The performance of seven operational high-resolution satellitebased rainfall products -Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Timeseries (TARCAT), and Tropical Rainfall Measuring Mission (TRMM) daily and monthly estimates -was investigated for Burkina Faso. These were compared to ground data for 2001-2014 on a pointto-pixel basis at daily to annual time steps. Continuous statistics was used to assess their performance in estimating and reproducing rainfall amounts, and categorical statistics to evaluate rain detection capabilities. The north-south gradient of rainfall was captured by all products, which generally detected heavy rainfall events, but showed low correlation for rainfall amounts. At daily scale they performed poorly. As the time step increased, the performance improved. All (except TARCAT) provided excellent scores for Bias and Nash-Sutcliffe Efficiency coefficients, and overestimated rainfall amounts at the annual scale. RFE performed the best, whereas TARCAT was the weakest. Choice of product depends on the specific application: ARC, RFE, and TARCAT for drought monitoring, and PERSIANN, CHIRPS, and TRMM daily for flood monitoring in Burkina Faso. ARTICLE HISTORY

Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow-only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias-insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.

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Abstract. This study evaluates the ability of different gridded rainfall datasets to plausibly represent the spatio-temporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrological modelling in the predominantly semi-arid Volta River basin (VRB) in West Africa. Seventeen precipitation products based essentially on gauge-corrected satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42 and TRMM 3B42RT) and on reanalysis (ERA5, PGF, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2 and JRA-55) are compared as input for the fully distributed mesoscale Hydrologic Model (mHM). To assess the model sensitivity to meteorological forcing during rainfall partitioning into evaporation and runoff, six different temperature reanalysis datasets are used in combination with the precipitation datasets, which results in evaluating 102 combinations of rainfall–temperature input data. The model is recalibrated for each of the 102 input combinations, and the model responses are evaluated by using in situ streamflow data and satellite remote-sensing datasets from GLEAM evaporation, ESA CCI soil moisture and GRACE terrestrial water storage. A bias-insensitive metric is used to assess the impact of meteorological forcing on the simulation of the spatial patterns of hydrological processes. The results of the process-based evaluation show that the rainfall datasets have contrasting performances across the four climatic zones present in the VRB. The top three best-performing rainfall datasets are TAMSAT, CHIRPS and PERSIANN-CDR for streamflow; ARC, RFE and CMORPH-CRT for terrestrial water storage; MERRA-2, EWEMBI/WFDEI-GPCC and PGF for the temporal dynamics of soil moisture; MSWEP, TAMSAT and ARC for the spatial patterns of soil moisture; ARC, RFE and GSMaP-std for the temporal dynamics of actual evaporation; and MSWEP, TAMSAT and MERRA-2 for the spatial patterns of actual evaporation. No single rainfall or temperature dataset consistently ranks first in reproducing the spatio-temporal variability of all hydrological processes. A dataset that is best in reproducing the temporal dynamics is not necessarily the best for the spatial patterns. In addition, the results suggest that there is more uncertainty in representing the spatial patterns of hydrological processes than their temporal dynamics. Finally, some region-tailored datasets outperform the global datasets, thereby stressing the necessity and importance of regional evaluation studies for satellite and reanalysis meteorological datasets, which are increasingly becoming an alternative to in situ measurements in data-scarce regions.