Suitability of 17 rainfall and temperature gridded datasets for largescale
hydrological modelling in West Africa

Dembélé, Moctar; Schaefli, Bettina; Giesen, Nick van de; Mariethoz, Grégoire

doi:10.5194/hess-2020-68

Cited by 6 publications

(4 citation statements)

References 140 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More information on satellite-based precipitation datasets can be found at http://ipwg.isac.cnr.it/ (last access: 10 December 2019) (IPWG, 2019). The modelling database is available at https://doi.org/10.5281/zenodo.3662308 (Dembélé, 2020).…”

Section: Resultsmentioning

confidence: 99%

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Dembélé

Schaefli

Giesen

et al. 2020

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Dembélé

Schaefli

Giesen

et al. 2020

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To address these uncertainties, the climate projection 140 datasets are evaluated with respect to ten satellite-based and reanalysis-based rainfall datasets and six temperature datasets (Table 2). The selected satellite-based and reanalysis-based rainfall datasets demonstrated the best performances for largescale hydrological modelling in the VRB as shown by Dembélé et al (2020c). Hereafter, the datasets composed of both satellite and reanalysis products will be referred to as observations.…”

Section: Multivariate Bias Correction Of Climate Datamentioning

confidence: 97%

“…The altitude ranges from zero to 940 m a.s.l., but the topography is predominantly flat with a mean altitude of about 255 m a.s.l., as most of the basin lies below 400 m a.s.l. (Dembélé et al, 2020c). The land cover of the basin area is composed of savannah (75%), cropland (13%), forest (9%), water bodies (2%) and bare land and settlements (1%).…”

Section: Study Areamentioning

confidence: 99%

Contrasting dynamics of hydrological processes in the Volta River basin under global warming

Dembélé

Vrac

Ceperley

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of twelve general circulation models (GCM) from CMIP5 that are dynamically downscaled by five regional climate models (RCM) from CORDEX-Africa is used. In total, 43 RCM-GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The corrected simulations are then used as input to the fully distributed mesoscale Hydrologic Model (mHM) for hydrological projections over the twenty-first century (1991–2100). Results reveal contrasting changes in the seasonality of rainfall depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture and terrestrial water storage) is only projected under RCP8.5. High and low flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water-energy-food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socio-economic development.

show abstract

“…Studies evaluating the impact of rainfall variability on hydrological response using process-based hydrological models are limited (e.g., (Beck et al, 2017;Satgé et al, 2021;Schreiner-McGraw & Ajami, 2020;Stisen & Sandholt, 2010)). Approaches instead typically rely on models that do not consider key dryland hydrological processes, such as transmission losses and focused recharge (Beck et al, 2017;Bitew et al, 2012;Dembélé et al, 2020;Li et al, 2012;Su et al, 2008;Tang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Quichimbo,

Singer,

Michaelides

et al. 2023

Hydrological Processes

View full text Add to dashboard Cite

Precipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance.

show abstract

Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa

Cited by 6 publications

References 140 publications

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Contrasting dynamics of hydrological processes in the Volta River basin under global warming

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Contact Info

Product

Resources

About