To make predictions for the regional hydrological cycle it has to be simulated with high spatial and temporal resolutions to resolve the relevant processes. We carry out these simulations with different atmospheric (WRF, MPAS), land surface (NOAH-MP) and hydrological models (WaSiM). In addition, we develop software systems to couple the models of the different compartments (WRF-Hydro), allowing for bidirectional fluxes of energy and matter. These models are then applied on different time scales, from high-resolution case studies of specific catchments for several months to climate predictions for larger regions, like central Europe or West Africa. Our newest developments include large eddy simulation (LES) to resolve vertical energy transport in the atmosphere with resolution of tens of meters. We also develop Deep Learning techniques to improve the spatial and temporal resolution of coarse climate models.

Rainfall is highly variable in time and space. The same is true for soil moisture and energy fluxes. They all are key variables for observing and modeling the hydrological cycle. To improve the spatial and temporal resolution of our observations, we employ new innovative measurement techniques and we build up dedicated observations networks in data-scarce regions. We work on the development of new sensing techniques, such as soil moisture observations from cosmic ray neutron sensing, and on the usage of opportunistic sensor data, e.g. from the existing network of commercial microwave links (CMLs) which form parts of the cellular network. Our dedicated observation networks are mainly installed within the framework of TERENO and WASCAL.

Water related problems often occur in regions with high pressure on water resources. Causes might be challenging or worsening climatic conditions, mismanagement of water resource, increasing pressure due to increasing population, or a combination of these. We still develop and apply our models and observational techniques to Germany and the Alps, benefiting from the provided infrastructure and easier accessibility. But we set a strong focus on the aforementioned regions were water related problems are already a significant issue and are projected to get worse over the next decades.

Developing countries often have lesser developed capacity for meteorological and hydrological consulting for decision makers. We set out to improve this situation by developing and running decision support system for water management. These systems are based on the hydrometeorological predictions of forecasting models that we run for our target regions. The scientific output is compiled into relevant info for the decision makers and visualized in interactive web frontend. Our current tool provides information on post-processed seasonal forecasts for selected catchments, like the Blue Nile, where water management is a crucial issue.

We assessed the potential economic value of the seasonal forecasting system SEAS5 for decision making in water management. For seven drought-prone regions analyzed in America, Africa, and Asia, the relative frequency of drought months significantly increased from 10 to 30% between 1981 and 2018. We demonstrate that seasonal forecast-based action for droughts achieves potential economic savings up to 70% of those from optimal early action. For very warm months and droughts, savings of at least 20% occur even for forecast horizons of several months. Our in-depth analysis for the Upper-Atbara dam in Sudan reveals avoidable losses of 16 Mio US$ in one example year for early-action based drought reservoir operation. These findings stress the advantage and necessity of considering seasonal forecasts in hydrological decision making.

Portele, T.C., Lorenz, C., Dibrani, B., Laux, P., Bliefernicht, J., und Kunstmann, H.: Seasonal forecasts offer economic benefit for hydrological decision making in semi-arid regions. Sci Rep 11, 10581, https://doi.org/10.1038/s41598-021-89564-y, 2021.

Lorenz, C., Portele, T. C., Laux, P., and Kunstmann, H.: Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions, Earth Syst. Sci. Data, 13, 2701-2722, https://doi.org/10.5194/essd-13-2701-2021, 2021.

We are collecting real-time attenuation data from 4000 commercial microwave links (CMLs) in Germany. From the CML attenuation data, path-averaged rain rates for each CML path can be derived using sophisticated signal processing and deep learning method. A comparison of CML-derived rainfall information with the official gauge-adjusted weather radar product of the German meteorological service (DWD) showed good agreement. We are now working on merging CML and radar data together with DWD and on transferring our methods to developing countries which typically have only very sparse rainfall observations networks.

Graf, M., Chwala, C., Polz, J., und Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931-2950, https://doi.org/10.5194/hess-24-2931-2020, 2020.

Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835-3853, https://doi.org/10.5194/amt-13-3835-2020, 2020.

It has been estimated that the annual world production of crops and livestock will need to be 60% higher in 2050 than in 2006 (FAO, 2016) to meet the growing food demand. In West Africa, the population is projected to increase by 54% in 2050 and 82% in 2100 (UN, 2017), while negative crop yield trends are expected. We assess the climate change impacts on agriculture and livestock in West Africa using the high-resolution regional climate models. We analyze agroclimatological indices for maize, sorghum, and millet under current (1981–2010) and future (2021–2100) conditions. Projections indicate delayed rainy seasons, reduced water availability, and increased growing degree days. Additionally, we assess heat stress for dairy cattle, showing a rise in severe heat stress events, leading to productivity losses. By 2071–2100, dairy cattle may experience up to 70 more days of extreme heat stress annually. Our findings highlight the urgent need for climate-adaptive strategies in agriculture and livestock management.

Dieng, D., Laux, P., Smiatek, G., Heinzeller, D., Bliefernicht, J., Heinzeller, D., Bliefernicht, J., Sarr, A., Gaye, A. T., and Kunstmann, H.: Performance analysis and projected changes of agroclimatological indices across West Africa based on high-resolution regional climate model simulations. Journal of Geophysical Research: Atmospheres, 123, 7950-7973. https://doi.org/10.1029/2018JD028536, 2018.

Rahimi, J., Mutua, J.Y., Notenbaert, A.M.O., Dieng D., and Butterbach-Bahl, K.: Will dairy cattle production in West Africa be challenged by heat stress in the future? Climatic Change 161, 665-685. https://doi.org/10.1007/s10584-020-02733-2, 2020.

We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. The evaluation is based on extensive observations at the Terrestrial Environmental Observatories (TERENO) Pre-Alpine Observatory for the Ammer river catchments in southern Germany. In the comparison with TERENO measurements, the fully coupled model slightly outperforms the classic WRF model with respect to evapotranspiration, sensible and ground heat flux, the near-surface mixing ratio, temperature, and boundary layer profiles of air temperature.

Fersch, B., Senatore, A., Adler, B., Arnault, J., Mauder, M., Schneider, K., Völksch, I., and Kunstmann, H.: High-resolution fully coupled atmospheric-hydrological modeling: a cross-compartment regional water and energy cycle evaluation, Hydrol. Earth Syst. Sci., 24, 2457-2481, https://doi.org/10.5194/hess-24-2457-2020 , 2020.

It is well accepted that summer precipitation can be altered by soil moisture condition. Coupled land surface – atmospheric models have been routinely used to quantify soil moisture – precipitation feedback processes. However, most of the land surface models assume a vertical soil water transport and neglect lateral terrestrial water flow at the surface and in the subsurface, which potentially reduces the realism of the simulated soil moisture – precipitation feedback. We use the coupled atmospheric-hydrological model WRF-Hydro with an option to tag and trace land surface evaporation in the modeled atmosphere, named WRF-Hydro-tag. Using WRF-Hydro-tag we can asses the contribution of lateral terrestrial water flow to summer precipitation It is found that lateral terrestrial water flow increases the relative contribution of land surface evaporation to precipitation by 3.6% in Europe and 5.6% in West Africa, which enhances a positive soil moisture – precipitation feedback and generates more uncertainty in modeled precipitation, as diagnosed by a slight increase in normalized ensemble spread. This study demonstrates the small but non-negligible contribution of lateral terrestrial water flow to precipitation at continental scale. 

Arnault, J., Fersch, B., Rummler, T., Zhang, Z., Quenum, G.M., Wei, J., Graf, M., Laux, P., Kunstmann, H., Lateral terrestrial water flow contribution to summer precipitation at continental scale - A comparison between Europe and West Africa with WRF-Hydro-tag ensembles. Hydrological Processes. 35:e14183. https://doi.org/10.1002/hyp.14183, 2021.