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Open AccessArticle

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

1
Leibniz Centre for Agricultural Landscape Research (ZALF), D-15374 Müncheberg, Germany
2
Wageningen University & Research—Environmental Research (Alterra), NL-6700 AA Wageningen, The Netherlands
3
Flemish Institute for Technological Research (Vito NV), B-2400 Mol, Belgium
4
National Agricultural and Food Centre, Soil Science and Conservation Research Institute, SK-827 13 Bratislava, Slovakia
5
Institute of Agriculture Systems and Bioclimatology, Mendel University, CZ-613 00 Brno, Czech Republic
6
Global Change Research Institute, The Czech Academy of Sciences, CZ-603 00 Brno, Czech Republic
7
Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria, Unità di Ricerca per i Sistemi Colturali degli Ambienti Caldo-Aridi, I-70125 Bari, Italy
8
Department of Agri-Food Production and Environmental Sciences, DISPAA, University of Florence, I-50144 Firenze, Italy
9
Institute of Biometeorology of the National Research Council (CNR-IBIMET), I-50145 Firenze, Italy
10
Climate Change Cluster, University of Technology, Sydney 2007, Australia
11
Institute of Meteorology, University of Natural Resources and Life Sciences, A-1180 Vienna, Austria
12
Thünen Institute of Biodiversity, D-38116 Braunschweig, Germany
13
Tropical Plant Production and Agricultural Systems Modelling, Georg-August-University, D-37077 Göttingen, Germany
14
Earth Science Institute of Slovak Academy of Science, SK-840 05 Bratislava, Slovakia
15
AGES—Austrian Agency for Health and Food Safety Ltd., A-1220 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Arjen Y. Hoekstra
Water 2016, 8(12), 571; https://doi.org/10.3390/w8120571
Received: 18 September 2016 / Revised: 23 November 2016 / Accepted: 28 November 2016 / Published: 5 December 2016
(This article belongs to the Special Issue Water Footprint Assessment)

Abstract

Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%–19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%–39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%–49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%–68% of the total actual evapotranspiration.
Keywords: water footprint; uncertainty; model ensemble; wheat; crop yield water footprint; uncertainty; model ensemble; wheat; crop yield

1. Introduction

The concept “Water Footprint” (WF) was introduced by [1], and later elaborated on by [2] as an indicator that relates human consumption to global water resources. Since international trade in commodities creates flows of so-called “virtual water” [2,3,4], by importing and exporting goods that require water for their production, the indicator provides valuable information for a global assessment of how water resources are used, although it was controversially discussed since water scarcity of the region is not accounted explicitly [5]. In recent years, WFs and virtual water was assessed for crops, goods, services, as well as on generic regional or national levels [2,4,6,7,8,9].
The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rainfed and irrigation water usage, respectively [9]. A third component, the grey water footprint, is defined as the volume of freshwater that is required to dilute the load of pollutants to achieve existing ambient water quality standards. More information about the parameters involved can be found in [10].
Crop productivity and water consumption together form the basis to estimate the water footprint of a specific crop. The WF calculation is based on the estimation of crop specific evapotranspiration during the growing season, which is related to observed crop yields usually from yield statistics of a region. Analyses of several ET formulas under various climate conditions [11,12], revealed that the FAO (Food and Agriculture Organization of the United Nations) Penman–Monteith equation [13] or the Priestley–Taylor formula had the best performance across the different climatic conditions [12,14]. FAO Penman–Monteith is recommended as the standard method for estimating reference and crop evapotranspiration in the water footprint manual to estimate the water footprint [15].
Agricultural production systems are very vulnerable to a potential decrease in water availability. The impacts of climate change (increasing temperatures, shifts of seasonal precipitation and decreasing summer rainfall) could cause water limitations in many areas of Europe [16,17]. A change of currently estimated water footprint values is expected under climate change. However, it is not clear how far the above-mentioned negative impacts of a changing climate can be compensated by the positive effects of increasing CO2. Climate change including increasing CO2 concentration of the atmosphere will affect crop growth as well as soil water dynamics. Moreover, crop response to climatic drivers strongly depends on the site conditions of their habitat [18,19,20]. Therefore, the assessment of WF under future climate conditions requires the simulation of crop yields as well, which may add further uncertainty in the estimate.
Uncertainty may originate from three sources [21]: (i) input data; (ii) parameterization; and (iii) model structure. While uncertainty analyses of models addressing the first point are usually using combinations of stochastically distributed inputs by using, e.g., Monte-Carlo simulations (e.g., [22]), for the other two aspects recent studies have shown that the application of ensembles of complex simulation models is a valuable tool to assess the uncertainty in the estimation of climate impact on crop growth [23,24,25,26,27] and water consumption [28]. To assess the uncertainty of model based assessments of WF an ensemble of different crop models was applied to field data sets from five locations from across Europe. The focus of the study was mainly to look at uncertainty originating from the use of different models. Only limited basic data were made available to allow only a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Although a separation between the uncertainty resulting from model structures and parameter uncertainty is not possible with this approach, the basic data provided in this study for each experimental site contained defined values for field capacity and wilting point and key phenological observations to keep the uncertainty caused by soil and crop parameterization at a limited level. Up to eight models were applied depending on the data set. In the comparison, we focused on cereal crops, mainly winter wheat. The objective of the study was to: (1) assess the uncertainty of the WF estimation caused by the choice of crop models; (2) analyze the response of models to management (irrigation, nitrogen fertilization) and site conditions (soils, CO2 concentration of the atmosphere); and (3) separate soil evaporation from crop transpiration to assess the difference of using evapotranspiration instead of crop transpiration for the crop water consumption assessment.

2. Materials and Methods

2.1. Experimental Data

The five datasets cover the European environmental zones of the Atlantic North, Atlantic Central, Continental and Pannonia according to [29] (Figure 1). The criteria to select data sets were that they provide: (a) meteorological and management data for several years; (b) different treatments or site conditions to analyze crop sensitivity on different inputs; and (c) data to evaluate the relevant outputs for the estimation of the water footprint, particularly crop yield and soil water (and if possible soil mineral nitrogen) status measurements. The basic characteristics of the experimental sites and the treatments used for the model inter-comparison are listed in Table 1.
Here some brief information for each site is presented:
The field experiment at Müncheberg (Mb), Germany was designed to study inter-annual variation in crop rotations, irrigation effects, and biomass development [30]. The crop rotation from 1992 to 1998, consisted sugar beet (Beta vulgaris L.), winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), winter rye (Secale cereale L.) and oilseed radish (catch crop). The rotation covered four parallel plots with a shift of one year to establish each crop each year. Treatments included rainfed agriculture versus irrigated regime. The complete dataset is published and accessible [31].
The Braunschweig (Bs) Free-Air Carbon Dioxide Enrichment (FACE) experiment was set up to investigate interacting effects of CO2 concentration and N fertilization on crop production [32]. The crop rotation was composed of winter barley, a mixture of three different ryegrass cultivars (Lolium multiflorum Lam.) as a cover crop, sugar beet, and winter wheat, grown in two consecutive cycles between autumn 1999 and summer 2005. Treatments included an ambient (374 ppm) and an enriched (550 ppm) concentration of atmospheric CO2, both with a standard and a reduced (−50%) supply of nitrogen (N) fertilizer. Although this experiment did not include climate change, it provided valuable data on the response of crop growth and response of transpiration to elevated CO2, as a main driver of global warming, since both responses are still a source of uncertainty in crop as well as hydrological models.
The data from Hirschstetten (Hi), Austria were taken from three lysimeters in the agricultural region Marchfeld [33]. The crop rotation from 1998 to 2003 included mustard (Sinapis alba, catch crop), spring wheat, mustard, spring barley, winter wheat, mustard (catch crop), potato (Solanum tuberosum L.), winter wheat (ploughed due to frost damage), maize (Zea mays L.), and winter wheat. The crops were grown on three soil types (Calcic Chernozem (S1), shallow and sandy Calcaric Phaeozem (S2) and Gleyic Phaeozem (S3)) in order to study the water cycle, and the influence of soil type and rotation.
The field experiment in Foggia (Fo), Italy [34] represented a durum wheat (Triticum durum) monoculture over ten years (1996–2005) on an alluvial clay-loam soil. Treatments were different nitrogen fertilization levels following straw incorporation in autumn (T2: straw without mineral N application; T3: straw + 50 kg·ha−1; T4: +100 kg·N·ha−1; T5: straw + 150 kg·N·ha−1).
The field experiment in Bratislava (Bt), Slovakia consisted of a crop rotation with winter wheat, maize, and spring barley. We grouped the treatments into rainfed with (RFF) and without nitrogen fertilization (RF0) and irrigated with (IRF) and without (IR0) N. All variants were performed with and without irrigation.

2.2. Model Runs and Model Ensemble

The simulation task for all modelers was designed to reproduce the field experimental treatments. Therefore, modelers were requested to simulate each treatment at each site, using observed information on daily weather (precipitation, minimum and maximum temperature, mean relative humidity, global radiation and mean wind speed), information on daily field management (previous crops, tillage, sowing, irrigation, fertilization and harvest) and soil properties (bulk density, texture, organic matter and water capacity parameters) as driving variables to the models.
We followed the idea of a “blind test” in order to mimic modeling practice in the event of scarce data, which is often practiced in regional climate impact studies [23,24,25,26]. Therefore, modelers were provided with a limited data set for each site depending on the availability of observation data to perform a minimal calibration of the region specific crop cultivars. The calibration data consisted of key phenological observations (dates of emergence, anthesis and maturity) for one soil of the dataset in Hirschstetten, one treatment in Bratislava and all treatments in Foggia, final biomass observations of one plot for Müncheberg, and phenological observations for the first four years at Braunschweig.
Depending on the data set four to eight modeling teams participated in the model inter-comparison. Not all models provided results for all data sets mainly because not all crops in the crop rotation could be simulated. Since the DSSAT model was applied by two groups, seven different models were applied. Differences in DSSAT versions were minor regarding wheat simulation, but differ in their way of crop parameter calibration options (see Table 2). The models consider various processes in a different way and with different complexity. Table 2 gives a summary of the main characteristics of the models and the sites, where they were applied. Modelers were asked to provide standardized model outputs on an annual and a daily basis. Within this study we analyzed the annual outputs only.
To calculate the water footprint the model outputs for crop dry matter (d.m.) yield, and the accumulated evapotranspiration and transpiration from sowing to harvest were used. Dry matter yields were transformed to yield with standard moisture to be in line with the calculation from yield statistics. The total water footprint was calculated in m3 per ton produced yield.
To assess the error that originates from the yield component of the models, a “reference water footprint” (WF_obs*) is calculated using the simulated evaporation and the measured crop yield.

3. Results

3.1. Simulated Water Consumption

The total actual evapotranspiration (ET) was simulated from sowing to harvest of the crop. Additionally, the models provided an output of the actual crop transpiration (Tr) only. Figure 2 shows both variables for the rainfed and the irrigated variants of the Müncheberg experimental site. Due to the shifted rotation, every column represents seven seasons of winter wheat. The error bars represent the inter-annual variability of the simulations of each model. The inter-annual variability of the simulated ET is relatively small with 6.3% and 5.8% on average across all models for the rainfed and irrigated variants, respectively. The absolute variation is similar for the transpiration resulting in higher coefficients of variation (CV%) due to lower absolute values of 14.7% and 12.8% for rainfed and irrigated variants, respectively. The error bars of the model ensemble mean (E-mean) represent the variation between models calculated on base of their multi-year averages. It revealed that the inter-model variability was higher than the inter-annual variability with 14.3% and 15.1% for ET and 26.8% and 26.6% for Tr of rainfed and irrigated variants, respectively. Transpiration contributes to 58% and 61% on average to the total evapotranspiration for rainfed and irrigated treatments, with the highest percentage for AQUACROP (71% and 77%) and the lowest for APSIM (45% and 49%), respectively. ET Model response to irrigation was similar showing an increase in ET and Tr, except DSSAT, which showed only a minor response.
Figure 3 shows the results of ET and Tr simulations for the FACE experiment at Braunschweig. We grouped the variants for the ambient (374 ppm) and the elevated (550 ppm) CO2 concentration. The meaning of the error bars is similar to Figure 1. Although the variability included the variance due to the two nitrogen levels, the variability between the seasons was lower than 7% for ET and lower than 13% von Tr, except for APSIM which showed a higher variance for Tr (24%). Simulated Tr contributed on average to 59% to ET for both CO2 concentrations ranging from 79% (374 ppm) to 76% (550 ppm) for HERMES and AQUACROP to 30% (374 ppm) to 28% for APSIM. The simulated response to elevated CO2 was different between the models. While AQUACROP, HERMES and APSIM showed a decrease in transpiration for the elevated CO2 concentration of 35, 40 and 18 mm, respectively, the two DSSAT simulations and DAISY showed nearly no response and CROPSYST and SWAP/WOFOST showed an increase by 15 and 19 mm, respectively. Inter-model variability was again higher than the inter-annual variability and CV% was 18% for ET and 29% and 25% for Tr at 374 and 550 ppm CO2, respectively.
Results of the ET and Tr simulations of seven models for the lysimeters at Hirschstetten, Austria are listed in Table 3. Inter-annual variability for ET and Tr is in the order of magnitude of 17% on average with only minor differences between soils. However, only two years of winter wheat were available for each soil. Lowest ET and Tr values were simulated by most of the models for the sandy Phaeozem (S2) having the lowest capacity for plant available water. Only SWAP/WOFOST and DSSAT showed minor differences between soils. Inter-model variability varied between soils with CV% between 13% (S2) and 19% (S1) for ET and 20% (S2) and 29% (S3) for Tr. Contribution of Tr on ET was simulated highest by HERMES and AQUACROP (77%–87%), while lowest for DAISY and CROPSYST (52%–58%) with an average across all models and soils of 68%.
Table 4 summarizes the results for the Italian site at Foggia cultivated with durum wheat. Differences of ET and Tr between the treatments were small for most of the models. Only AQUACROP, DSSAT and APSIM simulated different ET and TR amounts between treatments with a maximum difference in ET of 41 mm simulated by AQUACROP. Inter-annual variability of ET for the 10 years of each treatment were 6% (AQUACROP) to 16% (HERMES) and between 10% (AQUACROP) and 34% (CROPSYST) for Tr. However, the inter-model variability for the Italian site is slightly higher with a CV% of 13% (T5) to 15% (T2) for ET and 29% (T3,T4,T5) to 31% (T2) for Tr. Contribution of Tr to ET on average over all models and years was 53% and ranged from 28% (CROPSYST) to 67% (AQUACROP), indicating a higher portion of soil evaporation for this experimental site. Some models (APSIM, HERMES, and DSSAT) showed a stronger response of Tr to the increasing fertilization than for ET, which increased the percentage of Tr on ET, e.g., for DSSAT from 52% to 69% due to an earlier closure of the canopy.
The results for the fifth experimental site at Bratislava, Slovakia are shown in Table 5 for the aggregated treatments. Differences of ET between the irrigated and rainfed treatments varied between models. While DAISY and DSSAT simulated nearly no effect of irrigation, HERMES, CROPSSYST and AQUACROP showed differences of 20 to 37 mm. Interestingly, DAISY simulated even slightly lower Tr for irrigated than for rainfed treatments, which was an effect of sufficient simulated water supply under rainfed conditions on one side and of the reduction of atmospheric water demand on the irrigation days due to evaporation of water intercepted by leafs on the other hand, which led to slight reduction of transpiration for the irrigated treatments. The inter-annual variability of ET and Tr (CV%) ranged from 0.7% and 0.4% for AQUACROP and DAISY to 10% and 7.5% for DSSAT and HERMES, respectively. The inter-model variability for ET expressed as the CV% of the model ensemble was in the range of 14% to 16% (27%–34% for Tr) depending on the treatment showing a slight tendency to higher variability for the rainfed treatments. The percentage of Tr of the total ET was across all treatments and models at 58% with a maximum of 91% (IRF) and a minimum of 37% (RF0 and IR0) simulated by AQUACROP and DSSAT, respectively.

3.2. Simulated Crop Yield

For comparability, simulated and measured dry matter grain yields for winter wheat were transformed to standard yields as used in statistics by assuming a moisture content of 14%. Figure 4 shows the inter-comparison of yield estimations from eight models applied for the Müncheberg experimental site. The inter-annual variability of the yield estimations was 28% and 25% on average across all models for the rainfed and irrigated treatment, respectively. This was lower than the observed CV% of 43% and 33%, but confirmed that irrigation reduced the inter-annual variability. The ensemble mean slightly overestimated the observed grain yield by 0.7 and 0.35 t·ha−1, which correspond to 12% and 5% of the observed yields. Only AQUACROP and DSSAT2 showed a similar good performance, while SWAP and HERMES overestimated and APSIM and DSSAT underestimated grain yields. The difference between the two DSAAT simulations is an indicator concerning the magnitude of user impact on model performance.
The simulated yields of the FACE experiments at Braunschweig are shown in Figure 5. As expected, all models simulated an increase of grain yields under the elevated CO2 concentration. However, the magnitude was different ranging from 2.1% (APSIM) to 35% for CROPSYST. The ensemble mean showed a CO2 effect of +13.6%, which was close to the observed yield increase of 11.5% as described in [32]. AQUACROP and the ensemble mean were closest to the observed yields.
Yield simulations for the more loamy soils (S1 and S3) at Hirschstetten (Table 3) showed a close fit (<0.7 t·ha−1) for four out of seven models. DSSAT, APSIM and DAISY overestimated yield for these soils significantly. All models overestimated grain yield on the more sandy soil S2, which is also reflected by the ensemble mean. The inter-model variability expressed by the coefficient of variation of the ensemble mean was at 27% to 30%, which reflects a much higher model uncertainty for the yield estimation than for ET simulations.
Durum wheat yield simulations at Foggia showed even higher variations between the models (Table 4) from 25% to 39% especially for the treatments with higher fertilization. This is mainly because DAISY and DSSAT showed a strong response to the higher fertilization, while APSIM and HERMES showed no or only a slight response, which corresponds better to the observed yields showing nearly no response of crop yields as well. The simulated inter-annual variability was on average 39% ranging from 29% (APSIM) to 64% (HERMES) compared to 41% for the observed yields.
Crop yields at Bratislava (Table 5) were best estimated by the ensemble mean followed by AQUACROP. DAISY underestimated the fertilized treatments while overestimated the irrigated treatments. HERMES overestimated all treatments. The inter-model variability was 23%–26%, compared to an inter-annual CV% of 15% on average for the simulations of the rainfed treatments and 8.5% for the irrigated plots. Fertilization reduced in both cases the inter-annual variability. Inter-annual variability of observations showed CV% of 13.4% for rainfed and 16.6% for irrigated treatments. However, no fertilization reduced the observed inter-annual yield variability more than irrigation showing the lowest CV% of 1.7% and 7.3% for the rainfed and irrigated plots, respectively.

3.3. Water Footprint

Model results in Section 3.1 showed that there is a distinct difference between ET and Tr indicating that water consumption might be overestimated using ET. Therefore, we calculated water footprints alternatively using the simulated transpiration. To quantify the uncertainty caused by the inter-model variability of yield prediction we calculated the water footprint based on the simulated ET and the observed grain yields, which is annotated in Figure 6 and Figure 7 as “observed” and as “WF_obs*” in the tables. We used an overall water footprint instead of dividing it into WFgreen and WFblue for a better comparison of ranges of the model ensemble between locations.
Figure 6 contains the water footprints calculated for the Müncheberg field trial based on ET and Tr. Water footprints of the irrigated treatment were smaller than for rainfed variants for most of the models, which means that water use efficiency was higher due to a strong positive response of wheat yields on irrigation. However, DSSAT and DAISY showed an opposite trend indicating that the increase of water consumption was higher than the positive effect on crop yields. While the behavior is similar for ET and Tr based calculations for most models, the results of DSSAT2 showed an increase of WF_ET but a decrease of WF_Tr for the irrigated treatment. The inter-annual variability was estimated to be 27% on average for rainfed and 25% for irrigated treatments, which corresponded to the high inter-annual yield variability on the sandy soil (see Section 3.2). However, the variation between models for the ET based water footprint was relatively small with CV% of 15% and 18% of the rainfed and irrigated plots, respectively. Variation was slightly higher (21% for rainfed and 19% for irrigated) for the WF_Tr. Related to the estimated low contribution of Tr to the total ET, the water footprints based on Tr were on average across all models distinctly lower making 58% and 60% of the WF_ET for rainfed and irrigated treatments, respectively. Differentiation between green and blue water footprints revealed that the relative blue partition increased if Tr was used instead of ET for the calculation. The mean of the model ensemble was closer to the mean based on observed crop yields than any of the single models. DSSAT2 and AQUACROP simulations were closest on average over the two treatments.
The calculated water footprints for the two different CO2 concentrations of the FACE experiment at Braunschweig/Germany are shown in Figure 7. All models showed a reduction of the water footprints for the elevated CO2 concentration indicating a higher water use efficiency under higher CO2 concentration. However, the response of DAISY and DSSAT was very low. Although SWAP showed an increase of ET and Tr (see Figure 3 in Section 3.1) with rising CO2, this is over-compensated by the increase of yields resulting in a distinct reduction of the water footprint. Highest water footprints were calculated by APSIM, while HERMES resulted in lower values. The inter-model variability for WF_ET increases from 30% to 34% from ambient to elevated CO2, while the CV% of WF_Tr decreased from 26% to 19%. Inter-annual variability of WF_ET was at 17% and 21% (15% and 18% for WF_Tr) on average of all models for 374 and 550 ppm, respectively.
For Hirschstetten water footprints differed among soils (Table 3). However, the effect of soil on WF_Et and WF_Tr was small for CROPSYST, DSSAT and SWAP, which corresponded to their low sensitivity of crop yields on soils (see Section 3.1). Most of the other models showed higher water footprints for the sandy Phaeozem (S2), which reflect also the trend of the water footprints calculated on the base of observed crop yields. Only HERMES simulated even higher water footprints for S1, which is mainly due to a clear underestimation of yield in the first year. The inter-model variability was 31% to 33% for the WF_ET and 48% to 52% for WF_Tr. Since the models over-predicted yields on average, the WF_ETs were under-estimated compared to the values calculated with the measured yields, which is more pronounced on the sandy soil (S2), where WF_ET was only 53% of WF_obs*.
Water footprints calculated with observed durum wheat yields showed on average over all models a slight increase with increasing nitrogen fertilization (Table 4). However, the ensemble mean of the models for WF_ET and WF_Tr showed an opposite trend. Additionally, the inter-model variability was very high and varied from 36% for treatment T2 to 55% for T5. This is mainly due to the diversity of simulated crop yield response to the treatments since the variability of water footprints calculated with observed yields was only 15% to 18%. On the other hand, WF_ET showed a very high inter-annual variability of more than 70%. Due to the low percentage of Tr on ET (see Section 3.1) the difference between WF_ET and WF_Tr is especially high for the Italian site and WF_Tr was estimated on average over all treatments to be only 43% of WF_ET.
Results for Bratislava/Slovakia (Table 5) showed about 30% higher water footprints for non-fertilized compared to fertilized treatments, while the effect of irrigation was only −8% compared to rainfed. Inter-annual variability was reduced on average from 12% to 7% from rainfed to irrigated treatments. The inter-model variability was 18% and 18% for the fertilized treatments of rainfed and irrigated plots, compared to 32% and 30% for the unfertilized plots respectively. Using the observed crop yields for the estimation of the water footprints results the variability of the unfertilized treatments distinctly, which indicates that the uncertainty originated to a large extent from uncertainty of nutrient supply from the soils.

4. Discussion

The results from five sites across Europe showed that the uncertainty in the estimation of evapotranspiration (ET) expressed through the coefficient of variation of the model ensemble was in the order of magnitude of 13% to 19%. Similar variation (15%) was observed in a comparison of nine models applied to one of the rainfed plots of the Müncheberg data set [41]. Since the absolute standard deviations of ET and Tr were in the same order of magnitude, most of the uncertainty comes from the simulation of transpiration, which showed coefficients of variation from 13% to 34% due to the lower mean value. This result was in line with findings from [28], who compared sixteen crop models regarding their uncertainty of wheat water use covering four sites across the world. He found coefficients of variation for transpiration among models from 19.8% to 33.2% and came to the conclusion that transpiration contributed most to the uncertainty to crop water use. Uncertainty from the parameterization of soil hydraulic parameters was mainly reduced since models were provided with field capacity and wilting point values for each soil profile. The same holds for the length of the growing period since flowering and ripening dates were provided for rough calibration. Most of the modelers used the trial and error approach for calibrating the phenological development of the crops. The comparison of the results from the two DSSAT groups show, that transpiration simulations at Müncheberg were quite similar, while ET values differed more. However, at Braunschweig the differences between both groups were high for Tr and ET. Differences in the initialization of soil moisture and nitrogen of the models could be a reason for the differences in ET calculation, especially because the DSSAT simulations were re-initialized every year instead of using continuous simulation over the crop rotation and initial measured values were only provided for the first year. Although this could be accounted as input error it could also be related to the model structure which makes it difficult to run the model in a continuous mode. Finally, parameter errors are related to some extent related to model structure increasing with model complexity [42]. One example might be the discussion on the Tr response to elevated CO2 below. Beside the errors from inputs, parameters and model structure, the users of the models are another source of uncertainty [43], especially when trial and error approaches are used.
Another conclusion of the study of [28] was, that uncertainty increases with higher CO2 concentration. However, our results from the Braunschweig FACE experiment revealed, that the coefficient of variation for the simulated transpiration at elevated CO2 was slightly smaller than for the ambient concentration. Although some models did not reflect the reduction of water use caused by rising CO2 concentration as it was shown in a field chamber study with wheat by [44], the beneficial effect on crop yields was reflected by all models leading to a decrease of water footprint under elevated CO2. This was in agreement with the observed increase of water use efficiency [44]. However, the fact that reduction of water use was not reflected in some model results did not mean that the effect of increasing CO2 on stomata resistance was not considered at all in these models. In SWAP, for example, reduction of transpiration by increased stomata resistance under elevated CO2 was overcompensated by the increase in crop biomass and consequently in LAI. Other models use fixed or phenology driven kc factors and modify transpiration by factors (DSSAT) or by modifying stomatal resistance without considering changes in LAI (HERMES). The increase of water use efficiency or reduction in water footprints was even found under conditions of projected climate change, where potential evaporation increased due to warming [20,45].
In our study we found an increase of the estimated water footprints from North to South, which was also found in regional estimations e.g., by [15] or global studies [9,46]. Additionally, results from Bratislava showed the effect of insufficient fertilization on the water footprint, a situation, which can be often found in regions of high poverty, e.g., sub-Saharan Africa, leading to very low water use efficiency or high water footprints due to nutrient limited crop growth.
Water footprints estimated from simulated crop yields showed a high uncertainty indicated by the coefficient of variation of the model ensemble ranging from 15% to 18% for Müncheberg to 23% to 55% for the durum wheat in Foggia. Replacing simulated by observed crop yields could reduce the CV% for Hirschstetten, Foggia and Bratislava substantially leading to the conclusion that uncertainty of crop modeling contributed significantly to the uncertainty of the water footprint derived from simulated yields. Uncertainty of models was recently reported by several model inter-comparisons [23,25,27] showing very high ranges of model results for wheat yields, when models were applied only with minimum calibration. However, the inter-model variability could be significantly reduced when models are fully calibrated with suitable data for each location resulting that more than 50% of the simulated yields were below a CV% of 13.5%, which corresponds to the experimental error [25]. Uncertainty for durum wheat was higher in our study since not all models were applied for durum wheat before. In their model, inter-comparison of crop models applied for crop rotations [47] pointed out that model performance to predict crop yields was lower for crops that were not often simulated by the modelers before.
Our results for the fertilized and unfertilized plots of the Bratislava field experiment showed that nutrient limitation led to much higher inter-model variation of water footprints. Using measured crop yields reduced the CV% by 53% (from 30% to 14%). The variation in the nitrogen response of the models can add to the uncertainty of crop modeling. In their comparison of eleven models regarding their response to different nitrogen levels [48], came to the conclusion that uncertainty regarding the simulation of nitrogen release by mineralization was one of the main factors influencing the performance of crop models. Uncertainties are related to different structures of the N turnover modules in models [49], differences in their temperature responses [50] or in the estimation of initial mineralization parameters, which might be even a consequence of lacking long term history data on land management.
Soil information can have a strong influence on the regional assessment of climate impacts on crop yields [20] and water footprints [45]. The impact of soil was obvious for the lysimeters at Hirschstetten. Especially the overestimation of wheat yield for the sandy soil by some models led to a high inter-model variability and a distinctive underestimation of the water footprint compared to the calculation based on the observed yields. The uncertainty of rooting depth was identified as one major impact to the high variability and overestimation of another model ensemble for this soil [47].
Our results also indicated that the approach described in the water footprint manual [10] to use evapotranspiration for crop water use to calculate the water footprint of a crop might be worth to be discussed. We found that crop transpiration makes only 51%–68% of the total actual evapotranspiration on average across models showing a large variance between models. Similar relations were found by [28], who documented Tr to ET contributions of 56% to 77%. The rationale of the indicator is to represent consumptive water use by agricultural production at all and should be sensitive to agricultural management. However, water saving practices like advanced irrigation techniques or deficit irrigation are often applied when the crop canopy is mainly closed and soil evaporation plays a negligible role. Therefore, transpiration would be in most cases more responsive than ET, which is also shown in Figure 6 for the irrigated treatments. On average the contribution of the blue partition is 5% higher for TR compared to ET based WF. At Foggia, Tr showed a stronger response to increasing nitrogen supply compared to ET (see Section 3.1, Table 4). Other practices like mulching or tillage are mainly influencing evaporation during the time when no crop is on the soil. Therefore, these effects would not be accounted sufficiently for because the water footprint calculation just uses the ET between sowing and harvest. Inclusion of unproductive soil evaporation, which might not be accounted for the water consumption of a crop since it would occur even without crop cover, should therefore be discussed. An alternative would be to look at cropping systems as a whole including the fallow periods, but this would make it difficult to attribute the water consumption to a specific crop or product. Post-seasonal ET was not provided by all models and periods between harvest and sowing of the next crop varied due to different crop rotations, which made it difficult to compare results. For the durum wheat monoculture at Foggia, results of two models showed that post-seasonal ET contributes on average to 38% to the annual ET.
Finally, it should be noted that the model results should not be used to judge the suitability of a particular model, since information provided were basic and model performance could be certainly improved if more information would be available. We therefore did not apply model performance indicators. However, from the comparison of fully model derived water footprints to footprints using only simulated ET and observed crop yields we have to state that no model performed best on all sites and treatments and that, similar to other model inter-comparisons [19,20,21,22,23], the ensemble means were in most cases among the estimates closest to the footprints with observed yields.

5. Conclusions

The use of agro-ecosystem models is indispensable to assess impacts of climate change on crop production and resource use efficiency. However, limited opportunities to calibrate models on a regional scale and scarcity of management data at this scale imply higher uncertainties, especially regarding the prediction of crop production. Our study revealed that the uncertainty of crop yield prediction caused by the use of different models contributes more to the uncertainty in the assessment of future development of water use efficiency and water footprint calculation than the estimation of evapotranspiration. This is mainly because calculation of ET was much more standardized across the models and formulas for ET are similar. The insight that a regional calibration of crop models is recommendable to reduce uncertainty from yield predictions seems to be trivial. However, the uncertainty remains since the possibility to calibrate crop parameters for the future is limited. Recent model inter-comparisons have shown that the amount of information used for calibration has only a minor effect on most models’ climate response [51] and that crop response to external drivers, e.g., CO2 concentration or heat stress, is still an issue of research and source of uncertainty [28,51,52]. Increasing model complexity may cause higher parameter uncertainties, which was shown in the different responses of transpiration on elevated CO2 at Braunschweig. The choice of the most suitable model seems to be difficult since recent model inter-comparisons showed that there was no ultimate best model, which outperforms the ensemble mean or median [23,24,25,26,27].
Regarding the definition of water use for the water footprint calculation, our results indicate that the contribution of soil evaporation is not negligible and actual crop water use by transpiration is much less than the total evapotranspiration. Our results also show some evidence that Tr responds more sensitive than ET on different treatments. Therefore, the appropriateness to attribute actual seasonal evapotranspiration to crop water use requires a critical review for further water footprint and virtual water trade assessments.

Acknowledgments

The study was performed under the umbrella of COST ES1106. Kurt Christian Kersebaum and Domenico Ventrella received additional support from the German Federal Office for Agriculture and Food (BLE) (2812ERA 147) and Italian Ministry for Agricultural, Food and Forestry Policies, respectively, within the framework of JPI FACCE MACSUR. Anne Gobin obtained additional support from Belspo contract No. SD/RI/03A. Petr Hlavinka and Miroslav Trnka were supported by funding from the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Program I (NPU I), grant number LO1415, project IGA AF MENDELU No. TP 7/2015 with the support of the Specific University Research Grant and LD13030 project supporting Czech activities of the COST ES1106 action, Muhammad Anjum Iqbal got a fellowship by Alexander von Humboldt Foundation.

Author Contributions

Hans-Joachim Weigel and Remy Manderscheid performed the FACE experiment; Hans-Joachim Weigel, Remy Manderscheid, Domenico Ventrella, Anna Dalla Marta, Wilfried Mirschel, Kurt Christian Kersebaum, Josef Eitzinger, Johannes Hösch, Jozef Takáč and Pavol Nejedlik provided and prepared experimental data; Kurt Christian Kersebaum, Joop Kroes, Anne Gobin, Jozef Takáč, Petr Hlavinka, Miroslav Trnka, Domenico Ventrella, Luisa Giglio, Roberto Ferrise, Marco Moriondo, Qunying Luo and Munir Hoffmann did the simulations; Kurt Christian Kersebaum and Muhammad Anjum Iqbal analyzed the data and Kurt Christian Kersebaum wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Figure 1. Location of the experimental sites.
Figure 1. Location of the experimental sites.
Water 08 00571 g001
Figure 2. Simulated evapotranspiration (ET) and transpiration (Tr) for rainfed (_rf) and irrigated (_ir) variants of the Müncheberg field experiment from different models. Error bars of the model results show inter-annual variability, error bars of the ensemble mean the inter-model variability.
Figure 2. Simulated evapotranspiration (ET) and transpiration (Tr) for rainfed (_rf) and irrigated (_ir) variants of the Müncheberg field experiment from different models. Error bars of the model results show inter-annual variability, error bars of the ensemble mean the inter-model variability.
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Figure 3. Simulated evapotranspiration (ET) and transpiration (Tr) for ambient (374 ppm) and elevated (550 ppm) atmospheric CO2 concentration of the Braunschweig FACE experiment from different models. Error bars of the model results show inter-annual variability, error bars of the ensemble mean the inter-model variability.
Figure 3. Simulated evapotranspiration (ET) and transpiration (Tr) for ambient (374 ppm) and elevated (550 ppm) atmospheric CO2 concentration of the Braunschweig FACE experiment from different models. Error bars of the model results show inter-annual variability, error bars of the ensemble mean the inter-model variability.
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Figure 4. Simulated grain yields of winter wheat for rainfed (_rf) and irrigated (_ir) variants of the Müncheberg field trial from different models. Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability.
Figure 4. Simulated grain yields of winter wheat for rainfed (_rf) and irrigated (_ir) variants of the Müncheberg field trial from different models. Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability.
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Figure 5. Simulated winter wheat grain yields for ambient (374 ppm) and elevated (550 ppm) atmospheric CO2 concentration of the Braunschweig FACE experiment from different models. Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability.
Figure 5. Simulated winter wheat grain yields for ambient (374 ppm) and elevated (550 ppm) atmospheric CO2 concentration of the Braunschweig FACE experiment from different models. Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability.
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Figure 6. Water footprints of winter wheat calculated from simulations of different models for rainfed (_rf) and irrigated (_ir) variants of the Müncheberg field trial. Calculations were based on ET (WF_ET) and Tr (WF_Tr). Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability. “Observed” is calculated from simulated ET and Tr and observed yields. Dark blue columns in WF_ET_ir and Tr_ir show the blue WF based on ET and Tr, respectively.
Figure 6. Water footprints of winter wheat calculated from simulations of different models for rainfed (_rf) and irrigated (_ir) variants of the Müncheberg field trial. Calculations were based on ET (WF_ET) and Tr (WF_Tr). Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability. “Observed” is calculated from simulated ET and Tr and observed yields. Dark blue columns in WF_ET_ir and Tr_ir show the blue WF based on ET and Tr, respectively.
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Figure 7. Water footprints of winter wheat calculated from simulations of different models for ambient (374 ppm) and elevated (550 ppm) CO2 concentrations of the FACE experiment at Braunschweig/Germany. Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability. “Observed” is calculated from simulated ET and Tr and observed yields.
Figure 7. Water footprints of winter wheat calculated from simulations of different models for ambient (374 ppm) and elevated (550 ppm) CO2 concentrations of the FACE experiment at Braunschweig/Germany. Error bars of the model results and observations show inter-annual variability, error bars of the ensemble mean the inter-model variability. “Observed” is calculated from simulated ET and Tr and observed yields.
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Table 1. Characterization of experimental data.
Table 1. Characterization of experimental data.
Location/CountryTopographyPeriodClimate *Soil # S/Si/Cl/CorgTreatmentCrops
Müncheberg/GermanyLat: 52.52°
Long: 14.12°
Elev: 62 m a.s.l.
1992–19988.4 °C
563 mm
83/9/8/0.6shifted rotation, rainfed, irrigatedsugar beet, winter wheat, winter barley, winter rye (oil raddish)
Braunschweig/GermanyLat: 52.3°
Long: 10.45°
Elev: 79 m a.s.l.
1999–200510.0 °C
642 mm
69/24/7/1.0374/550 ppm CO2
2 nitrogen levels
winter barley, ryegrass (catchcrop), sugar beet, winter wheat
Hirschstetten/AustriaLat: 48.2°
Long: 16.57°
Elev: 150 m a.s.l.
1998–200310.9 °C
495 mm
1: 22/50/28/2.9
2: 68/19/13/1.3
3: 22/54/24/1.3
3 soilsgrain maize, winter wheat, spring barley, mustard , spring wheat, potatoes
Foggia/ItalyLat: 41.26°
Long: 15.30°
Elev: 90 m a.s.l.
1995–200515.9 °C
540 mm
13/39/48/1.5Straw burned Straw remained with 0, 50, 100 and 150 kg N/haDurum wheat
Bratislava/SlovakiaLat: 48.16°
Long: 17.23°
Elev: 130 m a.s.l.
1998–200610.7 °C
575 mm
19/59/22/1.7Rainfed, irrigated 2 nitrogen levels (0% and 100%) Residue managementw. wheat, maize, maize, maize, spr. barley, w. wheat, maize, spr. barley
Notes: * Annual mean temperature and annual precipitation for the given period. # Sand (S), silt (Si), clay (Cl) and organic carbon (Corg) content (mass%) in the plough layer
Table 2. Main characteristics of participating models.
Table 2. Main characteristics of participating models.
ModelAQUA CROPAPSIMDAISYDSSATHERMESSWAP/WOFOSTCROPSYST
4.54.6
AbbreviationAQAPDADTDSHESWCR
Light utilisation aTERUEP-RRUEP-RP-RTE/RUE
Yield formation bY(HI,B)Y(HI,B)Y(Prt)Y(HI(Gn),B)Y(Prt)Y(Prt,B)Y(HI_mw/B)
Crop phenology cf(T, DL, V)f(T, DL, V)f(T, DL, V)f(T, DL, V)f(T, DL, V)f(T, DL)f(T, DL, V)
Root distribution over depth dEXPLINEXPEXPEXPLINEXP
Stresses involved eW, N kW, NW, NW, NW, N, AW, N iW, N
Water dynamics fCCRCCRC/R
Evapotranspiration gPMPTPMPTPMPMPT
Soil CN-model h-CN, P(3), BCN, P(6), BCN, P(4), BN, P(2)-N, P(4)
Application atMb, Bs, Hi, Fo, BrMb, Bs, Hi, Fo Mb, Bs, Hi, Fo, BrMb, Bs, Hi, Fo, BrMb, Bs, Hi, Fo, BrMb, Bs, HiMb, Bs, Hi, Fo, Br
Calibration *T+R
Ph
T+R
Ph
T+R
Ph
Aut 1Aut 2+T+R
Ph
DF +Aut 3
Ph
T+R
Ph
Ph
Reference[35][36][37][38][20][39][40]
a Light utilization or biomass growth: RUE = Simple (descriptive) Radiation use efficiency approach, P-R = Detailed (explanatory) Gross photosynthesis—respiration; TE = transpiration efficiency biomass growth; b Y(x) yield formation depending on: HI = fixed harvest index, HI_mw HI modified by water stress, B = total (above-ground) biomass, Gn = number of grains, Prt = partitioning during reproductive stages; c Crop phenology is a function (f) of: T = temperature, DL = photoperiod (day length), V = vernalisation; d Root distribution over depth: linear (LIN), exponential (EXP); e Stresses involved: W = water stress, N = nitrogen stress, A = oxygen stress; f Water dynamics approach: C = capacity approach, R = Richards approach; g Method to calculate evapotranspiration: PM = Penman-Monteith, PT = Priestley –Taylor; h Soil CN model, N = N model, P(x) = x number of organic matter pools, B = microbial biomass pool; i nitrogen-limited yields can be calculated for given soil Nitrogen supply and N fertilizer applied; * T+R = trial-and-error, DF = default parameters, Aut = automatic calibration with 1 GeneCalc; 2+ GLUESelect and fine tuning by hand; 3 CALPLAT, Ph = phenology.
Table 3. Simulated actual evapotranspiration (ET), transpiration (Tr), grain yield (86% d.m.) and resulting water footprints based on ET (WF) and transpiration (WF_Tr) for winter wheat on three soils at Hirschstetten/Austria from different models. WF_obs* indicate water footprints calculated from simulated ET and measured yields. Ave is the average value, ± indicates the range of simulated values around the mean and the standard deviation of the ensemble mean, CV% is the coefficient of variation between models in percent.
Table 3. Simulated actual evapotranspiration (ET), transpiration (Tr), grain yield (86% d.m.) and resulting water footprints based on ET (WF) and transpiration (WF_Tr) for winter wheat on three soils at Hirschstetten/Austria from different models. WF_obs* indicate water footprints calculated from simulated ET and measured yields. Ave is the average value, ± indicates the range of simulated values around the mean and the standard deviation of the ensemble mean, CV% is the coefficient of variation between models in percent.
Model/SoilET (mm)Tr (mm)Yield (t·ha−1)Yield obs. (t·ha−1)WF (m3·t−1)WF_Tr (m3·t−1)WF_obs* (m3·t−1)
Ave±CV%Ave±Ave±CV%Ave±Ave±CV%Ave±Ave±CV%
APSIM S146911 31658.370.35 5.190.6756011 37822903
S23516 187284.940.41 2.540.3471348 375251383
S346222 309388.490.58 4.940.3754511 36320936
AQUACROP S145262 394575.150.85 5.190.6788127 76817871
S241361 324483.640.91 2.540.341164123 913961629
S348746 421385.200.89 4.940.3794975 82169986
CROPSYST S128650 167545.041.95 5.190.67620140 34124551
S232152 186435.481.70 2.540.3461495 348301264
S330456 172465.151.72 4.940.37624100 34225617
DAISY S149454 265207.771.66 5.190.6765270 35149953
S246061 240265.790.75 2.540.34821211 4281001813
S347860 252267.971.77 4.940.3761461 32540969
DSSAT S134639 22718.280.48 5.190.6742272 27517668
S235116 234178.410.89 2.540.3442464 280101384
S336252 253118.771.40 4.940.37417125 29034733
HERMES S140356 341314.522.31 5.190.671122450 974430778
S236260 279363.701.35 2.540.341060227 8292061428
S340138 338124.531.73 4.940.37999298 861302813
SWAP/WOFOST S135037 22775.140.72 5.190.6768327 44550674
S235240 23085.170.93 2.540.3468953 454691389
S335237 23155.210.79 4.940.3768144 45163712
Ensemble S14007619277786.331.72275.190.677062303350526277114719
S23764713249495.311.60302.540.3478425733518249147018713
S33977118278816.481.84284.940.376902113149324382414417
Table 4. Simulated actual evapotranspiration (ET), transpiration (Tr), grain yield (86% d.m.) and resulting water footprints based on ET (WF) and transpiration (WF_Tr) for winter wheat for four treatments at Foggia/Italy from different models. WF_obs* indicate water footprints based on simulated ET and measured yields. Ave is the average value, std indicates the standard deviation and CV% the coefficient of variation in percent (only for the ensemble mean).
Table 4. Simulated actual evapotranspiration (ET), transpiration (Tr), grain yield (86% d.m.) and resulting water footprints based on ET (WF) and transpiration (WF_Tr) for winter wheat for four treatments at Foggia/Italy from different models. WF_obs* indicate water footprints based on simulated ET and measured yields. Ave is the average value, std indicates the standard deviation and CV% the coefficient of variation in percent (only for the ensemble mean).
Model/TreatmentET (mm)Tr (mm)Yield (t·ha−1) Yield obs. (t·ha−1)WF (m3·t−1)WF_Tr (m3·t−1)WF_obs* (m3·t−1)
AvestdCV%AvestdAve stdCV%AvestdAvestdCV%AvestdAvestdCV%
APSIM T231028 178264.451.03 3.231.30718109 408551206824
T332331 196285.091.37 3.081.29664135 3997114181196
T433435 209315.651.81 3.041.24637165 3938514661210
T533834 214305.781.85 2.961.26632167 3958616151525
AQUACROP T234014 222173.320.18 3.231.30102562 667371324234
T334313 233183.420.18 3.081.29100580 682541527243
T436625 247253.570.29 3.041.24102978 696871532231
T538433 261353.780.42 2.961.261022100 6961061673247
CROPSYST T234625 96332.310.73 3.231.3017991180 413371335901
T334523 98332.350.74 3.081.2917661186 4123815071295
T434523 98332.350.74 3.041.2417661186 4123814971212
T534523 98332.350.74 2.961.2617661186 4123816261507
DAISY T244050 235353.061.03 3.231.301546410 82723917041223
T344050 236364.321.90 3.081.291201513 64729319391750
T444050 236375.172.03 3.041.24973377 52622019261640
T544050 236376.072.24 2.961.26824328 44418320952033
DSSAT T228330 146614.102.20 3.231.30926554 383671082698
T329828 179435.441.66 3.081.29591175 3364813011114
T430230 198436.541.61 3.041.24494153 3094613131066
T530631 211427.371.53 2.961.26442148 2924814411337
HERMES T233754 160483.112.01 3.231.3017091278 7314381293932
T333554 167483.722.34 3.081.291391975 64941114681340
T433552 170543.752.38 3.041.241386973 65140614601258
T533552 171573.762.41 2.961.261384974 65140615891561
Ensemble T23435315173513.390.77233.231.30128745435571194132720916
T33474914185514.061.14283.081.29110344740521154152721714
T43544713193544.501.55343.041.24104747245498153154320914
T53584713199584.851.86382.961.26101249249482158169422713
Table 5. Simulated actual evapotranspiration (ET), transpiration (Tr), grain yield (86% d.m.) and resulting water footprints based on ET (WF) and transpiration (WF_Tr) for winter wheat for rainfed and irrigated combined with fertilized and unfertilized treatments at Bratislava/Slovakia from different models. WF_obs* indicate water footprints based on simulated ET and measured yields. Ave is the average value, std indicates the standard deviation and CV% the coefficient of variation in percent (only for the ensemble mean).
Table 5. Simulated actual evapotranspiration (ET), transpiration (Tr), grain yield (86% d.m.) and resulting water footprints based on ET (WF) and transpiration (WF_Tr) for winter wheat for rainfed and irrigated combined with fertilized and unfertilized treatments at Bratislava/Slovakia from different models. WF_obs* indicate water footprints based on simulated ET and measured yields. Ave is the average value, std indicates the standard deviation and CV% the coefficient of variation in percent (only for the ensemble mean).
Model/TreatmentET (mm)Tr (mm)Yield (t·ha−1)Yield obs. (t·ha−1)WF (m3·t−1)WF_Tr (m3·t−1)WF_obs* (m3·t−1)
AvestdCV%AvestdAvestdCV%AvestdAvestdCV%AvestdAvestdCV%
AQUACROP RF048826 353536.351.02 5.740.10780102 55711745137
RFF50628 455477.860.76 7.501.8964628 57813751111
IR05254 403267.230.48 6.040.4472946 55812847185
IRF53615 486358.330.62 7.691.9864530 58414824192
CROPSYST RF039817 18995.330.30 5.740.1074718 355469324
RFF39514 190105.360.34 7.501.8973821 3554550126
IR042025 211205.900.56 6.040.4471433 358569520
IRF4293 22456.230.05 7.691.986881 3596589163
DAISY RF05967 27624.660.64 5.740.101299208 60190103914
RFF5977 27818.952.28 7.501.89699171 32785834209
IR05968 26915.100.49 6.040.441179135 5325799165
IRF5978 27119.671.47 7.691.9862790 28543817212
DSSAT RF043542 162115.351.30 5.740.10870167 3267175766
RFF43735 17315.531.01 7.501.8968852 2721603112
IR043744 162126.350.02 6.040.4477130 2881972341
IRF43835 17306.350.02 7.691.9868953 2731592116
HERMES RF046037 340258.281.96 5.740.1057294 4237180273
RFF45837 350269.912.18 7.501.8947366 36253651219
IR047633 357208.891.27 6.040.4454048 40539793109
IRF47830 3711811.220.76 7.691.9842617 33112663218
Ensemble RF04757516264865.961.43245.740.108542723245212280713517
RFF47977162891177.691.85247.501.896491041637911767811417
IR049172152811006.561.52236.040.447862363042811581011714
IRF49571143051258.362.15267.691.986151091836612769711717
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