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.
The importance of vegetation to understand terrestrial water storage variations