Multiscale Weather Generator
Random Algorithms for generating synthetic weather time series, especially precipitation and temperature, are important tools for hydrological modeling as well as for civil and agricultural engineering. The statistical models that generate this random sequences are called Weather Generators (WGs).
WGs can be used on the one hand side to study the input uncertainty of hydrological models by providing large ensembles of meteorological forcings that are statistically equal to the observations. On the other hand, they can be used to perform climate change impact studies by statistically downscaling output of climate models.
With the advance of distributed hydrological modeling, it is required to generate synthetic precipitation and temperature fields on large grids. Therefore, multi-site algorithms have to be extended, which is a non trivial task.
In our working group, we are focusing on the development of a WG which is robust to be applicable on large grids. Furthermore, we are investigating methodologies to consistently provide precipitation and temperature fields at multiple spatio-temporal resolutions, which is a key requirement for hydrological applications.
For further information, contact: Stephan Thober
Parametric uncertainty of a high-resolution dateset of water fluxes and states over Germany
Accurate and reliable predictions of water fluxes and state variables such as soil moisture or evapotranspiration are required for flood forecasting, drought mitigation, climate change impact assessment, water resource management, among others. The objective of this study is to estimate the hydrological fluxes and states on the domain of Germany using the mesoscale hydrologic model mHM.
To derive parameter sets which are valid on the domain of Germany 100 local parameters have been estimated for the seven inner German river basins Danube, Weser, Main, Saale, Neckar, Mulde, and Ems. The resulting 700 parameters sets have been applied in each of the seven catchments to determine the 100 parameter sets which perform best in all of the catchments simultaneously. Those 100 parameters are seen as the most representative for the domain of Germany and are used for an ensemble estimation of hydrological fluxes and states in the period of 1950-2013. This best performing parameter sets are characterized by ensemble median Nash-Sutcliffe efficiencies (NSE) exceeding 0.65 in all catchments.
The analysis of the data did show that the parametric uncertainty is dependent on the location within Germany and the time of the year. Furthermore, the uncertainties are varying between different hydrological variables. The coefficient of variation for the estimate of evapotranspiration is low compared to the coefficient of variation of the discharge. Furthermore the uncertainties with regard to model parameters are highest in the northeastern part of Germany which is characterized by a high aridity index.
Finally the parameter sets and data produced by this study have been the basis for a study investigating the floodevent 2013 in central Europe, a soil moisture drought study on the domain of Germany and a German drought monitoring system.
For further information, contact: Matthias Zink
Parameterization of potential evapotranspiration approaches for distributed hydrologic modeling
Reliable soil moisture products are needed for the estimation of plant available water or agricultural droughts. For the simulation of hydrological states, e.g. soil moisture, the estimation of evapotranspiration is crucial since it has the largest contribution to the water balance besides precipitation. In hydrological modeling the evapotranspiration is usually estimated based on potential evapotranspiration (PET). The common approaches for PET estimation and their parameterization are sufficient at the point or field scale for which they have been developed. But for spatially distributed estimations on the mesoscale, e.g. 4 km, their robust parameterization is still a challenge in current research.
The aim of this study is to find scale and location independent parameters for three different potential evapotranspiration formulations, which are applied in the mesoscale Hydrologic Model (mHM). PET is estimated using the 1) Hargreaves-Samani (HS), 2) Priestley-Taylor (PT), and 3) Penman-Monteith (PM) equations. The Hargreaves-Samani method is a temperature driven approach, whereas the other two methods are based on radiation. For estimating the parameters of the above mentioned PET formulations, the multiscale Parameter Regionalization technique is used.
Whereas only slight changes in the discharge hydrograph have been observed in the comparison of the three PET equations, the impact on soil moisture is significant. Especially during the summer period the soil moisture is lower for the Priestley-Taylor formulation compared to the Hargreaves-Samani and Penman-Monteith equation. This effect is due to lower estimates in PET for the latter ones.
For further information, contact: Matthias Zink