a multi-criteria optimisation of scenarios for the protection of water resources in europe

Report EUR 25552 EN

2012

Ad de Roo, Peter Burek, Alessandro Gentile, Angel Udias, Faycal Bouraoui, Alberto Aloe, Alessandra Bianchi, Alessandra La Notte, Onno Kuik, Javier Elorza Tenreiro, Ine Vandecasteele, Sarah Mubareka, Claudia Baranzelli, Marcel Van Der Perk, Carlo Lavalle, Giovanni Bidoglio

Support to the EU Blueprint

to Safeguard Europes

Waters

A multi-criteria optimisation of scenarios

for the protection of water resources in

Europe

European Commission

Joint Research Centre

Institute for Environment and Sustainability

Contact information

Prof.Dr. Ad de Roo

Address: Joint Research Centre, Via Enrico Fermi 2749, TP 261, 21027 Ispra (VA), Italy

E-mail: [email protected]

Tel.: +39 0332 78 6240

Fax: +39 0332 78 6653

http://ies.jrc.ec.europa.eu/the-institute/units/water-resources.html

http://www.jrc.ec.europa.eu/

This publication is a Reference Report by the Joint Research Centre of the European Commission.

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JRC25552

EUR 25552 EN

ISBN 978-92-79-27025-3

ISSN 1831-9424

doi:10.2788/55540

Luxembourg: Publications Office of the European Union, 2012

European Union, 2012

Reproduction is authorised provided the source is acknowledged.

Printed in Italy

3

Contents

Summary............................................................................................................................................. 5

Introduction......................................................................................................................................... 7

Setup of the study................................................................................................................................ 8

Scenarios......................................................................................................................................... 9

Studies from ENV used in this work.............................................................................................. 11

Basic data used.................................................................................................................................. 12

Meteorological data....................................................................................................................... 12

Meteorological data used in the scenario modelling ....................................................................... 15

Hydrological data for calibration and validation ............................................................................ 15

Irrigation water requirement data 2006 and 2030 ....................................................................... 15

Water abstraction for Livestock ..................................................................................................... 18

Public Water Withdrawals ............................................................................................................. 18

Industrial water withdrawals.......................................................................................................... 20

Energy water withdrawals EPRTR.............................................................................................. 21

Total water abstraction .................................................................................................................. 22

Point sources data.......................................................................................................................... 22

Cost of water retention scenarios ................................................................................................... 23

Cost of other scenarios .................................................................................................................. 24

Benefits (reduced economic loss for sectors, reduced flood damage) ............................................. 25

Modelling methods............................................................................................................................ 28

CAPRI .......................................................................................................................................... 28

LUMP ........................................................................................................................................... 29

LISFLOOD ................................................................................................................................... 30

EPIC for N & P fluxes................................................................................................................... 34

LISQUAL ..................................................................................................................................... 35

The Optimisation routine............................................................................................................... 45

The use of water-regions and macro-regions............................................................................. 49

Sensitivity analysis and uncertainty ............................................................................................... 51

The Baseline conditions .................................................................................................................... 54

Costs of the scenarios per region ....................................................................................................... 56

Results of the Individual Scenarios.................................................................................................... 58

The Baseline Scenarios.................................................................................................................. 58

The 11-Riparian zone scenario ...................................................................................................... 68

The 12-Afforestation scenario ....................................................................................................... 70

The 21-Urban25 scenario .............................................................................................................. 72

The 22-Urban50 scenario .............................................................................................................. 74

The 31-Grassland scenario............................................................................................................. 76

The 32-Buffer strip scenario .......................................................................................................... 78

The 33-Grassed waterways scenario .............................................................................................. 80

The 34-Crop practices scenario...................................................................................................... 82

The 43-Meander scenario .............................................................................................................. 84

The 51-N-fixing scenario............................................................................................................... 86

The 52-Optimum Fertilization scenario ......................................................................................... 88

The 71-Desalination scenario......................................................................................................... 90

The 91-Irrigation Efficiency scenario ............................................................................................ 92

The 93-Water Re-Use scenario ...................................................................................................... 94

The 94-Water Saving scenario....................................................................................................... 96

4

The 95-Leakage scenario............................................................................................................... 98

The 96-Wastewater re-use scenario ............................................................................................. 100

Results of the multi-criteria optimisation ......................................................................................... 102

Example of the procedure of the multi-criteria optimization for the Danube ................................ 103

Region 11 Danube: Crop optimisation ...................................................................................... 111

Region 11 Danube: Flooding optimisation ............................................................................... 113

Region 13 Iberia Mediterranean: Water saving optimisation ..................................................... 115

Region 13 Iberia Mediterranean: Crop optimisation.................................................................. 116

Region 13 Iberia Mediterranean: Flooding optimisation............................................................ 117

Region 10 France Atlantic: Water saving optimisation.............................................................. 118

Region 10 France Atlantic: Crop optimisation .......................................................................... 120

Region 10 France Atlantic : Flooding optimisation ................................................................... 122

Limitations of the current study, and further work needed ............................................................... 124

Conclusions..................................................................................................................................... 125

References....................................................................................................................................... 128

5

Summary

A modelling environment has been developed to assess optimum combinations of water retention

measures, water savings measures, and nutrient reduction measures for continental Europe. This

modelling environment consists of linking the agricultural CAPRI model, the LUMP land use model,

the LISFLOOD water quantity model, the EPIC water quality model, the LISQUAL combined water

quantity, quality and hydro-economic model, and a multi-criteria optimisation routine.

Simulations have been carried out to assess the effects of water retention measures, water savings

measures, and nutrient reduction measures on several hydro-chemical indicators, such as the Water

Exploitation Index, Environmental Flow indicators, N and P concentrations in rivers, the 50-year

return period river discharge as an indicator for flooding, and economic losses due to water scarcity for

the agricultural sector, the manufacturing-industry sector, the energy-production sector and the

domestic sector. Also, potential flood damage of a 100-year return period flood has been used as an

indicator.

The study shows that technically this modelling software environment can deliver optimum scenario

combinations of packages of measures that improve various water quantity and water quality

indicators, but that additional work is needed before final conclusions can be made using the tool.

Further work is necessary, especially in the economic loss estimations, the water prices and price-

elasticity, as well as the implementation and maintenance costs of individual scenarios.

The most promising individual scenarios are the following:

The Water saving in households scenario, which aims to achieve 25% water savings in the domestic

sector by simplistic measures (replacing showerheads, etc.), improves the Water Exploitation Index,

and reduces the amounts of abstracted and consumed water. Positive changes are observed in several

regions in the following order of importance: Great Britain (GB), Po (Milan area), Mediterranean

Iberia, southern Italy and Odra/Vistula (Warsaw area), with even higher local effects around populated

areas. Reductions in water consumption are also simulated in many other areas.

The Irrigation efficiency scenario aims to improve irrigation efficiency levels from the current

average of 74% (Eastern Europe) - 77% (Western Europe) to 93% by applying drip irrigation in all

areas. This scenario improves the Water Exploitation Indices and the Environmental Flow Indices,

especially in the Danube, Iberia/Mediterranean, southern Italy, Sicily, Sardinia, Greece/Evros, and the

France/Atlantic macro-region. Improvements are also simulated in other regions. An additional benefit

is also that the use of deep (geological) groundwater is reduced by around 20% in all areas. Due to the

larger amount of water available when less irrigation water is consumed, economic losses are also

reduced for industry, the public sector, and agriculture.

The scenario Water re-use in industry, which assumes that 50% of the water abstracted for industry

is re-used, leads to improvements in the Water Exploitation Index of around 10% in several regions,

with most effects being simulated in the industrial Elbe/Ems, GB, the Rhine/Meuse/Scheldt region,

southern Italy, Sardinia, Sicily, the Po and the Odra/Vistula region.

The Leakage reduction scenario, which assumes that 50% of the current leaking in the public water

supply is repaired, improves the Water Exploitation Index in all regions, most dramatically in GB

(24%), Ireland (38%), Po (6.2%), Adige/Balkan (5.8%), and Greece/Evros (4.1%), with local effects

even higher. It also improves the Environmental Flow Indicators by several days per year, especially in

the region of GB (8.6%), Ireland (5.8%), Sardinia (3.7%) and Sicily (2.2%).

6

The Urban-greening 25% scenario - establishing urban greening measures - reduces flood peaks

(Q50) slightly, for example by 0.7% in the region of GB. This scenario consequently also reduces the

potential flood damage by 27% in the region of GB in general, and by even more locally in England.

Further positive effects are simulated in the regions of the Rhine/Meuse/Scheldt (0.2% Q50 decrease,

12% flood damage decrease), Elbe/Ems (0.3% Q50 decrease, 6.5% flood damage decrease), Po (0.2%

Q50 decrease, 4.5% flood damage decrease) & Mediterranean Iberia (0.2% Q50 decrease, 6.0% flood

damage decrease). On the other hand, reduced runoff from cities results in less availability of water for

extraction, and thus leads to a slight deterioration of the WEI in those areas. However, environmental

flow (10th

percentile) conditions improve in GB by 0.4%, likely because of increased baseflow as a

consequence of increased infiltration. Because of reduced total river flow amounts, N concentrations in

rivers are simulated to slightly increase under the urban25 scenario in the regions of GB (0.2%) and

the Elbe/Ems (0.3%).

The N-fixing Scenario and the Optimum Fertilization Scenario both reduce Nitrate and Phosphate

concentrations in all regions that have significant agriculture, most dramatically in the Elbe/Ems

region (68% and 26%), France/Atlantic (61% and 25%), Denmark/northern Germany (61% and 45%),

the Rhine/Meuse/Scheldt (63% and 39%), and GB (75% and 26%).

The Re-Meandering Scenario, which increases the meandering of the current rivers by increasing the

length and storage capacity of the river bed reduces flood peaks in all European regions, and is

estimated to significantly reduce the flood damage potential especially in the Elbe/Ems (11%), Danube

(10%), Odra/Vistula (9.8%), Po (6.8%), Rhine/Meuse/Scheldt (5.3%) and France/Atlantic regions

(5.8%). At the same time, environmental flow conditions improve in some areas, for example in GB

(0.1%) and Ireland (0.3%), while environmental flow conditions are simulated to deteriorate by 0.5 to

0.9% in Mediterranean regions. Nitrate concentrations decrease in all regions, with maximum changes

of 20-25% in GB and Ireland.

The Crop Practices Scenario, which simulates the effects of the implementation of combined

methods of improved crop practices (reversed/reduced organic matter decline and increased mulching

and tillage), results in reductions of potential flood damages in all regions, including areas with high

absolute flood damages in regions such as the Odra/Vistula (6.2% reduction), the Rhine/Meuse (7.8%

reduction), Great Britain (15.9% reduction) and the Danube (8.2% reduction).

Installing desalination plants along the coastlines would improve the Water Exploitation Index in

several European macro-regions, and decrease the number of days during which Environmental Flow

cannot be respected, especially in Spain and Italy.

7

Introduction

In the context of the impact assessment for the forthcoming "Blueprint to Safeguard Europe's Water

Resources", the European Commission is developing a baseline scenario that will bring together

climate, land-use and socio-economic scenarios and look at the implication for water resources

availability and use under different policy scenarios. This work is carried out by the Joint Research

Centre of the European Commission. The objectives of the assessment are:

1. The development of an optimisation model linked with dynamic, spatially explicit water

quality and quantity models that allows for the selection of measures affecting water

availability and water demand based on environmental and economic considerations,

2. The application of this model to all European River Basins for a baseline scenario and a

number of alternative policy and socio-economic scenarios.

The aim of the modelling exercise is to help maximise the net social benefits gained from the use of

water by economic sectors including a range of components, such as welfare impacts for water users,

valuation of key ecosystem services provision, and valuation of the external costs of degrading

ecological and chemical status and energy consumption triggered by water abstraction and return.

8

Setup of the study

To achieve this, the JRC is building a hydro-economic model platform based on a combination of the

following elements:

- A simplified, pan-European, biophysical modelling tool, which integrates and/or mimics the

results of full runs of the Integrated Water Modelling Platform (IWMP). The IWMP is

currently developed by the JRC and already partially employed in support of the Blueprint. The

IWMP already allows for the definition and implementation of alternative scenarios of water

management in connection with socio-economic drivers. The following modelling tools,

maintained and used at the IES, will be primarily used: the JRC LISFLOOD model for water

quantity, the SWAT/GREEN/EPIC models for water quality, and LUMP (the JRC Land Use

Modelling Platform) for land use change scenarios. Agri-economic scenarios are provided by

the CAPRI model following on the work performed for the CAP assessment.

- An optimisation tool that builds on an existing optimisation model based on multi-criteria

analysis, and which has already been applied earlier in a pilot study on the river basins in

Catalonia, Spain1. This multi-objective optimisation, based on a genetic algorithm, has proven

powerful for addressing issues of conflicting management goals and objectives.

- A set of economic functions representing water demand and supply which allows the impacts

of strategic measures to be assessed, defined and discussed with stakeholders.

For this study, the biophysical modules described above must be combined with economic components

that account for the economic profits or losses at each demand site and the benefits to, for example,

ecological and environmental uses. The defined scenarios are analysed with this model.

The suite of models allows the variability of the quantity and quality of water resources to be

accounted for. All hydrologic elements will be linked to a network of sources and demand sites for

urban and industrial water uses, for energy production, for ecological and environmental flows, and for

agricultural needs. The latter will be based on specific modules accounting for river-basin scale

agricultural practices and their impacts on water. Withdrawals and return flows are developed for each

of these demand nodes, or determined using empirical relations between water and type of productive

uses. Taking economic and environmental constraints into account, the optimisation module is

envisaged to allocate available water to all end users while ensuring the best trade-offs for economic

and environmental sustainability.

The optimisation tool as described above can only work if it is fully and dynamically linked to a

biophysical model. Most of the existing biophysical models that are coupled with optimisation tools

only address sectoral aspects of the water management cycle, e.g. water distribution networks or

selected water quality components only.

In the short term, the existing JRC biophysical models (LISFLOOD, SWAT, etc.) are too complex to

achieve this dynamic coupling within the available time schedule of the Blueprint. Therefore, to meet

the Blueprint time schedule, we are using a pragmatic approach.

In the context of the present contract, the JRC is developing a - somewhat simplified - biophysical

modelling tool with simulations at pan-European scale on a 5x5 km regular grid, with a daily

simulation timestep. The modelling tool working title LISQUAL - integrates the results of full

1 http://www.analisisderiesgos.org/docs/TR/2010/Technical_Report_2010_23.pdf

9

simulation runs of the detailed water quantity model LISFLOOD and the water quality model output

produced by the EPIC model.

This approach offers two advantages: 1) continental coverage and dynamic coupling with the

optimisation model, 2) the taking into account of specific measures which are modelled at higher

resolution with models such as LUMP, the Land Use Modelling Platform.

Figure 1 Flowchart of the modelling exercise carried out in this study.

Scenarios

The identification of the classes of measures to be included in the scenarios is essential for the

definition of the objectives to be evaluated in the multi-criteria decision, e.g.

Water supply from other basins, the sea or fossil resources;

Increase water retention (artificial/natural);

Improve the quality of available water;

Improve efficiency in the use of water.

The table below excludes measures that address chemicals (other than N and P) and ecological status

which, while they are very relevant for the transformation agenda under the Blueprint, cannot be

integrated into the optimisation procedure with hydro-economic modelling in the context of this

assessment due to their higher complexity.

Measures such as water pricing or targets are not modelled per se. They are parameters, constraints or

outputs of the optimisation.

10

The measures listed below are included in the analysis:

Category Scenario Description

BASELINE2030 0.0 Baseline 2030 LUMP 2030, 2010 fertilisation application, 2010 point sources

BASELINE2006 0.1 Baseline 2006 As Baseline 2030, but with Landuse 2006

1-FOREST 1.1 Riparian Afforestation, CAP consistent Afforest areas from LUMP-CAP scenarios

1.2 Afforestation in mountainous areas Afforest areas in mountainous areas (LUMP)

2-URBAN 2.1 50% Green

Green infrastucture, Green roofs, Rain Gardens, Park Depressions; For all urban areas: Direct Runoff Fraction > 50%

2.2 25% Green

Green infrastucture, Green roofs, Rain Gardens, Park Depressions; For all urban areas: Direct Runoff Fraction > 25%

3-AGRICULTURE 3.1 Grassland Convert areas from LUMP-CAP scenarios to grassland

3.2 Buffer strips

5m wide grass buffer strips within arable fields, on slopes < 10%, every 200m; 2.5% of arable land converted to grassland, only on slopes < 10%

3.3 Grassed waterways 10m wide grass-covered areas in valley-bottom; 1% of arable land converted to grassland, in valley-bottoms > 5%

3.4 Crop practicies Reverse OM decline and increase mulching; increased infiltration, porosity, modified hydraulic parameters

4-NATURAL RETENTION 4.1 Wetlands Riparian wetlands along rivers; Change cross section

4.2 Polders Introduce flood retention polders along rivers

4.3 Re-meandering

4.4 Buffer ponds in headwater areas 1

natural retention ponds in headwater areas with 5000 m3 storage per 25km2

4.5 Buffer ponds in headwater areas 2

natural retention ponds in headwater areas with 10000 m3 storage per 25km2

5-NUTRIENTS 5.1 N-fixing winter crops updated N & P fluxes

5.2 optimum fertilisation application updated N & P fluxes

5.3 N-fixing winter crops & optimum fertilisation application updated N & P fluxes

6-POINT SOURCES 6.1 New wastewater treatment plants (WWTP) updated point information

6.2 Changing type of WWTP updated point information

7. WATER SUPPLY 7.1 groundwater extraction updated point water availability

7.2 desalination updated point water availability

7.3 large-scale water-transfer infrastructures transfer of water between river basins

8. TECHNICAL RETENTION 8.1 constructing dams and reservoirs new dams/resoirvoir to temporarily store water

8.2 hard infrastructure for flood risk

9. EFFICIENCY 9.1 Irrigation management optimizing crop water requirements

9.2 Water efficiency in power generation Save water in power generation, as compared to current use

9.3 Water efficiency in industrial processes Save water in industry, as compared to current use

9.4 Water efficiency in Buildings/households Save water in households, as compared to current use

9.5 Leakage reduction Fix all leakages 90% or 100% (reduce water abstraction)

9.6 Wastewater reuse for irrigation Reduce deep groundwater use for irrigation and replace by treated wastewater

Figure 2 Overview of the scenario measures included in this study.

11

Some assumptions were made in these scenarios:

Scenario 7.1 Desalination

o Plants are assumed to treat 60 Megalitres per day o Water is used at a maximum distance of 150km inland, inside the same Major Region,

but can contain more Water Regions

o Costs are calculated including distance to plant and height difference

Scenario 9.1 Irrigation efficiency

Change efficiency from the current average of 74% (Eastern Europe) - 77% (Western Europe) to 93% by applying drip irrigation. Costs 153 Euro/ha.

o Current efficiency stored in maps o ScenIrgf.map defines additional irrigation efficiency (16-19%) o Applied only in the non-drip irrigation areas (currently 3% in Eastern Europe, 18%

in Western Europe

Actual area of irrigation is used (hectare per 5x5km model pixel)

Costs: Adjusted for national price levels

Scenario 9.3 Water re-use in industry

Assumed that 50% of water is re-used

Costs are 0.013 Euro per re-used m3

Adjusted for national price levels

Water re-use for irrigation is treated in scenario 9.6

Scenario 9.4 Water savings in domestic a sector

25% savings achieved

since payback times are less than 30 years, 0 costs assumed

Scenario 9.5 Leakage reduction

50% reduction compared to current leakage

Scenario 9.6 Wastewater re-use for irrigation

50% reduction in the use of deep groundwater for irrigation; instead, treated urban wastewater is used for irrigation

Studies from ENV used in this work

The following studies of DG ENV have been used in this analysis:

The STELLA 2012 study on natural water retention measures including their costs, complemented by JRC hydrological and land use modelling

The COWI 2011 study on the investment needed to rehabilitate water distribution networks (ENV.G.1/FRA/2006/0073, September 2011)

Studies contributing to Water Scarcity and Droughts Gap Analysis

Discussion Paper Environmental flows in the EU in the context of the Expert Group on water scarcity and droughts, subcontracted to TYPSA (Jan 2012)

Water Scarcity & Drought indicator set development document (April 2012), on the Water Exploitation Index

12

Basic data used

Meteorological data

The meteorological variables of precipitation, minimum/maximum/average/dewpoint temperature,

wind speed, potential evapotranspiration, and evaporation rates for open water and bare soil surfaces

for driving LISFLOOD are derived from various data sources for the period 1/1/1990 until 31/12/2010.

These data sources include the JRC MARS database (http://mars.jrc.ec.europa.eu/mars/), data obtained

from MeteoConsult, SYNOP data, as well as data from the European Climate Assessment & Dataset

(ECA&D, http://eca.knmi.nl/). An overview of the definitions of the different meteorological variables

and the data sources used for those variables is given in Table 1.

Variable Definition Data Sources

ws Mean daily wind speed at 10 metres/second (m/s)

from 0-24 UTC

JRC MARS, SYNOP,

ECA&D

pr Precipitation (mm) between 6 UTC on the day

specified and 6 UTC on the following day

JRC MARS, SYNOP,

ECA&D, MeteoConsult

tn Minimum temperature (C) between 18 UTC and

6 UTC (i.e. during the preceding night)

JRC MARS, SYNOP,

ECA&D

tx Maximum temperature (C) between 6 UTC and

18 UTC (i.e. during daytime)

JRC MARS, SYNOP,

ECA&D

td Average of all available dewpoint temperature

measurements between 6 UTC on the day

specified and 6 UTC on the next day.

SYNOP

ta If the daily maximum temperature (tx) and the

daily minimum temperature (tn) is known, mean

daily temperature is calculated as

ta = (tx + tn) / 2.

JRC MARS, SYNOP,

ECA&D

e0 Penman potential evaporation from a free water

surface (mm/day)

JRC MARS

et Penman potential transpiration from a crop

canopy (mm/day)

JRC MARS

es Penman potential evaporation from a moist bare

soil surface (mm/day)

JRC MARS

Table 1 An overview of the observed meteorological variables as used in LISFLOOD and LISVAP

All meteorological variables are interpolated on a 5 x 5 km grid using inverse distance weighting with

a weight of d-2

and a maximum number of 5 points for the interpolation. Temperature variables are

first corrected using the elevation obtained from a DEM with a resolution of 1 x 1 km and using a

constant lapse rate of 0.006 (0.002 for dewpoint temperature) and are then interpolated onto the 5 x 5

km grid. An example of the spatial distribution of precipitation observations is given in Figure 4.

13

Figure 3 Spatial distribution of available precipitation observations on 15 Dec 2010.

Figure 5 shows the temporal distributions of the different meteorological variables from 01.01.1990

until 31.12.2010. On average more than 2000 5 x 5 km pixels have at least one precipitation

observation and more than 1,200 have a temperature or wind speed observation for the time period

between 1990 and 2003. Since about 2004 on average more than 2,500 5 x 5 km pixels for

precipitation and more than 2,000 for temperature and wind speed are observed at least once.

14

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Figure 4 Temporal distribution of the number of observations available for the interpolation of precipitation,

minimum/maximum/average/dew point temperature, and wind speed.

Potential evapotranspiration, and evaporation rates for open water and bare soil surfaces, are calculated

in two different ways during the 21-year time span. For periods before 23/01/2003 e0, et and es are

taken from the JRC MARS database directly and interpolated as described above. However, from

23/01/2003 onwards, LISVAP (van der Knijff, 2008), an evaporation pre-processor for LISFLOOD, is

used to derive the maps using the observed variables minimum daily temperature (tn), maximum daily

temperature (tx), dewpoint temperature (td) and windspeed (ws). Figure 6 shows the annual average

precipitation for Europe, computed from the data we have available for this study.

Figure 5 Annual average precipitation (mm) (1990-2010), based on spatially interpolated ground station

measurements, using the JRC CGMS/Mars database and the JRC EUFloodGIS database. Source: JRC / Salamon,

Burek (2011).

.

15

Meteorological data used in the scenario modelling

Weather data to carry out the scenario calculations are derived from baseline 1981-2010 climate

simulations from the data portal of the ENSEMBLES project. This is done to in a later stage carry out

climate change scenarios using the same setup and compare the results with the baseline scenario.

Within the ENSEMBLES project a large number of climate simulations using different combinations

of state-of-the-art General Circulation Models (GCMs) and Regional Climate Models (RCMs) have

been run for Europe. All climate simulations are forced by the SRES-A1B scenario defined by the

IPCC. For this work, we employ three climate simulations obtained from a combination of two GCMs

(HadCM3Q0 and ECHAM5) and three RCMs (HIRHAM5, RACMO2 and HadRM3) (see Table 1 for

details). These climate simulations have been selected in order to cover as much as possible of the

climate uncertainty.

Climate simulations from the 3 models have been bias corrected by JRC, based on the algorithms

developed by Piani et al. (2010) and recently applied to correct climate simulations from

ENSEMBLES by Dosio and Paruolo (2011) and Rojas et al. (2011).

Model Driving GCM RCM Institute

1 ECHAM5-r3 HIRHAM5 Danish Meteorological Institute DMI-HIRHAM5

2 ECHAM5-r3 RACMO2 The Royal Netherlands Meteorological Institute KNMI-RACMO2

3 HadCM3Q0 HadRM3Q0 UK Met Office, Hadley Centre for Climate Prediction and Research METO-HadRM3Q0

Table 1: Climate simulations used to force LISFLOOD in the period 1981-2010

Hydrological data for calibration and validation

Observed river flow data at gauging stations from Europe were used from the Global Runoff Data

Centre (GRDC). These daily observed data have been used to calibrate and validate the LISFLOOD

model setups for Europe. For Europe, historical river flow data from 435 stations (Figure 2) have been

used for model calibration and validation.

Irrigation water requirement data 2006 and 2030

A detailed description of the methods used to derive irrigation requirements for Europe can be found in

Wriedt et al. (2008, 2009). Irrigation requirements were estimated for 1984 until 2008. For the purpose

of the scenario study here, monthly averages were used derived from these 25-year period, and used

for the baseline 2006 scenario. Since an irrigation water requirement data set consistent with the 2030

land use scenario is not yet available, the 1984-2008 average irrigation data are also used for the

baseline2030 scenario and all measures scenarios.

The generation of the irrigation map followed a two-step procedure. First, irrigated area was

distributed to crop categories at sub-regional level (NUTS3) based on statistical information and

distribution rules. Next, the regional information was disaggregated to a high resolution dataset based

on the crop distribution and a global irrigation dataset (Siebert et al., 2005). EPIC uses a base-map to

identify irrigated areas, which originates from FAO (Siebert et al., 2005). Some boundaries between

the countries can clearly be seen on this map (e.g. FR-DE) and this causes high irrigation

requirements, different across boundaries and across rivers (Rhine for instance).

16

Figure 6 Irrigated area (Source: FAO, Siebert et al., 2005)

Based on crop growth, soil water and the EPIC nutrient model, we estimated irrigation water

requirements on a daily basis at a 10 x 10 km grid scale using automatic irrigation under different

strategies, including unlimited irrigation, various water stress thresholds, and a no irrigation strategy.

We then chose the irrigation strategy that yields at least 80% of the yield obtained under the unlimited

irrigation strategy, while also minimising water use. Selecting different irrigation strategies allowed us

to adapt irrigation more specifically to soil conditions and crop growth. For rice we took a fixed

irrigation strategy applying an amount of 3,500 mm per year. The calculated average irrigation

requirements for irrigated crops range from a minimum of 13 mm/yr in Switzerland to 900 mm/yr and

more for Spain, Portugal, Greece and Italy.

EPIC runs for the five dominant crops of each grid cell. This resulted sometimes in the loss of certain

irrigated crops in Germany and UK in particular. To solve this issue we forced EPIC to run for the

irrigated crops even though these were not in the 5 dominant crops. So for Germany and the UK we

forced EPIC to run for irrigated potatoes and sugar beet. In France, there initially was an

overestimation of irrigation in Brittany, which has been resolved with the final data used. A map of

irrigation requirements is shown in Figure 7. Clearly irrigation requirements for both Germany and UK

are low compared to those of Southern countries.

17

Figure 7 Average annual irrigation water requirements for Europe (mm per 25 km

2 grid). Source:

JRC/Bouraoui/Aloe (2012), updated from Wriedt (2008).

A graph showing the estimated versus reported abstraction using EPIC for year 2000 is shown in

Figure 8. No estimation was done on uncertainty as the raw data reported by Member States is already

subject to high unquantifiable uncertainty. However the estimations of EPIC are in the range of

reported abstraction values.

Figure 8 Estimated versus reported irrigation abstraction using EPIC for year 2000 (Aloe & Bouraoui, 2012)

18

Water abstraction for Livestock

Daily maps of livestock water withdrawal were calculated using the Food and Agriculture

Organization (FAO) livestock density maps and output from the CAPRI agricultural model. The water

use was calculated based on the specific water requirement (taken from literature) and spatial

distribution of each type of livestock (cattle, pigs, poultry, sheep and goats). A temperature function

was derived and applied to describe daily variations in water use for each livestock group.

Figure 9 Average annual livestock water withdrawal (2005-07) (mm/yr per 25 km2 grid).

Source: JRC/Mubareka, Vandecasteele, Bianchi (2012).

Public Water Withdrawals

The water withdrawal maps for the public, industry and energy sectors were calculated based on a

disaggregation of sectorial water use statistics. The OECD/EUROSTAT Joint Questionnaire on Inland

Water provides country-level statistics on annual freshwater abstraction by source and sector and water

use by supply category and user (Nagy et al.). The questionnaire covers the EU27 countries plus some

data on Iceland, Norway, Switzerland, Croatia, the Former Yugoslav Republic of Macedonia, Turkey,

Bosnia and Herzegovina, and Serbia. For the reference year 2006, we used the average sectorial water

withdrawals for the period 2005-7 from EUROSTAT, supplemented by the 2003-2007 average from

FAO - AQUASTAT. Where there were still missing values the respective sectorial European average

per unit was used. Since AQUASTAT provides only a total value for the industrial sector, we used the

EU27 average to split the totals into the respective proportion of water used for industry

(manufacturing), 40% and energy production, 60% of total.

19

Monthly public water withdrawal maps were computed, while industrial and energy withdrawals were

assumed to remain constant throughout the year. The final maps were aggregated to fit the 5k mask,

and values converted from hm3 to mm per year.

Figure 10 Average annual public water withdrawl for Europe (2006) (mm/yr per 25 km2 grid).

Source: JRC/Mubareka, Vandecasteele, Bianchi (2012).

2006

Public water withdrawals were assumed to be those made by residents and tourists in urban areas, so

that the spatial distribution of the withdrawals was assumed to be directly related to the combined

population and tourist density. Since tourists are known to have a higher water use than residents, the

tourist density maps were given a greater weight when assigning the water withdrawals we used a

ratio of 300/160 (Gssling et al., 2012).

Population density maps were available for 2006 at 100m resolution (Batista e Silva et al., submitted).

Tourist density maps were created using the number of nights spent by non-residents at NUTS2 level

(EUROSTAT) further disaggregated to NUTS3 level using the number of bedplaces (EUROSTAT).

The monthly distribution of tourism was calculated using the country-level percentage of nights spent

per month (EUROSTAT). For both cases, where data was missing national statistics or regional

averages were used, always taking the closest available year to 2006.

The total number of tourists per month at NUTS3 level for each country was disaggregated to the

refined Corine classes 111 and 112 (urban fabric), 141 (green urban areas), and 142 (sport and leisure

facilities). The number of nights spent abroad by residents per quarter year was also calculated and

subtracted from the population density maps. The final map, to which the country-level public water

withdrawal statistics were disaggregated, was then calculated as:

20

Weighted number of users per pixel = Population density map 2006 nights abroad map

(quarterly) + 300/160*tourist density map (monthly)

2030 A population density map for 2030 was computed, using population projections from EUROSTAT,

and modelled land use for 2030 from the EUClueScanner100 model. Both the tourist density maps and

the nights spent abroad maps were multiplied by a specific predicted annual growth factor taken from

the Tourism vision 2020 projections (WTO, 2000). The temporal (monthly) and spatial distribution of

tourism was kept constant.

The public water withdrawal per capita was also kept constant, so that the total public water

withdrawals for 2030 directly reflect the projected population and tourism densities.

Industrial water withdrawals

2006 All water used for manufacturing purposes was assumed to be withdrawn within industrial areas.

Where refined Corine land use maps were available (Batista e Silva et al., 2012), the water

withdrawals were disaggregated to the Industry classes 121 (industry and commercial units), 123 (port

areas), 124 (airports), 131 (mineral extraction sites), and 133 (construction sites).

Figure 11 Average annual Industrial water withdrawal (2005-2007) (mm/year per 25km2 grid). Source:

JRC/Mubareka, Vandecasteele, Bianchi (2012).

21

Where there was no coverage by the refined Corine land use map (for MD, UA, BY, RU), water

withdrawals were assumed to occur within the globcorine land use class 0 (Urban and associated

areas). For countries where no industrial water withdrawal totals were available (AD, FO, IM, LI,

MC, SM, VA) we used the EU27 average water use per pixel of industrial land.

2030 In order to calculate water withdrawals for 2030, we first computed a change factor per country. We

assumed the driving force for water withdrawals in time to be the GVA for industry.

Looking at historical data (1990-2010), there is a trend towards more efficient water use in industry

due to technological improvements, and therefore, on average, a decreasing water use per unit in

Europe. The average (decreasing) trend in total industrial water withdrawals for 2000-2006 for DE,

ES, FR & UK, was taken as an efficiency factor (-1.33 %/year) to correct for this observation. This

factor was subtracted from the (increasing) trends given by the GVA (%increase/year, for EU25, from

GEM-E3) for each country. The EU average trend in GVA was used to fill in for any missing values.

Country change factor (%/yr) = GVA for industry (%/yr) efficiency factor (%/yr)

For countries where the land use was kept constant (non-EU27), the 2006 industrial water withdrawal

map was multiplied directly by this change factor (x24 years) to compute the 2030 water withdrawals.

For the EU27 countries the total water withdrawal for 2006 was first multiplied by the country-specific

change factor (x24 years) to give the 2030 values, and then disaggregated to the industrial area for

2030 (modelled using EUClueScanner100, Lavalle et al., 2011).

Energy water withdrawals EPRTR

2006 Energy water withdrawals are those made for cooling during electricity production; we therefore

assumed this withdrawal to be attributed almost exclusively to thermal power stations.

For the EU27 countries, the country-level water withdrawals for use in the energy sector were

disaggregated using the number of thermal power stations per 5km pixel extracted from the European

Pollutant Release and Transfer Register data base, E-PRTR (any records having the same location

were first removed to avoid double counting).

For non-EU27 countries, for which this data was not available, the water withdrawals were

disaggregated to the Corine refined industry class 121, or to the globcorine class 0, depending on

availability. Where energy water withdrawal statistics were not available, the EU27 average

withdrawal per pixel was used.

2030 We assumed the driving force for water withdrawals in time to be energy consumption. The average

trend in water use 2000-2006 for DE, ES, FR, UK & PL was taken as an efficiency factor (-1.69

%/year), which was subtracted from the trends given by energy consumption (POLES output,

%increase/year) for each country. The EU average trend in energy consumption was used to fill in for

any missing values.

Country change factor (%/yr) = energy consumption (%/yr) efficiency factor (%/yr)

The 2006 energy withdrawals map was multiplied directly by this change factor (x24 years) to

compute the 2030 energy water withdrawals.

22

2030

The country change factors were computed as described in the EPRTR methodology. For the EU27

countries, the land use was updated to the modeled industry class 121. For non-EU27 countries, the

2006 energy withdrawals map was directly multiplied by the country change factors.

Total water abstraction

Figure 10 shows the sum of the water abstractions for irrigation, industry, livestock and domestic

purposes.

Figure 12 Total average annual water demand in Europe for irrigation, livestock, industry (manufacturing and

energy production) and the domestic sector. Source: JRC/Bouraoui, Wriedt, Mubareka, Vandecasteele, Bianchi

(2012).

Point sources data

We have used to UWWTP database distributed publically by the EEA. It covers 28 countries. We

cleaned as much as possible and then converted the data into PE (person equivalent) discharging to a

specific stream according to the following flow chart:

23

Figure 13 Flowchart to estimate point sources.

We have added the level of treatment to each treatment plants in order to generate the load of waste

water in terms of water and BOD, nitrogen and phosphorus. The discharged loads and their location

are available as input to LISQUAL and for further use in the optimisation.

Cost of water retention scenarios

For the natural water retention measures, the contractor Stella has collected the costs of measures,

specified per country. Five main categories of costs, including sub-categories, have been identified by

STELLA:

Land requirement: Acquisition and compensation;

Construction and rehabilitation: Investment, design and contingency;

Construction and rehabilitation: Operation and maintenance;

Administrative costs: Enforcement costs, monitoring, extension of networks;

Other costs.

For several scenarios, a unit investment cost and operation and maintenance cost is estimated per ha:

Figure 14 Unit investment and operation costs (source: Stella, 2012)

24

Cost of other scenarios

For the other scenarios, the sub-contractor IVM/VU (Free University of Amsterdam) has collected

information on unit cost across all EU member states of the measures:

Optimum fertiliser application

New wastewater treatment plants

Change type of wastewater treatment plant

Groundwater extraction

Desalination

Large-scale water transfer infrastructures

Constructing dams and reservoirs

Hard infrastructure for flood risk

Irrigation management

Water efficiency in power generation

Water efficiency in industrial processes

Water efficiency in building/households

Figure 15 Comparitive Price Levels (2010)

25

Benefits (reduced economic loss for sectors, reduced flood damage)

Not only costs of the measures are taken into account, but also some benefits, specifically:

reduced expected annual flood damage as a consequence of the measures in the scenario

reduced economic loss for the domestic sector

reduced economic loss for the agricultural sector

reduced economic loss for the industrial sector

reduced economic loss for the energy sector

The Expected Annual Flood Damage is calculated by combining the waterlevels of several return

periods of flooding, with 100m resolution potential damages calculated by combining local elevation,

water level, land use, and economic loss data which are a function of waterdepth and land use.

Economic losses are defined as the benefits forgone to water users as they receive less water than in a

normal year (i.e., less than their baseline water demand). The benefits of a unit of water at current

levels of provision are (under competitive conditions) equal to its market price. At lower levels of

provision, the forgone benefits per unit of water will typically rise for water users. The relationship

between the level of provision and the forgone benefits per unit of water can be depicted in an

(inverse) demand curve. The slope of the (inverse) demand curve expresses the sensitivity of the

forgone benefits (i.e. the economic losses) to the change in provision. If the slope is very steep, losses

increase sharply when the provision of water falls. If, in contrast, the slope is shallow, losses increase

slowly as the provision falls. Figure 15 presents an example of an (inverse) demand curve that is used

to calculate economic losses in this study. In this Figure, the economic loss due to a reduction in the

provision of water is measured by the area under the demand curve that has the difference between the

baseline water availability (3.70E+08 m3) and the restricted availability (for example 3.00E+08 m3) as

its base.

The slope of the demand curve at a specific level of provision is measured by the so-called price

elasticity of demand. This elasticity is negative, indicating that the price (i.e. benefit per unit)

increases as supply decreases. If the elasticity is close to zero, the slope is very steep and the demand

for water is said to be inelastic. If the elasticity is less than -1, the demand is said to be elastic.

This study constructed demand curves for the domestic, industrial, energy and agricultural sectors.

Note that we consider the economic welfare loss of water scarcity to private households to be just as

real as economic losses to industrial enterprises (including agriculture), although the former losses

may not become recorded in financial accounts. This approach of treating economic welfare losses to

households as real is in line with standard economic practice in Social Cost-Benefit Analysis and is

for example also used in the Californian CALVIN (California Value Integrated Network) economic-

engineering optimization model of Californias water supply system (Jenkins et al. 2004).

To construct demand curves, information is needed on market prices, the slope of the curves, and

baseline water demands for the sectors across Europe. Baseline water demand (m3) is in the base data

of LISQUAL (see above). There is considerable heterogeneity in water prices across uses and regions

in Europe. Figure 14, shows average residential water prices (including charges for sanitation) across

Europe (Kuik 2012). There is very little empirical information on the specific form of the demand

curves for water of industry and energy. Based on the review of literature of Kuik (2012), a price

elasticity of -0.71 has been used for all industrial and energy sectors.

There is more empirical evidence on the relationship between price and demand for water for the

residential sector. Based on the review of European studies by Kuik (2012), this study uses country-

specific price elasticities that range from -0.187 to -0.402.

26

There is also more empirical evidence on the relationship between the price of irrigation water and

demand in agriculture. The evidence, however, shows extreme variation across locations and crops.

This study uses an extremely simplified linear demand curves that are cut-off from above by a so-

called choke price, i.e. a price of irrigation water at which irrigation is stopped, resulting in

maximum damage for agriculture. Depending on the ratio of irrigation water availability and the total

irrigation water demand, the economic loss is calculated. Choke prices of 0.35 Euro/m3 for low-value

crops and 1.25 Euro/m3 for high-value crops are used, according to data provided by Kuik (2012).

Figure 16 Water prices used in this study (source: Kuik 2012)

The calculation of the economic losses across scenarios is a very rough first approximation, due to the

lack of data on location- and use-specific water prices, the dearth of empirical estimates of the

sensitivity of demand for water of industrial and energy firms to different water prices, and the huge

variety and context-specificity of agricultural demand for irrigation water. It is even impossible to say

at this moment whether our assumptions lead to an over- or underestimate of the real economic costs

of water scarcity. There is a huge scope for improvement in this area.

27

Figure 17 Example of the Economic Loss Model which estimates the loss (in Euro's) if water availability (m3) is less

than the water demand (the value at zero loss). For cost of water 0.6 Euro per m3 is used, and for price elasticity -

0.71.

28

Modelling methods

CAPRI

CAPRI stands for Common Agricultural Policy Regionalised Impact analysis and is both the

acronym for an EU-wide quantitative agricultural sector modelling system and of the first project

centred around it. CAPRI was developed with European Commission research funds, the first phase

being 1996-1999. Operational for more than a decade (1999), the CAPRI model supports decision-

making related to the Common Agricultural Policy and Environmental policy related to agriculture

based on sound scientific quantitative analysis.

CAPRI is a global, comparative static partial equilibrium model for primary and secondary agricultural

commodities, designed for ex-ante impact assessment. The model has a European focus but trade

policies from seventy-seven countries, divided into 40 trade blocks, are incorporated through the

global, multi-commodity market module. The agricultural sector for the EU27 plus Turkey, Norway

and the Western Balkans is taken into account within the supply module through 280 regional models

or 1900 farm-regional models. Together, the market and supply module are iteratively linked. The

outputs include farm supply details, such as land requirements for commodities on a NUTS 2 level.

Certain variables, namely crop shares, yields, stocking densities, and fertilizer application rates, are

downscaled to 1x1 km grid cells.

CAPRI builds upon an analysis of observed historical trends, on expert information for particular

issues, and on standard economic modelling.

The CAPRI baseline used for this study was generated in the frame of the EC4MACS project, funded

by the EU-LIFE program.

The baseline for agricultural policies includes the following assumptions:

The Health Check of the Common Agricultural Policy (CAP)

Agricultural premiums are largely decoupled from production levels

Biofuels as projected by PRIMES (see details later)

Impacts of the Nitrates Directive

The economic and sectorial outlook for the EU economy until 2030 is based on the latest short- term

forecasts published by DG-ECFIN in May 2009 and the DG ECFIN Ageing 2009 report of April 2009,

which include projections of long-term GDP, population and labour force developments. The multi-

sector and multi-country general equilibrium model GEM-E3 has been used in EC4MAC to ensure

internal consistency among the relevant aspects while reproducing the short-term GDP forecasts as

well as the long-term GDP and demographic projections of DG-ECFIN. The baseline scenario

assumes for the EU-27 that economic recovery will compensate for some of the loss in GDP during the

recent recession, but in the longer term will not entirely compensate these GDP losses.

An extensive assessment of uncertainties related to the CAPRI model has been performed in the frame

of EC4MAC and is available at:

http://www.ec4macs.eu/content/report/EC4MACS_Publications/UNC_Rep_final%20in%20PDF/CAP

RI_Uncertainties_Final.pdf

The outputs from CAPRI are used to drive the agricultural land use allocations performed by LUMP in

the various simulations described in this report. This ensures consistency between the economic and

market assumptions (CAP compliant) and the physical results of the allocation. During the simulation,

the socio-economic assumptions set in the CAPRI baseline are not modified.

29

Furthermore, the CAPRI output is used for the EPIC N & P modelling used in this project.

LUMP

The JRC Land Use Modelling Platform (LUMP) is used to simulate CAP-consistent land use scenarios

for the year 2030 at a 100x100m spatial resolution at a pan-European scale. This scenario is used as a

baseline for calculations in this study.

For several specific natural water retention measures, specific runs are done with LUMP to simulate

alternative land use patterns for 2030. This is done for e.g. afforestation scenarios in riparian areas,

afforestation in mountainous areas, and a CAP-consistent run converting arable land into grassland.

Upscaling to the 5km spatial resolution LISFLOOD hydrological model is done with a series of 80

tables defining sub-grid variability within a 5km grid, aimed at maintaining the detailed 100m LUMP

simulations. Land use/cover scenario baseline 2030

- Future land claims for arable land and pasture were derived from the CAPRI-PE 2030 scenario with the assumption of national-flat rates.

- Future land claims for urban land were derived from Eurostat data (EUROPOP2008), based on a single convergence scenario, whereby demographic structural differences between countries are assumed to fade out by 2150.

- Land use change from forest or semi-natural vegetation to agricultural land is only allowed outside protected areas (i.e. Natura 2000).

- Land use change from agricultural land to urban or industrial land is only allowed outside protected areas (i.e. Natura 2000).

- Abandoned land is driven by economic factors, i.e. emerges as a result of the decline in agricultural claims, and thus its definition does not take directly into consideration any other variable related with economic or demographic conditions (e.g.

holdings with low income or proportion of farmers close to retiring age).

- Land use change to arable land and permanent crops is encouraged in Less Favoured Areas (art.18 and 20) and discouraged in environmental sensitive areas: a 50m strip width along water courses in currently designated Nitrate Vulnerable Zones; and in

erosion sensitive areas (where erosion is between 20 and 50 ton/ha/year and higher than 50 ton/ha/year).

30

LISFLOOD

LISFLOOD is a GIS-based spatially-distributed hydrological rainfall-runoff model developed at the

JRC. It includes a one-dimensional hydrodynamic channel routing model (De Roo et al., 2000; Van

der Knijff et al., 2010). LISFLOOD is currently used at the JRC for simulating water resources in

Europe and Africa. Driven by meteorological forcing data (precipitation, temperature, potential

evapotranspiration, and evaporation rates for open water and bare soil surfaces), LISFLOOD calculates

a complete water balance at a daily time step and every grid-cell. Processes simulated for each grid

cell include snowmelt, soil freezing, surface runoff, infiltration into the soil, preferential flow,

redistribution of soil moisture within the soil profile, drainage of water to the groundwater system,

groundwater storage, and groundwater base flow. Runoff produced for every grid cell is routed

through the river network using a kinematic wave approach. Although this model has been developed

with the aim of carrying out operational flood forecasting at the pan-European scale, recent

applications demonstrate that it is well suited for assessing the effects of land-use change and climate

change on hydrology (Feyen et al., 2007; Dankers and Feyen, 2009).

To account properly for land-use dynamics, some conceptual changes have been made to render

LISFLOOD more land-use sensitive. Combining land-use classes and modelling aggregated classes

separately is known as the concept of hydrological response units (HRU). This concept is used in

models such as SWAT (Arnold and Fohrer, 2005) and PREVAH (Viviroli et al., 2009) and is now

implemented in LISFLOOD on the sub-grid level. A forest fraction map, water fraction and direct

runoff fraction have been derived from the 100m resolution land use Land Use Modelling Platform

(LUMP) maps. The spatial distribution and frequency of each class is defined as a percentage of the

entire 5 x 5 km grid. To address the sub-grid variability in land use, we model the within-grid

variability by running the soil modules separately for fractions of land use.

Figure 18 Input data used for the European LISFLOOD model setup

31

The model has also options to simulate lakes, reservoirs, and retention polders, which are relevant for

low-flow analysis (as they tend to increase low flows) as well as for simulating flood protection during

high flows. In the current setting for Europe, 173 lakes and reservoirs are included. Several large lakes

and reservoirs are also included for Africa.

The current pan-European setup of LISFLOOD uses a 5-km grid and spatially variable input

parameters and variables obtained from European databases (Figure 1). Elevation data are obtained

from the Shuttle Radar Topography Mission (SRTM) (Farr et al., 2007) and river properties were

obtained from the Catchment Information System (Hiederer and de Roo, 2003). Soil properties were

obtained from the European Soil Geographical Database (King et al., 1994) whereas porosity,

saturated hydraulic conductivity and moisture retention properties for different texture classes were

obtained from the HYPRES database (Wsten et al., 1999). Vegetative properties and land use cover

were obtained from the JRC Land Use Modelling Platform (LUMP). The meteorological and

hydrological data used are described in the next chapter.

For Africa, a 0.1-degree resolution is established.

The LISFLOOD model output can be any internal variable calculated by the model, either as time

series, summary maps or stacked maps over the complete time period. Examples of output are

discharge hydrographs, summary maps of evapotranspiration, soil moisture or groundwater recharge.

For modelling water supply and water demand, the model output is the daily accumulated amount of

surface and groundwater in millimetres for each grid cell (daily local runoff).

With a grid size of 5 x 5 km (Europe) and 0.1 x 0.1 degree (Africa), LISFLOOD is developed for

simulating medium and large river basins. Satisfactory results can be obtained in basins of a few

hundred km2 up to the size of the entire Danube basin. A limiting factor is the availability of good,

accurate and homogenous input data for the entire pan-European or pan-African scale, for example soil

data or measured discharge data. Human influences (e.g. dams, reservoirs, polders, irrigation) also are

difficult to quantify. This is an especially important factor for low-flow simulations.

With a 5-km grid resolution, the current European-wide model setup uses spatially variable parameters

on soil, vegetation and land use derived from European datasets. The Shuffled Complex Evolution -

University of Arizona (SCE-UA) algorithm (Duan et al., 1992) was selected for carrying out the

calibration of the LISFLOOD model at the European scale. A set of 9 parameters that control

infiltration, snowmelt, overland and river flow, as well as residence times in the soil and subsurface

reservoirs, have been estimated for 479 catchments by calibrating the model against historical records

of river discharge. The results of 435 of these catchments have been used to set up the model, while

44 were rejected because of unreliable data input. The calibration period varied between the different

catchments depending on the availability of discharge measurements, but all spanned between 1 and 9

years between 1996 and 2009. A more detailed description of the calibration of LISFLOOD for

different European catchments is given by Feyen et al. (2007, 2008).

32

Figure 19 Location of the 435 discharge gauging stations used in the calibration of the hydrological model

The location of the stations used in the calibration and validation are represented by the black dots in

Figure 2. This overview shows that the coverage is sufficient in most parts of northern and central

Europe. For the Balkan area, Greece, southern Italy and parts the Iberian Peninsula, no discharge series

were available at the time of the model calibration. For catchments where discharge measurements

were not available, simple regionalisation techniques (regional averages) were applied to obtain the

parameters.

Figure 20 Observed versus simulated averages, 95% and 99% discharge for each of the 435 calibration stations for

a 3-year validation period

Figure 3 shows observed versus simulated averages, and 95% and 99% discharge for each of the 435

calibration stations shown in Figure 2, for a 3-year validation period. This period varyies between the

different catchments depending on the availability of discharge measurements but includes the latest

available data. Visual inspection and the values for the coefficient of determination (r2) show that the

observed flow statistics are reasonably well reproduced by the LISFLOOD simulation with a general

tendency towards better performance for average flows and with increasing catchment size.

33

Notwithstanding the overall good agreement between the observed and simulated flow statistics, large

discrepancies do occur at a small number of stations. Deviations from the observation-based statistics

may be attributed to errors in meteorological forcings, the spatial interpolation of meteorological data,

as well as to errors in the hydrological model, its static input and the calibration of its parameters.

Some of the differences may also be due to manmade modifications of flow regimes in many

catchments, which are not accounted for in the hydrological model.

34

EPIC for N & P fluxes

Nitrogen and phosphorous fluxes from agriculture are calculated with the pan-European set-up of the

EPIC model, ran at 10x10km grid resolution.

A land use map (LUM) was developed combining the land use distribution of CORINE Land Cover

2000 with the FSS statistics on crop areas for the year 2000. 43 crop categories were distinguished

according to FSS crop categories. The crop areas were distributed to arable land, grassland and

permanent cropland of the CORINE data set. FSS crop areas were directly assigned to corresponding

land use classes where possible. Field crops were distributed to irrigated and non-irrigated agricultural

land. Where FSS crop areas do not match CORINE land cover data, a set of enlargement and shrinking

rules was applied to ensure consistency with FSS data. Altogether, the LUM contains 98 land use

classes in 1ha resolution for EU25. The land use raster grid with 100m resolution was then tabulated to

obtain crop areas per 10km grid cells.

For each of the crop available in the land use map, fertilization was taken from CAPRI in the form of

manure (consistent at NUTS2 level with the number of animal unit present reported by EUROSTAT)

and mineral fertilizer consistent at NUT0 with the national data reported to EUROSTAT. Baseline

irrigation was calibrated for year 2000 and then auto-irrigation was used for all scenarios for the

irrigated crops. For the baseline EPIC was run with the observed weather taken from the JRC MARS

database.

For the optimized fertilization, EPIC was run then using an auto-fertilization option where an

application of fertilization is performed each time a crop is under nitrogen stress. For catch crop we

use non fertilized alfalfa

Four scenarios were run:

Baseline scenario with crop distribution and fertilizer application per crop from CAPRI

Scenario identical to the previous one but with a winter/catch crop cover

Scenario with optimum fertilization with crop distribution identical to scenario 1

Scenario with optimum fertilization with crop management identical to scenario 2 (crop from

CAPRI with winter/ catch crop)

Using EPIC, the following fluxes are calculated for each scenario with a daily timestep, but are used

with a monthly average values in LISQUAL

Nleach (nitrogen from leaching)

Pleach (phosphorous from leaching)

Nrunoff (nitrogen in surface runoff)

Prunoff (phosporous in surface runoff)

35

LISQUAL

The aim of the LISQUAL model is to be able to carry out integrated simulations of water quantity and

quality on continental and sub-continental scale, in order to assess a variety of measures and policy

options. The model is taking into account water abstraction, demand and consumption. Furthermore,

the model includes principles such as environmental flow and the water exploitation index. Also, the

model contains an economic loss estimation routine which estimates the economic loss if water

availability is not sufficient to meet the demand.

Using these individual daily fluxes allows later on in the LISQUAL model routing of water after

additional abstractions or return flows.

Spatial and Temporal Inputs All inputs specified below are spatial inputs, i.e. values can be assigned to every single 5*5km grid,

the spatial resolution of the model. The temporal resolution is 1 day, with where appropriate daily

input data, monthly average input data, or annual average input data.

Water quantity (from LISFLOOD runs) o Total runoff produced in cell, contributing to river o Individual water fluxes (required for chemical load estimates)

Surface runoff Preferential flow Subsurface flow

Water quality (from EPIC runs): o N concentration in runoff from agricultural land o N concentration in leaching from agricultural land o P concentration in runoff from agricultural land o P concentration in leaching from agricultural land o Equilibrium Phosphate concentration (Error! Reference source not found.) o Average atmospheric Nitrogen input

Point sources o N & P point sources from waste water treatment

Additional meteorological data: o Daily 2m surface temperature (to estimate water temperature) o Potential Evapotranspiration data (to estimate lake evaporation)

Topographic data o River flow network; o River dimension (cross section) parameters; o MacroRegions: 21 large European regions (no water transfers between those regions);

these consist of one or several entire river basins;

o WaterRegions: 1246 smaller European sub-regions; these regions are used as units for computing e.g the water exploitation index; water transfers between these regions - but

within a certain MacroRegion are possible;

Land use data o Fraction of arable land within 5*5km pixel o Fraction of sealed (urban) area within 5*5km pixel

Water abstraction, demand and consumption data o Average monthly irrigation water need (from EPIC) o Average daily abstraction of water for households o Average monthly livestock water need o Average annual industrial water abstraction o Average annual water abstraction for energy production

36

o Current irrigation efficiency o Current leakage loss of water o Current desalination installations and their capacity o Current water re-use in industry o Current percentage water savings in households

Economic data o Water price o Price elasticity for domestic water use o Comparative price levels

For scenarios o Any change in one of the above parameters o Additional irrigation efficiency o Leakage reduction o Percentage water re-use in industry o Percentage water savings in households o New desalination plants with their capacity and area of water use

Figure 21 Equilibrium Phosphorous Concentration. Values are a function of lithology

37

Model equations

Kinematic wave routing of river discharge (from LISFLOOD), using the LISFLOOD calibration parameterization, which is also used in the operational EFAS flood warning system;

Routines to simulate the effects of lakes and reservoirs;

Daily routing of surface and river N and P, as a function of flow velocity;

Exponential first order - removal of Nitrogen, as a function of water temperature, water depth and flow velocity;

First order Phosphorous removal, taking into account an equilibrium phosphorous concentration depending on sediment characteristics, derived from geological maps

Irrigation water use, taking into account irrigation demand, and irrigation efficiency

Industrial water use, taking into account abstractions, consumptive use and potential re-use of water

Domestic water use, taking into account abstraction, leakage, and water savings

Livestock water use, taking into account abstraction and consumptive use

Environmental Flow

The WFD does not specify the term environmental flows but requires the flow regime should provide

conditions consistent with the achievement of the values specified for the Biological Quality

Elements. In this context, environmental flows to achieve the Good Ecological Status can be defined

as the hydrological regime necessary to achieve the values specified for the biological quality elements

in order to be classified as Good Status. The concept of environmental flow is taken into account by

calculating the 10th

and the 25th

percentile of daily river discharge at each location on a monthly basis,

based on model simulations of a period of 30 years (1981-2010), while not including the current water

abstraction and consumption. This method is based on work by Sanchez & Schmidt (2012) and

bilateral discussions with JRC staff (W. Van De Bund, D. Van Ham). The method here corresponds to

a simplified application of the Range of Variability Approach (RVA), as part of the Level1-

Preliminary Eflow Assessment.

An example for the 10th

percentile map for June is given below. Next, the LISQUAL routine flags

every day that the environmental flow threshold is not respected -i.e. discharge is lower than the

environmental flow thresholds.

At the end of a scenario simulation run, the total number of days at which discharge is lower than the

10th

percentile is calculated. If this values exceeds 10 percent of the days so for this report larger than

37 days per year the scenario does not fulfil the environmental flow requirements. If for a scenario

the number of days that the environmental flow threshold is exceeded is larger than the baseline, this

indicates that the scenario has negative consequences in view of the environmental flow principle.

38

Figure 22 Example Environmental Flow map: 10th percentile for July.

Water Exploitation Index A Water Exploitation Index is calculated along the lines of the Water Scarcity and Drought Expert

Group (WSDEG) 2012 document on indicators.

Within LISQUAL, a Water Exploitation Index + (here called WEIcns) is calculated as:

WEIcns = (abstraction return flow) / (external inflow + internal flow)

With:

Internal flow = net generated water (rainfall evapotranspiration + snowmelt)

External inflow = inflow from upstream areas

Abstraction = water abstraction in the region

Return flow = water abstraction minus water consumption in the region

(Water consumption = abstraction minus return flow)

The index is calculated for single entire years (from 1st October until 1

st October) for the entire

simulation period, in this case 30 years, and then averaged. A monthly calculation would be

39

technically possible but requires detailed information and simulation of seasonal water storages, which

is a considerable effort in data acquisition, as is recognized by the WSDEG.

In addition, a WEI(abs) is calculated as follows:

WEIabs = abstraction / (external inflow + internal flow)

which is essentially the WEI+ (WEIcns) without the return flow

Figure 23 Example of the WEI+(cns) indicator, here fore the Baseline2030 scenario.

Instead of calculating the 30-year average of the individual annual WEIabs indicators, we also

investigated the use instead of a median (50th

percentile, WEI50) WEI, the maximum WEI (100th

percentile, WEI100), or the intermediate 80th

and 90th

percentile WEI (WEI80 and WEI90). The 80th

,

40

90th

and 100th

percentile give the WEI for one or more extreme years within the 30-year simulation

period

Regio

n

WE

Icns (

-)

WE

Iabs (

-)

WE

Iabs50 (

-)

WE

Iabs80 (

-)

WE

Iabs90 (

-)

WE

Iabs100 (

-)

01 N. Scandinavia 0.001 0.004 0.004 0.005 0.005 0.007

02 S. Scandinavia 0.004 0.019 0.018 0.022 0.024 0.031

03 Baltic 0.018 0.061 0.059 0.075 0.085 0.115

04 Denmark/N.Germany 0.025 0.095 0.090 0.114 0.120 0.153

05 Odra/Vistula 0.082 0.282 0.270 0.362 0.416 0.568

06 Elbe to Ems 0.073 0.281 0.266 0.346 0.391 0.533

07 Rhine/Meuse/Scheldt 0.043 0.160 0.155 0.185 0.201 0.265

08 GB 0.021 0.073 0.074 0.088 0.095 0.105

09 Irland/N.Ireland 0.006 0.017 0.017 0.019 0.021 0.026

10 France Atlantic 0.050 0.100 0.095 0.119 0.138 0.209

11 Danube 0.143 0.238 0.224 0.308 0.355 0.475

12 Iberia Atlantic 0.029 0.061 0.058 0.075 0.084 0.131

13 Iberia Mediterranean 0.198 0.322 0.301 0.398 0.466 0.784

14 France Mediterranean 0.027 0.069 0.067 0.084 0.094 0.116

15 Po 0.075 0.154 0.146 0.181 0.211 0.272

16 Corsica 0.008 0.061 0.056 0.075 0.084 0.118

17 Sardinia 0.164 0.232 0.201 0.308 0.345 0.502

18 Sicily 0.358 0.475 0.448 0.593 0.694 0.921

19 South Italy 0.197 0.297 0.282 0.372 0.407 0.596

20 Adige/Balkan 0.020 0.045 0.042 0.055 0.064 0.073

21 Greece/Evros 0.097 0.159 0.150 0.202 0.232 0.288 Figure 24. WEI indicators for the 21 European macro-regions for the Baseline 2030 scenario.

Later on in the report, well keep on using the average WEIabs and WEIcns. As a rule of thumb, figure

24 indicates that WEI90 is around 50% larger than the average WEI, and WEI100 the maximum

WEI in a single year is approximately double the average WEI. However, scenarios tend to react in a

non-linear way to changes, so this rule of thumb should be taken with care.

41

Figure 25 WEIAbs 50th, 80th, 90th and 100th percentile indicators for the Baseline 2030 scenario. The 50th

percentile is the medium Water Exploitation Index, whereas the 100th

percentile represents the maximum Water

Exploitation Index in a single year within the 30years simulated.

42

Model output The LISQUAL model produces the following output. In addition, any internal model variable could be

output as well:

Spatial output (maps) used in the Blueprint optimization process:

Discharge o Daily discharge maps o Maps with maximum annual discharge

Used to calculate flood return periods

Figure 26 50year return period river discharge

Average NO3 concentration in rivers

Average PO4 concentration in rivers

Water Exploitation Index o WEI-abs (not including return flow, using abstraction only) o WEI+ (WEI-cns, including return flow, using net consumption)

Environmental Flow indicator o Number of days with discharge lower than 10th percentile environmental flow o Number of days with discharge lower than 25th percentile environmental flow

Costs o Costs of the scenario

43

Calculated before the start of a run

Economic Losses (further explained on page 25) o Expected Annual Flood damage as a consequence of the scenario

Calculated in post-processing, after a run o Estimated economic loss for the domestic sector o Estimated economic loss for the industrial sector o Estimated economic loss for the energy sector o Estimated economic loss for the agricultural sector

Model assumptions

Environmental flow: thresholds 10 & 25 percentile of daily discharge are used, per month, so the January 10-percentile is used to evaluate January daily discharges in the scenarios, etc.

o Env10flw monthly maps o Env25flw monthly maps

So called water regions are used to calculate the Water Exploitation Index Plus indicator. These regions are typically single river basins or subbasins, but transboundary catchments are

split in separate regions following country borders.

Irrigation water use: o Effective crop water use from irrigation is estimated using the EPIC model by JRC;

actual irrigation water abstraction is larger due to an efficiency lower than 100%.

o An initial CurrentIrrigationEfficiency is used, which aims to mimic irrigation efficiency as in 2010. According to Kuik (2012), this efficiency is around 77% in

Western Europe, and 74% in Central in Eastern Europe; The Efficiency is expressed as

a fraction (0.77 and 0.74).

o An AdditionalIrrigationEfficieny is used for scenario calculations; it is used as a fraction, so can at maximum be 0.25, totaling 100%

Map: scenirgf.map

Only applied in irrigation areas o It is assumed that 5% of the not-efficiently used irrigation water (the difference

between abstraction and crop use) is lost (evaporated), while 95% of that part is return

flow.

Manufacturing Industry water use: o Industrial water abstractions are estimated by JRC using Eurostat and similar data

sources, using the 100m landuse data to downscale;

o Consumptive water use in industry IndustryConsumptiveUseFraction is assumed at 0.15; values between 0 and 1; this is water which is not returned (evaporates during

cooling etc);

a multi-criteria optimisation of scenarios for the protection of water resources in europe

Documents

meteorological data

hydrological data

basic data

report eur

water abstraction

protection of water

europa server http

roo address