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The simulation of fast-reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, these sub-hourly data are typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution. Here, two different models for the disaggregation of precipitation data from a daily to sub-hourly scale are presented. The first one is a conditional disaggregation model based on first-order Markov chains and copulas (WayDown) that keeps the input daily precipitation sums consistent within disaggregated time series. The second one is an unconditional rain generation model based on a double Poisson process (LetItRain) that does not reproduce the input daily values but rather generates time series with consistent rainfall statistics. Both approaches aim to reproduce observed precipitation statistics over different timescales. The developed models were validated using 10 min radar data representing 10 climate stations in Germany and South Korea; thus, they cover various climate zones and precipitation systems. Various statistics were compared, including the mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles, and annual maxima. To account for the model uncertainties, 1000-year-equivalent ensembles were generated by both models for each study site. While both models successfully reproduced the observed statistics, WayDown was better (than LetItRain) at reproducing the ensemble median, showing strength with respect to precisely refining the coarse input data. In contrast, LetItRain produced rainfall with a greater ensemble variability, thereby capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall and underestimation thereafter. Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) information and the climate projections at a daily and 10 min scale. Both methods showed similar results for the respective stations and Representative Concentration Pathway (RCP) scenarios. Several consistent trends, jointly confirmed by disaggregated and daily data, were found for the mean, variance, autocorrelation, and proportion of wet periods. Further, they presented similar behaviour with respect to annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale (i.e. a similar systematic underestimation). [ABSTRACT FROM AUTHOR]

Evaporation plays an important role in the water balance on a different spatial scale. However, its direct and indirect measurements are globally scarce and accurate estimations are a challenging task. Thus the correct process approximation in modelling of the terrestrial evaporation plays a crucial part. A physically based 1D lumped soil–plant–atmosphere model (BROOK90) is applied to study the role of parameter selection and meteorological input for modelled evaporation on the point scale. Then, with the integration of the model into global, regional and local frameworks, we made cross-combinations out of their parameterization and forcing schemes to show and analyse their roles in the estimations of the evaporation. Five sites with different land uses (grassland, cropland, deciduous broadleaf forest, two evergreen needleleaf forests) located in Saxony, Germany, were selected for the study. All tested combinations showed a good agreement with FLUXNET measurements (Kling–Gupta efficiency, KGE, values 0.35–0.80 for a daily scale). For most of the sites, the best results were found for the calibrated model with in situ meteorological input data, while the worst was observed for the global setup. The setups' performance in the vegetation period was much higher than for the winter period. Among the tested setups, the model parameterization showed higher spread in performance than meteorological forcings for fields and evergreen forests sites, while the opposite was noticed in deciduous forests. Analysis of the of evaporation components revealed that transpiration dominates (up to 65 %–75 %) in the vegetation period, while interception (in forests) and soil/snow evaporation (in fields) prevail in the winter months. Finally, it was found that different parameter sets impact model performance and redistribution of evaporation components throughout the whole year, while the influence of meteorological forcing was evident only in summer months. [ABSTRACT FROM AUTHOR]

We investigated the depth–duration relationship of maximum rainfall over all of Germany based on 16 years of radar-derived quantitative precipitation estimates (namely, RADKLIM-YW, German Meteorological Service) with a space–time resolution of 1 km 2 and 5 min. Contrary to the long-term historic records that identified a smooth power law scaling behaviour between the maximum rainfall depth and duration, our analysis revealed three distinct scaling regimes of which boundaries are approximately 1 h and 1 d. A few extraordinary events dominated a wide range of durations and deviate to the usual power law. Furthermore, the shape of the depth–duration relationship varied with the sample size of randomly selected radar pixels. A smooth scaling behaviour was identified when the sample size was small (e.g. 10 to 100), but the original three distinct scaling regimes became more apparent as the sample size increases (e.g. 1000 to 10 000). Lastly, a pixel-wise classification of the depth–duration relationship of the maximum rainfall at all individual pixels in Germany revealed three distinguishable types of scaling behaviour, clearly determined by the temporal structure of the extreme rainfall events at a pixel. Thus, the relationship might change with longer time series and can be improved once available. [ABSTRACT FROM AUTHOR]

We investigate the depth-duration relationship of maximum rainfall over the whole of Germany based on 16 yrs of radar derived Quantitative Precipitation Estimates (namely, RADKLIM-YW, German Meteorological Service) with a space-time resolution of 1 km and 5 min. Contrary to the long-term historic records that identified a smooth power law scaling behavior between the maximum rainfall depth and duration, our analysis revealed three distinct scaling regimes of which boundaries are approximately 1.5 h and 1 d. Few extraordinary events dominate a wide range of durations and deviate to the usual power law. Furthermore, the shape of the depth-duration relationship varies with the sample size of randomly selected radar pixels. A smooth scaling behavior were identified when the sample size is small (e.g. 10 to 100), but the original three distinct scaling regimes became more apparent as the sample size increases (e.g. 1000 to 10 000). Lastly, a pixel wise classification of the depth-duration relationship of the maximum rainfall at all individual pixels in Germany revealed three distinguishable types of scaling behavior, clearly determined by the temporal structure of the extreme rainfall events at a pixel. Thus, the relationship might change with longer time series and can be improved once available. [ABSTRACT FROM AUTHOR]

Often detailed point information from future climate projections is needed for hydrological and other impact models. Therefore, two problems must be solved, first the bias correction is needed for the unbiased application of projected data and second the general scale gap between gridded model data and pointwise observation data has to be taken into account. The latter is often ignored, but contributes systematically to the model bias as model output is often referring to mean values or budget closing on grid level, while measurements are typically point measurements by nature. We present an approach, which solves the aforementioned problems by applying a parametric quantile mapping (QM) with a special focus on extremes in precipitation. We selected 16 model combinations of the multi-model ensemble matrix of the projects Euro-Cordex (Coordinated Downscaling Experiment – European Domain) and ReKliEs-De (Regional climate projections ensemble for Germany) as climate projections. Since the study area is the Free State of Saxony in Germany, 17 observation stations from the reference dataset by ReKIS (Referenzdatensatz V1.0 (10/2017), Regional climate information system – www.rekis.org) are used as the observation dataset. We fitted two parametric distribution functions with the maximum likelihood method to datasets of various situations (i.e. seasons, stations and model combinations). First, the often used Gamma distribution is fitted to the datasets. Second, a dynamical mixed Gamma and generalized Pareto distribution is fitted, to be able to describe the extremes more in detail. The different correction functions are generated to correct the bias between gridded model data and pointwise observation data. A cross-validation is carried out to evaluate the performance of the different fitted distributions. Therefore, 10 years from 1971-2015 are picked randomly as validation period and the remaining years are used for the calibration of the fitting process. This procedure is repeated 30 times for each single situation. Subsequently, the Akaike information criterion (AIC) and the Cramér-von Mises criterion (CvM) are calculated as skill scores to select the most appropriate distribution for each situation. However, the analysis of the skill scores shows different pattern of the chosen distributions, the AIC and CvM often suggest different distributions. Hence, to analyse which skill score delivers the better bias corrected model data, both skill score decisions are used to correct the model dataset for each situation to generate two corrected model datasets for the period 1971-2015. The final evaluation is performed by a comparison of quantile-quantile plots of the two corrected model datasets with respect to the observation data. The analysis shows that both distributions have their entitlement and that more reliable results can be generated, if not only one distribution is prescribed for each situation. [ABSTRACT FROM AUTHOR]

Precipitation usually exhibits scale-invariant behaviour on a large range of temporal and spatial scales which has been explained physically as well as empirically in many studies. Here, we investigate intensity-duration relationships from radar data for Germany from 2001 to 2016. A newly released radar composite product from the German Meteorological Service (Radolan-YW, DWD 2018) with a resolution of 5 minutes and 1x1 km² was used for the analysis. After a conversion of the whole raster data set into time series, the maximum intensities per duration were derived with 1) moving window, 2) fixed window, and 3) interpolated fixed window aggregation. Out of the data set, the maximum and percentiles of maximums were plotted against their duration and compared with existing theoretical as well as empirical relationships from point observations. The results show unexpected scale breaks after around 1 hour, which suggests a non-consistent scaling behaviour of the radar data. This implies a careful usage of the data set in context of extreme value statistics as well as its application. [ABSTRACT FROM AUTHOR]

In some catchments, especially in low mountain ranges, fog deposition can make a considerable contribution to the water budget. Yet, fog deposition is usually not measured, hence gaps between observed and expected discharge in the water balance of these catchments arise. These gaps are ignored, or either corrected by calibration, like by decreasing the evaporation or corrected with general approaches like an additional fog water input depending on the elevation.We present an approach, which is capable to estimate physically justified the fog deposition in dependence on the vegetation. The method requires only limited data and meteorological standard measurements are used. The calculations are done on a spatial basis and have been performed for Germany. First, liquid water content (lwc) at ground level is derived and interpolated to grid cells. The temporal resolution of the lwc-product is 1 hour and the spatial resolution 1 kilometre per grid cell. Second, deposition velocities based on measurements are determined for the study area. Last, the fog deposition can be estimated from the lwc-product, the deposition velocity and the actual vegetation properties (i.e. height of vegetation, Leaf Area Index).Depending on the catchment area, the additional precipitation input due to fog precipitation contributes up to 50 % to the annual precipitation. This is a considerable portion of the overall input and needs to be discussed, in order to avoid misleading effects of water budget model calibration in mountain ranges. [ABSTRACT FROM AUTHOR]