Quantile mapping (QM) is routinely applied in many climate change impact studies for the bias correction (BC) of daily precipitation data. It corrects the complete distribution, but does not correct for errors in the annual cycle. Therefore, QM is often applied separately to temporal subsamples of the data (e.g. each calendar month), which reduces the calibration sample size. The question arises whether this sample size reduction negates the benefit from applying QM to temporal subsamples. We applied four QM methods in a cross‐validation approach to 40 years of daily precipitation data from 10 regional climate model (RCM) hindcast runs, without and with (semi‐annual, seasonal, and monthly) subsampling. QM subsampling improved the BC of daily RCM precipitation; less distinct for independent data but considerably for the calibration data. The optimal subsampling timescale for the correction of independent data depended on the chosen QM method and ranged between semi‐annual and monthly. Overall, a sub‐annual QM improves the forcing for climate change impact studies and thus their reliability.

No abstract

No abstract

Abstract. It is expected that the biomass productivity of forest stands will be influenced by global climate change. In order to adapt forest management to this fact a model based approach was developed in cooperation with forestry experts. The concept aims at detecting the link between climate, terrain and soil parameters with the biomass productivity of some tree species. This article gives an insight into the first two steps of this approach. At first the WaSiM-ETH 8.2 model was parameterised to simulate various forest sites. Furthermore, different drought stress indices were applied to the simulated water balance time series. The impact of variations of climate, topography and soil characteristics on water balance was plausibly simulated. All drought stress indices detected years which were dominated by dry conditions. However, the indices related to soil water content were more selective than those related to evapotranspiration. Drought stress indices for one future climate projection have shown an increasing frequency of drought stress during vegetation period. Thus, the first two steps are capable components to detect the link between water balance with climate, terrain and soil parameters.

In this paper, a method combining graphical and statistical techniques is proposed for surface resistance calibration in a distributed hydrological model, WaSiM-ETH, by comparing daily evapotranspiration simulated by model WaSiM-ETH with corresponding daily evapotranspiration retrieved from remote sensing images. The study area locates in Nahe catchment (Rhineland-Palatinate, Germany, 4065 km 2 ) forest regions. The remote sensing based observations are available for a very limited number of days but representative for most soil moisture conditions. By setting canopy resistance (rc) at 150 s/m, soil surface resistance (rse) at 250 s/m or at 300 s/m for deciduous forest and setting rc at 300 s/m, rse at 600 s/m or at 650 s/m for pine forest, the model exhibits its best overall performance in space and time. It is also found that with sufficient soil moisture, the model exhibits its best performance in space scale.