white paper novel concepts in hy- drometeorological risk assessments€¦ · the concept is...

White Paper – novel concepts in hy-

drometeorological risk assessments

IMPREX has received funding under the European Union HORIZON 2020

Grant agreement° 641811

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White Paper - Novel concepts in hydro-

meteorological risk assessments

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Deliverable 5.1

Deliverable Deliverable title

Related Work Package: 5

Deliverable lead: H. de Moel

Author(s): Hans de Moel, Gabriela Guimarães Nobre, Philip Ward (IVM/VU)

Johannes Hunink, Peter Droogers (FW)

Emma Aalbers (KNMI)

Stefan Lüdtke, Heidi Kreibich, Kai Schröter (GFZ)

Saskia van Vuren (HKV)

Marjolein Mens, Marnix van der Vat (Deltares)

Contact for queries [email protected]

Grant Agreement Num-

ber:

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 white paper explains four novel concepts related hydromete-

orological risk assessments. For each concept, the current state of

the concept is described as a starting point for researchers and

practitioners. Moreover, directions in which these promising con-

cepts can be further developed are explored.

The following four concepts are addressed:

• Future weather and compound events

• Link between climate variability and risks

• Risk-based water allocation

• Probabilistic flood damage assessments

Developments in these four concepts will take place over the

course of the IMPREX project. This goes not only for methodolog-

ical developments, but the concepts will also be tested in case

http://www.powerstep.eu/



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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 Euro-

pean 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 5-12 2015 De Moel First set up after kick-off meeting discussion

v.1 24-02 2016 All authors First draft compilation of all chapters

v.2 7-03 2016 All authors Full draft after revision by all authors

v.3 11-04 2016 All authors Incorporation of review feedback into final version

v.4 22-11-2016 De Moel Added graphics of novel concepts

v.5 25-6-2017 De Moel Adjustments after review comments EU

study areas with stakeholders in order to illustrate their potential

usage from a practical point of view.

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Deliverable 5.1

Table of contents

Versioning and Contribution History ............................................................................................................. 4

1 Introduction ........................................................................................................................................... 6

2 Future weather and compounded events ............................................................................................. 8

2.1 Current state of the concept ......................................................................................................... 9

2.2 Way forward ................................................................................................................................ 10

2.3 Usefulness of the concept to stakeholders .................................................................................. 11

3 Climate variability and flood/drought risks .......................................................................................... 12

3.1 Current state of the concept ....................................................................................................... 13

3.2 Way forward ................................................................................................................................ 14


4 Methods to support Drought Risk Management ................................................................................. 15


4.2 Way forward ................................................................................................................................ 18


5 Probabilistic impact assessment .......................................................................................................... 20


5.2 Way forward ................................................................................................................................ 22


6 Concluding remarks ............................................................................................................................. 24

7 References ........................................................................................................................................... 25



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1 Introduction

Hydrometeorological risks such as droughts and floods can have huge repercussions on the well-being of

human society, both in terms of human suffering and economic impact. Flood events can cause enormous

direct damages (Barredo 2009), indirect economic impacts (Koks et al. 2015) and fatalities (De Bruijn et al.

2014). Flood events can occur relatively sudden, particularly with flash floods, but in some conditions warn-

ing times of several days are available due to weather forecasting, or when downstream regions can see

upstream flood waters approaching days in advance. Droughts, on the other hand, are a much more grad-

ual hazard, building up over months and becoming more severe over time. Both hazards, however, are

directly related to hydrometeorological weather phenomena such as storms, precipitation events and dry

spells. Correspondingly, these hazards are also influenced by changes in climate. This relates to anthropo-

genic climate change, which creates large uncertainties on what the future (decades/centuries) looks like.

Besides these large-scale changes in climate, hydrometeorological events are also influenced by shorter

term variations in climatic conditions driven by various regional or global climatic oscillations (such as the

El Niño Southern Oscillation or North Atlantic oscillation).

In the climate and risk (research) communities, risk is usually described as the product of the hazard (the

physical event), exposure (assets and population potentially in harm’s way) and vulnerability (the suscepti-

bility of the exposed units to the hazard). Managing hydrometeorological risks is thus a multi-disciplinary

task, requiring understanding of the various processes that eventually determine the impact. Such pro-

cesses include both the processes that drive the hazard (i.e. meteorology, hydrology), and the processes

that determine the impact of the hazard on society (i.e. engineering, geography, economy).

In flood and drought management, risk management approaches are increasingly developed and applied,

recognizing the need to assess both hazard and consequences (combination of exposure and vulnerability)

for decision making. This means that measures to reduce risk can both entail measure that reduce the

hazard, and measures that reduce the exposure or vulnerability. For instance, measures can be: (1) the

forecasting of the meteorological conditions, (2) preventing flood waters with natural water retention

measures; or (3) providing irrigation water or change to drought-resistant crops in otherwise rain-fed agri-

cultural areas.

Risk assessments form the basis for decision making in the management of such hydrometeorological risks.

In such assessments, (model) inputs from different disciplines are combined in order to assess the overall

risk. This requires both in-depth knowledge on the individual components (i.e. the weather, the hydrology,

the economic impact), and a way to combine all this information in a useful and correct manner. With risk

management approaches – and thus risk assessments – becoming more prevalent, there is a clear need for

improved knowledge and methods to assess such risks.

Whilst a lot of knowledge is present in the risk community (and the IMPREX consortium) to assess hydro-

meteorological risks, also the shortcomings of current approaches and practices are well known. This

holds for the determination of the hazard, the determination of the consequences, the tools available to

manage risks and the substantial resources (both in work and data requirements) necessary to perform

such analyses. The (academic) research communities on these topics are continuously developing new

concepts to address these challenges. With the knowledge base present in the IMPREX consortium, it is

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Deliverable 5.1

possible to pick up some of the challenges that are currently present in the field of hydro-meteorological

risk assessments. Within IMPREX four novel concepts have been chosen to investigate deeper. These four

concepts cover by no means all of the challenges faced in hydro-meteorological risk assessments, but

each one is built on recent advances in research and links closely with specific expertise of the consortium

members.

This white paper aims to introduce four promising new concepts in the realm of hydrometeorological risk

assessments. These four concepts are:

➢ Future weather and compound events. The impact of a future climate is usually addressed using

results from GCMs. However, when it comes to extreme weather (such as pluvial flooding), the

responsible processes are usually not resolved in GCMs. Rather, the (spatial and temporal) resolu-

tion of numerical weather forecast models is necessary for that. This concept therefor explores the

benefit to use high resolution daily tested numerical weather prediction models (in conjunction

with climate models) to project high impact weather events in a future climate (future weather).

Moreover, the possible simultaneous occurrence of events that together generate a high impact

(such as storm surge and heavy rainfall) is addressed as well.

➢ Link between climate variability and risks. Assessing hydro-meteorological risks from a to z involves

the combination of many different models (climate, meteorological, hydrological, economic, etc.).

Correspondingly, a full-fledged assessment requires a lot of time and resources. However, at the

annual time-scale, changes in the impact ultimately results from changes in the climate. By explor-

ing a direct relationship between natural climate variability and flood and drought impacts (i.e.

flood damages, crop losses), it may therefore be possible to find links that would allow for fast and

practical impact assessments on the basis of known climatic oscillations.

➢ Methods for drought risk management. In the risk community, risk is often indicated by expected

annual damage, which integrates the impacts of events with different probabilities into an average

annual impact (in euros). This is well established in the realm of flood risk management, but its

application in drought and water resources management is much more complex because of the

many different users/sectors and ways in which shortage of water can result in damage. Therefore,

a novel framework to estimate drought risk in terms of expected annual damage including different

levels and users will be explored.

➢ Probabilistic impact assessments. Despite known uncertainties and poor transferability, the go-to

method to estimate flood damage remains the use of depth-damage curves, which typically uses

only a single (hazard) variable to predict flood damage. An approach capturing more damaging

variables which also makes uncertainty more explicity would be through the use of Bayesian net-

works or decision trees. This would improve significantly the description of damaging processes

since they capture the joint probability distribution of all input variables and model the probabilistic

dependency among the variables as well as their inherent uncertainty information.

In the following chapters, for each of these novel concepts the current state of knowledge is provided,

giving a starting point for interested researchers and practitioners. Moreover, ways on how to bring these

concepts forward are detailed, resulting in a research agenda for these concepts. In the IMPREX project,

many of these topics will be addressed to further develop and (ultimately) operationalize these concepts,

including testing in IMPREX case-study areas.



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2 Future weather and compounded events

Coastal areas are threatened by water from multiple sides: storm surges hitting the coast, sea level rise,

high river discharges from heavy or persistent rainfall or sudden snowmelt in the hinterland, and heavy

local rainfall. When the meteorological conditions associated with these events are physically and

statistically independent, the chance that they will happen simultaneously is small and can be estimated in

a straightforward manner (i.e. the product of the probability of the individual events) for e.g. the design of

flood defence structures. However, if a weather system associated with the development of a storm surge

also brings heavy rainfall to the hinterland, the individual weather variables are physically and statistically

related. Strongly dependent on the temporal and spatial scale of the system, the individual impacts of the

simultaneously or successively occurring weather variables, themselves not necessarily extreme, can add

up resulting in a severe so-called compound event (e.g. Kew et al. 2013, Van den Hurk et al. 2015, Klerk et

al. 2015; Wahl et al., 2015). The general definition of a compound event adopted here is ‘an extreme impact

that depends on multiple statistically dependent variables or events’, after Leonard et al. (2014).

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Deliverable 5.1

It is only in the last years that, in the view of climate change, a proper analysis of compound events has

(re)gained attention in climate impact research and that appropriate methods for the analysis of compound

events are being explored (White, 2007, Seneviratne et al. 2012, Leonard et al. 2014). Conventionally, to

assess climate risks, univariate climate statistics (e.g. return periods of precipitation or water levels) are

derived from either historical observations or (downscaled) climate model simulations. The joint probability

of partially dependent events herewith cannot be quantified, and this approach does not give insight in

changes in the occurrence of compound events in a future climate setting. Rather, what is required is a

method that examines the synoptic weather system associated with the compound event, exploring the

physical dependencies between the variables, the mutual interaction of the variables with the physical

system and of the individual impacts (see Leonard et al. 2014). The impact depends strongly on the spatial

and temporal structure of the correlation between the various variables, and thus a proper analysis of

compounding events relies heavily on the understanding of their role in generating high impacts.

Realistically simulating compound events requires the use of high resolution weather models – opposed to

climate models as commonly used – run under the climate conditions of interest and coupled to impact

(hydrologic and/or hydraulic) models. The approach of impact tailored, high resolution simulations of

(future) weather events is known as the ‘Future Weather’ concept, recently conceptually introduced by

Hazeleger et al. (2015), and applied in the Dutch climate scenarios KNMI’141 (KNMI, 2015, Lenderink et al.,

2012).

2.1 Current state of the concept

Future Weather (FW) explores plausible scenarios based on synoptic weather events associated with high

impact hydrological events in the current as well as in the future climate. The concept is developed to

complement the conventional approach in climate impact research. Where the latter is mainly based on –

simply put – statistical derivatives of climate model ensembles, the Future Weather concept acts at the

physical understanding of specific weather events and their impact, be it single variate or compound

events2. An elaborate description of the concept can be found in Hazeleger et al. (2015); here we briefly go

through the elements the concept comprises.

• Central in the Future Weather approach is the impact of (future) events, as experienced by

stakeholders / end-users. Future weather cases thus should be identified and specified in

consultation with them.

• The event under study is described in terms of its statistics in the current climate, either based on

observations or a large ensemble of high resolution weather simulations, to indicate the rarity of

the event.

• The physical mechanisms (the synoptic weather system) leading to the event are analysed and

described.

• A future analogue of the current event is developed by running a high-resolution weather model

under future boundary conditions. The selection of the boundary conditions is crucial for the

plausibility of the future event. They can for example be selected from a large climate model

ensemble; current boundary conditions can be smartly manipulated to represent future

1 The analysis of weather conditions causing substantial societal impact in the Netherlands and future changes in them, started after the develop-

ment of the last generation climate scenarios for the Nether lands KNMI’06.

2 In practice the two approaches might not always be as clearly identifiable as described here, and often are a mix, using the benefits of both.



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conditions; models resolving synoptic scale weather systems can be pushed to the desired synoptic

situation (see Rasmijn et al. 2014).

Until now, the Future Weather concept has mainly been applied to analyse (future changes in) precipitation

in the Netherlands and the hinterland for a variety of spatial and temporal scales, see Lenderink et al. (2012)

for an overview. The most vivid example might be the future analogue of the August 2010 heavy

precipitation event in the east of the Netherlands, showing an increased maximum intensity and spatial

extent of the precipitation field in a warmer climate (Lenderink and Attema, 2015). Although not yet as

frequent, compound flooding has been subject to research using the scope of Future Weather as well. Van

den Hurk et al. (2015) analysed a compound surge and precipitation event, as occurred in winter 2012 in

the North of the Netherlands. Sea water levels were higher than the inland water level for five consecutive

days preventing excess water to be discharged in the Wadden Sea. Using a large regional climate model

ensemble coupled to a water balance model to simulate inland water levels, it was shown that indeed there

was a physical dependency between the storm surge and heavy precipitation, but also that the highest

inland water levels were associated with astronomical neap tide – completely uncorrelated with the

synoptic weather system. For a much larger system, namely the Rhine catchment, Kew et al. (2013)

identified the synoptic weather systems causing both heavy precipitation and storm surges using a large

global climate ensemble and meteorological proxies for river discharge and storm surge. They found that

the probability of a simultaneous occurrence of a storm surge in the North Sea at the mouth of the Rhine

and heavy 5-20 days extreme precipitation in the Rhine catchment could be four times higher than for the

events being independent. Klerk et al. (2014) confirmed the relationship between high discharge and sea

water levels by coupling the meteorological simulations to a hydrological model, but only with a time lag

between the two events. Comparison of the cases (not all strictly within the FW approach) shows how the

compound event depends on the spatial and temporal scales of the system that is analysed. Moreover,

they illustrate that using an impact model with the right processes and states and thus being able to

accurately model the system’s memory and travel times might reveal unexpected dependencies or factors

contributing to or weakening the compound event.

2.2 Way forward

The concept has been shown to be suitable to analyse (future) precipitation and compound flooding. To

develop the concept further and at the same time directly serve stakeholders coping with risks of extreme

impacts of future weather, the concept will be applied to a large variety of future weather cases. For the

users this is directly relevant in that it exposes and maps the vulnerability of ‘their’ system. For the scientific

community it has added value because compound events in systems with a wide range of spatial and

temporal scales and orientations are analysed, giving insight into potential relevant scales for dependency

of weather variables and their joint impact.

As mentioned, a realistic simulation of the interaction of the weather variables with the physical system

requires coupling of the weather model to an impact model. An issue that should be considered carefully

when doing this is the usually unpreventable bias correction of the weather variables that are input in the

impact model. Especially in the analysis of compound events, with inherent physical dependencies between

weather variables, this may distort the analysis, since the interdependencies of the bias corrected variables

might be changed (Leonard et al., 2014) or not all relevant characteristics of a variable are captured (Klerk

et al., 2014).

For the Future Weather concept in general, ‘Future Weather cases’ that are produced indeed provide

tangible, clear, easily communicable examples of what the weather could look like in the future. At the

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same time, we should be able to communicate the plausibility and preferably even the probability of such

an event. Framing the physical understanding of specific weather events within a statistical context

therefore will be part of the future developments around the concept.

2.3 Usefulness of the concept to stakeholders

As sketched above, FW adds to the physical understanding of drivers of high impact hydro-meteorological

events, and provides information at the scale where climate change is experienced: at the local scale as

changes in high-impact weather. It is at this scale that reliably information is required by stakeholders to

assess the vulnerability of the water system and/or society to these changes. FW provides the information

for specific cases, co-designed by the stakeholders. Moreover, the cases can be designed such that they

can be used as a stress test for climate adaptation design.

Another feature of FW is related to the perception of climate change, which is likely to be influenced by

the (recent) experience of extreme weather events. FW may aid in increasing the awareness of climate

change and its impact by visualizing future weather cases and relating these to present day experiences.

This may generate more (public) support for adaptation measures.

Within IMPREX, FW will be applied in case studies in the Netherlands and the United Kingdom. In consul-

tation with the stakeholders, risks to compound flooding will be analysed in the current and future cli-

mate.



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3 Climate variability and flood/drought risks

Every year, human societies experience disasters from natural hazards. Between 1980 and 2012, weather-

related or hydrometeorological hazards have resulted in a total of US$2.6 trillion in assets losses and 1.6

million fatalities (Munich Re, 2013). Globally, these hazards represent 87% (18,200) of total disasters, and

the two main sources of threat are floods and droughts (World Bank, 2013). Many regions of the European

Union (EU) territory are facing major challenges driven by the onward change and variability of climatic

conditions. During the last decade, important countries that supply the territory with agricultural goods

have more frequently experienced droughts above normal values (ESPON, 2013), and when looking at the

future, the challenges are even greater. In addition to the influence of atmospheric variations, anticipated

climate change is affecting a range of factors associated with drought and floods, and high confidence exists

that increased temperatures will raise the frequency of hydrological hazards and the risk of agricultural

drought (Dai, 2011; IPCC, 2013). Hydrometeorological events bring risk to the EU regions, particularly to

flood and drought-prone areas that host crucial economical and human activities.

Considering the current and future impact, the development of adaptation measures to mitigate flood and

drought damages are required. However, our capability to prepare for disasters is challenged by large

uncertainties, and our limited understanding of important driving forces of hydrometeorological hazards

such as climate variability (Apel et al., 2004). Large-scale indices of climate variability are often basis for

seasonal forecast models, which provide information regarding the upcoming weather condition in

monthly to seasonal scale. The oscillation of the climate greatly affects the magnitude and frequency of

hydrometeorological variables, as previously demonstrated in many scientific studies. However, the

relationship between these large-scale indices and its impact on society, such as flood damages and crop

productivity, has received little attention in the scientific literature. Once a significant connection is

identified, climate variability, flood/drought impact and seasonal forecasts could reveal crucial information

for disaster risk management.

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Deliverable 5.1


Several studies at the global scale have shown that river discharge and precipitation across large fractions

of the globe are related to El Niño Southern Oscillation (ENSO) (Dettinger and Markgraf, 2000; Dettinger

and Diaz, 2000; Ward et al., 2010; Ward et al., 2014; Sun et al., 2015). Investigations on the pan-European

scale demonstrate that hydrometeorological variables are physically influenced by large-scale climate

variability (Fraedrich, 1994; Mariotti et al., 2002; Shaman and Tziperman, 2011). For instance, Bouwer et

al. (2008) demonstrated that the mean and peak discharges of European basins are sensitivities to four

different atmospheric indicators. Particularly for Europe, a wide range of teleconnection indices have been

shown to be related to hydrometeorological variables. Therefore, flood and drought across the continent

cannot be explained by one single climate mode, but regionalization is necessary, selecting the best

performing index for each location.

Large-scale climate variability, such as ENSO and North Atlantic Oscillation (NAO), is known to influence

local and regional climate and accounts as an important driver when aiming to improve the seasonal

forecast of local weather condition and extreme events. Even though the relations between climate

variability indices and hydrometeorological variables have been widely investigated, little is known about

the relationships with the direct impact of hydrological extremes. Ward et al. (2014) and Veldkamp et al.

(2015) demonstrated for the first time that ENSO strongly influences flood risk and water scarcity events

in large parts of the world. Relationships between this climate mode and agricultural drought impacts have

been studied for famine warning purposes in Africa, applying remote sensing-based impact indicators

(Phillippon et al., 2012). Researchers also assessed the links between crop production and indices of

atmospheric oscillation on a global (Iizumi et al., 2014) and European scale (Gonsamo and Chen, 2015).

To establish teleconnections between climate variability and impacts, various impact indicators can be

used: (i) damage and disaster databases from re-insurance companies or similar (ii) for drought: anomaly

indicators from crop production databases (iii) remote sensing-based impact indicators. Each of these

impact indicators has its strengths and weaknesses, and they should complement each other when

establishing relationships with indices of climate oscillation. Empirical models that could describe such links

can be used to build seasonal forecasting systems for flood and drought impact in Europe, which has so far

not been achieved (Brown et al., 2010). At a number of operational meteorological centers, the time-scale

ability to predict weather variability has improved (Palmer et al., 2004; Cantelauble and Terres, 2005;

Scaiffe et al., 2014). Combined with empirical models on potential impacts, this creates opportunities for

the management of hydrometeorological risks and can potentially result in economic benefit for end-users

(Cantelauble and Terres, 2005).



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3.2 Way forward

Following an initial methodology proposed by Ward et al. (2014), more investigation should focus on the

influence of indicators of atmospheric oscillation on flood/drought risk. Therefore, we propose to take a

step further than current practice, bypassing the intermediate physical variables (“leapfrogging”) and

establishing relationships directly with impact indicators. Such a leapfrogging approach is very instrumental

(fast and practical) to assess flood/drought risks. Evidently, to develop adaptation strategies a thorough

understanding of the physical system is still needed (Droogers and Bouma, 2014). Considering the

complexity of the climate variability patterns affecting Europe, a complete investigation requires the use

of multiple indices of large scale atmospheric drivers. Also, flood and drought impacts can be measured in

different ways, thus numerous indicators should be studied to build empirical models. This multi-index

impact-based approach should result in regionalized relationships: for each zone the strongest set of

indicators explaining most variance in impacts must be selected. These relationships will allow the seasonal

prediction of flood and drought impacts across Europe.


The methods and relationships that follow from this approach can be fed into risk outlooks and early

warning systems for the agricultural sector in those areas that are directly affected by floods and droughts.

The seasonal predictions of impacts enable stakeholders for better disaster prevention, mitigation and

preparedness (Dilley and Heyman, 1995). Also, a seasonal prediction on the likelihood of drought can allow

agricultural systems to anticipate better and plan better for its consequences, thus improving the climate

resilience and sustainability of the agricultural sector (Rosenzweig and Hillel, 2008).

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Deliverable 5.1

4 Methods to support Drought Risk Management

Drought is one of the major natural hazards frequently causing large impacts worldwide (UNISDR 2009;

WMO 2013). Impacts from drought events in Europe (e.g. Rhine basin in 2003, Po and Ebro basins in 2012),

and drought events outside Europe (Amazon basin in 2005 and 2010, Sacramento-San Joaquin delta in

2007-2009, Yangtze delta in 2006 and 2011) are just a few examples that demonstrate how vulnerable

societies are to droughts.

Droughts originate from a period of below-normal precipitation and may result in a reduction of water

available from rivers, streams, reservoirs and aquifers. To prepare for future droughts, it is widely acknowl-

edged that countries should move from crisis management to risk management (OECD 2013; Rossi and

Cancelliere 2013; Wilhite 2011). Many countries respond ad-hoc to droughts with emergency management

and disaster relief, but this is not considered a sustainable solution in view of climate change and socio-

economic developments (Fu et al. 2013; Wilhite et al. 2014). Proactive drought risk management, a sys-

tematic process to prevent, mitigate and prepare for drought-induced disaster (UNISDR 2009), is therefore

promoted over reactive emergency management.



16

Large uncertainties about future climate change and socioeconomic developments impose a challenge for

societies to cope with possible consequences arising from drought events in the future (IPCC 2012; Wilhite

et al. 2014). Subsequently, there is a strong wish to explicitly account for uncertainty and risks in drought

management practice. Since drought risk is the interaction of the natural drought hazard and its impacts

on society and the environment, a method for drought risk assessment should consider both the probability

of drought-related hazard events, as well as their possible socio-economic and environmental conse-

quences. For the stakeholder, an analysis of drought risk may be more meaningful than the analysis of

drought likelihood, as non-climatic drivers of risk may have a strong impact (population growth, water con-

sumption per capita, technological development in agriculture, etc.). A drought risk assessment method

can be used to: (1) quantify drought risk and how it is affected by climate change and socio-economic

developments, and (2) assess the cost-benefit ratio of measures to prevent water shortage and/or reduce

drought impact, i.e. to reduce drought risk to an acceptable level.


A risk-based approach to support water resources management is rather new (Hall and Borgomeo 2013).

In recent studies supported by the European Commission3, conceptual frameworks for drought risk man-

agement have been developed, including definitions of drought hazard, exposure and vulnerability.

Hazard in the context of water resources management is the occurrence of a drought, defined as a tempo-

rary situation of precipitation and/or streamflow below a user-defined threshold. The drought hazard is

further characterized by its spatial extent, intensity, severity, frequency and duration. Vulnerability is de-

fined as the potential to be harmed and is related to all system characteristics that determine the water

use, for example population, land use, economic activity, environment, agriculture. Vulnerability can be

further decomposed into exposure (water demand) and susceptibility. Unlike with flood risk analysis, where

hazard can be analysed separate from vulnerability analysis, drought hazard is much more intertwined with

vulnerability. Hydrological drought becomes meaningful when the user-defined threshold is related to the

water use. In practice, a (hydrological) drought hazard analysis therefore takes into account water demand

and focuses on the probability of a water shortage, thus combining hazard and exposure. The next step in

a drought risk analysis is then the translation from water shortage into societal impact. This requires

knowledge on the value of water to society (susceptibility). Impacts of water shortage differ between dif-

ferent water users and understanding coping mechanisms requires detailed socio-economic research.

Much work has been done in the hazard part and the impact part (exposure and vulnerability) of drought

risk assessment:

- Drought hazard & exposure: Compared to other natural hazards, droughts are difficult to determine,

because they are slow-onset and their non-structural impacts cover large geographic regions (Wilhite

et al. 2014). Furthermore, human activities in one part of a basin may influence drought occurrence in

other locations, for example due to excessive irrigation, deforestation, and over-exploiting groundwater

resources (Mishra and Singh 2010). Much of the drought literature focuses on the identification and

characterization of drought on different spatial scales through the use of drought indicators (e.g. McKee

et al. 1993; Mishra and Singh 2010; Palmer 1965; Veldkamp et al. 2015). Hisdal and Tallaksen (2000)

and Van Loon (2015), among others, give an overview of common methods to quantitatively describe

meteorological and hydrological drought events.

3 DroughtR&SPI: http://www.eu-drought.org/, MEDROPLAN: http://www.iamz.ciheam.org/medroplan/, DEWFORA: http://intranet.iamz.ci-

heam.org/dewfora-e-learning/, Xerochore: http://www.feem-project.net/xerochore/index.php

http://www.eu-drought.org/

http://www.iamz.ciheam.org/medroplan/

http://intranet.iamz.ciheam.org/dewfora-e-learning/

http://intranet.iamz.ciheam.org/dewfora-e-learning/

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Deliverable 5.1

- Drought impact (vulnerability, susceptibility): Quantifying water shortages for different users is only the

first step into an estimation of drought impacts. How water shortage translates into a disruption of the

socio-economic functioning of society depends on its vulnerability. Water shortage due to droughts may

have a negative impact on water quality and the environment as well as all social and economic activities

that depend on water supply such as domestic and municipal water use, agricultural production, power

generation, and industry. Estimating such impacts is a complex task because of the indirect and diffuse

impacts of droughts and the different mechanisms in which drought can cause damage in different sec-

tors. Because of this complexity, in practice water shortage is often used as proxy instead (Rossi and

Cancelliere 2013). Methods for economic drought impact quantification have been developed in several

studies over the past decade. Griffin (2006) and Young and Loomis (2014) are two standard works on

the economic valuation of water resources and recent research synthesised current knowledge about

cost assessment methods for various hazards, including droughts, looking at different sectors such as

housing, industry, transport, agriculture, the environment and human health (Logar and Bergh 2013;

Meyer et al. 2013).

Most literature on drought risk assessment focuses on either parts of the risk assessment. Few integrated

studies have had practical policy implications (Harou et al. (2009). A recent attempt to carry out a policy

analysis jointly considering probability of drought-related hazard events, as well as their possible socio-

economic and environmental consequences, is presented by Deltares et al. (2015) who developed a con-

ceptual framework for drought risk analysis for the Dutch government. Inspired by the Netherlands’ flood

risk analysis framework, they showed how probability of water availability can be combined with a physical

dose-effect relationship (e.g. between water availability, water demand and impact on a sector) and in turn

with an economic damage function (translating the physical effect of water shortage into a welfare effect),

to obtain a risk curve (cumulative distribution function of the economic damage). They then used the ex-

pected annual economic damage as a risk metric. They successfully applied the framework for four sectors:

shipping, drinking water, agriculture and nature, but also concluded that underlying data to determine

welfare effects is often lacking.



18

4.2 Way forward

Figure 1 shows the conceptual framework for the quantification of drought-related risks, considering both

the probability of drought-related hazard events, as well as their possible consequences. In general, the

method will allow a cost-benefit analysis of current water resources management practice as well as quan-

tify cost-efficiency of alternative drought risk reduction strategies, for any region where water shortage

may occur. To move the framework forward, the following aspects need attention:

- Further development of the underlying conceptual framework, i.e.;

o Considering a number of end-users/sectors rather than focussing on one, since sectors may suffer

from droughts simultaneously;

o Focussing on extreme event characterisation: historical observations are often too short to capture

low-probability drought events;

o Impact modelling (including economic analysis, integrating hydrology and impact modelling) and

combining hazard and impact in a probabilistic way;

- Making a step towards a decision-support tool and illustrating the merits of drought risk assessments in

decision making, i.e.:

o to support the decision on water supply service levels for all regions and sectors

o to analyse the impact of climate variability and socio-economic developments on drought risk

o to assess the cost-benefit ratio of measures to reduce drought risk (prevent water shortage and/or

mitigate drought damage) (cost-benefit analysis)

In order for such developments to be relevant to policy makers, it is best to develop the framework in

concrete case studies in cooperation with end-users. Within the IMPREX project this will be demonstrated

for a number of case studies in the Rhine-Meuse delta region.

Figure 1. Conceptual framework for drought risk assessment

19

Deliverable 5.1


Based on this concept, tools can be designed to support quantitative risk-informed decision-making for

fresh water management for the Netherlands. Within the IMPREX project, such methods and tools will

provide decision support for the decision on water supply levels by the Delta Programme. Water is currently

allocated according to a prearranged ranking system (a water hierarchy scheme based on a list of priorities),

for when water availability drops below a critical low level. With a risk-based tool, the aim is to have supply

levels available that are based on the probability of occurrence and economic impact of water shortage,

and that are transparent for all water users in the regional water systems and the main water system. The

research and the case studies will be conducted in close collaboration with the Dutch Ministry for Infra-

structure and Environment, the Freshwater Supply Programme Office, the Dutch governmental organisa-

tion responsible for water management (Rijkswaterstaat), the Foundation for Applied Water Research,

(STOWA, knowledge centre of the water boards) and a number of water boards.



20

5 Probabilistic impact assessment

In view of increasing disaster losses (Barredo 2007, Kundzewicz et al. 2013) we need to work for a signifi-

cantly improved and more efficient reduction of natural hazard risks. Integrated risk management is the

way forward and has been more and more implemented in recent years. The basis for efficient risk man-

agement is a comprehensive, reliable risk assessment. Risk assessments extend the hazard analysis to an

additional impact analysis that investigates exposure and vulnerability/susceptibility of elements at risk like

companies or residential buildings. However, impact assessments in the framework of hydrometeorological

risk analyses have in common that complex damaging processes are described by relatively simple, deter-

ministic approaches, which are associated with high uncertainty (Meyer et al. 2013). Probabilistic damage

modelling (as opposed to deterministic) is expected to improve significantly the description of damaging

processes and inherently provides uncertainty information. While this is true for most hazards, we will

focus in the following on the example of flood impact and damage assessments.

21

Deliverable 5.1


Presently, deterministic depth-damage functions (dose-response curves relating water depth to damage)

still provide the standard approach to estimate direct flood damage (Merz et al. 2010; de Moel et al. 2015).

That is, most methods focus on inundation depth as the determining factor for flood damages. They are

set up for certain objects or land use units using bivariate statistical analysis of empirical or synthetic flood

damage data and are specific for the region or country for which they were developed (Penning-Rowsell

and Chatterton 1977, Green 2003).

However, several studies provide impact quantifications of additional damage determining factors like du-

ration of inundation, sediment concentration, contamination of flood water, availability and content of

flood warning, precautionary measures and the quality of external response in a flood situation (e.g. Smith

1994, Wind et al. 1999, Penning-Rowsell and Green 2000, Thieken et al. 2005, Kreibich et el. 2005, 2009).

Such knowledge has been integrated into the development of still deterministic but multi-variable damage

models. For instance, Zhai et al. (2005) developed a multi-variate regression model with inundation depth,

house ownership, house structure, length of residence and household income to estimate damage in pri-

vate households in Japan. For private households and companies in Germany the multi-variable damage

models Flood Loss Estimation Model for the private sector (FLEMOps) and for the commercial sector

(FLEMOcs) were developed, applied and validated at the micro- and meso-scale (Thieken et al. 2008,

Kreibich et al. 2010, Seifert et al. 2010, Elmer et al. 2010).

Various challenges still remain however:

➢ High uncertainty: Multi-variable models outperform depth-damage functions and are as such an

improvement in flood damage modeling (Apel et al. 2009; Merz et al. 2013). Still, flood damage

modeling is subject to considerable uncertainty (Merz et al. 2004, de Moel and Aerts, 2011), which

results from various sources including an incomplete knowledge and representation of the damag-

ing process, which crystallizes for instance in generalizations concerning the damage influencing

variables and aggregated input data. It is crucial to capture and quantify uncertainties in flood dam-

age estimates for risk communication and informed decision making.

➢ Unclear transferability: The transfer of damage models in time and space is critical and leads to

significantly increased uncertainty (Thieken et al. 2008). Commonly, damage models do not per-

form well, when they are applied in different regions than those for which they have been devel-

oped (Jongman et al. 2012, Cammerer et al., 2013, Schröter et al. 2014). Flood damage models,

which have been derived on data from geographical regions with comparable socio-economic,

building and flood event characteristics, perform better than those from different regions with

differing characteristics.

➢ Changing susceptibility: Only few flood damage models take risk mitigation measures like private

precaution into account as a damage determining variable. Doing so supports temporally dynamic

flood damage and risk modeling and enables the evaluation of risk management and climate ad-

aptation strategies.

Probabilistic, multi-variable flood damage modelling tackles these challenges. Recent studies used bagging

decision trees (Merz et al. 2013) or Bayesian Networks (Schröter et al. 2014) for estimating flood damage

of residential buildings on the micro-scale. Both approaches are improving the description of the damage

processes and inherently provide quantitative information on uncertainty associated both with the random

heterogeneity of input data and model structure. A decision (regression) tree is a flowchart structure that

recursively divides the data into groups and estimates a classification (regression) model. Bagging decision



22

trees are an ensemble of multiple decision trees and describe well the damaging processes since they do

not require a global relation between input variables and damage, and non-linear and non-monotonic de-

pendencies can be represented by the tree structure. They are able to consider interactions between input

variables (Breiman 1984, Merz et al. 2013). Bayesian networks can capture the joint probability distribution

of all input variables and model the probabilistic dependency among the variables (Nielsen and Jensen

2007). As such they can better model damaging processes. Additionally, Bayesian networks offer the pos-

sibility to consistently update the model with additional information at hand (Li et al. 2010; Pollio et al.

2007). Schröter et al. (2014) showed that Bayesian Networks improve the spatio-temporal transferability

of damage models. Overall, both model approaches are multi-variable models, which can also consider

private precaution as input variable to estimate flood damage. While their suitability and improvements in

damage modelling using bagging decision trees and Bayesian models have been proven scientifically on the

micro scale, these models are still relatively far from operational applicability. Additionally, up-scaling ap-

proaches for their use at regional – European – level still need to be developed.

5.2 Way forward

To enable the applicability of probabilistic damage models (like Bayesian networks) in meso- to large scale

flood risk analyses, they need to be adapted to the information available at that level. An up-scaling proce-

dure for the probabilistic damage models from the micro-scale to the meso-scale is necessary (e.g. Thieken

et al. 2008, Kreibich et al. 2010). The damage model structure may be preserved, but all the input variables

need to be estimated area-wide. This may involve reduction of input variables. For this purpose, suitable

data representations or proxies need to be identified. Exposure and susceptibility characteristics can be

correlated with area-wide (statistical) information accounting for spatial variations e.g. on NUTS levels and

linked to land use data (e.g. the German ATKIS Basic DLM or the European-wide land-cover data CORINE).

The transferability challenge may be tackled as follows: Flood damage modelling for the European scale

could be undertaken with an up-scaled Bayesian network flood damage model, which will gradually be

updated for the different European countries or regions with local/regional information where available as

for instance empirical damage data or country specific flood damage functions. The general structure of

the Bayesian network may remain the same, since we expect similar damaging processes in the whole of

Europe. As such, a consistent approach for Europe can be achieved and empirical damage data intensive

model structure development can be avoided. For regions/countries where local information is available,

the Bayesian network parameters will be updated (Li et al. 2010). This approach will improve country spe-

cific damage modelling by incorporating spatially differentiated information and will enable consistent un-

certainty analyses.

The influence of risk mitigation measures on flood damage can be included through susceptibility indicators

used as model input variables. This approach shall support temporally dynamic flood damage modelling

and the evaluation of climate adaptation strategies.

23

Deliverable 5.1


The concept of probabilistic impact assessment can be used by stakeholders when integrated into risk anal-

yses, which commonly are the basis for decisions in risk management. Since risk analyses are typically deal-

ing with extreme events and failure scenarios which have hardly been observed, they are associated with

considerable uncertainty. Therefore, decreasing this uncertainty with improved impact assessments as well

as quantifying it with probabilistic approaches will most likely lead to better decisions in risk management

(Downton et al. 2005, Pappenberger and Beven 2006). For instance, if a decision on basis of the benefit-

cost-ratios has to be taken between two alternative protection measures, this might be strongly influenced

by their uncertainty. Risk-averse decision makers which definitely want to avoid a benefit-cost-ratio below

one may prefer the alternative with the smaller uncertainty, although it might have a lower benefit-cost-

ratio than the other. Others may decide for the alternative which has the higher benefit-cost-ratio, alt-

hough it might have a higher uncertainty including the possibility of a benefit-cost-ratio below one.



24

6 Concluding remarks

For each of the four concepts introduced in this report, it is not a-priori known if the research will reach

the desired product. As such, Work Package 5 can basically be regarded a methodological lab where the

developments of each these concepts take place. The research agendas focus on the described ways for-

ward and the further development of these concepts towards useful concepts or models for researchers

and practitioners. Thus, the novel concepts will be developed further from a methodological point of view,

in order to proof their potential to significantly improve hydro-meteorological risk assessments.

Moreover, within IMPREX these concepts will also be applied and tested in case study areas that are avail-

able in the IMPREX project. This will take place in the sectoral work packages (WP7-12), most notably in

WP7 (on flooding) and WP11 (on agriculture and droughts). For this, the team of WP5 is also part of these

sectoral work packages allowing for close collaboration. Together with other partners in these sectoral

work packages and the stakeholders there, we aim to illustrate the practical applicability and usefulness

for decision making of the novel concepts. Finally, requirements for further improvements will be identified

over the course of the project.

25

Deliverable 5.1

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