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Abstract: Scientifically sound decisions in sustainable water management are usually based on hydrological modeling which can only be accomplished by meteorological driving information. Especially in regions with weak infrastructure this task is hampered by limited hydro-meteorological information in sufficient spatial and temporal resolution. We investigated three approaches to provide required meteorological fields driving the distributed hydrological model: the results of the mesoscale meteorological model MM5 which are available near real time, the TRMM product 3B42 available with approximately one month delay, and station data available with a delay of one year or more. The study site is the White Volta catchment in the semi-arid environment of West Africa. The results for 2004 show that the meteorological model is able to provide meteorological input data for near real time water balance estimations. In this study the TRMM product does not improve the simulation results. Besides missing important meteorological data, also gridded information on land surface properties (albedo, LAI, etc.) is usually difficult to obtain, albeit it is an essential input for distributed hydrological models. This information is commonly taken from “static” tables depending on the land use. Satellite remote sensing provides worldwide spatially detailed information on land surface properties which is particularly suitable for large regions in remote settings. Therefore the MODIS products for albedo and LAI were processed to annual time series including the identification and replacement of low quality observations by interpolation. The impact using MODIS data on the spatial distribution of water balance variables occurs mainly on local scale. The hydrological simulations using MODIS LAI and albedo values result in higher annual evapotranspiration and lower total discharge sums for 2004. Altogether it is concluded that hydrological decision support systems in regions with weak infrastructure can benefit significantly from the integration of atmospheric modeling and satellite-derived land surface data. [Copyright &y& Elsevier]

Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, MERRA-2, CAMS, and SARAH-2) with 37 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions for the year 2020. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that satellite data performs better than reanalysis data under different atmospheric conditions. Nevertheless, both data sources exhibit significant bias of more than 150 W/m2 in terms of RMSE under cloudy skies compared to clear skies. The new measure of overall performance clearly shows that the hourly GHI derived from CAMS and SARAH-2 could serve as viable alternative data for assessing solar energy in the different climatic zones of West Africa. [ABSTRACT FROM AUTHOR]

Abstract: Savannahs are highly dynamic ecosystems but many of their properties and the related balances of energy, carbon, nitrogen, and water are still poorly understood. A particular scientific issue is the quantification of trace gases emitted from the soil of savannah ecosystems and their interaction with regional and global climate and air chemistry. Therefore it is important to develop and evaluate land-surface models that on the one hand represent vegetation and soil dynamics and on the other hand provide energy and water fluxes in a temporal resolution suitable for the application in climate/air chemistry models. In this paper, we present a consistent coupling between a common land-surface model (OSU) and a widely used biogeochemical model (DNDC) that is a first step for a full coupling of climate/air chemistry and biogeochemical processes. For consistency reasons, both models are linked to a general physiologically based plant model to provide the physical boundary conditions as well as the carbon and nitrogen in- and output variables. Evaluation is carried out with measurements of soil temperature, latent heat flux, soil water content, and soil emission data from two vegetation periods collected at a natural grassland site in Bontioli Nature Reserve, Burkina Faso (Africa). The results demonstrate that simulations of biogeochemical processes based on soil environmental conditions, calculated either with the land-surface model or with the unchanged biogeochemical model, do not differ significantly from each other. The OSU model simulates more realistic day-to-day variation of soil temperature as DNDC but the sensitivity of the biogeochemical simulation to this variation is small. In contrast, the sensitivity to differences in soil water content is high, but simulation results of both models are very similar on the daily scale and hardly depend on spatial soil resolution. [Copyright &y& Elsevier]