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Questions related to historical and future water resources and scarcity have been addressed by several macroscale hydrological models. One of these models is the Variable Infiltration Capacity (VIC) model. However, further model developments were needed to holistically assess anthropogenic impacts on global water resources using VIC. Our study developed VIC-WUR, which extends the VIC model using (1) integrated routing, (2) surface and groundwater use for various sectors (irrigation, domestic, industrial, energy, and livestock), (3) environmental flow requirements for both surface and groundwater systems, and (4) dam operation. Global gridded datasets on sectoral demands were developed separately and used as an input for the VIC-WUR model. Simulated national water withdrawals were in line with reported Food and Agriculture Organization (FAO) national annual withdrawals (adjusted R2 > 0.8), both per sector and per source. However, trends in time for domestic and industrial water withdrawal were mixed compared with previous studies. Gravity Recovery and Climate Experiment (GRACE) monthly terrestrial water storage anomalies were well represented (global mean root-mean-squared error, RMSE, values of 1.9 and 3.5 mm for annual and interannual anomalies respectively), whereas groundwater depletion trends were overestimated. The implemented anthropogenic impact modules increased simulated streamflow performance for 370 of the 462 anthropogenically impacted Global Runoff Data Centre (GRDC) monitoring stations, mostly due to the effects of reservoir operation. An assessment of environmental flow requirements indicates that global water withdrawals have to be severely limited (by 39 %) to protect aquatic ecosystems, especially with respect to groundwater withdrawals. VIC-WUR has potential for studying the impacts of climate change and anthropogenic developments on current and future water resources and sector-specific water scarcity. The additions presented here make the VIC model more suited for fully integrated worldwide water resource assessments. [ABSTRACT FROM AUTHOR]

This paper uses hindcasts (1981–2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill. [ABSTRACT FROM AUTHOR]

Seasonal predictions of river flow can be exploited among others to optimise hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. This paper presents the development of the system and the evaluation of its skill. The variable infiltration capacity (VIC) hydrological model is forced with bias-corrected output of ECMWF's seasonal forecast system 4. For the assessment of skill, we analysed hindcasts (1981-2010) against a reference run, in which VIC was forced by gridded meteorological observations. The reference run was also used to generate initial hydrological conditions for the hindcasts. The skill in run-off and discharge hindcasts is analysed with monthly temporal resolution, up to 7 months of lead time, for the entire annual cycle. Using the reference run output as pseudo-observations and taking the correlation coef- ficient as metric, hot spots of significant theoretical skill in discharge and run-off were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, northern Germany, Romania and Bulgaria (mainly from November to January), western France (from December to May) and the eastern side of Great Britain (January to April). Generally, the skill decreases with increasing lead time, except in spring in regions with snowrich winters. In some areas some skill persists even at the longest lead times (7 months). Theoretical skill was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is generally substantially less than theoretical skill. This effect is stronger for small basins than for large basins. Qualitatively, the use of different skill metrics (correlation coefficient; relative operating characteristics, ROC, area; and ranked probability skill score, RPSS) leads to broadly similar spatio-temporal patterns of skill, but the level of skill decreases, and the area of skill shrinks, in the following order: correlation coefficient; ROC area below-normal (BN) tercile; ROC area above-normal (AN) tercile; ranked probability skill score; and, finally, ROC near-normal (NN) tercile. [ABSTRACT FROM AUTHOR]

Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the second of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. Whereas the first paper presents the development and the skill evaluation of the system, this paper provides explanations for the skill. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with meteorological forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4 (S4). WUSHP is probabilistic. For the assessment of skill, hindcast simulations (1981-2010) were carried out. To explain skill, we first looked at the forcing and found considerable skill in the precipitation forecasts of the first lead month but hardly any significant skill for later lead months. Seasonal forecasts for temperature have more skill. Skill in summer temperature is related to climate change and more or less independent of lead time. Skill in February and March is unrelated to climate change. Sources of skill in runoff were isolated with Ensemble Streamflow Prediction (ESP) experiments. These revealed that beyond the second lead month simulations with forcing that is identical for all years (ESPall) produce more skill in runoff than the simulations forced with S4 output (Full Hindcasts). This occurs because interannual variability of the S4 forcing has insufficient skill while it adds noise. Other ESP-experiments show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, at the end of the melt season, initial conditions of snow contribute significantly to the skill, also when forecasts start much earlier. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Finally, predictability of evapotranspiration was analysed in some detail, leading among others to the conclusion that it is due to all potential sources of skill but mostly to forcing. [ABSTRACT FROM AUTHOR]

Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. The present paper presents the development and the skill evaluation of the system. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4. The system is probabilistic. For the assessment of skill, we performed hindcast simulations (1981-2010) and a reference simulation, in which VIC was forced by gridded meteorological observations, to generate initial hydrological conditions for the hindcasts and discharge output for skill assessment (pseudo-observations). Skill is analysed with monthly temporal resolution for the entire annual cycle. Using the pseudo-observations and taking the correlation coefficient as metric, hot spots of significant skill in runoff were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, North Germany, Romania and Bulgaria (mainly from November to January) and West France (from December to May). The spatial pattern of skill is fading with increasing lead time but some skill is left at the end of the hindcasts (7 months). On average across the domain, skill in discharge is slightly higher than skill in runoff. This can be explained by the delay between runoff and discharge and the general tendency of decreasing skill with lead time. Theoretical skill as determined with the pseudo-observations was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is mostly and often substantially less than theoretical skill, which is consistent with a conceptual analysis of the two types of verification. Qualitatively, results are hardly sensitive to the different skill metrics considered in this study (correlation coefficient, ROC area and Ranked Probability Skill Score) but ROC areas tend to be slightly larger for the Below Normal than for the Above Normal tercile. [ABSTRACT FROM AUTHOR]

This contribution deals with the skill of a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. The system basically consists of the Variable Infiltration Capacity (VIC) hydrological model forced with bias-corrected output of ECMWF's Seasonal Forecasting System 4. Our analyses focus on skill in runoff hindcasts determined with so-called pseudo-observations, i.e. with runoff data generated with VIC forced with historical meteorological observations (1981-2010; skill in discharge against true observations are available but beyond the scope of the present analyses). For specific parts of the year, hot spots of significant discrimination skill were identified in Fennoscandia, the southern part of the Mediterranean, Poland and northern Germany, Romania and Bulgaria, western France and the eastern side of Great Britain. Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. To explain the skill in runoff, we first analysed the meteorological forcing. Most importantly for runoff, the precipitation forecasts contain hardly any significant skill beyond the first lead month. Seasonal forecasts of temperature have more skill, which is partly related to climate warming. To explain the skill in runoff, we also performed a suite of specific hydrological hindcasts akin to Ensemble Streamflow Predictions (ESP), which each isolate a different source of skill. These hindcasts show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, initial conditions of snow contribute significantly to the skill. The combination of specific hindcasts and pseudo-observations revealed several interesting features. The most noteworthy is that beyond the second lead month, full hindcasts with S4-forcing are less skilful than hindcasts with forcing without interannual variation (like ESPs) because the S4-forcing consists mainly of noise which enhances degradation of the skill. [ABSTRACT FROM AUTHOR]