the agricultural demands by

30%.

From the interaction with stakeholders it became clear that they will only integrate the results in the Water and

Drought Management Plans in case the meteorological forecast and the climate change predictions demonstrate

enough skill to reduce the uncertainty existing in the current management tools. If not, it will only introduce a

new source of uncertainty in the currently used methodologies for drought planning and management in JRB and

not be useful for the targeted end-users. That stresses the importance of comparing the new tools with the

current one.

Discussion and conclusions 2.5

In addition to the comments and discussion about the results presented in previous paragraphs, and based on

those results, let’s think about two different teams that are competing in producing forecasts for decision

making., Team A uses the ARMAX approach, while team B uses the AR approach. And let’s concentrate only on

the 7-months forecasts of total water storage in JRB system.

In Table 1 and Table 2, it can be seen that for the normal year 2003/2004, the October and March respectively,

probabilistic forecasts of team B seem more accurate than the ones produced by team A for all months of the

forecast period (an “x” is placed in the storage zone corresponding to the observed value, and the probability

value for each approach is the value depicted in the graphs previously presented).



Probabilities of storage

Table 1 Probabilities per storage interval for the (normal) year 2003/2004 from the October 7 -months forecast coming

from the ARMAX and the AR(1) modelling approaches, and historic observations’ marks.

Table 2 Probabilities per storage interval for the (normal) year 2003/2004 from the March 7-months forecast coming from

the ARMAX and the AR(1) modelling approaches, and historic observations’ marks.

Moreover, when we go to the dry year 2005/2006, it can be seen in Table 3 that team B would appear again more

accurate than team A for the October forecast. But, by the contrary, team A would appear as more accurate for

the March forecast than team B, as it can be seen in Table 4.

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Deliverable n°11.3

Table 3 Probabilities per storage interval for the (dry) year 2005/2006 from the October 7-months forecast coming from

the ARMAX and the AR(1) modelling approaches, and historic observations’ marks.

Table 4 Probabilities per storage interval for the (dry) year 2005/2006 from the March 7-months forecast coming from the

ARMAX and the AR(1) modelling approaches, and historic observations’ marks.

It has to be noticed that, for a practical application in Júcar River Basin in drought management, it is desirable to

be accurate in the March forecast, because it corresponds to the irrigation season, which is the period in which

most water is used, and therefore, the most critical period for drought management.



In order to further illustrate the discussion, in Figure 32 and Figure 33, the October and March 7-months forecast,

respectively, of the expected values of the total storage in JRB system for the normal year 2003/2004 are

presented. Also, in this type of results, team B (AR approach) appears as more accurate than team A (ARMAX

approach). Tables 1 to 4 summarize the decision making based on the forecast probabilities, and its comparison

with the historical values. The figures below provide a continuous comparison of the historical evolution with the

expected values of the forecast.

Figure 32 Observed storage level and expected values from AR and ARMMAX options in Alarcón, Contreras and

Tous reservoirs integrated in some total capacity thresholds (10%-50%) for volumes reached in the first

7 months of the hydrological years 2003/2004 (normal year).

Figure 33 Observed storage level and expected values from AR and ARMMAX options in Alarcón, Contreras and

Tous reservoirs integrated in some total capacity thresholds (10%-50%) for volumes reached from

March to September of the hydrological years 2003/2004 (normal year) .

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Deliverable n°11.3

But, in the October 7-months forecast of the expected values of the total storage in JRB system for the dry year

2005/2006 depicted Figure 34, it can be seen that team B forecast is over the observed result, while team A is

below the observed result. Both are at similar distance from the observation (each one in a different side),

although team A is a little bit more distant than team B. Furthermore, a similar situation can be found in the

March 7-months forecast of the expected values of the total storage in JRB system for the dry year 2005/2006

depicted in Figure 35, it can be seen again that team B forecast is over the observed result, while team A is below

the observed result, even though team A is a little bit more distant than team B. So, for the dry year, the accuracy

is about the same in terms of forecasted expected values, but team B tends to be overoptimistic, which for

practical application to drought management in JRB is unsecure.

Figure 34 Observed storage level and October 7-months forecast of expected values from AR and ARMAX

approaches of total storage in JRB system for the hydrological year 2005/2006 (dry year).

Figure 35 Observed storage level and March 7-months forecast of expected values from AR and ARMAX

approaches of total storage in JRB system for the hydrological year 200 5/2006 (dry year).



So, as general conclusions from this limited analysis, AR approach seems more accurate for normal years, while

ARMAX approach seems to be more adequate for dry years, which are the critical periods for drought

management. This last conclusion gives reason to expect that the inclusion of the improved forecasts of

precipitation from WP3 of IMPREX can improve drought management by means of the proposed ARMAX

approach.

However, this is an analysis of only two years and results are not conclusive, but they are encouraging in terms of

the use of ARMAX with seasonal forecasts as opposed to the use of AR, with flows alone.

On the other hand, we also expect that, in future times, better skilled seasonal forecasts and better schemes to

link forecasts to local hydrological conditions will be produced. This would decrease the uncertainties attached to

the currently used in the generation scheme. In this way, if better seasonal forecasts were available, they could be

incorporated in the probabilistic risk assessment, reducing uncertainty in the probabilistic impacts forecast that

are used for decision making in design and approval of seasonal action plans and mitigation measures. Better

short term and seasonal forecasts could also help farmers to design their crop selection and irrigation strategies

for every growing season.

Thus, this tool is an option to improve drought management. In the end, results obtained are an easy

understandable representation of a possible future that allows stakeholders to make decisions. Moreover, this

methodology can be extended to any system with a proper calibration adapted to that area.

Next steps 2.6

As said before, this is an analysis of only two years and results are not conclusive. In the following months UPV

team will work in extending the analysis to more years. Also, the number of series used in SIMRISK simulation is

15, i.e., one for each ensemble provided in the 7-month forecast from ECMWF. It would be desirable that the

ARMAX approach would be used to generate let’s say 70 series from each ensemble, which will render around

1000 series to be used by SIMRISK, in which case the statistical and probabilistic approach would have more

significance, which is desirable. It has not be done so far because the connection between the ARMAX analyser

and generator (developed using Mathlab) had no automatic connection with SIMRISK inputs, and the transfer was

done manually. Therefore, UPV team will work in the following months in order to get an automatic transfer,

which will facilitate the use of more time series.

In addition, other areas of work are ongoing, as the one using the hydrological model HBV in EVALHID, as

mentioned in the introduction.

Besides, an evaluation of the effect of temperatures on demands will be considered as a second part of the

analysis, looking at multiple hazards simultaneously. Moreover, if there is enough time, the economic impacts

derived from water shortages will be estimated despite the fact that economic indicators are not perceived as

robust basis for decision making, as explained above.

Finally, the approach will be used in an integrated analysis in WP13 including results for other sectors (Urban

supply, hydroelectricity, etcetera).

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Deliverable n°11.3

Berkel 3

Introduction 3.1

The aim of the IMPREX Berkel case study is to investigate the feasibility of a risk approach to decision-making

about freshwater management in a semi-natural area, where several sectors are depending on the amount of

water and water quality. The feasibility of the method is investigated by deriving the present day integral drought

risk, in which all relevant sectors are incorporated. This will lead to insight in which sector has the highest drought

risk, or, where the (scarce) water can have most added value by reducing the drought risk. At the moment, such

considerations are not made, because the damage for different functions is simply not known. Using a risk

approach might give a more objective and solid ground for management decisions.

In the east of the Netherlands, a range of organisations are collaborating on the Eastern Netherlands Freshwater

Supplies (ZON1) program to ensure that there will be adequate supplies of good water for use in the future. The

organisations involved need to establish a picture of the consequences of agreements relating to the availability

of water and possible water shortages. In addition, information is needed about possible action, together with

sound arguments as a basis for decisions and measures. In consultation with ZON, it was decided to adopt the

Berkel catchment as a pilot area.

Characteristics of the Berkel catchment

The Berkel catchment drains freely, with water entering the downstream section from the Twenthe Canal. Water

is used in a range of ways (urban area, agriculture, drinking water extraction, nature, leisure) and there is a range

of possible measures to prevent drought, such as measures relating to inlets, the restructuring of waterways and

water retention in the subsurface.

The Berkel River begins in Germany and the river flows into the IJssel in the Netherlands. It is fed by various

streams and by water from the Twenthe Canal. Figure 36 provides an overview of the main watercourses and the

in the Berkel catchment. The Berkel provides part of the catchment with water. Water is diverted from the main

watercourses of the Berkel to lateral waterways by means of small inlet structures in several locations. These

inlets are used, for example, to flush urban water systems or to supply water for livestock or arable farming.

Almost half of the Berkel catchment is located in Germany and so discharge rates in the catchment are heavily

dependent on amounts of water coming in from Germany. It is not known how much water extracted from the

Berkel at inlets, to be directed to agricultural areas (for uses like watering the crops). Nor is it always clear why

water is let in and how effective these interventions are. At the same time, sustaining the current structure of the

water system requires management and maintenance. An understanding of the benefits of water distribution

through inlets and the costs of the management and maintenance of the water system could help to make a

proper assessment of the structure, and management and maintenance, of the water system, the distribution of

water and any decisions about possible measures.

Water availability is limited in the Berkel during the summer. When there are shortages, the emergency plan and

the water agreement for drought (Waterakkoord Twenthekanalen / Overijsselsche Vecht (2011), Calamiteitenplan

Waterschap Rijn en IJssel (2015)) come into operation. There is a priority sequence for water distribution

(“verdringingsreeks”) in which urban water quality has the highest priority and agriculture (a ban on overhead

irrigation using surface water) the lowest. Agriculture is still allowed to extract groundwater when there is a ban

1 Cooperation partners are the Provinces Overijssel, Drenthe and Gelderland, water authorities Vechtstromen, Drents Overijsselse Delta, Rijn

en IJssel and Vallei en Veluwe, Rijkswaterstaat Oost-Nederland, inbound municipalities and drinking water company Vitens



on overhead irrigation, except in locations close to nature areas. These plans mainly provide solutions for drought

conditions that occur regularly, with repeat periods of up to approximately ten years. These plans are not based

on a risk assessment, but on a perception of acceptable and less acceptable drought damage.

Figure 36 Berkel catchment with main watercourses and inlets, and it location in The Netherl ands.

The summer of 2018 (the moment of writing) turned out to be a very dry one. The precipitation shortage at the

beginning of August is very close to the highest ever measured in The Netherlands (in a period of more than 100

years), and the discharges of the important rivers are very low (Rhine) and low (Meuse). This led to several

interventions by the Water board in the Berkel catchment:

- All inlets to neighboring catchments and agricultural areas are closed. The only inlet that is maintained is

the inlet to the area where the groundwater extraction by Vitens is located.

- Closing of the inlet together with the high precipitation shortage, has led to a reduction in crop yield or to

extra costs for irrigation by means of groundwater extraction. These effects are not quantified yet.

- Extra water from the Twenthe canal is pumped into the Berkel to maintain minimum water levels in the

Berkel.

- The Verdringingsreeks (priority sequence for water distributions) was close to becoming active.

- Up to September there a no major issues regarding water quality.

It is interesting to note that the Verdringingsreeks has not been activated in this exceptionally dry period.

Apparently, the system and the water management is more robust than foreseen.

The aim of the risk approach in this area is:

- to establish an understanding of the occurrence of shortages in present and future climate conditions;

- to establish an understanding of the welfare effect that occurs as a result of these shortages;

- to be able to make a well-founded assessment of water distribution during drought and possible

investments to mitigate impacts in the water system.

- learn from current hydrological extremes to anticipate future conditions.

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Deliverable n°11.3

Water consumers

The main water consumers in the area are depicted in Figure 37, without ranking. Not all water users are water

consumers. Urban water, for example, benefits from enough water of reasonable quality. The same amount of

water will provide for leisure.

Figure 37 Relevant use functions (sectors depending on water) in the Berkel catchment.

Arable agriculture depends primarily on the groundwater level, and the availability and quality of surface water.

Water depths for shipping and water quality are important for leisure purposes. Water quality is also important for

urban water in order to minimise smells and health risks. Drinking water and industry mainly use deeper

groundwater where supplies are adequate. During droughts, extraction for drinking water companies and

industry does contribute to the drying out of the area.

The Natura2000 areas depend on groundwater levels, soil moisture levels, and water depths and discharge

dynamics in watercourses. In streams, stagnation/near-stagnation and the drying up of the streams have an

adverse effect on aquatic flora and fauna. And the drying up of fish passages, for example, impacts fish stocks

negatively.

Groundwater levels may decline on the long term as a result of the extraction of groundwater and climate change.

Groundwater extraction can also have a major impact during the course of a single season. In the current climate,

groundwater stocks are replenished adequately in the winter. Lower groundwater levels reduce the availability of

water for agriculture and nature, and for mitigating land subsidence in areas with peat soils.



Method 3.2

General

The method applied here was developed in WP5 and it is described in deliverables 5.2 and 5.5 (as planned). The

primary focus here is on the description of the specific application of the method to this case.

The following information was required for the implementation of the drought-risk approach:

- Information about hydrology: the statistics for the hydrological parameters that play a role in the

physical effect of a given use function.

- Information about the dose-effect relationship: the relationship between hydrological parameters and

the physical effect of a given use function.

- Information about the welfare effect (damage): the expression of the physical effect as the welfare

effect.

Effects by use function

The relevant hydrological parameters and the physical effects of drought are identified for each use function. The

hydrological parameters and the physical effects are transferred into welfare effects. They can be expressed as

monetary and non-monetary values.

In this case study, reduced crop yields as a result of drought in agriculture are expressed as monetary values.

Problems in the urban area caused by reduced water quality are expressed as non-monetary values. Problems

with the drying out of terrestrial nature can lead to a reduction in natural variety and food availability, and a

knock-on effect on the intrinsic (non-monetary) value of nature, and possibly on welfare as well (monetary). The

effects are elaborated for each use function on the basis of existing local information and key figures. Information

about the hydrology, and therefore the probability of drought, is derived from model results and measurements.

Hydrological models and measurement data

This study drew on the National Hydrological Model (LHM), which schematises both the groundwater and surface

water systems for almost all of the Netherlands, including interaction. Figure 38 shows the components of the

national model and how they are interrelated. At the moment of this research, calculations are available for this

analysis for a period of twenty years (1960-1980) (this period will be extended, but the run-time of the model is

long). Groundwater and soil moisture levels were calculated with a spatial resolution of 250 by 250 m. This is a

practical resolution for this regional scale. In terms of surface water, the Berkel has been schematised in the LHM

as a single water body, with a single total balance and one water level. This is not enough detail for the

elaboration of use functions that depend on the surface water. It is therefore decided to use (limited) series of

measurements of water depth and water temperature at various locations in the Berkel catchment for these use

functions. An overview of the measurement locations used for water depth and water temperature can be found

in Figure 39. A drawback of this approach is that by using the limited series of measurements, it is not possible to

arrive at any conclusions about future developments in risk as a result of climate change and socio-economic

developments. This drawback will be addressed in paragraph 3.5, the discussion. In future work (within IMPREX),

risks will also be derived for future scenarios.

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Deliverable n°11.3

Figure 38 Schematic representation of the National Hydrological Model with the four model components .

Figure 39 Measurement locations used for water depth and water temperature in the surface water system .



Workshops

Stakeholders are actively involved in this study. Stakeholders supply the local knowledge and information needed

to define the use functions and possible effects. In addition, their input is essential to achieve a practical result

that fits in with the realities of water management. There were therefore several consultations with stakeholders,

and two workshops were organised, one at the start and one more towards the end of the work presented here:

1. One workshop looked at the use functions and welfare effects, and discussed the following questions with the

stakeholders:

- Who are the main water consumers in a normal year and in a very dry year?

- What is the main consequential welfare effect caused by water shortages in a dry year and in a very dry

year?

- What considerations are made in a dry year and in a very dry year?

2. The other workshop reviewed the results, and put a number of questions to the stakeholders:

- What could you use the results for?

- What is missing for a clearer picture?

- What criteria other than monetary effects are important in the public domain?

- How could you quantify these criteria or assess them qualitatively?

Results 3.3

Agriculture

Agriculture in the Berkel catchment consists mainly of grassland and maize. It is not clear whether the areas which

are supplied with water from the Berkel contain different types of vegetation than the areas where no water is let

in. Table 5 shows the crop types in the area, with the number of hectares.

Table 5 Overview of the number of hectares per crop type in the Berkel catchment

Crop type Area in hectares Crop type Area in hectares

Grassland 22,281 Cereals 946

Maize 8,447 Miscellaneous 341

Potatoes 481 Trees 31

Sugar beet 150 Total 32,678

Figure 40 shows how hydrological parameters are translated into physical effects and expressed as welfare effects

for each type of crop. The physical effects of drought on agriculture are expressed as a reduction in crop yields.

The yields are dependent on adequate soil water availability (which is a hydrological parameter). To define the

reduction in the crop yield due to soil moisture shortage, the agricultural yield model AGRICOM is used. The

reduction in the yield has been defined as the difference between the potential and the actual agricultural yields

(kg/crop/area). Since there are rarely any years where the yield is equal to the potential, this means that also

under non-drought conditions some yield reduction will apply. Wageningen University and Research Centre has

developed a price tool in which changes in agricultural yields are expressed as a welfare effect taking price

elasticities and imports/exports in the agricultural sector into account.

Figure 40 Effect chain for agriculture: from probability of hydrological effects to physical effects and welfare

effects

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Deliverable n°11.3

Recreational shipping with 'Berkelzomp' boats

Recreational shipping with 'Berkelzomp' boats is an important tourist attraction in the Berkel catchment. The

negative effects of drought are that, if the water is not deep enough for the boats, they will not sail and there will

be no revenue. To determine the impact of drought, the required sailing depth and the average revenue during

the season were determined. The model results were used to calculate the number of days per year when the

depth in the Berkel was lower than required.

Figure 41 shows the steps from hydrological effect to welfare effect. The physical effect is the number of trips per

year that did not take place because the water was not deep enough. The welfare effect for the company is the

number of trips per year that do not take place because the water is not deep enough, multiplied by the number

of people per trip and the ticket price per person. Because we have no information about the fixed and variable

costs of the Berkelzompen company, the welfare effect has not been corrected for the ratio between them. In

addition to direct effects, indirect effects are also likely to occur. For example, the revenue of local catering

outlets will also be affected if sailing is not possible. For the time being, this factor has not been considered.

Figure 41 Effect chain for the Recreational Shipping user function with the Berkelzomp boats: from probability of

hydrological effects to physical effects and welfare effects

In addition to the loss of revenue for the Berkelzompen company, the welfare effect at the regional and national

levels can also be taken into consideration. A substitution effect occurs in the case of tourists from the

Netherlands. These tourists may engage in different activities in their own country and so there will be no welfare

loss at the regional and national levels. The lost income from foreign tourists does have a national welfare effect.

The loss of revenue is equal to the number of trips per year that do not take place because the water is not deep

enough, multiplied by the expenditure of the number of foreign tourists.

Urban water quality

Urban areas can have problems during dry periods because of poorer water quality. This can cause nuisance, for

example in the form of smells, reduced amenity value, and negative health effects (welfare effects). Because it is

difficult to express these effects as monetary values, it was decided, when looking at urban water quality, to

consider the extra maintenance of watercourses with the aim of preventing the negative effects listed here. This

is not the actual welfare effect of water shortage, but a means to prevent negative welfare effects, and can

therefore be seen as the ‘economical damage’ of water shortages in urban areas.

To determine the extra maintenance work needed on watercourses to prevent negative effects such as smells,

reduced amenity value and negative effects on health, it has been assumed that more mowing and dredging is

required as a result of faster plant growth at a water temperature of more than 20°C and that more maintenance

is required in the transition from oligotrophic to eurotrophic water. The costs of maintenance increase from € 1.40

per m2/year to € 5.67 per m2/year (Kamsma, 2014).

Measurement series for water temperature are available for a limited number of locations. The measurement

frequency varies between eight times a year and monthly measurements. The number of times the water

temperature exceeds the threshold value of 20°C was determined on that basis. Table 6 shows the surface area

covered by urban water.



Table 6 Overview of surface water in the urban areas in the Berkel catchment (Zutphen, Lochem, Borculo and

Eibergen)

Town Area covered by water in urban areas [m2]

Zutphen 697,500

Lochem 335,000

Borculo 130,000

Eibergen 93,750

Figure 42 shows the procedure for working up hydrological parameters into physical effects and stating them as

additional maintenance costs for the management of the water area in urban areas.

Figure 42 Effect chain for the Urban Water Quality use function: from probability of hydrological effects to

physical effects and welfare effects

Nature

The Nature use function involves a distinction between aquatic and terrestrial nature. The physical effects for

nature are expressed as intrinsic values such as the loss of species or the reduction of numbers per species. In

order to determine the species loss in aquatic nature during droughts and to value that loss, information about

the water depth and the flow rate of the waterways in the area is required, as well as the limit values for different

species at which species loss occurs. The duration of the exceedance of the limit values (in the case of aquatic

nature, this is usually when watercourses fall dry) is also a determinant of species loss. This information can be

obtained from the hydrological model. In addition, drought can also lead to species loss in aquatic nature as a

result of eutrophication. Parameters such as temperature, seepage, drainage and nutrients are important here.

The consequences of drought for terrestrial nature depend on the soil moisture level. Here also, it is important to

know at which level of soil moisture species loss occurs, and what the limit values are for the different species in

the Berkel catchment. As well as species being lost, new species may be found in aquatic and terrestrial nature

and these may be seen as assets. This factor has not been taken into consideration.

In the ”‘verdringingsreeks”, which determines the priority of water consumers during water shortages, nature that

would suffer unrepairable damage is the highest priority. This means all other water using sectors can be limited

in their water use to secure water availability for nature as much as possible. This does not imply there will be no

shortages and thus negative effects for nature, but it shows nature is considered as important. However, the

effects on the Nature function are difficult to express in monetary values. One way to do this involves looking at

the effect of changed nature on ecosystem services or estimating the recovery costs of nature (see also Deltares,

Stratelligence and LEI, 2016). To determine these indicators, more information is needed about the limit values at

which species are lost as a result of drought. Data were not available as the study was being conducted and so the

effects on nature have not been considered further in this risk analysis. Figure 43 does set out the steps needed to

determine the effect on nature.

A future possibility for the aquatic nature could be to use a method derived for the Water Framework Directive

(WFD-Explorer method), in which tolerance levels for different species are compared with environmental factors

in the water body (Wortelboer, in prep.). For the ARC-NSC case study this method is already applied (described in

paragraph 0). Another option to determine the welfare effect for water shortages for nature could be the loss of

nature areas for inhabitants and tourism. This could partly be an economical effect, as a reduction of income from

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Deliverable n°11.3

tourism. A larger effect would be more qualitative: the presence of attractive areas and biodiversity. An attempt is

made to determine this effect, but this has not yet lead to a applicable method.

Figure 43 Effect chain for the Aquatic Nature (top two chains) and Terrestrial Nature (bottom chain) use

functions: from probability of hydrological effects to physical effects and welfare effects .

Industry

The main industry in the Berkel catchment is Friesland Campina (dairy processing), which uses water as process

water and cooling water. Process water is not highly dependent on the temperature; it depends mainly on the

water supply. Both temperature and quantity are important for cooling water. The surface water must not warm

up too much as a result of the discharge of cooling water. It is therefore important to know to what extent

Friesland Campina uses process water and cooling water in the production process.

Friesland Campina extracts the water from the deeper groundwater. It does not discharge process and cooling

water into the surface water. Any discharges go directly to the waste water treatment plant. As a result, the

surface water does not suffer any adverse effects from the heated discharge water.

Friesland Campina is authorised to extract groundwater at all times. There are no circumstances in which a ban on

groundwater extraction applies. As a result, the company never suffers from a shortage of water in dry conditions.

It is not foreseen that this will change in the future.

The effect chain for the Industry use function is therefore not relevant here and so it has not been elaborated

further. Groundwater extraction may have an effect on agriculture or nature by causing the drying out of the area.

The extent to which this is a factor and to which compensation measures are applied is not known. In is possible

that, when there is a certain balance in the present situation, in the future this balance gets disturbed when there

is less groundwater recharge in summer, and higher extraction during the year. This is a topic for further research.

Drinking water

The drinking water company Vitens is one of the most important water consumers in the area. Like Friesland

Campina, Vitens extracts water from the deeper groundwater. There are no circumstances in which a ban on

groundwater extraction applies. As a result, the drinking water company itself is not affected by dry conditions.

The effect chain for the Drinking Water use function has therefore not been elaborated further. As with the

Industry use function, there may be indirect effects on other use functions because water extraction does affect

the water levels in watercourses, groundwater levels and soil moisture levels in the affected area. For 2040, an

increase in drinking water demand of 30% is foreseen. Vitens is exploring possibilities to provide in this extra

demand without negative effects for the groundwater levels, or example by actively increasing the water storage

in the neighbouring higher area and using this as a buffer.



Risk approach 3.4

The results of the application of the first version of the risk tool will be presented and discussed in this chapter.

They provide a provisional picture of the Berkel risk profile for the current situation (present climate without

climate change). This application looks at two situations:

1. the current situation,

2. the situation without a water inlet from the Twenthe Canal to the Berkel.

First of all, the risk for one sector – agriculture – was analysed separately. Recreational shipping and urban water

quality were then also analysed in brief and compared with the agriculture sector.

The figures presented in this chapter are indicative and they are intended to test the method for the risk approach

and show stakeholders the concept. The welfare effect is expressed as the expected annual level of drought

damage. There has not yet been any assessment of the reliability of the outcomes (by means of a comparison

with occurred loss of crop yield, for example) and so they cannot yet be used for a genuine social cost-benefit

analysis, for example.

Risk without intervention

Agriculture

In the absence of a regional model, The National Hydrological Model (LHM) and the AGRICOM agricultural effect

model are used to establish a picture of the risk of drought for agriculture in the entire Berkel area. The LHM is

used to model the groundwater and surface water system in the current situation for a 20-year time series. This

means that current land use is simulated in combination with the historical hydrological and meteorological

conditions for the years 1961-1980 (actual precipitation, evapotranspiration and discharges). A more recent

simulation is not available at the start of this research. The agricultural yield loss is calculated for each year using

AGRICOM and broken down into the different crop categories and surface areas used by AGRICOM. AGRICOM

found that the total surface area used for agriculture for which the agricultural yield loss was calculated was

34,000 ha. Agriculture in the Berkel catchment consists mainly of grassland and maize. Together, these crops also

incur most of the calculated drought damage.

Figure 44 shows the loss of agricultural yield in the period of 20 years (1961-1980) considered. The figure shows

that the welfare effect due to drought is highest in the year 1976. This was also, climatologically, the driest year in

the time series. The average probability of the climate conditions of 1976 in the Netherlands is considered as once

every 100 years. The year 2018 will probably become the second-driest year (see Figure 45), but is not included in

the analysis. In welfare effect, 1976 is followed at some distance by five other relatively dry years, including 1973

and 1975. There is hardly any welfare effect owing to drought in the other years.

Figure 44 The welfare effect of drought over time for each crop type (without taking the price effect into account)

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Deliverable n°11.3

Figure 45 Precipitation deficit in The Netherlands in 2018 and dry reference year 1976.

The annual expected loss of income from agriculture for the Berkel (based on the average for the 20-year time

series) is approximately € 2.92 million (see Figure 46). The expected actual annual agricultural yield is

approximately € 55 million, so the annual expected welfare effect of drought is about 5% of the annual expected

yield. The pie chart shows once again that grassland and maize are the main crops.

Figure 46 Distribution of the expected annual welfare effect for agriculture for each crop type (without taking the

price effect into account).

The relatively large welfare effects for the crop types grassland and maize are mainly caused by the production of

grass and maize, which affects the production of cattle feed: lower roughage production on the farm and in the

region can be offset by the purchase of concentrated feed. The extra costs involved have been included in the

calculation of the welfare effects for the consumers of grass and maize.

Recreational shipping with 'Berkelzomp' boats

The annual expected welfare effect is also calculated for recreational shipping using the 'chain' described in

Section 3.3 and historical measurement series of water levels covering 1969 through to 1994. Water levels were

measured daily during this period. It can be seen from Figure 47 that from 1982 the welfare effect due to drought

is much higher than in the years before. Probably a change in water management, for example a different weir



regime, is implemented in 1982. For the years 1972, 1973, 1974 and 1976 measurements are only available for one

location. In this location, water levels in summer are always relatively high, probably due to inlet of water.

Therefore, no welfare effect is calculated for these years, whereas we do suspect that there will be a welfare effect

on other locations. The year 1976 is known in the Netherlands as a very dry year. The absence of this year from

the measurement series therefore has an impact on the calculated welfare effect. The average expected annual

welfare effect is € 5900. The average annual revenue is equal to the number of recreational vessels multiplied by

the number of people a year and the ticket price. This amounts to approximately € 178,000. The annual expected

welfare effect of drought is about 3% of the annual revenue. This percentage is similar to the effect for agriculture

(5%). However, the annual expected welfare effect in euro’s is significantly lower for recreational shipping than

the welfare effect for agriculture.

To investigate the effect if 1976 would be taken into account, it is assumed that for most of July, August and

September (based on precipitation shortages that period) water levels have been too low sail with the

Berkelzomp. This leads to a welfare effect of € 17,400 for 1976. The average annual welfare effect including this

value for 1976 would be € 6550. The average annual revenue would be € 200,000. This is still significantly lower

than the welfare effect for agriculture.

Figure 47 The welfare effect over time for recreational shipping with the 'Berkelzompen' (there are no estimates

for 1972, 1973, 1974 and 1976 for the effect of drought on recreational shipping).

Urban water quality

The welfare effect for the urban area is based on the mowing regime for urban waters. The aim of extra

maintenance on the watercourses is to prevent the negative effects such as smells, other disruptive effects,

impaired amenity value, and negative effects on health. The annual expected value of additional management

and maintenance is determined for the four largest urban centres with urban water, in other words Zutphen,

Lochem, Borculo and Eibergen. The cost for additional management is estimated at almost € 360,000 (see Figure

48). This cost is a maximum value for a dry year. Zutphen accounts for the largest share of this amount. With its

canals around the inner city and other waters in residential areas, this city has most urban water. The additional

amount needed for management and maintenance must be assessed in relation to the total municipal

expenditure on the maintenance of watercourses. No information is available about the annual amount of this

expenditure, since it is not known which years extra maintenance is necessary. However, in relation to the annual

water tax this will be negligible.

The costs for extra management and maintenance of watercourses are not the actual welfare effect. Welfare

effects of drought in urban areas include the effects of poor water quality on property prices (because waterside

houses may become less attractive properties) and the additional costs for health care (caused by health

complaints). These however are not taken into account, due to a lack of data on these costs.

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Deliverable n°11.3

Figure 48 Distribution of the annually expected welfare effect for the Urban Water Quality function for Zutphe n,

Lochem, Borculo and Eibergen.

Agriculture, recreational shipping and urban water quality combined

Finally, the welfare effect in euro for the three user functions for which the welfare effect is calculated are

compared with each other (see Figure 49). This comparison has clear limitations because the time period, the

damage figures, model data and measurement data etcetera on which the various welfare effects are based differ

significantly from each other. As a result, the baseline situations differ for each use function. Nevertheless, based

on the results it is expected that the annual welfare effect will be largest for the agricultural sector by comparison

with recreational shipping and urban water quality. The effect per sector as percentage of the annual revenue is

equal for agriculture and recreational shipping (with 5 and 3 %) and negligible for urban water quality.

In practice, other criteria will be taken into account in the administrative context or management strategy than

the welfare effects considered here, such as the cultural value of sailing with the Berkelzompen boats and related

spending for hotels and catering. In addition, the water authority or the municipality will, as more water bodies

are created in new urban areas ('living on the waterside'), attach more importance to deteriorating water quality

as it is experienced by inhabitants. These matters are difficult to express in monetary terms (data as house prices

of the house on the water side, compared to other houses, could be used) but they are taken into consideration

when deciding about measures to be taken and priorities for the distribution or redistribution of the available

water.

Figure 49 Distribution of the annual expected welfare effect for the functions agriculture, recreational shipping

with Berkelzompen and urban water quality for Zutphen, Lochem, Borculo and Eibergen .



Risk with measure (shutting down the inlet of water from the Twenthe Canal)

Shutting down the inlet of water from the Twenthe Canal to the Berkel catchment has been explored as an

alternative management strategy for the Berkel catchment.

In the National Hydrological Model, the Twenthe Canal is modelled separately as a watercourse, but the Berkel is

not. Nevertheless, it is possible to obtain a very broad indication of the effect of the measure, in other words when

the inlet from the Twenthe Canal at Lochem into the Berkel of 1.3 m3/s is reduced to 0 m

3/s. The effects of

stopping the inlet of water on the loss of agricultural yield were also considered. The model showed that the

measure mainly affects the GLG (mean lowest water table, as calculated over the time series of 1960-1980). In the

area between Lochem and Zutphen, the water table fell by a maximum of approximately 10 cm. This is shown in

Figure 50, where the area in red is the area where there was a sharp fall in the water table. The dark red shows a

fall of 10 cm; the lighter colour indicates a smaller fall. The annual expected welfare effect for agriculture was

recalculated. The results show that the measure has hardly any effect: the annual expected welfare effect

increases by € 50,000, which is less than 2% of the annual expected effect (€ 2.92 million).

Figure 50 Impact of stopping the inlet from the Twenthe Canal (from 1.3 m3/s to 0 m3/s) on the mean lowest

water table as calculated with the National Hydrological Model .


Discussion

The aim of the IMPREX Berkel case study is to investigate the feasibility of a risk approach to decision-making

about freshwater management in a freely drained area like the Berkel. Therefore the drought risk in a baseline

situation is compared with the situation involving an intervention. The annual drought damage for the sectors

explored is not very high (ranging between 0 to 5% of the annual turnover). The intervention that is investigated

(stopping the water supply from the Twenthe Canal) has a very small effect on annual drought damage.

The intervention reduces water availability for agriculture, in areas where in the basic situation surface water level

are kept at a certain level with inlet of water from the Twenthe Canal. The expected effect of this measure is

reduced soil moisture availability in these areas, and therefore reduced crop yields. The water from the Twenthe

Canal that is not used here, can be used for agriculture or other functions like nature elsewhere. This intervention

is not a drought mitigation measure for the agricultural area, since it only reduces the water availability for

agriculture. Measures that reduce drought risk for the agriculture can also be investigated. Examples of such

measures are increasing the bottom level of the ditches (and thereby reducing the draining function of these

ditches and increasing the groundwater storage) and increasing the inlet capacity. The risk profile with these

kinds of measures is investigated in further research.

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Deliverable n°11.3

The results provide a first impression of the current risk profile, with a limited number of use functions (water consumers) being considered. The future development of the risk as a result of climate change and socio-economic developments is not yet taken into account. The risk is expressed as an expected annual welfare effect. In the risk approach for the Berkel, use functions of agriculture, urban water quality and recreational shipping are considered. The risk approach is not applied to other relevant use functions such as nature, industry and drinking water. This was not yet possible for nature because the effect chain (hydrology-dose effect-welfare effect) was not complete. Interviews with the drinking water company Vitens and with Friesland Campina have shown that there are no circumstances in which a drought can affect their groundwater extraction (no ban will be imposed). As a result, Friesland Campina and the drinking water company Vitens are not affected by drought. However, there may be indirect effects due to lower water and groundwater levels in the area of extraction. This will result in an indirect welfare effect on functions that depend on water levels in watercourses, groundwater levels and soil moisture levels. The Industry and Drinking Water use functions were not considered, nor were the indirect effects of extraction. In future work (ongoing at the moment of writing), interviews will be held with Vitens to learn more about these indirect effects. This is incorporated in the next phase, in which also the future situation with climate change and socio-economic changes is considered. For the Berkel water system no appropriate regional water system model is available, that adequately describes the interaction between the groundwater and surface water systems. For further research, especially to define the risk profile for the future situation, a more detailed groundwater model is needed. For this purpose, a detailed model is built for part of the Berkel catchment. This is not done within the IMPREX project, but will be used in the next step for Imprex (ongoing work). For now, in order to establish a risk for the situation without climate change and socio-economic development, model results from the National Hydrological Model Instruments (LHM) are used for the agriculture use function. Measurement series of water levels, water temperature and discharge rates are used for the other use functions. The MetaSWAP model component in the LHM contains information about soil moisture levels with a resolution of 250x250 m. This information about soil moisture levels is used for agriculture in combination with the effect module for agriculture (AGRICOM) and the WER price tool (to determine the effect of price elasticity). The LHM (version 3.0.1) provides hardly any model results for the surface water in the Berkel catchment and it provides information only about the total surface water balance in the Berkel (water demand and supply), in other words information at the 'district level'. Local measurement points in the Berkel, with information about water levels, water depths and flow velocities in time, are lacking. As a result, the LHM outcomes cannot be used for the risk assessment relating to use functions such as urban water quality and recreational shipping, while a picture of the time-dependent development of hydrological variables at multiple locations in the surface water is needed to determine physical effects. For the urban water quality and recreational shipping use functions, measurement series (some of which were not continuous) are used in combination with simple assumptions and key figures about costs and benefits taken from various sources, including the literature. An initial assessment of the drought risk was established. The outcomes of the risk approach are preliminary and they should not be interpreted as absolute numbers. They still include a broad margin of uncertainty. However, in spite of the limitations of the current version of the Drought Risk Tool for the Berkel, it has become clear that the agricultural sector will have the largest annual expected welfare effect (however not very large) relative to recreational shipping and urban water quality. To demonstrate the added value for the consideration of measures, the effect of a measure on the risk for

agriculture is examined. The effect of stopping the inlet of water from the Twenthe Canal to the Berkel (in other

words, 0 m3/s instead of max. 1.3 m

3/s (the usual inlet rate)) on the risk is determined. The analysis showed that

the measure has hardly any effect: the effect is mainly visible in the area between Zutphen and Lochem (in the

form of a limited fall in the average lowest water level). The welfare effect of the measure is very limited. To

establish an idea of the magnitude of the effect, the costs of the measure can be compared with the welfare

effect.



Conclusions

The annual expected value of the welfare effect of drought for the Berkel area is determined with limited

accuracy. This does however give insight on which of the use functions has a higher expected welfare effect, for

the current situation and for a different water management strategy (no inlet from Twenthe Canal). It occurs that

not all drought effects on use functions can be expressed in welfare effects with the same accuracy. For example,

the value that is attached to nature or to water quality in an inhabited area, is difficult to capture. This leads to a

denial or an underestimation of certain effects of drought in the risk calculation.

The extremely dry summer of 2018 shows that the water system and management practises are robust. This

drought did not lead to activating the Verdringingreeks (priority sequence in water distribution). This is in line

with the findings of the study that annual average drought effects on welfare are 0-5% of the annual turnover (for

the use functions agriculture, recreational shipping and urban water quality).

For stakeholders it is not yet clear how the results of a risk approach can contribute to decision-making or

decisions relating to the assessment of specific measures, tipping-point analyses on the regional and national

scales, arriving at agreements about water availability and/or contributions to the Decision-Support Information

Profile (BOI) of the Freshwater Delta Programme. The results are too uncertain to see their use. To improve this

uncertainty, analyses are being made with a more detailed regional model. The results of these calculations are

however not yet available (but will become available within IMPREX). These uncertainties are related to the

spatial scale and related expected accuracy of the pilot study.

The stakeholders stated that they wish to keep things simple. Clear and understandable arguments are

important. This is a relevant remark, since a risk analysis and the demanded accuracy easily lead to complex

assumptions and calculations. Also, a detailed model is needed to arrive at recognisable calculation results.

In the Berkel area drought is not experienced as a problem by stakeholders. There are very few complaints from

farmers about inadequate water supplies. The water authority is mainly held responsible for damage caused by

too much water. If dry periods become more frequent in the future or if they last longer, there will be a higher

demand for groundwater use for irrigation.

Since the effects on nature a not considered in the risk profile, the total drought risk is not yet known.

Groundwater use for irrigation, and groundwater use by industry and the drinking water company, might result in

decreasing groundwater levels in summer and therefore in shortages for nature. The future situation (2050) is

considered in ongoing work and will be reported later in IMPREX (Deliverable 5.5). However, also in this work

there will be no welfare effect calculated for nature. It is taken into account in a qualitative manner only.


As mentioned, the work is conducted in close collaboration with a number of end users. The method developed in

WP5 is developed with close involvement of The Dutch Ministry for Infrastructure and Environment, the

Freshwater Supply Programme Office, the Dutch governmental organisation responsible for water management

(Rijkswaterstaat), the Foundation for Applied Water Research, (STOWA, knowledge centre of the water boards)

and a number of water boards. The application of the method in the Berkel area is carried out in cooperation with

a range of organisations are collaborating on the Eastern Netherlands Freshwater Supplies, ZON. These include

water boards and the Province of Gelderland, and STOWA is closely involved as well. Also the water users (e.g.

agricultural cooperation and the drinking water company) are involved. The involved parties are stakeholders and

knowledge parties. They are involved in several work-shops, meetings and interviews. Discussions are had over

the use of the method. Here it became clear that working on a relatively small spatial scale as the Berkel river

basin, involving the water users directly, leads to a demand for high detail and high accuracy outcome.

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Deliverable n°11.3

As yet, the risk profiles have not been used for management decisions on this local scale. For the local scale more

detailed analysis is needed to convince the stakeholders, since in the present analysis, the results of the model

simulations that form the basis of the risk analysis, are criticized. Secondly, by not being able to address all effects

(agriculture, recreational shipping, water quality, nature) at the same level, it is difficult to obtain a full drought

risk-profile. Work is done on the risk calculation for present day as well as future scenario’s and with a more

detailed hydrological model, leading to more realistic results. The method is promising in providing valuable

information for long-term planning. Water authorities can use the risk profile to define whether or not measures

are needed in the water supply system, on a management level. Based on the risk profiles, insight can be given on

water availability for specific water users. These users can subsequently decide on specific local measures.

Next steps 3.7

To determine the future development of the drought risk, calculations with future climate and socio-economic

developments are made. Also analyses are being made with a more detailed regional model to reduce the

uncertainties in the present analysis. For both analyses, the results are not yet available. These results will be

reported in Deliverable 5.5.

Some aspects of the method need more work:

- Whether to summarise the overall risks for all user functions as a total risk or to consider the use

functions individually (since effects on all functions cannot equally be defined in a monetarily value). And,

in the latter case, whether to state the effects as monetary values or as other units, like loss of species for

nature, number of affected hectares for nature, value of houses, etcetera. An option would be to use

monetary and individual risk functions, and accept to need to do more bold assumptions, for example on

nature.

- The approach to the quantification of all kinds of less uniform welfare effects, such as effects of drought

in urban areas, such as stating the effect in terms of property prices (because waterside properties

become less attractive due to drought).

- What should be the spatial scale of the risks of the use functions, the regional system or the national

system? The focus may vary depending on the different parties involved.

- Determine whether the risk approach leads to new insight in the spending limit for measures to improve

water distribution and allocation.

- Determine whether the risk approach indeed leads to a different water allocation scheme compared to

using a water-availability approach. In other words, are the differences in the economic damage between

sectors giving rise to a different prioritization of water supply?

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Deliverable n°11.3

ARC-NSC 4

Introduction 4.1

The area to be considered in the third case study consists of the Amsterdam-Rhine Canal and the North Sea Canal

(ARC-NSC) and the areas that depend on water coming from this system. The aim of the IMPREX ARC-NSC case

study is to investigate the feasibility of a risk approach to decision-making about freshwater management in the

ARC-NSC area on the basis of a case in which the drought risk in a baseline situation is compared with the

situation involving an intervention. The selected measure in the case study is 'high-priority flushing for the

Amsterdam-Rhine Canal'. This intervention reduces salt intrusion into the Amsterdam-Rhine Canal from the

North Sea Canal and it requires water that cannot therefore be used elsewhere. The reduced salt intrusion is

expected to diminish damage to agriculture, nature and drinking water in the case study area during dry

circumstances. The risk approach will be applied to all these functions in order to assess the overall risk. The

agricultural risk will be compared with the results of a risk assessment based on characteristic years. This will

provide insight in the added value of using the proposed risk approach over the current method.

In order to be able to assess the impact of an intervention reducing salt intrusion, an algorithm for salt has been

developed, making it possible to establish a relationship between the effects of a lower discharge and the chloride

concentration in the ARC-NSC. This provides for stating this effect in terms of the impact on nature and drinking

water supplies. For the effect on agriculture, a comparison is made between different calculation methods

(potential impact, actual impact or long-term impact) for the welfare effect.

Characteristics of the ARC/NSC system

The Amsterdam-Rhine Canal takes in fresh water from the Lower Rhine/Lek or through the Merwede Canal from

the Waal (see Figure 51). An adequate flow of fresh water is important to prevent salt intrusion from the sea locks

at IJmuiden. The water in the ARC-NSC can be let in by the neighbouring water authorities to their belt canal

systems and be used for various functions such as irrigation in agriculture and level maintenance for the

protection of peat dikes. If the ARC-NSC water contains too much chloride, agriculture can be adversely affected.

Higher salt concentrations can also have an effect on nature and drinking water supplies. Operational water

management in the ARC-NSC area is designed to maintain water levels in the polders and in the belt canal

systems, and to prevent excessively high chloride concentrations. The high chloride concentrations are limited by

flushing the system. The salt load from the polders is ultimately discharged into the ARC, NSC or other open

waters (North Sea, Markermeer lake).

The Hoogheemraadschap Stichtse Rijnlanden and Amstel Gooi en Vechtstreek water authorities (both of which

are located on the eastern side of the area) take in water directly from the ARC. Usually, the Hoogheemraadschap

van Rijnland water authority takes fresh water from the Hollandse IJssel near Gouda during periods of drought. If

the chloride concentration at Gouda becomes too high to let water in, the Small-Scale Water Input (KWA) system

goes into operation and fresh water is brought in from the Amsterdam-Rhine Canal to the area managed by the

Hoogheemraadschap van Rijnland water authority. This system operated during the drought of the 2003 and

2018.



Figure 51 Schematisation of the Amsterdam Rhine Canal-North Sea Canal water system, with in red the

discharges in m3/s at the different water ways.

An overview has been made together with the water management authorities and other stakeholders of the main

water consumers in times of drought and the effects that can occur during dry periods. That has resulted in to an

overview of the functions in the case study area exposed to the highest risk (see Figure 52). Agriculture emerged

as the function with the highest risk of drought damage, followed by shipping, nature, flood risk management and

drinking water.

In the current situation, droughts have a limited effect on the system, partly owing to the implementation of

measures such as the Small-Scale Water Input (KWA) system2. Climate and socio-economic change may result in

more frequent water shortages in the future, possibly leading to damage to agriculture and drinking water

because there is not enough water or because the available water is too salty. The focus of the regional

stakeholders, such as regional water authorities, is on preventing future water shortages and its impacts. That

implies making freshwater supplies more robust and allocating water according to economic principles. However,

to be able to prevent future shortage the current drought risk should be firstly understood.

The ARC-NSC is a preeminent example of a system where the water can be controlled in many ways. Therefore,

there are several steps that can be taken that have an effect on water allocation, not only in the main water

system, but also at regional management authorities and by users. There are therefore several windows of

opportunity to prevent water shortages in the future. The economic benefits of freshwater measures and the

alternative management of water have, in the past, only been identified to a limited extent. To facilitate sound

decisions about possible measures, the water authorities and Rijkswaterstaat wish to establish more robust

economic support for measures and to make the risks of drought transparent.

2

The small-scale water supply system increases the water supply to the western part of the Netherlands in times of droughts. This is done by

additional supply of water from the river Lek and Amsterdam-Rhine Canal.

65

Deliverable n°11.3

Figure 52 Overview of sectors and functions with a high risk of drought (without ranking) (outcome of working

session on 10 May 2016).

The risk approach for freshwater supplies was developed in response. The approach considers both the

probability of drought-related hazard events, as well as the probability of socio-economic and environmental

consequences of droughts per end-users/sectors. The risk approach starts with estimating the hydrological effects

of droughts using -in this case study- the Dutch National Hydrological Model, which consists of models for the

unsaturated zone (MODFLOW), the saturated zone (MetaSWAP), the regional distribution of surface water

(MOZART) and the national distribution of surface water (SOBEK). The results include the allocation of water in

the case study area, taking into account priorities of water allocation and operational management. Model results

for 50 years are produced to be able to define return periods for water shortage. The output of the hydrological

models serve as an input for the available impact modules, which produces physical impacts for the respective

time series. Physical impacts include amongst others agricultural crop yield reduction and drought (depth)

limitations for navigation. This case study uses the crop yield model ‘AGRICOM’, which is shortly described in the

coming chapters. The last step is to translate the physical impacts in economic impacts using damage functions,

resulting in welfare effects of droughts. As welfare effects are estimated for 50 to 100 years, exceedance

probabilities of welfare effects can be obtained. The expected value of all welfare effects together is seen as the

drought risks. The drought risk can be calculated for the different use functions of water and for different spatial

scales. The ARC-NSC area is a test case for the application of the risk approach to level-controlled areas. The

results of this case study can be used to determine the wider applicability of the approach.



This chapter describes the results of the application of the risk approach to the ARC-NSC case study. First a

description is given of the applied methods Then the hydrological results are described, followed by the

calculation of the welfare effects on agriculture, nature, drinking water and shipping. The chapter ends with a

discussion of the added value of the risk approach and issues relating to application.

Method 4.2

The risk approach, as described in the introduction, is applied. With this approach, the current and future

economic risk of water shortages in the area can be assessed. Furthermore, the economic benefits of measures

that increase water availability can be estimated based on a reduction in risk as a consequence of a particular

measure. The risk approach is demonstrated in this case study and compared with a characteristic year approach.

Furthermore, some additional methodological tests were done in order to further develop the approach. The case

study goals were determined together with local stakeholders.

A workshop was organised with the water authorities concerned from the ARC-NSC area, during which it was

decided to apply the risk approach first to the current situation and current climate. The measure under

consideration was the granting of a high priority to the flushing of the Amsterdam-Rhine Canal, which is expected

to reduce salt concentrations. The underlying idea was to make an appraisal of the trade-off between water

quality problems affecting the use functions agriculture, nature, drinking water, and industry on the one hand

and, on the other hand, problems caused by impaired water quantity with an effect on the agriculture, shipping

and infrastructure use functions. The hydrological calculations draw on the National Hydrological Model (LHM) as

this is the only available model in the area that can assess water shortages, and the model is already linked to

various relevant effect models. As the hydrological models does not produce satisfactory results regarding salt

concentrations, an additional analysis has been conducted in order to be able to analyse the impact of higher salt

concentrations.. The water authorities were actively involved during the study phase in elaborating the risk

approach and giving feedback on the results.

Determination of chloride concentrations

Chloride can result in damage to a number of functions in the ARC-NSC area: agriculture, nature, drinking water

and industry. Therefore, assessment of chloride concentration in the Amsterdam-Rhine Canal and the North Sea

Canal is important to determine the full welfare effect of water shortages. The National Hydrological Model

(LHM) does not provide the chloride concentration in the ARC-NSC as model output. Arcadis (2016) derived a

relationship between discharge rates (and the associated duration) and salt intrusion on the basis of a 3D salt

model. These results were used to convert the LHM discharges into chloride concentrations.

Arcadis (2016) established a picture of time-dependent salt intrusion for different discharge rates for the salinity

contours 0.27 PSU (Cl level 150 mg/l) and 0.9 PSU (Cl level 500 mg/l), see Figure 53. The salt intrusion for these

contours in relation to discharge and the duration of the shortfall are shown. Contour plots were established for

three discharge rates: 9, 16 and 23 m3/s. A chloride concentration of 150 mg/l is the maximum salinity for the

intake of surface water for drinking water production. The chloride concentration of 500 mg/l is the boundary

between relatively sweet brackish and relatively salt brackish water and it is therefore an important limit value in

nature management. This information was adopted as the starting point for the quantification of welfare effects

for nature and drinking water.

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Deliverable n°11.3

Figure 53 Intrusion of the salt tongue in the ARC [in km] for chloride concentrations 150 and 500 mg/l in the

scenario 'New Lock with selective extraction' and three discharge rates in the ARC (9, 16 and 23 m3/s).

The horizontal axis shows the number of days with a discharge of 9 m3/s it takes before the 500 mg/l

contour is reached for Nigtevecht (5 days, line 1) and Nieuwersluis (52 days, line 2).

Chloride concentrations were calculated for two locations on the Amsterdam-Rhine Canal that are important for

the drinking water and nature functions. The Nieuwersluis location is important for drinking water supplies

because there is an intake point here for drinking water extraction. Intake is shut down when the chloride

concentration of 150 mg/l is exceeded, with possible welfare effects as a result. The effects on the nature function

were considered for the Nieuwersluis and Nigtevecht locations, which are important because there is a direct

connection here with nature areas that are susceptible to high salt concentrations.

The hydrological calculations with the LHM produced a time series of 50 years for the discharge rate of the

Amsterdam-Rhine Canal at Weesp for the baseline situation, as well as for the measure high priority for flushing.

The average discharges per decade from the LHM were converted into daily values and then into a running

average discharge for time windows ranging from 1 to 7 weeks, with steps of 1 week. The minimum discharge per

time window was then determined for each year on that basis. The discharges associated with the baseline

situation and the measure are depicted in figure 55. With high-priority flushing, the discharge is at least 23 m3/s.

There are no extreme years in which discharge is lower because the model grants the highest priority to the

flushing demand, and this amount of water is available. When flushing is given a low priority, the minimum

weekly average discharge is 12 m3/s in the most extreme year. This information is the basis for assessing the salt

concentrations on the ARC/NSC.

Figure 54 The relationship between the return period and the average discharge for different time windows for the

baseline situation (flushing salt has a low priority) (left) and high-priority flushing (right).



An indication was given for each time window and year of whether the limit concentrations are exceeded. This

was done by interpolating the data as presented in Figure 54. The normative shortfall period was then selected for

each return period. The results are shown in Figure 55.

A chloride concentration of 150 mg/l was exceeded at Nieuwersluis in none of the scenarios. In the baseline

situation also (low-priority flushing), the discharge is still adequate to stop the salt tongue extending to the

Nieuwersluis location. There are therefore no effects on drinking water extraction and nature reserves that are

directly linked to this location.

There are no exceedances at Nigtevecht in the high-priority flushing scenario. In the baseline situation (low-

priority flushing), the discharge is inadequate to prevent salt intrusion.

Figure 55 Chloride exceedance duration at Nieuwersluis and Nigtevecht with high- and low-priority flushing. The

dotted lines represent the salt concentration without planned measures to reduce salt intrusion at the

sluice of IJmuiden.

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Deliverable n°11.3

Results 4.3

The multi-year hydrological results are the basis to estimate the welfare effects of drought for the most important

users of freshwater such as agriculture, shipping, drinking water and nature. These welfare effects are described

for each user of water. Furthermore, the impact of the intervention (lower priority for flushing) is estimated. The

welfare effect together with the probability exceedance will give the drought risk.

Agriculture

The impact on Dutch agriculture of drought and a lower priority for flushing was calculated using the AGRICOM

model (Mulder en Veldhuizen, 2016). This effect was then stated as the national welfare effect using the

agricultural pricing tool (Peerlings et al., 2017). AGRICOM calculates the drought and salt damage to crops and

the resulting loss of yield in kilos on the basis of the evapotranspiration deficit determined with the LHM (not

enough water available for maximum evaporation). AGRICOM values the loss of revenue using fixed crop prices in

order to determine the loss in terms of euros. The Agriculture price tool calculates the price effects on the various

crops depending on whether crops have a large international market share or are only traded in their own region

(see inset).

The welfare effect for agriculture has been estimated for the alternatives 'highest priority for flushing' and 'lowest

priority for flushing' (see Section 4.2). The welfare effect can be calculated in various ways, for example with or

without the effects of yield loss on the price, as calculated in the price tool. The calculation method has an effect

on the size of the welfare effect. This chapter first discusses the assumptions before presenting the results for the

high-priority alternative using different calculation methods. Finally, the results for the alternative examined will

be discussed in brief.

Price effects in agriculture

Reduced yields due to drought can affect the market price of a product. The size of this effect depends on how large the

change in production is by comparison with the market as a whole. If it is small, prices will not be affected. This is the case, for

example, when less Dutch cereals are produced due to drought. Dutch cereals account for a small proportion of the world

market and so the price will not change. If the relative change is large, for example in the case of bulbs, the price will change

when Dutch yields fall.

In the other case, the market is local and production in the region is equal to demand in the region. This will be the case with a

local product that is not traded outside the region, cattle feed being one example. Demand will not change a great deal if the

price changes since cattle must eat and cattle feed is not imported from outside the region.

In order to calculate the impact on the national economy due to the effects of drought on agriculture, a number of

assumptions were made. In all situations, supply was assumed to be price-insensitive. In other words, crops go on to the

market regardless of the prevailing price. The assumption was that production is fully determined by the price in the previous

period. Furthermore, it was assumed that variable costs have already been occurred before the start of a drought. It was

assumed for the international market that production does not change over the years.



A change in national welfare was defined as a change in the consumer surplus and producer surplus. The national welfare

effect of drought can be described with the following demand and supply curve in which p = price, q = supply, o = old and n =

new (see Figure below). The demand curve shows that demand rises as the price falls. In the case of the supply curve, supply

remains constant when the price rises: supply is price-insensitive. When supply falls due to drought, the price - in this example

- rises. The farmer loses the income BC, but receives the income AB in return because of the higher price. The new producer

surplus is therefore a 0. As a result of the higher price, fewer consumers will be willing to pay for the product, as a result of

which the consumer surplus will fall. Without price change and drought, the consumer surplus is: C. As a result of drought, this

will decline to: A. Part of the consumer surplus returns as income for the farmer: AB. The other component is welfare loss:

ABC. When a product is exported, a higher price has an effect on foreign consumers, but not on Dutch consumers. Since only

changes in national welfare are taken into account, the loss of consumer surplus outside the Netherlands should be deducted

from the total welfare loss to obtain loss of Dutch welfare. If there is no price effect, the consumer surplus will not change : the

price remains the same and therefore the same number of consumers is prepared to pay for the product. The welfare loss BC

will then be entirely for the account of the farmer.

It was assumed for the following crops that the price will change when production changes:

- arboriculture and bulbs (large national and international market share)

- grass and maize (local products not traded outside the region)

In the case of cereals and sugar beets, drought in the Netherlands will never affect the price because the price is determined on

the international market and the Dutch share of this market is small. In the case of the other products (potatoes, fruit, and

'other'), it is less clear what the effect of drought in the Netherlands will be on the price but, for the time being, it has been

assumed that the price will not change if Dutch production changes (Peerlings et al., 2017).

In order to investigate how different assumptions influence the welfare effect of drought and the alternatives, a

range of calculation methods were used. For example, the welfare effect was calculated both with and without a

price effect. In addition, loss of yield was calculated in two different ways. The usual way is to define the

difference between the potential yield and the actual yield as affected by water shortage. The potential yield is

defined in AGRICOM as the yield in a given year without water shortages, in other words including losses that

cannot be attributed to water shortages such as crop losses, storage, disease, etc. The actual yield is the

calculated yield in a given year, including losses due to water shortages. This calculation approach is in line with

the maximum yield that can be achieved if there had been enough water. The second method is more in line with

farmers' perceptions. The actual yield is compared with the long-term average historical yield. For farmers, the

years with damage are the years in which yields are lower than expected because of drought. Accordingly, this

calculation approach matches farmers' perceptions more closely than an approach calculating the difference

between the potential and actual yields.

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Deliverable n°11.3

We calculated the welfare effect of drought and the two alternatives in the following ways:

1. No price effect, the welfare effect is based on the difference between potential and actual crop

yields;

2. No price effect, the welfare effect is based on the difference between the actual crop yield and the

long-term average actual crop yield, only yields below average are considered to represent the

welfare effect of drought;

3. Price effect, the welfare effect is based on the difference between potential and actual crop yields

and the associated prices which are determined by the yield.

In the case of the variant in which a price effect is included, it should be noted that no correction was made for

exports. As a result, it was assumed that the entire welfare loss due to the loss of income is confined to the

Netherlands.

Other assumptions were:

- The calculation of the welfare effect included the entire area used for agriculture: both the area in which

irrigation may be used and the entire rain-dependent area.

- Irrigation costs are included in the welfare effect in all variants.

Results for the welfare effect in the current situation

Figure 56 shows the welfare effect for the three alternative calculation variants for the baseline alternative. The

largest welfare effect was found for the extremely dry year 1976 in all variants but the absolute size of the effect

varied widely. The largest welfare effect was found in variant 3 (with a price effect and the difference between the

potential and current yields), with a welfare loss of M€ 1200. It was followed by variant 1 (without a price effect

and the difference between potential and actual yields), with a welfare loss of M€ 700. Finally, the welfare loss in

variant 2 (without a price effect and compared to the multi-year average) was smallest at just under M€ 300. The

welfare effect on average for the multi-year series also varied considerably between the different variants. The

welfare effect was highest in variant 3 with an average of M€ 317/year and lowest in variant 2 at M€ 38/year. The

welfare effect in variant 1 was, at M€ 270/year, close to the welfare effect in variant 3.

The low welfare effect in variant 2 was in line with expectations since the approach looks only at the negative

outliers with respect to the average. In average years crop yield is lower than potential yield, as soil moisture

conditions are never optimal in all areas.

The high welfare effect in variant 3 can be explained as follows. The higher price will, depending on the assumed

price elasticity, compensate for some of the damage suffered by farmers because of the reduction in the crop

yield. The higher price means that fewer consumers will be prepared to pay for the product. Depending on the

details of the supply and demand curves for the product, the consumer may suffer a negative effect owing to the

price increase that is larger than the benefit for the producer. As a result, the welfare effect can be higher in a

situation in which the price effect is included than in a situation in which a price effect is excluded.

However, it should be pointed out here that the consumer component of the national welfare effect must be

compensated for by the export share (the export share of bulbs, for example, is approximately 70%). That results

in a reduction in the welfare effect of variant 3. Variants 1 and 2 implicitly assume that the entire welfare loss is for

the account of farmers and so there is no way to correct for exports in these variants.



Figure 56 Distribution of the welfare effect by year for the three variants.

Figure 57 shows the exceedance probability of the annual welfare effect for variants 1, 2 and 3. Variants 1 and 3

always result in a welfare effect: the potential yield is not achieved everywhere in any year. The welfare effect

does fall to zero in variant 2 because the yield is higher than average at a given moment. Once again here, it can

be seen that the welfare effect in variant 3 is higher than in variant 1. The difference is mainly seen in the years

with a small exceedance probability. This was as expected because the price will react sharply in the years with a

very high yield loss (in which there is a low probability of exceedance). If the negative effect on the consumer is

already larger than the benefit for the producer as a result of the price increase, the perceived negative effect will

become increasingly large in relative terms as the price increases further. This resulted in the ever larger

differences in welfare effect between variants 1 and 3 in the years with low exceedance probabilities.

Figure 57 Exceedance probability of annual welfare effect for the three variants.

Before long hydrological time series were available, characteristic dry years were used in the Netherlands to

estimate the risk of droughts. For the Dutch situation three characteristic years were used. The year 1976 is

characterized as a dry year with a probability of 1/100, the year 1989 represents a year with a drought probability

of 1/10 and the year 1967 represents an average dry year. In order to assess the risk of droughts the economic

impacts in these years were assessed, after which the results were interpolation to be able to produce a drought

risk curve. When comparing this method with a risk-based method, which considers the economic impacts of

droughts in multiple years, an indication of the added value of the risk based method is obtained.

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Deliverable n°11.3

Figure 58 illustrates the difference between the risk based approach and the ‘traditional’ approach using

characteristic years with a stated return period. In previous analyses, a certain year was associated with a certain

return period. Interpolation between the economic impacts associated with these years provided an estimation of

the risk. For example, the year 1976 was characterized as a year with a return period of 1/100, while 1989 is seen as

an average year (return period ½). The expected value of the welfare effect on agriculture that was estimated

based on the difference between potential and actual crop yields shows that characteristic years overestimate3

the risk on the agricultural sector. The differences are, on the one hand, caused by the interpolation between the

years and on the other hand by not assigning a for agriculture accurate return period to a year. When using the

risk approach the expected value (i.e. the average of the distribution) of the welfare effect is 270 Meuro per year,

while the expected value is 331 Meuro per year when using characteristic years. This difference is significant,

especially when used in a cost benefit analysis, which will sum the expected value of multiple years. If the welfare

effect is estimated based on the difference between the long-term average crop yield the actual crop yield, the

expected value using a risk approach is higher (38 Meuro) then using the characteristic years (18 Meuro). This

shows that the risk approach improves risk estimations in comparison with the ‘traditional’ method, this will help

policy makers to better assess risks and benefits of fresh water measures.

Figure 58 Illustration of the impact of using characteristic years and the risk approach on the final risk calculation .

Impact of the intervention

The effects of the baseline in comparison with the high flushing (or salt avoidance scenario) are limited. The

hydrological calculation results show negligible differences in the study area. Water availability and the salt

concentration for agriculture hardly change at all and this is reflected in the welfare effects: there are hardly any

differences between the baseline scenario and salt avoidance scenario (high priority flushing). Figure 59 and

Figure 60 show that, in variant 3, the average benefit of high priority flushing is € 16,000 per year. The differences

are even smaller in the other variants.

3 As a more precise estimation of the risk shows lower total risk values than using characteristic years.



Figure 59 Welfare effect in millions of euros per year for the baseline alternative (Low-priority flushing) and

alternative 1 (High-priority flushing). The differences between the welfare effects for the two

alternatives are very small.

Figure 60 Difference between the expected value of the welfare effect (drought risk expressed in welfare

indicators) for high-priority flushing and low-priority flushing in the five areas managed by water

authorities in the ARC-NSC. The effect of low-priority flushing is slightly negative (€21,600) for the

HHNK water authority (there is a more negative welfare effect); for the other wate r authorities, there is

no change or a slight positive effect.

Nature

Reduced water availability and increased chloride concentrations can have an effect on nature: a loss of intrinsic

natural value, in other words the value of nature for nature itself. This can be expressed as species loss or as a

reduction in numbers per species, which cannot be stated in monetary terms. In addition to the loss of intrinsic

value, a change in the biodiversity of aquatic nature can also have an effect on the economic value of nature. This

factor was not included in the analysis here.

In the ARC area, aquatic nature in particular is sensitive to higher chloride concentrations. Salt intrusion in the

ARC primarily affects the nature areas in the area managed by the Amstel Gooi en Vecht water authority because

these areas are directly connected to the northern section of the Amsterdam-Rhine Canal, where high chloride

concentrations are possible. Areas originally replenished by fresh water (seepage or rainwater) are most sensitive

to chloride. These are the seeps on the edge of the Gooi area, Loosdrechtse Plassen and Noorderpark. Brackish

seepage is found in the Groot Mijdrecht polder and further to the west around Ankeveen, Horstermeer and the

Nieuwe Keverdijkse polder. Here, less water would result in less flushing and therefore damage due to salt. The

most critical locations are the Noorderpark and the Loosdrechtse Plassen, where there are local species with low

chloride tolerance that cannot survive at higher chloride levels [correspondence Bart Specken, Waternet].

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Deliverable n°11.3

Figure 61 Locations considered for the nature function.

The intake point for water from the ARC for the Loosdrecht Plassen is at Nieuwersluis (see Figure 61). Another

important location is Nigtevecht, where the ARC is connected directly to the Vecht river. The Ankeveense

Plassen, among other bodies of water, obtain water from the Vecht system. The effects on nature were estimated

using the WFD-Explorer method, in which tolerance levels for different species are compared with environmental

factors in the water body (Wortelboer, in prep.) for the quality elements 'other water flora' and 'macro-fauna'. The

results provided indications of the change in the capacity to accommodate WFD reference species. Here, we

assumed that any reduction in that capacity would be associated with a fall in the intrinsic value of nature. When

determining the effects on nature, it was assumed that higher chloride concentrations occur in the summer

season (the growing season for aquatic plants). It was also assumed that the chloride concentrations at the inlet

points are representative for the concentrations in the entire area covered by the lakes (the buffering of

freshwater in the area was therefore disregarded). At present, these results cannot yet be compared directly with

the results of the other water consumers, such as agriculture and shipping, because these results are not stated in

monetary terms4. It may be possible, in time, to state the results as ‘nature points’

5, which is a method to estimate

impacts on biodiversity, and, on that basis, to determine a risk in terms of ‘nature points’.

4

Intrinsic value cannot be expressed in monetary terms as it represents the value of nature for nature itself. 5

Nature points are estimated by multiplying the surface of nature times the nature quality times a weighting factor (PBL, 2014).




No exceedance was found of the chloride concentration at the Nieuwersluis inlet (Figure 61) in the ARC. The

model results therefore indicate that there is no risk of negative effects in the Loosdrechtse Plassen due to the

intrusion of water into the ARC from the North Sea Canal for either aquatic plants or macro-fauna.

At the Nigtevecht inlet (Figure 61), no exceedance of the chloride concentration was found in the high-priority

flushing variant and no effect on nature is therefore to be expected. In the case of the baseline scenario, an

exceedance of the chloride concentrations was found and an effect on nature is therefore possible.

In the baseline scenario (low-priority flushing), the exceedance duration for the different chloride levels increases

with the return period. An exceedance duration of 2 weeks occurs once every 2-3 years at a concentration of 150

mg/l, once every 4-9 years for a concentration of 300 mg/l and once in about 25 years for a concentration of 500

mg/l. A period of two weeks in which the chloride concentration is exceeded is probably too short to eliminate the

sensitive species of aquatic plants entirely. In addition, 25 years is a long enough time for aquatic vegetation to

fully recover (Wegen naar Natuurdoeltypen 2). A recurrent increased concentration of 300 mg/l once every 4-9

years will lead to micro-succession (Noordhuis, 2016), in which there are fluctuations over time in the density of

species but in which vegetation as a whole does not undergo any significant change. There will be more

physiological damage and some species of aquatic plants will be lost temporarily in the case of the exceedance of

a chlorine concentration of 300 mg/l lasting more than 3 weeks, which occurs once every 10 years. Because of the

seed bank in place, these species will be able to recover in a few years. Even in this situation, the effects are likely

to remain negligible. A period of 2 weeks with a concentration of 150 mg/l every two or three years would not

seem to represent a problem for aquatic plants.

The data in the WEW database indicates a limited susceptibility of reference species of macrofauna to increases in

the chloride content. Above 1000 mg/l, there is a clear decline in the potential number of species present. 1000

mg/l was not used as a signal value in this study but given the prolonged salt intrusion at Nigtevecht, this level

may be exceeded, possibly resulting in damage to organisms (and particularly to sensitive species).

Drinking water

There may be welfare effects in the drinking water sector as a result of high chloride concentrations. At a

concentration higher than 150 mg/l, the intake of surface water for drinking water production will stop. Welfare

effects may result in various ways:

- additional costs for drinking water supplies using alternative routes;

- additional investment costs for supplementary water treatment;

- costs for interventions to change consumer behaviour (campaigns to reduce drinking water use).

It has been concluded that there will be no limitation of supplies of drinking water because the drinking water

sector always has enough alternative sources of drinking water in the short term and because it will take adequate

measures, such as increasing their buffer capacity, in the long term to prevent any limitation of supplies. The

welfare effect therefore consists of the costs of the investments that drinking-water companies have to make in

order to adapt to changing circumstances. This is a similar approach as used in the Berkel case.

If the salt concentration at the location of the intake point at the Nieuwersluis drinking water extraction location

exceeds 150 mg/l in the future, it will be necessary to invest in additional water treatment (such as fast and slow

sand filtration and reverse osmosis). This investment amounts to approximately € 15 to 20 million (Sikma, 2015).

In the long term, the Nieuwersluis drinking water extraction location may also be used for normal intake purposes

in order to deliver the amount of drinking water required for Amsterdam and its surrounding areas and possibly

for supplies to other drinking water companies. It is therefore expected that the importance of emergency intake

will increase in the future. In combination with increasing salt intrusion in the Amsterdam-Rhine Canal as a result

of climate change, the new sea locks and increasing movement through the locks, this makes a sound analysis of

the salt-related risks important.

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Deliverable n°11.3


It was assumed in this study that drinking water producers will not accept an annual probability of a failure of

supplies of more than 1%. If this probability increases, drinking water companies will invest in additional backup

capacity so that they can cope with the consequences of a possible fall in the availability of fresh water. RO

(reverse osmosis) as a treatment technology is one of the conceivable measures that can almost always be used.

On the basis of the additional costs for investment and production relating to RO, Ecorys has drafted a damage

function (KWR, 2017; Ecorys, 2017). The damage function for the drinking water company in this region

(Waternet) shows that the current backup arrangements provide a capacity of approximately 6 million m3/year.

Should this capacity prove inadequate, it will be necessary to turn to RO plants or other, cheaper, options (Ecorys,

2017).

Hydrological calculations with the National Hydrological Model (LHM) indicate that the current exceedance

probability for the standard of 150 mg/l is 0 (zero) for the Nieuwersluis inlet point, based on a 100 year time series.

Potentially the standard could be exceeded at lower return periods. This means that there are no welfare effects

for drinking water. Welfare effects may be found if climate and socio-economic scenarios are taken into account,

which will be analysed later in the context of IMPREX .

Shipping

The welfare effects for shipping in the ARC-NSC study area are caused by limitations on movements through a

number of locks. By contrast with the large rivers outside the study area, there are no limitations on navigation

depth here. To quantify the welfare effects, the method and the numbers from (IMPREX, 2016) were used. In an

IMPREX working session with water management authorities in April 2017, it was agreed that the welfare effects

for shipping are related to the use of the Small-Scale Water Input (KWA) system. Less lock capacity is available for

shipping during these periods. It is possible that the 'extra flushing' has an additional effect on the available

capacity for shipping but the relationship between inlet quantity and limitations on movements through the locks

has not been elaborated at this level of detail and it cannot therefore be applied in this case. The damage for

shipping in the baseline situation is therefore equal to the damage associated with the 'high-priority flushing'

measure.


The welfare effects for Spaarndam and the Princess Irene Locks are both related to the deployment of the Small-

Scale Water Input (KWA) system. On the basis of assumptions about the increase in the waiting time, the costs

per hour spent waiting at the lock and the number of shipping movements (see Table 7), it has been estimated

that the welfare effects for the Princess Irene Locks amount to € 31,000 per day of KWA and € 6200 per day of

KWA for Spaarndam. These amounts are based on 2008 price levels. For 2017, the total damage per day

associated with KWA is € 44,400.



Table 7 The number of extra hours per day spent on waiting when there are restrictions on shipping movements through

locks and when a ship must wait for the duration of one lock movement on the basis of passages through locks in the case

study area (Table 8) and own calculations.

Number of extra hours spent waiting on a day

with restrictions on shipping movements through

locks* for the baseline year 2008 IJmuiden

Sea Lock Irene Lock Beatrix Lock

Large lock in

Spaarndam

container vessels 1 0 0 0

non-container vessels 55 0 0 0

all professional vessels 0 49 64 10

ferries 0.5 0 0 0

cruise vessels 0.5 0 0 0

recreational vessels 0 0 6 20

*The calculations are based on 365 days per year, even though cost-benefit analyses for travel time losses usually take

holidays etc. into account, in other words times when there is less traffic. Calculations for road traffic are based on 342 days

per year. This number of days is not known for shipping. Furthermore, no information is available about the days on which

movements through the locks are limited.

The 50-year hydrological calculation with the LHM includes four periods during which the KWA was activated.

Table 8 shows the welfare effects in these years. It should be noted here that, in practice, water inlet at the

Princess Irene Locks is usually at night, which will probably result in less disruption for shipping.

Table 8 Welfare effects for shipping in the period 1961-2010.

Year Number of days KWA Welfare effect (€)

1964 20 880,000

1976 30 1,320,000

1991 10 440,000

2003 10 440,000

The 'high-priority flushing' measure has no effect on the implementation frequency of the KWA. There is

therefore no difference in the welfare effects for shipping between the current situation and the situation in which

the measure is implemented. In a risk-based consideration of the implementation of this measure, the shipping

effects in the current situation do not play a role in the economic assessment. It is possible that 'more flushing' will

result in more damage for shipping because the lock capacity for shipping will be reduced further. However, the

welfare effects cannot yet be calculated in such detail.

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Deliverable n°11.3

Risk profile 4.4

In order to get an overview of the risks, a risk profile can be obtained combining the different risks for the use

functions of water. The differences between the risk profile for the high priority flushing and baseline scenario

(low priority flushing) can be seen as benefits. As there is (almost) no difference between the risk with and without

the intervention, the same risk profile can be used to assess the risk (see Figure 62). The risk profile shows that

agriculture risk is dominant in this region. Although the shipping sector faces some risk of droughts, the expected

value of this risk is only 60,000 euro per year. In comparison with agricultural risk this is insignificant. No risk for

the drinking water sector and industry is assumed under the current socio-economic circumstances and climate

and the risk for nature is not expressed in monetary values and therefore not included in the risk profile. There can

be concluded that in this region the current economic risk (excluding nature) is limited to the agricultural sector.

Figure 62 Exceedance probability of the annual welfare effect.


The aim of the IMPREX ARC-NSC case study is to investigate the feasibility of a risk approach to decision-making

about freshwater management in the ARC-NSC area on the basis of a case in which the drought risk in a baseline

situation is compared with the situation involving an intervention. The measure used in the case study was 'high-

priority flushing for the Amsterdam-Rhine Canal'. This intervention reduces salt intrusion into the Amsterdam-

Rhine Canal from the North Sea Canal and it requires water that cannot therefore be used elsewhere.

By drawing up an algorithm for salt, it is possible to establish a picture in a practical way of the effects of a lower

discharge on the chloride concentration in the ARC-NSC. It is then possible to state this effect in terms of the

impact on nature and drinking water supplies. For the effect on agriculture, a comparison is made between

different calculation methods (potential, actual or long-term) for the welfare effect.

The current risk has been calculated for shipping and agriculture. The comparison shows that, in this area, the risk

for agriculture is much higher than for shipping. Including the effects outside the study area could alter this

conclusion. Water from outside the study area is required to give flushing a high priority. Although the extraction

of water from the rivers Lek and Waal are limited, during drought periods this could have adverse effects on

shipping and drinking water supply outside the study area. For shipping no welfare effects are expected in the

current situation and so there is no risk. Adverse effects can be expected for nature. Although it is possible to

estimate the effect on nature with the WFD Explorer these cannot be quantified in such way that the results can



easily be included in the risk approach. By expressing the results of the WFD Explorer, possibly accompanied by

quality factors (such as the presence of amphibians), as nature points, risk can be expressed in nature points.

However, these points will not be directly comparable with the other risks.

The model calculations show that the intervention 'more flushing' has no negative effect on the agricultural yield

in the study area because the extra water required can be supplied from the Lek and Waal rivers. In the study area,

the intervention influences nature only, and in a positive way. The negative effects of flushing are found outside

the area in the rivers Waal and Lek. Bringing in additional water from the Waal may have adverse effects on ship-

ping due to the reduction of the navigating depth and the associated need to limit the size of cargoes. In addition

to the effects on shipping, a reduced discharge in the Lek or Waal can lead to higher salt intrusion in the down-

stream sections of the rivers. This can also have an indirect reverse effect on the ARC-NSC area if the chloride

concentration in the Hollandse IJssel increases and the KWA has to be implemented more often. These effects

outside the study area are not investigated, as these considerations go beyond the boundaries of the study area.

On the basis of the risk approach implemented here, limited to the ARC-NSC study area, the risk approach could

not be fully demonstrated. Given the very limited effects of the intervention in the study area considered, it is

difficult to demonstrate whether the risk approach provides added value. The added value of the risk approach

over the ‘traditional’ approach has been shown. The risk method leads to a more reliable annual welfare effect.


As mentioned, the work is conducted in close collaboration with a number of end users. The Dutch Ministry for

Infrastructure and Environment, the Freshwater Supply Programme Office, the Dutch governmental organisation

responsible for water management (Rijkswaterstaat), the Foundation for Applied Water Research, (STOWA,

knowledge centre of the water boards) and a number of water boards are involved as stakeholders in the

research. We follow the concept learning by doing, and use the outcomes of demonstrations of the method for

further development and refinements of the generic risk assessment framework and tool. For this learning and

development of the tool, we closely cooperate with the end users. This is done in practice by organizing several

workshops with the main stakeholders to discuss the main assumptions and present the approach. In addition,

stakeholders are involved in collecting data and selecting the focus of the study.

The users consider the approach as promising, which is demonstrated by a current application of the approach in

the Dutch Delta Programme Fresh Water that aims to adapt to (future) droughts. Lessons learned from the case

studies are used to draw general assumptions for nationwide analysis based on risk expressed in monetary terms.

This analysis includes assessment of the current and future risk as well as differences in risk of potential fresh

water measures that are defined by the national and regional governments. Final decisions on fresh water

measures (supply, demand and measures to reduce vulnerability) should be made by 2021.

Next steps 4.7

Work on the ARC-NSC case study within IMPREX will continue in order to improve the risk approach and

demonstrate the added value. As the current risk is limited, the risk approach will be applied to assess future risk

in the ARC-NSC region, including climate change and socio-economic scenarios. Furthermore, the measure ‘low

priority flushing’ will be assessed; with the difference that water supply from the river Waal will be limited. The

expectation is that the risk will increase under these assumptions. Besides the use of 50 year time series, extreme

dry years will be determined based on RACMO and probabilistic ‘ARMA’ time series both developed in work

package 5 of IMPREX. The impact of these extreme years will be analysed to assess the added value of including

more extreme years in the risk approach. Finally, the impact of (model) uncertainty on the risk profile will be

examined by unravelling the individual uncertainty factors with help of representatives of regional water

authorities.

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Deliverable n°11.3

Comparing the case study results 5

In all three case studies a drought risk is determined, as a probabilistic assessment of economic drought impacts.

The exact definition differs for the Spanish and Dutch case studies. In the Júcar case study, drought risk is defined

as the probability reservoir storage levels, or, for this work package, of experiencing water deficits by the

agricultural sector. In the case studies Berkel and ARC-NSC, drought risk is defined as the probability of

occurrence of the drought, times the welfare effect it causes. This drought risk can be expressed as a yearly

expected damage value or an exceedance probability of the annual welfare effect.

The case studies demonstrate that probabilistic drought risk assessments provide valuable information for

operational water management as well as for long-term planning. In the Júcar basin the concept of drought risk is

used to produce seasonal forecasts of reservoir water levels. The stochastic ARMAX model has been used to

generate probabilistic river flows for multiple years. River flows serve as input in the SIMRISK model simulating

the management of the system, which results in exceedance probability of reservoir water levels. Reservoir levels

are an important indicator for water managers to take decisions and determine amongst others agriculture

productivity loss and loss in hydropower. Next steps include defining long term drought risk for usage in planning

and translation of the results in economic damage. In the Berkel and ARC-NSC case studies, drought risk is

defined as the yearly expected value of the welfare effects of droughts or as the exceedance probability of the

annual welfare effect. Historical time series of precipitation, evaporation and river discharges6 with a length of 20

to 50 years, serve as input in a series of hydrological models for the surface water system, groundwater system

and unsaturated zone. The AGRICOM model is applied in order to assess the impact on the agricultural sector.

Impact on the other relevant sectors (use functions) are assessed based on simple dose-effect relationships. These

have been further translated into welfare effects. The drought risk is presented as a risk curve for the different use

functions of water. The information on drought risk can be used to determine if measures should be taken to

reduce the impacts of droughts. By comparing the welfare effect for the use functions, it can be determined in

which sector the available water is of most economic value (note: economic value will not always be the only

defining factor). Furthermore, the benefits – described as a reduction in risk (either a reduction in negative effect

or a reduction in probability of occurrence) – can be assessed to economically evaluate the impacts of measures

that increase water availability or reduce vulnerability of actors to droughts.

The largest difference between the drought risk approaches as applied in the Spanish and the Dutch case studies

is the focus on either seasonal forecasts or long-term planning. Especially in regions that already face extensive

droughts and who have the ability to manage the allocation of fresh water resources, seasonal forecasts are

valuable. The Júcar basin is an example of such a region. In the Netherlands, the current droughts are less

prolonged and there are no reservoirs that can be managed to reduce the impact of droughts. The most short-

term drought measures, which can profit from seasonal forecasts, are relatively small. Dutch policy makers are

especially interested in future drought risk to be able to decide whether or not additional measures should be

taken. Although not demonstrated in the case studies yet, the developed drought risk approach can answer this

question by using climate enforced long-term time series and socio-economic scenarios. This analysis is currently

carried out and will be reported in deliverable 5.5. Furthermore, the Júcar case study will also estimate long-term

drought risk for planning based on the ARMAX model. Also these results will be reported in deliverable 5.5. The

Dutch case studies have worked with time series of 20 to 50 years which are used to determine return period of

6 In order to use these series for a risk evaluation in the present day climate, it has been be explored if the historical time series of discharges,

precipitation and evaporation historical time series are representative for the current climate. There is a positive trend in annual precipitation

over the years (Stowa, 2015), however, the precipitation trend in the ‘summer half year’ is not significant meaning that there is no significant

impact of this trend on dry periods. Furthermore, there is no significant trend in river discharge of the Rhine and Meuse over the years.

Therefore it has been decided to take the historical time series as representative for the current climate.



certain impacts. The Júcar case study uses stochastic time series based on the ARMAX model. The advantages of

using deterministic (historical) time series instead of probabilistic series is that – if available - they are relatively

straightforward, easily understandable and no probabilistic model has to be developed. These time series allow

for statistical analysis and determination of probabilities as well, but for a more limited range of return periods.

These time series do not include information on the extent of an extreme event or on return period of more than

about 10 to 50 or - when extended - 50 to 100 years. This information is especially valuable if there is an indication

that the extreme events can lead to disruptive situations, for example problems with drinking water supply or

bankruptcy of a large part of the agricultural businesses. As this probability is expected to be higher in the Júcar

basin, it is especially important to include extreme events with smaller return periods in the drought risk in this

basin. The probabilistic ‘ARMAX’ time series based on precipitation and discharge developed provide valuable

information on the extreme droughts with high return periods (e.g. 1000 years).

In The Netherlands, the most extreme drought in the last 100 years was not disruptive, indicating that the impacts

of drought with a small (1/50 to 1/150 years) return period are limited. However, the impact in an extreme climate

change scenario or at very small return periods can still be substantial. Therefore, the contribution of extreme

events to drought risk is currently tested in the ARC-NSC case study. A purely stochastic method is being used,

which combines an autoregressive modelling approach (ARMA) and a copula method for incorporating

dependences between a precipitation and river discharge time series (see the deliverables of work package 5 for a

comprehensive description of the model). Besides an ARMA model, GCM/RACMO time series were used to derive

synthetic time series of Rhine River discharges. However, it was observed that the performance of the

hydrological model for drought periods was poor; low flow discharges of the Rhine River are systematically

underestimated. For that reason, it was decided to implement the ARMA model to assess the impact of extreme

droughts on the drought risk.

Another difference is the expression of the risk in economic terms. Dutch stakeholders ask for expression of the

impacts in economic terms (impact on the welfare in The Netherlands) in order to include these impacts in a social

cost benefit analysis, while the Spanish stakeholders prefer expression of the impacts in physical terms (reservoir

levels). The case studies show that a risk approach is able to respond to both needs.

A third difference is the size of the pilot area, and therefore the spatial scale on which the risk is determined. In

the Júcar case study, the whole Júcar River Basin is involved. In the Berkel case study only a small river basin is

studied, and the ARC-NSC pilot area covers the canals and especially the regions depending on water supply from

the canals. The Dutch case studies are relatively small-case compared to the Júcar case study. This difference in

scale leads to a difference in expected detail and accuracy of the results. In the Dutch case studies it is observed

that the demanded accuracy is high, leading to the conclusion that the National model that is used, does not

provide enough detail on all demanded output. This goes especially for the Berkel case study. Therefore, a

drought risk analysis on an even smaller (sub-basin of the Berkel) scale is carried out with a much more detailed

hydrological model. Results of both approaches will be compared in deliverable 5.5.

In summary, although the approaches differ, both approaches are using information on drought risks to inform

policy makers on drought management and planning. The case studies give a valuable overview of the different

possibilities and methods of using drought risk. Both studies are not completely finished and will provide new

results in the near future, which will be reported in deliverable 5.5. The new results will give a more profound

overview of the added value for future planning.

In Table 9 a comparison between the case studies is made. From this, the applicability of each method for other

areas can be derived.

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Deliverable n°11.3

Table 9 Comparison of the case studies on key-elements.

Júcar basin Berkel and ARC-NSC

Aim Improve drought forecasts on the short

and medium term in order to improve

water system management and assess

the effects of new measures.

In future work, and especially in WP13,

long term forecasts including climate

change will be used to obtain risk

assessment, in order to define the

program of measures for river basin

plans

Give more insight in current and future drought

risk to assess the (economic) impacts of

measures. Facilitate management decisions on

water distribution, water demand measures

and measures that reduce vulnerability to

droughts.

Definition of risk Risk perception from Júcar River Basin

Stakeholders, and therefore, decision

making, is more based on the

information related to future reservoir

storage (collective risk perception as

implemented in the Drought Plans), and

to future deficits (sectoral and individual

risk perception) than on expected

economic losses.

Nevertheless, in IMPREX project,

estimates of expected economic losses,

and exceedance probabilities will also be

obtained, and the results will be

discussed with stakeholders.

Probability of occurrence of the drought, times

the welfare effect it causes. This drought risk

can be expressed as a yearly expected damage

value or an exceedance probability of the

annual welfare effect.

Time frame Seasonal (in this deliverable), and long

term (in future work to be reported in

D5.5 and D13.4)

2014 and 2050

Risk model Hydrological series are produced with

ARMAX model for seasonal forecasts,

and ARMA model for long term

analysis. The risk assessment model is

SIMRISK model in both cases. SIMRIK

performs a Monte-Carlo analysis using

hundreds Of hydrological time series

generated with ARMA(X) model.

50 year deterministic time series, use of a

model network including water allocation and

impact models (agriculture, shipping and

nature), in the future phase an ARMA model

will be tested.

Effects considered

Agriculture, hydroelectricity, urban

supply, environmental requirements

Agriculture, Shipping, Drinking Water, Nature

Spatial scale Large: river basin of 22,186.61 km2 Current application Small: maximum several

hundreds of km2. Also applicable on larger

spatial scales.

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Deliverable n°11.3

Implications 6

The management instrument will help to identify opportunities and measures and to quantify their subsequent

consequences. By combining this with probability of occurrence we can quantify drought-related risks in present

and future conditions (under the influence of climate and socio-economic change) associated with various

measures (which may include controlling water flows in major waterways, local infrastructural solutions to

supplement water and local adaptations by the end users). This also allows the economic comparison of various

interventions, which helps to prioritize operational and strategic actions and investments. We follow the concept

learning by doing, and use the outcomes of demonstrations of the method for further development and

refinements of the generic risk assessment framework and tool. The case study applications so are considered as

a proof of concept.

Given the very limited effects of the investigated measures and the relatively low drought risks in the Dutch study

areas considered, it is difficult to demonstrate whether the risk approach provides added value. Applying the

method in a highly managed and complex water system, such as the ARC-NSC system is challenging, as the

responses to drought highly depend on the type of event. Differences in the management and operation of the

system during droughts have a direct impact on the (long term) drought risk. In the case of ARC-NSC not all

management and operation rules are well presented in the model, which is affecting the estimation of the

drought risk. However, the ARC-NSC case study also indicated that the approach can improve the current

assessment of the impacts of droughts and therefore also the estimation of costs and benefits of drought

measures. The method is found to be promising and will therefore be used in the Dutch Delta Programme Fresh

Water, which aims to prepare the Netherlands on the impacts of climate change. The developed risk approach will

be used to estimate the current and future risk (under changing climatic and socio-economic circumstances) of

droughts and the impacts of fresh water measures. For the Júcar case study, the risk assessment methodology

provides useful information for first level early warning and action against drought, as well as for risk perception

by the public. In order to manage droughts, a more elaborated and detailed information system is needed. The

drought risk method is an option to improve drought management. Moreover, this methodology can be extended

to any system with a proper calibration adapted to that area.

The approach is expected to be applicable in other countries. A (simple) water allocation model, time series (or

stochastic time series) and damage functions are required to obtain a risk profile. Better insight in current and

future drought risk around the world will help policy makers to decide on the need to implement drought

(mitigating/adaptation) measures. Furthermore, this approach can help to select drought measures that are most

beneficial from a societal perspective.

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Deliverable n°11.3

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Grant Agreement N° 641811