Journal of Hydrology 373 (2009) 527–544

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Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

Estimating irrigation water requirements in Europe

Gunter Wriedt *, Marijn Van der Velde, Alberto Aloe, Fayçal BouraouiJoint Research Centre, Institute for Environment and Sustainability, TP 460, Via Enrico Fermi, 2749, I-21027 Ispra (VA), Italy

a r t i c l e i n f o

Article history:Received 5 August 2008Received in revised form 18 May 2009Accepted 24 May 2009

This manuscript was handled by G. Syme,Editor-in-Chief

Keywords:AgricultureIrrigationWater abstractionsCrop water requirementEuropeLarge-scale modeling

0022-1694/$ - see front matter � 2009 Elsevier B.V. Adoi:10.1016/j.jhydrol.2009.05.018

* Corresponding author. Tel.: +39 0332 78 9776; faE-mail addresses: gunter.wriedt@jrc.ec.europa

(G. Wriedt).

s u m m a r y

In Southern Europe, irrigated agriculture is by far the largest consumer of freshwater resources. However,consistent information on irrigation water use in the European Union is still lacking. We applied the cropgrowth model EPIC to calculate irrigation requirements in the EU and Switzerland, combining availableregional statistics on crop distribution and crop specific irrigated area with spatial data sources on soils,land use and climate. The model was applied at a 10 � 10 km grid using different irrigation strategiesover a period of 8 years. The irrigation requirements reflect the spatial distribution of irrigated areas, cli-matic conditions and crops. Simulated net irrigation requirements range from 53 mm/yr in Denmark to1120 mm/yr in Spain, translating into estimated volumetric net irrigation requirements of 107 mio. m3

and 35,919 mio. m3, respectively. We estimate gross irrigation demands to be 1.3–2.5 times higher thanfield requirements, depending on the efficiency of transport and irrigation management. A comparisonwith national and regional data on water abstractions for irrigation illustrates the information deficitrelated to currently available reported data, as not only model limitations but also different nationalapproaches, country-specific uncertainties (illegal or unrecorded abstractions), and restrictions of actualwater use come into play. In support of European environmental and agricultural policies, this work pro-vides a large-scale overview on irrigation water requirements in Europe applying a uniform approachwith a sufficiently high spatial resolution to support identification of hot spots and regional comparisons.It will also provide a framework for national irrigation water use estimations and supports further anal-ysis of agricultural pressures on water quantity in Europe.

� 2009 Elsevier B.V. All rights reserved.

Introduction

In the European Union (EU) agriculture is an essential drivingforce in the management of water use having significant impactson water quantity and water quality. This is true especially in theMediterranean region (OECD, 2006) where irrigated agriculture isa major water user accounting for more than 60% of total abstrac-tions (e.g. Spain 64%, Greece 88%, Portugal 80%) (OECD/Eurostat,2000). About 75% of the 16 million ha agricultural land equippedfor irrigation in the EU concentrate in the Mediterranean countriesFrance, Greece, Italy, Portugal and Spain (Eurostat, 2000, 2003). Thehigh water demand of agriculture and population in the Mediterra-nean are exacerbated by the limited natural availability of waterresources and high climatic variability (MGWWG, 2005). Climatechange is expected to intensify problems of water scarcity and irri-gation requirements in the Mediterranean region (IPCC, 2007;Goubanova and Li, 2006; Rodriguez Diaz et al., 2007). In Centraland Northern European countries agricultural water abstractionsaccount for less than 1% of total abstractions (e.g. Belgium 0.1%,Germany 0.5%, Netherlands 0.8%, OECD/Eurostat, 2000). In these

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x: +39 0332 78 5601..eu, gunter.wriedt@gmx.de

regions, irrigation is supplementary and used to optimize produc-tion in dry summers, especially when water stress occurs at a sen-sitive crop growth stage. The ongoing debate on climate changeimpacts on the water cycle has raised concerns regarding futurewater availability also in these regions (Weatherhead and Knox,2000).

Irrigated agriculture causes various direct or indirect problems,including leaching of nutrient and pesticides, soil salinization,overexploitation of aquifers (leading to ground subsidence andsea-water intrusion), modification of natural flow regimes anddamages to water dependent ecosystems. Locally, percolationand conveyance losses may also rise groundwater tables and in-crease salinization problems (Döll and Siebert, 2002; Stigteret al., 2006).

Potential water savings by new irrigation technology and im-proved irrigation management may be off set by increases in irri-gated areas and appropriate measures are necessary to limitwater use to sustainable levels.

The 6th Environment Action Programme (EAP) (1600/2002/EC)and the Water Framework Directive (WFD, 2000/60/EC) set out themain policy objectives in relation to water use and water stress atEU level. They aim at ensuring a sustainable use of water resources.An accurate estimation of irrigation demands (and other wateruses) is therefore a key requirement for better-informed water

528 G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544

management (Maton et al., 2005). A large scale overview onEuropean water use can contribute to the development of suitablepolicies and management strategies. There is however a significantlack of information since ‘‘the information needed by policy deci-sion makers on aquifer recharge and pumping by farmers, irriga-tion pollution emissions from either surface or subsurface water,soils, transport and fate processes [. . .] is not available in countrieswith significant irrigated agriculture such as Spain, Italy, Portugaland Greece” (Albiac et al., 2005).

Methodologies applied in EU Member States to assess irrigationabstractions include water metering, questionnaires, water usecoefficients, water rights, pumping hours or electricity consump-tion and model-based estimates (Nagy et al., 2007) and compari-son of data is therefore difficult. In France, water withdrawalsexceeding 8 m3/h are subject to authorization and the countingof withdrawals by appropriate means is required (MEDAT, 2009),but not adequately enforced (WWF, 2003). England and Waleshave strict licensing systems in place and the water licensed andactually abstracted is reported annually (Weatherhead, 2000). InSpain, water authorities assign water allocation rights to farmersthat can be reduced in times of water scarcity (Rodriguez-Diazet al., 2007) and are the basis to estimate irrigation water use. Nev-ertheless, despite metering obligations there is no knowledge andcontrol of irrigation water use (Avellá and García-Mollá, 2009).WWF/Adena (2006) estimate that 45% of all water abstracted fromaquifers in Spain is abstracted illegally (and unrecorded). Exhaus-tive knowledge of irrigation water use is missing also in Italy dueto the fragmentation and complex organization of public agenciesin combination with private water supplies beyond public control(Zucaro and Pontrandolfi, 2005). Government reported figures re-sult from indicative modeling studies (ISTAT, 2006). Reliable infor-mation is missing also in Greece (WWF, 2003; Panoras andMavroudis, 1996) and Portugal (WWF, 2003). Especially in waterrich countries with minor irrigation, no irrigation water assess-ments have been implemented, as for example in Switzerland(Weber and Schildt, 2007). The main problems estimating irriga-tion water use are missing obligations to measure water abstrac-tions, lacking enforcement of legal obligations, and illegalabstractions (exceeding legal abstraction rights or undeclaredand unauthorized abstractions). WWF (2003) conclude in a studyinvolving 10 countries (Austria, Croatia, France, Greece, Hungary,Italy, Poland, Portugal, Spain and Turkey) that information instru-ments in all surveyed countries were considered to be inadequateor to have deficits, resulting in poor knowledge on and control ofthe real water use by agriculture ‘on the ground’. These issues givelittle confidence in the accuracy and consistency of reported data.Irrigation requirements estimated by independent modeling ap-proaches will be useful to fill gaps in the reported data, to supportdata comparison and to provide unbiased estimates at continentalscale.

Various deterministic modeling tools have been developed tocalculate crop irrigation requirements and to assist in irrigationplanning and water management. Bastiaanssen et al. (2007) givea comprehensive overview on the state-of-art of modeling irri-gated soils with numerous examples of models, applications andtechnical developments. Apart from developing numerical models,additional research focuses on integrating remote sensing, inversemodeling and optimization and the integration of decision-makingprocesses as well as economical and sociological aspects of irriga-tion. We highlight only a few issues relevant for the presentedstudy. The standard modeling approach is based on the FAO guide-line to estimate crop water requirement (Allen et al., 1998). Netirrigation requirements per unit irrigated area are calculated as dif-ference between the crop-specific potential evapotranspirationand the effective precipitation. A simple soil water balance modelaccounts for soil moisture content and its impact on actual crop

evapotranspiration. The FAO approach was also implemented infield scale models (e.g. CROPWAT, Smith, 1992; ISAREG, Pereiraet al., 2003). Other soil water balance models implement irrigationoperations that are triggered by certain thresholds of soil moistureand refill soil moisture content to field capacity. This allows gener-ating irrigation schedules internally, adapting to actual weatherconditions and setting constraints in timing and irrigation rates.Examples are the EPIC model (Sharpley and Williams, 1990;Williams, 1995) or the soil water balance components of inte-grated hydrological models such as WASIM (Schulla, 1997; Schullaand Jasper, 2007) and SWAT (Neitsch et al., 2005). The EPIC modelhas been used for scheduling irrigation in several environmentsand for different crops (wheat: Debaeke, 1995; maize: Cabelgu-enne et al., 1993; Sunflower: Texier et al., 1992; Rinaldi, 2001)and offers the additional advantage of a dynamic crop growthmodel.

The combination of GIS and field scale soil water balancemodels allowed estimating irrigation water demands in irrigationdistricts and at regional scale. The spatially distributed modelGISAREG (Fortes et al., 2005) applies ISAREG to multiple fieldsand irrigation districts. Knox et al. (1996, 1997) developed aGIS based procedure to map water demands in England andWales. A GIS-based approach was developed by Portogheseet al. (2005) for regional assessment of net irrigation require-ments in Southern Italy. Based on the FAO approach, it calculatesmonthly water balances on raster data with a spatial resolutionof 1 km. Integrated water balance models such as WASIM andSWAT are typically applied to catchments and medium-sizedriver basins.

At global scale, the WaterGAP model (Döll and Siebert, 2002;Döll et al., 2003) implements the FAO approach at daily time stepsto estimate irrigation requirements at a spatial resolution of 0.5�.Crop specific differences can be accounted for by adjusting poten-tial reference evapotranspiration with crop specific correction fac-tors that can also adapt to crop development stage. However, theapproaches by Portoghese et al. (2005) and Döll et al. (2002,2003) accumulate soil water deficits over the entire vegetationperiod to estimate monthly and seasonal irrigation requirements.Consequently, different irrigation practices are not considered,although irrigation scheduling and irrigation rates may have con-siderable impact on water use.

GEPIC (Liu et al., 2007; Liu, 2009) is a large-scale implementa-tion of the EPIC model that has been applied to simulations of cropgrowth and water productivity at continental and global scale andruns at a spatial resolution of 0.5�.

In our assessment we use a spatially distributed implementa-tion of the EPIC model (Bouraoui and Aloe, 2007) for Europeanscale applications. It has been used to analyze agricultural lossesof nutrients and pesticide in the EU (Bouraoui and Aloe, 2007;van der Velde et al., 2009). Previously we have developed a Euro-pean irrigation map for distributed agricultural modeling (Wriedtet al., 2009) in support to the EU-wide policy developments thatrequire large-scale overviews with sufficiently high resolution.This spatial distribution of irrigated crops was developed since itis a prerequisite for any spatial assessment of European irrigationwater requirements. The map is directly compatible with theaforementioned modeling tool, whose capability to calculate cropirrigation requirements makes it a promising tool to analyze alsoagricultural pressures on water resources.

The objective of the research presented here is to estimate irri-gation requirements in the EU applying a spatially distributedagricultural model. We present the methodological approachesand results of the estimation. A comparison is made with nationaland regional water abstraction data. Further, potential applica-tions and methodological limitations of the approach arediscussed.

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 529

Materials and methods

Our spatially distributed implementation of the EPIC modelcovers the territory of the EU and Switzerland on a 10 � 10 kmgrid. The model was linked to a spatial database containing rele-vant information on weather, soils, crop areas and irrigated cropareas for each grid cell. We calculate net irrigation requirementsfor different pre-defined irrigation strategies and for the domi-nant irrigated crops within each grid cell, using the auto-irriga-tion option of EPIC. Then we determine the optimum irrigationstrategies for each cell and crop and calculate the resulting netirrigation requirements and volumetric net irrigation require-ments per cell. Gross irrigation requirements are estimated at re-gional level considering efficiency of irrigation methods andwater transport. A flow-chart of the calculations is shown inFig. 1.

Calculation of irrigation requirements

The European agrochemical geospatial loss estimatorEPIC is a continuous simulation model that can be used to study

the effects of management strategies on agricultural productionand soil and water resources (Gassman et al., 2005). It has beenused before in a global assessment of crop yields at national level(Liu et al., 2007), in irrigation scheduling (Rinaldi, 2001), climatechange studies (Mearns et al., 1999; Niu et al., 2009) and soil or-ganic carbon assessments (Wang et al., 2005).

Bouraoui and Aloe (2007) developed a spatially distributed EPICimplementation for Europe, based on (i) the EPIC model (Sharpleyand Williams, 1990; Williams, 1995) simulating soil hydrology,nutrient cycling, and crop growth, (ii) the so-called EAGLE databaseholding all relevant input data to perform EPIC simulations atEuropean scale (Mulligan et al., 2006), and (iii) a GIS interface pro-viding all functionalities to apply the EPIC model at European scaleand to access simulation results. The EPIC model runs with a daily

Fig. 1. General comp

time step on 10 � 10 km grid cells covering the EU and Switzer-land. These results in a total of 49,157 grid cells with specific soil,climate and land use attributes.

Meteorological data were obtained from the MARS unit of theJRC (Micale and Genovese, 2004). Measured daily data (rainfall,minimum and maximum daily temperature, vapour pressure, windspeed and global radiation) of more than 1500 meteorological sta-tions across Europe were interpolated to a 50 km grid generating2572 virtual meteorological stations. The database currentlyimplements climatic time series from 1990 until 2002.

Soil data were taken from the European Soil Bureau Database(ESDB 2.0), which is the only comprehensive source of data onEuropean soils harmonized to the standard international classifica-tion of FAO. ESDB provides information on texture, bulk densityand organic matter content. We calculated wilting point, fieldcapacity, saturated porosity and hydraulic conductivity usingpedo-transfer functions of Rawls and Brakensiek (1985) and theequations by Van Genuchten (1980). Mapping units differ in size(ranging from a few square-kilometers to hundreds of squarekilometers) and shape. The EPIC model runs on the mean valuescalculated for each 10 � 10 km grid cell.

Crop management information was taken from the EuropeanCrop Growth Monitoring System (Lazar and Genovese, 2004). Cropgrowth parameters were provided from the Blackland Researchand Extension Center with the EPIC model.

Land use information is based on a European land use map(LUM) developed at JRC (Grizzetti, 2007). The land use map com-bines regional data on crop areas of the year 2000 (Eurostat,2000) with the land cover distribution given in CORINE Land Cover2000 (ETC, 2005). In addition to the original CORINE land cover dis-tribution, the LUM gives the distribution of 43 crop categories(according to crop categories used in the European Farm StructureSurvey), compliant with regional data and the distribution of ara-ble land, permanent cultures and grassland given in CORINE. Werecently compiled a European irrigation map (EIM, Wriedt et al.,

utation scheme.

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2009) combining regional European data on irrigated areas (totalsand for selected crops) with the LUM and the Global Map of Irri-gated Areas (Siebert et al., 2007). A detailed description of themap generation is given in Wriedt et al. (2009) (see also Supple-mentary Material). The overlay of both maps allowed us to calcu-late the crop specific irrigated areas for each 10 � 10 km cell. Thecrop categories defined in the LUM and EIM refer to individual cropspecies (for example maize, soft wheat) and to crop categoriesgrouping different species according to certain characteristics (forexample vegetables, fruit and berry orchards, and other). Gener-ally, all crop categories have been associated with a ‘representa-tive’ crop for modeling. A complete list of crop categories is givenas Supplementary Table.

The regional statistical data contained in the LUM and EIM referto NUTS2 and NUTS3 regions (‘provinces’ and ‘districts’ accordingto the European Nomenclature of Territorial Units for StatisticsNUTS). A customized regional layer (mixed NUTS2 and NUTS3units) was used to import regional statistics into the EAGLE data-base and to aggregate EPIC modeling results to statistical units(Fig. 2).

Soil hydrology and irrigation in the EPIC modelThe EPIC model is composed of various sub-models including

climate, soil hydrology, crop growth and nutrient cycling. Herewe highlight only some key features relevant to the estimation ofcrop water requirements. For a detailed model description includ-ing model equations see Williams (1995).

A curve number approach is used to calculate surface runofffrom precipitation. Remaining water infiltrates into the soil and astorage routing technique simulates water flow through soil layers.If soil water content exceeds field capacity, water can percolatedownwards to the next layer, the percolation rate depending onhydraulic conductivity and thickness of the layer. No percolationoccurs when water content is below field capacity. Simultaneously,

Fig. 2. Regions (according to the European Nomenclature of Territ

lateral flow from each layer is considered. We used the Penman–Monteith method (Monteith, 1965) to calculate crop-specific po-tential evapotranspiration, thereby separating soil evaporationand crop transpiration following Ritchie’s approach (Ritchie,1972). The potential plant transpiration is distributed to soil layersbased on the depth of the root zone. If the soil water content in alayer is less than 25% of plant-available soil water, the ‘reductionwater content’, the actual transpiration is reduced linearly accord-ing to the ratio of soil water content and the reduction watercontent.

The phenomenological development of crops is based on a dailyheat unit accumulation, affecting harvest date and senescence, leafarea growth, root depth and crop height, partition of biomassamong roots, shoots and yield. Biomass is calculated from conver-sion of intercepted photosynthetic active radiation. Crop yield isdetermined using a harvest index concept. The harvest indexdetermines yield as fraction of above-ground biomass. Waterstress, nutrient stress and temperature stress can limit phenome-nological development and crop growth.

Irrigation schedules can be specified explicitly by the user. Amore flexible way is the automatic irrigation option, performingirrigation operations within pre-defined settings according to ac-tual weather conditions and crop requirements. An irrigation oper-ation can be triggered by predefined thresholds of either plantwater stress level (0–1), soil water tension (kPa) in the ploughlayer, or soil water deficit (mm) of the root zone. A maximum irri-gation rate can be set to limit total water use. Also a minimum ratecan be required not to irrigate at unrealistic small rates. Further, itis possible to define a minimum number of days required betweenirrigation operations.

The simulated irrigation can be interpreted as net irrigationrequirement. The net requirement depends, however, on the cho-sen irrigation strategy. Therefore the effect of different irrigationstrategies has to be taken into account.

orial Units for Statistics NUTS2 and NUTS3) and crop regions.

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 531

Model setup to estimate irrigation requirementsThe automatic irrigation option was activated for all irrigated

crops to implement flexible demand-based irrigation schedulingand application rates. The irrigation controls of the EPIC models al-low various irrigation strategies to be implemented reflectingnumerous options for a farmer to decide how and when to irrigatein the field.

To evaluate the impact of irrigation strategy on yields and irri-gation water use, we defined different irrigation strategies (theassociated model settings are listed in Table 1):

� Irrigation strategy S0 assures optimum water supply, thesoil water content is always kept at field capacity. Thisreference-scenario provides a theoretical maximum irrigationrequirement.

� Irrigation strategies S1, S2 and S3 trigger irrigation based on asoil water deficit of 50, 100 and 150 mm, respectively.

� Strategy SX is rain-fed agriculture without irrigation.

We set a minimum irrigation interval of 3 days. Increasing thewater deficit triggering irrigation (S0–S3) also affects the intervalsbetween irrigation operations determined by the cumulative dailyevapotranspiration and the time required to reach the soil waterdeficit.

We calculated irrigated area per crop category for each10 � 10 km grid cell based on the European Irrigation Map (EIM).To save computation time, the EPIC model was run for the fivedominant irrigated crops within each cell only. We assumed thatirrigation requirements of the remaining crops will not consider-ably affect average requirements per cell. We simulated irrigationrequirements for a simulation period of 8 years from 1995 to 2002.

The net irrigation requirement (mm) for each cell is the area-weighted average net irrigation requirement of these five crops.The volumetric net irrigation requirement (m3) per cell was ob-tained multiplying the net irrigation requirement with total irri-gated area (ha) per cell (and converting from mm to 1000 m3/ha).

Model results extracted include annual values and average an-nual values of yield (t/ha), biomass production (t/ha), precipitation(mm), potential evapotranspiration (mm), actual evapotranspira-tion (mm), surface runoff (mm), subsurface runoff (mm), percola-tion, irrigation (mm) and water stress (days).

Table 1Relevant EPIC parameter settings implementing different irrigation strategies defined for

EPIC parameter Scenario S0 S1Description No water deficit Low w

BIR Irrigation trigger 0.99 �50BIR < 0 Soil water deficit (mm)0 < BIT < 1 Water stress factor (–)EFI Runoff fraction (–) 0 0ARMN Minimum application rate (mm) 1 40ARMX Maximum application rate (mm) 150 60

Table 2Water use in rice cultivation.

Irrigation depth (mm) Region C

4492 Andalusia, Spain T2921 Andalusia, Spain O2300 Camargue, France2100–5000 Camargue, France C1500–3000 Camargue, France P1590 Aragon, Spain M

Generating the final result setFrom the different irrigation strategies, a new data set was gen-

erated, selecting the optimum irrigation strategy per crop and persite based on the average annual irrigation requirements. The opti-mum strategy was considered to support agricultural productionwith the lowest possible irrigation requirements. The challenge isto define a reference production level for crops and regions thatshould be obtained, especially given the strong relation of yieldand water availability. We chose to set the minimal required pro-duction level (per crop and cell) equal to 80% of the yield obtainedunder full irrigation (strategy S0), thus excluding all strategies thatfailed to reach this target. From the remaining irrigation strategiesthe strategy with the lowest irrigation water requirement was cho-sen as the final strategy. The rain-fed strategy SX was excludedfrom this selection, as the statistical data by definition refer toareas ‘irrigated at least once a year’. Finally, new result tables werecreated based on the final strategies selected per site and crop. Inaddition to average results, we extracted simulation results forthe years with minimum and maximum irrigation requirementsto analyze the range of irrigation requirements during the simula-tion period.

Treatment of specific cropping systems – rice productionThe EPIC modeling approach can not represent irrigation of rice.

In Europe, rice is dominantly grown as paddy rice in flooded fields(Ferrero, 2006; FAO, 2004). In addition to evapotranspiration, aconsiderable amount of water is lost by percolation from theflooded field and the true application rates are highly dependenton local soil conditions (hydraulic conductivity) and managementpractice. Rice cultivation requires water applications in the rangeof 1500–5000 mm (Table 2; Aguilar and Borjas, 2005; Chauvelon,1996; Chauvelon et al., 2003; Nogues and Herrero, 2003), depend-ing on local conditions. Given these extremely high irrigation rates,it can be expected that rice cultivation has a considerable impacton the regional water demand. While the lower range of reportedirrigation values corresponds to rice evapotranspiration, the higherrange of values refers to soils with considerable percolation losses.

EPIC does not simulate infiltration and percolation from floodedfields. For European scale mapping we assumed significant perco-lation in addition to transpiration and provisionally assigned a con-stant irrigation requirement of 3500 mm to rice crops, which is

the EPIC simulations.

S2 S3 SXater deficit Moderate water deficit High water deficit No irrigation

�100 �150 –

0 0 –90 140 0

110 160 0

omment Source

raditional flooding management Aguilar and Borjas (2005)ptimised flooding strategy Aguilar and Borjas (2005)

Chauvelon (1996)ooperative systems Chauvelon et al. (2003)rivate systems Chauvelon et al. (2003)odel estimation Nogues and Herrero (2003)

Table 3Comparison of irrigation strategies: relative yield change with respect to irrigationstrategy S0 and irrigation requirement (mm/yr) by crop region.

S0 S1 S2 S3 SX

Yield change compared with S0Mediterranean 0 �0.04 �0.16 �0.66 �0.81Alpine 0 0.01 �0.02 �0.09 �0.15Continental 0 0.01 �0.01 �0.13 �0.26Atlantic 0 0.05 0.01 �0.10 �0.16Boreal 0 0.05 0.05 0.00 �0.02

Irrigation (mm/yr)Mediterranean 1220 886 724 171 0Alpine 456 189 127 59 0Continental 569 273 205 105 0Atlantic 521 215 147 54 0Boreal 355 148 96 42 0

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about two-third of the reported irrigation range. For comparison togovernment reported water abstraction, we used the entire rangeof reported irrigation applications (1500–5000 mm) to identifythe potential contribution of rice irrigation.

From water demand to potential water abstractions

Net irrigation requirements (or volumetric net irrigation waterrequirements) constitute only a part of the total water abstractedfor irrigation purposes (or gross irrigation requirement). Additionalwater abstraction results from the need to compensate for lossesduring transport (infiltration and percolation or evaporation), theneed to apply water in excess to prevent salinization (‘leachingfraction’) and the water use efficiency of the irrigation method.

Starting in 2003, a survey of irrigation methods was included inthe FSS, reporting the area covered by specific irrigation methods(surface irrigation, sprinkler irrigation, drip irrigation and mixedmethods). Regional data for 2003 were provided by Eurostat(2003). We assumed that the ratio of irrigation methods is compa-rable to the year 2000, which forms the basis for estimating cropareas in this assessment. It should be mentioned, however, thatthere is a general trend of replacing surface irrigation by sprinkleror drip irrigation.

Based on the regional volumetric net irrigation requirement, weestimate regional gross irrigation requirements by multiplicationwith the inverse of a regional scheme efficiency (Brouwer et al.,1989) where the scheme efficiency es can be expressed as productof regional application efficiency ea and transport efficiency ec:

WA ¼ 1es� IWD where es ¼ ec � ea ð1Þ

Indicative field application efficiencies according to Brouweret al. (1989) range from 0.60, 0.75 and 0.90 for surface irrigation,sprinkler irrigation, and drip irrigation, respectively. We calculateda regional application efficiency using the indicative values foreach irrigation method and calculating a weighted average basedon the area covered by each irrigation method.

The transport efficiency mainly depends on the length of the ca-nals, the soil type or permeability of the canal banks and the con-dition of the canals. For earthen canals, Brouwer et al. (1989) giveindicative values of 0.60–0.80 for sandy canals and of 0.80–0.90 forclayey canals. Highest efficiencies of 0.95 were assigned to linedcanals. There are no simple rules of thumb to estimate conveyanceefficiency at regional scale due to the possible diversity ofirrigation schemes, differing in size, irrigation infrastructure, main-tenance, management, etc. In addition, supporting data at Euro-pean scale are lacking. We used the minimum efficiency of 0.6and the maximum efficiency of 0.95 of all values given by Brouweret al. (1989) for our calculations. Then we calculated the resultingrange of scheme irrigation efficiency and determined minimumand maximum gross irrigation water requirement per region. Theregional values were applied uniformly within each region, as cur-rently no further assumptions on the spatial distribution of irriga-tion methods below regional level could be made. The resultingscheme efficiencies at regional average range from 0.36 to 0.85,meaning that irrigation requirement is multiplied with a factor(1/es) in the range of 2.78–1.17 to estimate potential waterabstractions. These two factors determine the extremes withinwhich true abstractions should be located.

Comparison with reported data

We compared simulation results with reported data at nationaland regional level. National water abstraction data are collectedregularly by Eurostat via the OECD/Eurostat Joint Questionnaireon Inland Waters. These data include annual water abstraction

data per sector at national level. Abstractions for agriculture, fish-eries and forestry are combined as agricultural abstractions whilea separate indicator on agricultural water abstractions forirrigation is sometimes included. However, not all information isprovided regularly and consistently by the EU’s Member States.Government reported water abstractions (OECD/Eurostat, 2000)for irrigation and agriculture were compared with irrigationrequirements and with potential water abstractions assuming highand low irrigation scheme efficiency for the year 2000. Reportednational water abstractions were expressed as ‘Statistical irriga-tion’ in mm/yr, dividing reported water abstractions by reportedirrigated area. The simulated irrigation was calculated accordinglydividing the regional (national) volumetric irrigation requirementby regional (national) irrigated area. Where countries reportedagricultural water abstractions and do not specify irrigationabstractions, the latter were replaced by agricultural abstractions.This may introduce some bias in regions where irrigation is rela-tively unimportant and watering livestock accounts for the major-ity of agricultural water abstractions. Given the differentmethodologies applied in Member States to estimate irrigationabstractions and the problems of lacking legal enforcement, illegaland unrecorded abstractions, the data have little value for modelvalidation, but a comparison may still reveal relevant patterns ordiscrepancies enabling a mutual improvement of reported dataand model calculations.

Another comparison was made with regional water abstractiondata for the year 2000 from France (IFEN, 2007). The French dataset includes Mediterranean agricultural systems as well as therain-fed agricultural systems of Western and Central Europe. Theregional comparison was based on volumetric water requirementsand reported abstractions.

Results and discussion

Effect of irrigation strategies

Irrigation strategies have different impacts on crop yield, bio-mass and net irrigation requirement (Table 3). For simplificationwe present results of average net irrigation water requirement(Fig. 3) and yield reduction (Fig. 4) for different crop regions(Fig. 2).

Irrigation requirements (Fig. 3) decrease from strategy S0 in theorder S1, S2, S3, SX, according to the higher water deficit requiredto trigger irrigation. In the Mediterranean crop region, they rangefrom 1220 mm/yr (S0) to 171 mm/yr (S3). Changing from strategyS0 to S1 cuts irrigation requirements by approximately 30% (from1220 to 886 mm/yr in the Mediterranean). The absolute net irriga-tion requirements are highest in the Mediterranean and lowest inthe boreal crop region reflecting the climatic conditions.

Fig. 3. Average irrigation requirement for different irrigation strategies and crop regions.

Fig. 4. Average yield for different irrigation strategies and crop regions (given as relative yield to irrigation strategy S0).

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 533

Crop yields (Fig. 4) are given as relative change with respect tocrop yield in irrigation strategy S0, averaged over all crops and theentire crop region. The decrease of yields from strategy S0 to SX ishighest in the Mediterranean (81%), while the decrease is less than20% in the Atlantic, Alpine and Boreal crop regions. This reflects thesubstantial requirement for irrigation in the Mediterranean agri-culture, while the other parts of Europe receive sufficient rainfallfor crop cultivation. Except for the Mediterranean, yields in strat-egy S1 are 1–5% higher than in S0. Especially in moderate climatesand on soils with low pore volume above field capacity, frequentirrigation (S0) may have impacts on soil aeration, negatively affect-ing crop growth and yields. The EPIC model explicitly simulatessoil aeration stress; this process can therefore explain thesefindings.

The results show that irrigation strategy, irrigation requirementand crop yield are not independent. Exceeding a certain irrigationlevel does not substantially increase crop yields while considerable

water savings could be achieved (with respect to S0) with no or lit-tle yield reduction. This finding supports the idea of applying def-icit irrigation practices (FAO, 2002) to enhance water savings inagriculture.

The findings suggest that especially irrigation strategies S0 andS1, which were rarely chosen as optimum strategies, apply irriga-tion water in excess. This can be explained when reviewing someconcepts of soil water modeling: Crop evapotranspiration falls be-low the potential rate only after falling below a limiting soil watercontent, which lies below field capacity (Shuttleworth, 1992). Tomaintain crop production it is therefore not necessary to fill soilwater storage up to field capacity and some elasticity of waterrequirement has to be taken into account. Strategies S2 and S3seem to be the most appropriate strategy in a general sense,although other strategies were favored locally.

Providing a selection of defined irrigation strategies can notcover all possible combinations of scheduling and application

534 G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544

rates. The chosen strategies cover a broad spectrum of an infinitenumber of possible strategies. The water deficits chosen to triggerirrigation translate also into different irrigation intervals (from afew days up to several weeks), as required to achieve the waterdeficit under given climatic conditions. We tested additional irriga-tion strategies, but the results were similar to the chosen strategiesS2 and S3. Selecting an ‘optimum’ strategy is not strictly focused onmaximizing yields but focuses at a tradeoff between irrigationwater use and yields (within a specified tolerance to the highestsimulated yield). Irrigation for quality control is not explicitly con-sidered, although this issue is important at least for certain crops.Examples for quality aspects related to irrigation are sugar contentof (sugar beet, vine), carbohydrates in potatoes, oil or water con-tent (olive oil versus table grapes). The selection approach gives,however, some flexibility to adapt also to changing conditions ofclimate and crop patterns.

It is likely that actual irrigation strategies differ from the strat-egies suggested by the model, as management practices also de-pend on irrigation technology, education and habits, wateravailability and economic aspects of irrigation. With respect toour focus on a large-scale overview on irrigation requirements,we consider this limitation to be acceptable.

Based on a regional survey of actual irrigation strategies itwould be possible to pre-define strategies by location and crop.This would also allow to better consider quality irrigation, but flex-ibility to adapt strategies is then lost.

Fig. 5. Spatial distribution of irrigation strategies selected for final results based on. Thenumber of crops simulated in a particular site.

Irrigation requirements

The final result set was selected choosing an optimum strategyper site and crop. The spatial distribution of the selected irrigationstrategies is displayed in Fig. 5. The frequency is the number ofcrops irrigated with a specific strategy divided by the total numberof crops per site. Strategies S0 and S1 are only marginally relevantwith very limited distribution. As shown before, they possibly indi-cate locations where soil parameters are not adequately represent-ing agricultural soils. Strategy S2 and S3 were the most frequentlychosen strategies and are distributed all over Europe. The averagenet irrigation requirements in the final result set are displayed inFig. 6. They are not only determined by climatic conditions, but re-sult from the interaction of climate, soil properties and crop com-position at each site. The general patterns reflect well the differentirrigation requirements in Northern and Southern countries,though at smaller scale complex patterns exist reflecting specificlocal conditions. Cross-cutting geographical locations, crop typesand local soil and climate, the average site irrigation requirementsrange from 0 mm/yr up to 2368 mm/yr (including correction forrice cultivation).

During the 8-year simulation period, irrigation requirementsvaried considerably (Fig. 7) reflecting inter-annual variability of cli-matic conditions. The highest ranges (exceeding 600 mm) were ob-served in Southern Portugal, Southwest Spain and Crete. In Centraland Northern Europe (United Kingdom, Belgium, Netherlands,

frequency is the number of crops irrigated with a certain strategy divided by total

Fig. 6. Average net irrigation requirement (mm/yr) in EU and Switzerland (simulation period 1995–2002).

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 535

Luxemburg, Germany, Denmark and Sweden) the range was below200 mm. The maximum irrigation requirement indicates dry-yearwater requirements. However, the time series of 8 years is tooshort to derive statistically valid ‘design dry-year’ requirementsor any other statistics representing long-term conditions. Anextension of the climatic database to include longer periods is un-der way.

We obtain volumetric net irrigation water requirements (Fig. 8)by multiplying irrigation requirements with irrigated area for each10 � 10 km cell. The spatial patterns therefore do not only reflectthe irrigation requirements, but also the distribution of irrigatedareas.

Fig. 9 illustrates the effect of irrigation (Strategy S2) on cropyields in comparison with the no-irrigation strategy SX. The rela-tive increase in yield shows distinct regional differences. In Central,Northern and Eastern countries, relative yield increase due to irri-gation is less than two (=100% increase relative to SX), reflectingthe supplementary and temporary character of irrigation in thesecountries. On the contrary, in Southern countries, the relative yield

increase is considerably higher by orders if magnitude (>5). Theseextremely high relative yield increases reflect the severe limitationof agricultural production by climatic water scarcity (having verylow yields without irrigation) and show that in these regions irri-gation is of substantial importance to maintain agriculturalproduction.

Screening of model behaviour

Analysing the extensive output of complex large-scale inte-grated models efficiently is a challenge due to the high numberof spatial units and output variables. Nevertheless, some insightin model behaviour is required to better judge the plausibility ofmodel results and identify simulation problems. We present an ini-tial screening of model outputs (crop water deficit and irrigation)in relation to climatic drivers and soil parameters.

Fig. 10 displays the relations of simulated irrigation and cli-matic water deficit (left), crop water deficit (middle) and the rela-tion of irrigation and crop yield (as yield index, right) for maize.

Fig. 7. Range of irrigation requirements (Max–Min) in EU and Switzerland (simulation period 1995–2002).

536 G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544

Climatic water deficit was determined calculating the daily waterbalance (P-ETP) and summarizing all negative values (water defi-cit) to the average annual value. The crop water deficit is the differ-ence between potential crop evapotranspiration and actualevapotranspiration under rain-fed conditions (ETPc-ETA, averageannual values). The crop water deficit accounts for effects of soilwater storage and crop development, and the relation is evenstronger than for the climatic water deficit. The yield index is theratio of irrigated and rain-fed yield. The figure suggests a non-lin-ear positive relation. At the upper end of the irrigation range,rain-fed yields decrease considerably and irrigation becomessubstantial to maintain crop production. Thus the very high yieldindices. At the lower end, approximately below 200–300 mm, irri-gation has only minor impact on the yield index, reflecting thetemporary character of irrigation to occasionally overcome dryperiods. The findings show that the model represents the climaticimpact on irrigation requirement sufficiently. Variability (appr.±90 mm) results from variations of specific climatic conditions, soilparameters, and management schedules. There are also interac-

tions between crop development, nutrient cycling, climate and soil,which come into play.

In Figs. 11 and 12 we demonstrate the impact of soil conditionson simulation results, using a sample of 25 cells with identical cli-mate in central France. Each climatic station (given on a50 � 50 km grid), is associated with 25 cells having different soilconditions. The climatic station was selected as an example withthe highest soil textural heterogeneity in all associated cells. Sandcontent was chosen as an indicator of soil physical properties, aswe have strong correlation between soil and silt content, availablewater capacity and saturated conductivity (the latter two derivedby pedo-transfer functions). The crop water deficit has a positivelinear relation to sand content, reflecting the lower water storagecapacity of sandy soils (Fig. 11, left). Consequently, rain-fed yieldsdecrease linearly with crop water deficit and sand content (Fig. 11,middle, right). In Fig. 12 we demonstrate the effect of different irri-gation strategies (S0-full irrigation) and S2 (min. soil water deficitof 100 mm to irrigate). Under the full irrigation strategy S0, irriga-tion is related to sand content and water deficit, but the change of

Fig. 8. Average volumetric net irrigation requirement per 10 � 10 km cell in EU and Switzerland (1000 m3/yr/site, simulation period 1995–2002).

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 537

irrigation is less than 10%. This is reasonable, considering that irri-gation refills soil water storage only up to field capacity (no perco-lation) and irrigation basically serves to maintain cropevapotranspiration. Irrigation has different effects on yieldsaccording to soil type, while the absolute yield levels are similar.Under strategy S2, we observe similar relations for most cells.However, compared with S0 much less irrigation is required toachieve similar crop yields. Selecting a final strategy, S2 wouldbe favored compared with S0. It is important to note that the effectof irrigation strategy (S0 versus S2) exceeds the effect of soil (Sandcontent 0–60%) by far. For some cells (exceeding 60% sand or430 mm crop water deficit), no irrigation is applied and yieldsare on rain-fed level. This is caused by the specific setting of theirrigation strategy S2, requiring a soil water deficit of �100 mmto trigger irrigation. If soil water capacity is less, this water deficitis never fulfilled and irrigation can not be triggered. In this case, S0would be preferred to S2 in the final selection. The selection proce-dure assures that the most appropriate strategy is maintained. Thisbehaviour can also point to soils and soil properties not appropri-

ate for agricultural use, as the soil map defines dominant soils,which may not be the ones where agriculture takes place.

In Fig. 13 we present a spatial sensitivity analysis of net irriga-tion requirements in response to soil heterogeneity (soil propertiesrepresented by sand-content). We define the sensitivity u of theoutput variable vo to the input parameter vi as the ratio of theircoefficients of variations (c):

u ¼ stdevðvoÞmeanðvoÞ

�stdevðv iÞmeanðv iÞ

¼ cðvoÞcðvoÞ

ð2Þ

A sensitivity greater than one suggests that the output variationis greater than the input variation, indicating high sensitivity ofmodel output to model input. Vice versa, a coefficient smaller thanone suggests that the input variation is greater than the outputvariation. This indicates that the output is less sensitive to the in-put variable.

For each climate station we calculated the local sensitivity uover the subset of all associated cells (max. 25) for maize, requiringa minimum of 10 cells with maize cultivation for each climate

Fig. 9. Relative yield-increase of irrigation strategy S2 compared with the no-irrigation strategy SX (yield index), indicating the dependence of agricultural production onirrigation.

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station. Given the small sample, results are indicative only. Formost climate stations the sensitivity is below 1, indicating that soilproperties have minor impact on the net irrigation. Only few sta-tions show uncertainty above 1, indicating existence of cells withproblematic soil parameterization as identified before. These find-ings are consistent with the previous findings.

Generally, the model responds clearly to climatic conditions atEuropean level (water deficit and irrigation) and to soil texture atlocal level (water deficit), which gives us some confidence thatthe model is appropriate for the study. The indicative findings sug-gest that sub-grid heterogeneity of soils is a minor problem, but arevision of soil parameters may be required to better consider agri-cultural soils rather than landscape-representative soils.

Comparison with reported water abstractions

Reported national abstractionsReported water abstractions for the year 2000, the calculated

irrigation requirement for the same year and the potential abstrac-

tions assuming high and low irrigation efficiency are displayed inTable 4 and Fig. 14. National average irrigation scheme efficiencies(es) range from 0.41 (low efficiency) to 0.70 (high efficiency). Theseefficiencies correspond to national multiplication factors (1/es) of2.42 and 1.26.

The following countries were omitted from the analysis forobvious inconsistencies in reported data or missing informationon water abstractions: Estonia, Finland, Latvia, Lithuania, Malta.The countries were ordered with increasing reported irrigationwater abstractions grouped in (i) EU15 and Switzerland and(ii) Eastern European Countries.

Ideally reported abstractions should fall within the uncer-tainty range defined by net irrigation requirement and waterabstractions under low efficiency irrigation. This uncertaintyranges reflect the water saving potential of irrigated agriculturethat could be achieved by reducing conveyance losses, improvedapplication efficiency, changes in irrigation practices (schedul-ing), change of crops and reuse of treated sewage effluent.According to a recent study on EU water saving potential, the

Fig. 12. Impact of soil and water deficit on irrigation and crop yield for maize under identical climate for two irrigation strategies S0 (upper panel) and S2 (lower panel):relations of irrigation to soil texture (as sand-content) (left), irrigation to crop water deficit (middle-left), yield response to soil texture (middle-right) and absolute yield tocrop water deficit (right). Example from Central France.

Fig. 10. Relation of net irrigation requirements to climatic water deficit (left), soil-crop water deficit (middle) and yield response to irrigation for maize (dashed line on yieldindex = 1 is the rain-fed yield level).

Fig. 11. Soil impact on water deficit and crop yield for maize under identical climate: relations of crop water deficit to soil texture (as sand-content) (left), yield to crop waterdeficit (middle) and yield to soil texture (left). Example from Central France.

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 539

saving potential of irrigated agriculture is about 43% (Ecologic,2007).

Fig. 14 compares reported national irrigation abstractions withcalculated irrigation requirements and the corresponding range ofabstractions. Countries are grouped into EU15-countries andSwitzerland (CH–PT) and new Member States (Eastern Europeancountries CZ–BG), each group sorted according to reported abstrac-tions. For some countries, reported abstractions overestimate cal-culated irrigation requirements (Sweden, Belgium, and Poland)but typically they underestimate calculated values. Roughly speak-ing, reported and calculated values are positively related reflectingthe high water demands in the Mediterranean and South-East

Europe in contrast to Northern, Central and Western Europe. Largediscrepancies between reported and calculated values occur. Forexample, data for Italy seem to correspond reasonably well to sim-ulation results, but they are based on simulations as well. However,the reported irrigation is about 900 mm higher than in Spain andGreece with comparable or even more severe climatic conditions.Reported data from Spain and Greece, however, are likely to under-estimate true abstraction due to lacking data and a high percentageof illegal and unrecorded abstractions. It is estimated that Spanishagriculture abstracts about 3.600 hm3 water per year illegally,about 45% of total agricultural water abstractions (WWF/Adena,2006) and not included in reported data. The irrigation abstractions

Fig. 13. Indicative sensitivity of maize irrigation requirements to soil texture (expressed as sand-content), as ratio of the respective coefficients of variation by climate station(up to 25 crop cells). Values <1 indicate low sensitivity, values >1 indicate high sensitivity.

Table 4Comparison of reported data (Eurostat, 2000) with simulation results given as reported and calculated irrigation requirement (in mio. m3/yr and mm/yr) and calculatedabstractions (expressed as gross irrigation in mm/yr) respecting low and high efficiency of irrigation systems.

CTRY Irrigatedarea (ha)

Reported irrigationabstractions(mio. m3)

Net irrigationrequirement(mio. m3)

Efficiency range(high–low)

Reported irrigationb

(mm/yr)Calculated irrigationrequirement (mm/yr)

Calculatedabstraction(low eff.) (mm)

Calculatedabstraction(high eff.) (mm)

AT 28,277 68 103 1.4–2.2 239 364 797 503BE 2885 10a 3 1.4–2.3 347 97 220 139BG 77,435 731 634 944 819 1883 1064CH 44,237 6 0 13 29 17CZ 15,896 9a 28 1.4–2.2 59 176 385 243DE 220,270 163a 223 74 101 233 132DK 201,185 165 107 1.4–2.2 82 53 118 75ES 32,06,214 21,763 35,919 1.4–2.2 679 1120 2486 1570FR 15,66,535 4872a 6349 1.4–2.2 311 405 905 572GR 11,59,281 7600a 12,776 1.4–2.2 656 1102 2421 1529HU 65,924 173 760 1.4–2.2 262 1152 2568 1622IT 24,50,993 38,360 22,381 1.5–2.3 1565 913 2136 1349LU 49 408 28 65 37MT 332 2 1.3–2.0 627 1252 791NL 61,824 76a 50 1.4–2.2 123 80 177 112PL 33,392 110 17 330 50 116 65PT 256,022 6551a 2427 1.4–2.3 2559 948 2176 1374RO 393,850 513a 2030 1.4–2.3 130 515 1164 735SE 53,044 107 22 1.4–2.2 202 42 93 58SI 1680 7a 6 1.4–2.2 399 380 821 519SK 106,882 77 409 1.5–2.4 72 382 913 576UK 146,603 106 62 72 42 98 55

a Approximated by total agricultural abstractions.b Reported irrigation abstractions/irrigated area.

540 G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544

reported for Portugal (resulting from dividing water abstractions byirrigated area) are 2.7 times higher than calculated and 3.7 timeshigher than in the neighboring country Spain, which seems unreal-istic and may indicate possible inconsistency of the underlying dataon water abstractions and irrigated area. In countries with signifi-cant rice production, considering the additional requirements tocompensate percolation can have substantial impact on irrigationrequirements. The discrepancies observed for Eastern Europeancountries require further analysis to separate the impact of modeluncertainties and limitations of the statistical information. They

can also be related to inconsistencies in reported data on irrigatedabstractions and irrigated area.

There are also conceptual problems comparing simulated irriga-tion requirements with reported water abstractions, even whencorrected for water losses and efficiencies. Simulated irrigationrequirements are determined by climatic and edaphic conditionsand standardized crops (not taking into account regional varietiesand differences in management). In contrast, reported abstractionsrepresent actual water abstractions additionally affected by irriga-tion systems and management practices, legal restrictions, water

Fig. 14. Comparison of reported national water abstractions for irrigation with calculated irrigation requirements and resulting abstractions assuming low and high efficiencyof irrigation practices. All units converted to mm/yr. Grouping by (i) EU15 and Switzerland and (ii) Eastern European Countries.

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 541

shortage and economic reasons: Lilienfeld and Asmild (2007) andCancela et al. (2006) show that irrigation management and main-tenance of irrigation systems are key factors determining actualwater use, counterbalancing potential water savings of irrigationtechnology. Legal restrictions on irrigation can be imposed in manycountries, for example by assigning water rights as in Spain or issu-ing temporal interdictions as in the Netherlands. Economic aspects,such as market prices of crops, water prices and costs for irrigationtechnology and maintenance affect the marginal income achievedby irrigation and thus feed back on irrigation water use. On thecontrary, unlimited access to water or low costs favors inefficientirrigation and excess water use.

An explicit consideration of these conceptual issues in theassessment approach may help to better transfer simulated netirrigation requirements into estimates of actual abstractions,which would be a better basis for comparison with reported data.

The total volume of irrigation requirements and abstractions bycountry (Supplementary material) is scaled by the irrigated area,pronouncing the difference in irrigation water use between theMediterranean and the rest of Europe.

Reported regional abstractions in FranceThe comparison with regional abstraction data from France was

directly based on comparison of water abstractions and irrigationrequirement, without taking into account irrigation efficiency(Fig. 15). An initial screening suggested different relations inSouthern France (Corse, Languedoc, Provence) and the remainingpart of France. The two zones separate dominantly rain-fed agricul-ture (temporary irrigation) and dominantly irrigated agriculturecoinciding with structural differences in irrigation practices. TheFrance dataset was therefore split into two subsets. We observeapproximate linear relations between reported and simulated data.However, calculated irrigation requirements underestimate re-ported data in Southern France by 30% (ratio 0.7), while calculatedrequirements are 170% of reported data in the remaining part ofFrance. In Southern France, public irrigation water supply andoff-site surface water are dominant water sources (Eurostat,2003), indicating a high level of institutional organization and irri-gation infrastructure. In this zone, water abstractions are likely tobe better monitored, while conveyance and application losses canbe high due to long distance transport. Accounting for losses we

can well explain the differences in simulated and reported abstrac-tions. In Central, Western and Northern France groundwater is themajor source of irrigation and sprinkler is the dominant irrigationmethod (Eurostat, 2003). This suggests dominant on-site abstrac-tion and short distance transport (low losses) and use of efficientpressurized sprinkler irrigation systems. True abstractions are pos-sibly systematically underestimated by reported data, as only wellsexceeding a certain capacity require authorization (Dubus I.G.,2007, oral communication) and a considerable amount of unre-corded abstractions may exist. This regional comparison showsthat even on a sub-national scale, reported data can deviatesystematically from simulation results.

Discussion of modeling uncertainties

Large-scale modeling faces specific challenges, mostly related tosub-scale heterogeneity and simplifications required to modellarge geographical areas. An overview on state-of-the-art and fu-ture perspectives is given by Döll et al. (2008). The uncertainty ofmodel results caused by spatial aggregation and/or simplificationof model parameters and the error of input data requires carefulconsideration.

Climate is the main driver of irrigation requirement in the mod-el. Uncertainty in precipitation therefore remains a major chal-lenge for large-scale hydrologic modeling (Döll et al., 2008) andis likely to affect estimates of water deficits and irrigation require-ments made in our assessment.

Reported data on irrigated areas to calculate volumetric irriga-tion requirements are another source of input uncertainty. Thereis, however, a direct relation between input and output uncer-tainty, and this uncertainty does not affect the comparison with re-ported abstractions, as both relate to the same total irrigated area.

Soil data are important parameters controlling soil water bal-ance. The internal heterogeneity of modeling units can not be con-sidered appropriately, as soil data have to be derived from mapsand databases of sufficient geographical coverage. This excludesthe use of high resolution soil data and requires the use of inter-preted and aggregated continental and global scale data sets. Soilmapping units define a dominant or typical soil within the unitand the information on potential internal heterogeneity of soiltypes and texture is lost. The dominant soils need not be the ones

Fig. 15. Comparison of regional water abstraction data with calculated volumetric net irrigation requirements (in mio. m3) for France, indicative linear regression fits for thetwo geographical areas (France – South and France – rest).

542 G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544

where agriculture takes place (for example in mountainous regionswith dominant regosols, where agriculture is limited to valleydeposits with deeper soils). Future improvement of soil parametersshould therefore focus on characterizing ‘representative’ agricul-tural soils. We showed before, that soil parameters closely affectcrop growth and evapotranspiration, while irrigation requirementsare relatively stable over a broad range of soil conditions.

The simplifications made in the model put constraints on modelvalidation to local and crop specific information. Rice is so far theonly crop where results were checked crop specifically and thesimulated values were provisionally corrected by literature values.Given the lack of appropriate validation data, model validation re-mains a challenge. Integration of remote sensing data may be anoption to better constrain model results; but first attempts havenot yet been promising.

These issues are equally valid for comparable large-scale mod-els and modeling studies, such as the aforementioned modelsWaterGap and GEPIC. Approaches to integrate sub-grid heteroge-neity, the appropriate combination of scale and models and theintegration of sensitivity and uncertainty analysis are key prob-lems for future research (Döll et al., 2008).

Conclusion and final remarks

Our assessment provides a view on irrigation requirements athigh spatial resolution with large geographical coverage (EU andSwitzerland), taking into account the spatial distribution of crops,soil conditions, and climate and crop management. The spatial pat-terns of irrigation water use reflect the distribution of irrigatedareas and the climatic water requirements at sufficient resolutionto support detailed analysis of regional differences in agriculturalpressures on water resources. The approach can provide reason-able estimates of net irrigation requirements. Selecting differentirrigation strategies allowed us to adapt irrigation more specificallyto soil conditions and crop growth.

A comparison with reported irrigation abstractions revealeddiscrepancies between reported abstractions and model estimates.These discrepancies require careful and country-specific interpre-tation, as not only model uncertainties come into play, but alsothe quality of reported data has to be questioned due to the varietyof methods applied and the often insufficient enforcement of mea-surements or compliance with legal obligations.

Future improvements of the modeling approach should aim at arevision of underlying soil parameters towards improved represen-tation of agricultural soils rather than landscape-representativesoils. Apart from the modeling, the assessment in general can beimproved by more detailed consideration of legislative aspectsand water availability (requiring a comprehensive water resourceassessment at European scale) allowing a better estimation of ac-tual water use.

The bottom up approach allows aggregating results from thesite-scale (10 � 10 km) to regional and national estimates. Thedata sets created are therefore also useful to identify hot spots ofirrigation water use at various spatial levels and communicatingthe findings across different administrative levels.

The work presented in this paper contributes to the develop-ment of agri-environmental indicators carried out in the EuropeanCommission. A previous indicator on water use intensity (IRENAIndicator 10, EEA, 2005) was based on the distribution of irrigatedareas at regional level, but did not include quantitative informationon water use. The assessment of water use via the OECD/EurostatJoint Questionnaire does currently not provide the spatial detailfor regional comparisons. Ongoing activities aim at overcomingthese limitations and further developing agri-environmental indi-cators on irrigation and water abstraction in agriculture. We donot claim that the model assessment is more accurate than re-ported data, but given the problems currently related to reporteddata, only assessments like the one presented in the article can of-fer a framework of consistent information, using ‘official’ data, ex-plicit assumptions and a single approach in all Member States. Themodeling approach provides an independent estimate not biasedby problems of unrecorded water abstractions or by national dif-ferences in accounting and reporting. Instead, it focuses on thewater needs based on the reported crop areas and irrigated areasapplying official data and a unique methodology. This referenceinformation supports evaluation and comparison of national andregional statistics which are typically based on different methodol-ogies and helps to identify inconsistencies and to modify assess-ment strategies.

Especially in Southern Europe, irrigation is a key driver of wateruse. Quantitative and qualitative degradation of water resources isfrequently observed, providing an example of the so-called ‘trag-edy of the commons’ (Hardin, 1968). This demonstrates the needfor appropriate water management. Apart from its direct contribu-tion to the development of agri-environmental indicators in the

G. Wriedt et al. / Journal of Hydrology 373 (2009) 527–544 543

European Commission, the data and tools presented can be appliedto a broad range of questions. In combination with data on wateravailability (precipitation, runoff and recharge) and water abstrac-tions for other uses, it will be possible to develop indicators ofwater stress, to map hot spots of water quantity problems and toanalyze the regional drivers of water quantity problems at Euro-pean scale. The modeling approach can directly be used to simulateleaching of nutrients and pesticides, mapping irrigation relatedwater quality problems. The model can be linked to different cli-matic data and land use scenarios, thus supporting also analysesof land use change impacts and climate change impacts on irriga-tion water demands.

Acknowledgements

The authors thank the reviewers for their constructive criticismand comments.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.jhydrol.2009.05.018.

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