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withdrawals into account) for historic climate. It also identifies reasons for some of the differences between model results. Understanding how the models perform differently for naturalized conditions and current climate provides important information with which to understand why some models might respond differently in future runs using climate projections. The models participating in WaterMIP cover a wide range of characteristics, ranging from physically based models run at subhourly time steps to more
withdrawals into account) for historic climate. It also identifies reasons for some of the differences between model results. Understanding how the models perform differently for naturalized conditions and current climate provides important information with which to understand why some models might respond differently in future runs using climate projections. The models participating in WaterMIP cover a wide range of characteristics, ranging from physically based models run at subhourly time steps to more
://eosweb.larc.nasa.gov/PRODOCS/srb/table_srb.html ) interpolated to half degree. Downward shortwave radiation was adjusted at the monthly time scale using CRU cloud cover and the local linear correlation between monthly average (interpolated) ERA-40 cloud cover and downward shortwave radiation ( Sheffield et al. 2006 ; Weedon et al. 2010 ). Troy and Wood (2009) compared unadjusted ERA-40 radiation fluxes with other reanalysis products and observations across northern Eurasia. ERA-40 does not include adjustments for the effects of seasonal and decadal
://eosweb.larc.nasa.gov/PRODOCS/srb/table_srb.html ) interpolated to half degree. Downward shortwave radiation was adjusted at the monthly time scale using CRU cloud cover and the local linear correlation between monthly average (interpolated) ERA-40 cloud cover and downward shortwave radiation ( Sheffield et al. 2006 ; Weedon et al. 2010 ). Troy and Wood (2009) compared unadjusted ERA-40 radiation fluxes with other reanalysis products and observations across northern Eurasia. ERA-40 does not include adjustments for the effects of seasonal and decadal
storage due to precipitation being stored as snow and ice ( Hisdal et al. 2001 ). Soil moisture droughts are commonly referred to as agricultural droughts. Here, for simplicity, they are together with runoff and groundwater droughts termed hydrological droughts. In this paper, summer droughts defined as precipitation, soil moisture, runoff, or groundwater below a predefined threshold level are examined. This definition is applicable to both meteorological and hydrological time series, and is of direct
storage due to precipitation being stored as snow and ice ( Hisdal et al. 2001 ). Soil moisture droughts are commonly referred to as agricultural droughts. Here, for simplicity, they are together with runoff and groundwater droughts termed hydrological droughts. In this paper, summer droughts defined as precipitation, soil moisture, runoff, or groundwater below a predefined threshold level are examined. This definition is applicable to both meteorological and hydrological time series, and is of direct
). There is already evidence that rainfall, runoff, and evaporation have increased, and will continue to do so ( Wentz et al. 2007 ; Huntington 2006 ). However, rising CO 2 concentrations may also reduce evaporation because of stomatal closing under elevated CO 2 concentrations. Superimposed on the effects of climate change will be the other impacts of human activities, such as land cover change and exploitation of water resources. In the short term at least, these latter influences will have an
). There is already evidence that rainfall, runoff, and evaporation have increased, and will continue to do so ( Wentz et al. 2007 ; Huntington 2006 ). However, rising CO 2 concentrations may also reduce evaporation because of stomatal closing under elevated CO 2 concentrations. Superimposed on the effects of climate change will be the other impacts of human activities, such as land cover change and exploitation of water resources. In the short term at least, these latter influences will have an
four soil layers, each with a temperature and moisture content, and the four soil layers have thicknesses from the surface downward of 0.1, 0.25, 0.65, and 2.0 m. On the surface, there are lying snow and canopy water stores. The canopy water is the rainfall intercepted by plant leaves that is available for free evaporation. The method of partitioning precipitation into canopy interception and throughfall is described by Dolman and Gregory (1992) . The total moisture flux from the surface is made
four soil layers, each with a temperature and moisture content, and the four soil layers have thicknesses from the surface downward of 0.1, 0.25, 0.65, and 2.0 m. On the surface, there are lying snow and canopy water stores. The canopy water is the rainfall intercepted by plant leaves that is available for free evaporation. The method of partitioning precipitation into canopy interception and throughfall is described by Dolman and Gregory (1992) . The total moisture flux from the surface is made
et al. 1993 ). The standardized precipitation index (SPI; McKee et al. 1993 ) and the Palmer drought severity index (PDSI; Palmer 1965 ) are the most commonly used amongst the drought indices. As a matter of fact, drought indices contain a large amount of data on rainfall, streamflow, snow, and other indicators that transform these huge datasets into a comprehensible picture. Hence, a drought index value is typically a single number—more useful than a raw dataset for decision making. The
et al. 1993 ). The standardized precipitation index (SPI; McKee et al. 1993 ) and the Palmer drought severity index (PDSI; Palmer 1965 ) are the most commonly used amongst the drought indices. As a matter of fact, drought indices contain a large amount of data on rainfall, streamflow, snow, and other indicators that transform these huge datasets into a comprehensible picture. Hence, a drought index value is typically a single number—more useful than a raw dataset for decision making. The
climates based on vegetation, temperature, and precipitation. Five major climate zones are differentiated: equatorial zone (A), the arid zone (B), the warm temperate zone (C), the snow zone (D), and the polar zone (E). In Fig. 1 , the regional distribution of these climate zones is shown. Fig . 1. Global distribution of (a) NSC and (b) RMSE in context of the five main Köppen–Geiger climate types for the period 1995–2001. The dots (•) represent the mean NSC and RMSE calculated for each 0.5° grid cell a
climates based on vegetation, temperature, and precipitation. Five major climate zones are differentiated: equatorial zone (A), the arid zone (B), the warm temperate zone (C), the snow zone (D), and the polar zone (E). In Fig. 1 , the regional distribution of these climate zones is shown. Fig . 1. Global distribution of (a) NSC and (b) RMSE in context of the five main Köppen–Geiger climate types for the period 1995–2001. The dots (•) represent the mean NSC and RMSE calculated for each 0.5° grid cell a
dataset covers the period 1958–2001 and is based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ). The ERA-40 data were interpolated to 0.5° and only considered over land points using the land–sea mask from the Climate Research Unit dataset TS2.1 (CRU; Mitchell and Jones 2005 ). A correction for elevation differences between ERA-40 and CRU was applied. For 2-m temperatures, a correction of the monthly means with CRU data was performed
dataset covers the period 1958–2001 and is based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ). The ERA-40 data were interpolated to 0.5° and only considered over land points using the land–sea mask from the Climate Research Unit dataset TS2.1 (CRU; Mitchell and Jones 2005 ). A correction for elevation differences between ERA-40 and CRU was applied. For 2-m temperatures, a correction of the monthly means with CRU data was performed
, followed by the introduction of three performance metrics. The results of the analysis are then presented and discussed. The paper concludes with comments on the ability of the multimodel ensemble to simulate European, large-scale hydrology, with special emphasis on low and high river flows. 2. Models and observations a. Individual models and ensemble mean Table 1 lists the nine models that were considered in this study and summarizes their evapotranspiration, snow, and runoff schemes. Table 2
, followed by the introduction of three performance metrics. The results of the analysis are then presented and discussed. The paper concludes with comments on the ability of the multimodel ensemble to simulate European, large-scale hydrology, with special emphasis on low and high river flows. 2. Models and observations a. Individual models and ensemble mean Table 1 lists the nine models that were considered in this study and summarizes their evapotranspiration, snow, and runoff schemes. Table 2
.3), particularly for the wet anomalies. Scatterplots (not shown) reveal a systematic decrease in A only for basins with elevations of >1000 m MSL. Hence, only a few very high (mountain) basins in the dataset ( Fig. 1c ) dominate this correlation. Results for those basins likely suffer from known challenges of the simulation of mountain hydrology such as strong gradients and snow accumulation and melt. There is no systematic relation with elevation for mid- to low-elevation basins. The lack of availability of
.3), particularly for the wet anomalies. Scatterplots (not shown) reveal a systematic decrease in A only for basins with elevations of >1000 m MSL. Hence, only a few very high (mountain) basins in the dataset ( Fig. 1c ) dominate this correlation. Results for those basins likely suffer from known challenges of the simulation of mountain hydrology such as strong gradients and snow accumulation and melt. There is no systematic relation with elevation for mid- to low-elevation basins. The lack of availability of
