a multi-criteria optimisation of scenarios for the protection of water resources in europe
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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
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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: ad.de-roo@ec.europa.eu
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
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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
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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
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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%).
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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.
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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.
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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
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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.
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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.
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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
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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.
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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.
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ws td ta tx tn pr
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).
.
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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).
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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.
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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)
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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.
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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:
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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).
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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.
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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:
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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)
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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)
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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.
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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.
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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.
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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.
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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).
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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
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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).
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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.
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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.
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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)
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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
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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
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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.
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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
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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
,
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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.
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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.
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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
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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);
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