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.
Our understanding of environmental systems is underpinned by observational data, whose unavailability and uncertainties hinder research and operational applications. Among other factors, atmospheric data quality is of prime importance for the reliability of hydro-meteorological and climatological studies (Ledesma and Futter, 2017; Zandler et al., 2019). Precipitation is one of the major components of the water cycle, which has led to numerous initiatives on understanding its generation, and estimating its amount and variability on Earth (Maidment et al., 2015; Cui et al., 2019). In hydrological modelling (Singh, 2018; Beven, 2019), precipitation is the most important driver variable that determines the spatio-temporal variability of other hydrological fluxes and state variables (Thiemig et al., 2013; Bárdossy and Das, 2008).
With the development of distributed hydrological models that facilitate large-scale predictions (Clark et al., 2017; Fatichi et al., 2016; Ocio et al., 2019), there is a growing need to inform and evaluate those models with distributed observational datasets to improve spatio-temporal process representation (Baroni et al., 2019; Paniconi and Putti, 2015; Hrachowitz and Clark, 2017). A key challenge is the spatio-temporal intermittency of precipitation, which is a major challenge for its measurement and its spatial interpolation (Tauro et al., 2018; Acharya et al., 2019; Bárdossy and Pegram, 2013; P. D. Wagner et al., 2012), especially in regions with particular features such as complex topography, convection-driven precipitation or snowfall occurrence. A comprehensive description of precipitation measurement techniques can be found in previous studies (e.g. Tapiador et al., 2012; Stephens and Kummerow, 2007; Kidd and Huffman, 2011; Levizzani et al., 2020). The drawbacks of in situ measurements of precipitation include limited and uneven areal coverage, deficiencies in instruments and costly maintenance (Kidd et al., 2017; Awange et al., 2019; Harrison et al., 2019), and they have led to the advent of precipitation estimation from space (Barrett and Martin, 1981). Precipitation estimates from space are spatially homogeneous and cover inaccessible regions with uninterrupted records over time (Beck et al., 2019b; Funk et al., 2015).
The advent of satellite-based rainfall products (SRPs) has opened up new avenues for water resources monitoring and prediction, especially in data-scarce regions (Serrat-Capdevila et al., 2014; Sheffield et al., 2018; Hrachowitz et al., 2013). Although the use of SRPs in hydrology is increasing (Xu et al., 2014; Chen and Wang, 2018), they have not been fully adopted for operational purposes yet (Ciabatta et al., 2016; Kidd and Levizzani, 2011). The limited uptake of SRPs in hydrology is due to measurement bias, inadequate spatio-temporal resolutions (e.g. for extreme-event simulation) and shortness of the records for some applications (e.g. climate change impact assessments), and the scepticism of some potential users with regard to the data quality (Marra et al., 2019). In the past decades, a large number of SRPs have been developed with different objectives, spatial and temporal resolutions, input sources, algorithms and acquisition methods (Ciabatta et al., 2018; Ashouri et al., 2015; Brocca et al., 2019). Several studies provide a review of SRPs (e.g. Maidment et al., 2014; Sun et al., 2018; Maggioni et al., 2016; Le Coz and van de Giesen, 2019).
In addition to SRPs, there are also atmospheric retrospective analysis (or reanalysis) datasets of precipitation. A reanalysis system is composed of a forecast model and a data assimilation scheme that integrates spatio-temporal observations of meteorological variables (i.e. temperature, humidity, wind and pressure) to generate gridded atmospheric data (Lorenz and Kunstmann, 2012; Schröder et al., 2018). Precipitation is one of the reanalysis model-generated fields that generally has more uncertainties than the meteorological state fields (Roca et al., 2019). Reanalysis datasets are often used in hydrological modelling (Tang et al., 2019; Duan et al., 2019; Gründemann et al., 2018), and sometimes they are preferred over SRPs because of their usually long-term records suitable for climate change studies and because of their higher performance in predictable large-scale stratiform systems (Seyyedi et al., 2015; Potter et al., 2018).
Despite the progress in satellite instruments, which has led to substantial advances in improving precipitation estimates (Sorooshian et al., 2011; Tang et al., 2019), there are known inconsistencies among the available SRPs (Sun et al., 2018; Tapiador et al., 2017). SRPs are subject to inherent errors originating mainly from precipitation retrieval instruments and algorithms, sampling frequency, and inadequate representation of cloud physics in some regions (Laiti et al., 2018; Alazzy et al., 2017; Romilly and Gebremichael, 2011). While on the one hand SRPs are subject to systematic biases, reanalysis products on the other hand have uncertainties resulting from their model forcing parameters, low spatial resolution with poor representation of sub-grid processes and the model physics (Bosilovich et al., 2008; Laiti et al., 2018). Uncertainty quantification both in SRPs and reanalysis data is subject to intense research (e.g. Maggioni et al., 2016; Gebremichael, 2010; Awange et al., 2016; Westerberg and Birkel, 2015). The error quantification of SRPs and reanalysis products is usually done by comparing them with in situ measurements (e.g. Dembélé and Zwart, 2016; Thiemig et al., 2012; Beck et al., 2019a; Caroletti et al., 2019; Satgé et al., 2020), or by assessing their reliability as forcing for hydrological models (e.g. Duethmann et al., 2013; Pan et al., 2010; Nkiaka et al., 2017). Other evaluation approaches include triple collocation, which is a technique that estimates the variance of unknown errors of three independent variables without a reference or observed variable (e.g. Massari et al., 2017; Alemohammad et al., 2015; McColl et al., 2014; Roebeling et al., 2012). Compared to the ground-truthing approach, the hydrological evaluation approach has received limited attention (Camici et al., 2018; Poméon et al., 2017).
In rainfall–runoff modelling (Peel and McMahon, 2020), the non-linearity of hydrological processes (Blöschl and Zehe, 2005; Clark et al., 2009) can reduce or amplify the errors in the input rainfall data used and result in a satisfactory or poor representation of the hydrological responses (Maggioni and Massari, 2018; Nijssen, 2004). Consequently, the hydrological model can give a good representation of a hydrological state or flux variable for the wrong reasons (cf. Kirchner, 2006), thereby potentially leading to unfortunate consequences for water resources management (Zambrano-Bigiarini et al., 2017). When testing models as hypotheses (Beven, 2018; Pfister and Kirchner, 2017), type I errors (i.e. false positive model acceptability; Beven, 2010) should be avoided to ensure a high predictive skill of the model and its correctness for good decision-making. This sheds light on the importance of assessing the reliability of hydrological predictions generated with the use of SRPs and reanalysis products (Behrangi et al., 2011; Kuczera et al., 2010). In this context, knowing the adequacy and coherence of meteorological data in reproducing hydrological processes is a prerequisite to data selection for water resources management (Casse et al., 2015; Laiti et al., 2018).
In the context of hydrological evaluation of precipitation datasets, some limitations can be identified in previous studies. Some studies only evaluate a small number of precipitation datasets or do not consider reanalysis products (e.g. Bitew and Gebremichael, 2011; Ma et al., 2018; Liu et al., 2017; Bhattacharya et al., 2019). Usually, the influence of temperature datasets in combination with rainfall datasets is not tested (e.g. Satgé et al., 2019; Camici et al., 2018; Casse et al., 2015; Qi et al., 2016; Zhang et al., 2019), with the exception of a few studies (e.g. Laiti et al., 2018; Lauri et al., 2014), despite the importance of this interaction for evaporation simulation. Most studies evaluate a single hydrological state or flux variable, generally streamflow (e.g. Poméon et al., 2017; Seyyedi et al., 2015; Shayeghi et al., 2020; X.-H. Li et al., 2012) or soil moisture (e.g. Brocca et al., 2013). Some studies use lumped or semi-distributed models, therefore averaging the rainfall amount over large areas (e.g. Duan et al., 2019; Tang et al., 2019; Tobin and Bennett, 2014; Gosset et al., 2013; Shawul and Chakma, 2020), which reduces the bias effect that could occur at the pixel level with a fully distributed model. Often, the model is not recalibrated for each precipitation dataset (e.g. Voisin et al., 2008; Su et al., 2008; L. Li et al., 2012; Tramblay et al., 2016), which is, however, a prerequisite for reliable input field assessment (Stisen et al., 2012). Moreover, some studies perform a global-scale analysis and ignore regionally tailored products (e.g. Beck et al., 2017b; Mazzoleni et al., 2019; Fekete et al., 2004), which can outperform global products (e.g. Thiemig et al., 2013). Finally, to the best of our knowledge, no study has evaluated the simultaneous impact of various precipitation and temperature datasets on the spatial patterns of several hydrological processes (i.e. soil moisture and evaporation).
In light of the above, we propose to study the adequacy of different combinations of 17 precipitation datasets (10 SRPs and 7 reanalysis products) and 6 temperature datasets from reanalysis, when used as forcing data for a fully distributed hydrological model, in reproducing the spatio-temporal variability of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage). In total, 102 rainfall–temperature input data combinations are tested with the mesoscale Hydrologic Model (mHM) by recalibrating the model for each of the input data combinations. The experiment is carried out in the poorly gauged and predominantly semi-arid Volta River basin (VRB), located in West Africa, over the period 2003–2012. It is noteworthy that the goal of this study is not to estimate the intrinsic quality of the meteorological forcing (i.e. precipitation and temperature) but rather to understand the impact of the propagation of associated uncertainties on the simulation of hydrological processes (Bhuiyan et al., 2019; Falck et al., 2015; Marthews et al., 2020).
The VRB case study is particularly interesting from both scientific and
societal perspectives. On the one hand, precipitation modelling in tropical
monsoon climates is a challenging task due to strong seasonality and diurnal
variations of rainfall (Turner et al., 2011; Pfeifroth et al., 2016; Cook
and Vizy, 2019), and due to isolated convection systems in semi-arid regions
(Taylor et al., 2017; Mathon et al., 2002; Parker and Diop-Kane, 2017). On
the other hand, open-access and good-quality datasets are needed for water
resources management in West Africa (Roudier et al., 2014; Serdeczny et
al., 2017; Di Baldassarre et al., 2010; Dinku, 2019). The following research
questions are addressed:
What is the impact of different gridded rainfall and temperature datasets on
the simulation of hydrological fluxes and state variables? How important is the choice of meteorological datasets for the
representation of spatial patterns versus temporal dynamics?
Overall, the objective of this work aligns with the efforts to solve the current scientific challenges in hydrology (i.e. uncertainty in large-scale measurements and data, spatial heterogeneity and modelling methods; Blöschl et al., 2019; Wilby, 2019). Moreover, a growing interest in using satellite remote-sensing data in hydrological modelling is expected (McCabe et al., 2017; Peters-Lidard et al., 2017; Wilkinson et al., 2016). Therefore, knowing the suitability of the input data for hydrological modelling is a prerequisite for reliable spatio-temporal predictions, as the goal is to increase model performance with minimum uncertainty (Beven, 2016; McMillan et al., 2018; Savenije, 2009).
The adequacy of the rainfall and temperature datasets to plausibly reproduce various hydrological processes is tested with all the 102 possible combinations of 17 rainfall and 6 temperature datasets used as meteorological forcing (see Sect. 2.2). Different temperature datasets are used to allow flexibility in rainfall partitioning into evaporation and runoff because temperature is a key variable for the calculation of potential evaporation (Kirchner and Allen, 2020; Zheng et al., 2019; Van Stan et al., 2020). The hydrological model is recalibrated for each of the 102 combinations of rainfall–temperature datasets (Fig. 1).
Flowchart of the methodology used to evaluate the suitability of meteorological datasets in reproducing plausible hydrological processes.
The differences in the performance of model outputs are assumed to result from the propagation of the input data uncertainty through the model simulations (Nikolopoulos et al., 2010; Fallah et al., 2020). In the case of uncertainties resulting from the hydrological model structure, these uncertainties can be assumed to remain consistent for all the input datasets, and therefore it should not hinder the interpretation of the results, because only the parameters change during model calibration, not the model structure (Raimonet et al., 2017).
This study evaluates 17 rainfall products composed of 10 satellite-based products (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42 and TRMM 3B42RT) and 7 reanalysis products (JRA-55, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2, PGF and ERA5) (Table 1). Widely used global and Africa-tailored datasets were selected based on their availability in the period for which streamflow data are available for the hydrological modelling (2000–2012). For SRPs that have multiple versions, the gauge-corrected version was selected to avoid the known systematic biases found in the SRPs as compared to ground measurements (Jiang and Wang, 2019; Pellarin et al., 2020). The selected rainfall datasets include single and multi-sensor, with various merged and gauge-corrected products obtained from rain gauges, microwave sensors in low Earth orbits and infrared sensors on geostationary satellites (Maggioni and Massari, 2018; Thiemig et al., 2013; Golian et al., 2019). Moreover, six different datasets of air temperature (at 2 m above ground) are used for the calculation of potential evaporation, and they are obtained from the following reanalysis products: JRA-55, EWEMBI, WFDEI, MERRA-2, PGF and ERA5.
Meteorological datasets with spatial resolution used; the table
presents the characteristics of the datasets used in this study, although
different spatial and temporal resolutions can be available from the data
providers. G: gauge; S: satellite; R: reanalysis; P: precipitation;
Continued.
In addition to the meteorological datasets (Table 1), an ensemble of datasets is required for the set-up and the calibration and evaluation of the hydrological model (Table 2). The streamflow datasets obtained from different organizations (see Acknowledgements) were pre-processed (i.e. gap-filling and quality control) in the work of Dembélé et al. (2019).
Multiple satellite datasets are used to evaluate the modelled hydrological
fluxes and state variables. For the evaluation of the modelled water
storages, the GRACE-derived terrestrial water storage (
Modelling datasets. ESA CCI SM: European Space Agency Climate Change Initiative Soil Moisture; GIMMS: Global Inventory Modeling and Mapping Studies; GLEAM: Global Land Evaporation Amsterdam Model; GLiM: Global Lithological Map; GMTED: Global Multi-resolution Terrain Elevation Data; GRACE: Gravity Recovery and Climate Experiment; WFDEI: WATCH Forcing Data methodology applied to ERA-Interim data.
The transboundary Volta River basin (VRB) covers approximately 415 600 km
Physical and hydroclimatic characteristics of the Volta River
basin.
The VRB is a data-scarce region, not like places in Europe and USA where a large amount of ground measurements are widely and freely accessible. The few datasets collected by local organizations in the VRB are not easily accessible due to the transboundary nature of the basin, which is shared among six countries. Moreover, the VRB region has a low density of meteorological stations (cf. Fig. 1 of Dembélé and Zwart, 2016, and Fig. 1 of Satgé et al., 2020). A thorough evaluation of satellite and reanalysis datasets with ground measurements in the VRB cannot be limited to a few stations because of the large size of the basin and the strong spatial variability of rainfall. Moreover, a robust ground evaluation would require independent in situ measurements that are not used in the development of the SRPs and reanalysis datasets (Beck et al., 2019a), as they are a luxury in West Africa. These limitations in in situ data availability further motivate the hydrological evaluation of SRPs and reanalysis datasets.
The fully distributed mesoscale Hydrologic Model (mHM, version 5.9; Samaniego et al., 2010; Kumar et al., 2013) is used in this study. It is a conceptual model that simulates dominant hydrological processes (e.g. evaporation, soil moisture, subsurface storage and discharge) per grid cell in the modelling domain. The Muskingum–Cunge method (Cunge, 1969) is used for routing the total grid-generated runoff using a multiscale routing model (Thober et al., 2019). A multiscale parameter regionalization technique (MPR; Samaniego et al., 2017) is used to account for sub-grid variability of the basin physical characteristics (e.g. soil texture, topography and land cover). For this study, 36 global parameters are determined through model calibration (Table S24).
In this study, the Hargreaves and Samani method (Hargreaves and Samani,
1985), solely based on air temperature data, is used to calculate the
reference evaporation (
Actual evaporation (i.e. all evaporative fluxes including transpiration,
The modelling experiment covers the period 2000–2012 with a 3-year model warm-up period (2000–2002), 6 years for model calibration (2003–2008) and 4 years for model evaluation (2009–2012). The model is calibrated and evaluated with the available daily in situ streamflow datasets from 11 locations (Fig. 2a), while the evaluation with satellite datasets of evaporation, soil moisture and terrestrial water storage is done at a monthly time step to avoid the impact of mismatches in the daily data retrieval periods among the satellite data sources. An illustration of natural variability of streamflow (Fig. S16), precipitation (Figs. S1 and S5) and temperature (Figs. S3–S4 and S6–S8) is provided in the Supplement.
A multisite calibration strategy is adopted by simultaneously constraining
the model with the 11 streamflow (
The model is calibrated solely with
In addition to
In addition to
The spatial pattern representation of hydrological processes is assessed by
using a bias-insensitive and multi-component metric developed by
Dembélé et al. (2020b). The proposed spatial pattern efficiency
(
The relative variation in model performance is assessed with the
second-order coefficient of variation (
The results are presented and discussed for the entire simulation period (2003–2012, i.e. combined calibration and evaluation periods) because reliable meteorological datasets are expected to produce a plausible representation of hydrological processes independently of the modelling period (Bisselink et al., 2016). Separated results are provided for the calibration and evaluation periods in the Supplement.
Similar model performance patterns are obtained with
Kling–Gupta efficiency (
The analysis of the components of
The model performance for the temporal dynamics of monthly terrestrial water
storage (
The rainfall datasets show different performances across climatic zones,
with ARC showing the highest score for all the climatic zones except the
Guinean zone, where CMORPH-CRT ranks first. The choice of the rainfall
dataset leads to an average
Pearson correlation coefficient (
Figure 5 shows the model performance for the
temporal dynamics of monthly soil moisture (
The spatial patterns of
Pearson correlation coefficient (
Maps of long-term (2003–2012) average of annual soil moisture
(
For the entire VRB, the choice of the rainfall dataset leads to an average
variation of 61 % for the
Spatial pattern efficiency (
Pearson correlation coefficient (
Maps of long-term (2003–2012) average of annual actual evaporation
(
The model performance for the temporal dynamics of monthly actual
evaporation (
As for
The
Spatial pattern efficiency (
This study builds upon and expands existing research studies on the evaluation of meteorological datasets in several ways:
the evaluation of the spatial patterns of multiple hydrological processes
(i.e. streamflow, actual evaporation, soil moisture and terrestrial water
storage) in addition to the more classically evaluated temporal dynamic, the evaluation of a high number of both satellite-based and reanalysis
rainfall datasets considered in combination with different temperature
datasets, the assessment of the model performance across four considerably different
climatic zones from semi-arid to sub-humid.
The overall outcome of this analysis is the ranking of all the meteorological datasets based on their ability to simulate various hydrological processes across different climatic zones in the VRB (Table A1). It is worth noting that the overall ranking shows which product is best or worst at simulating a given hydrological flux or state variable. However, the ranking does not systematically tell whether a dataset is good or bad. Only the skill scores can be used to make a judgement on the adequacy of a given dataset to produce plausible model outputs.
The results show that there is no single rainfall dataset outperforming the others in reproducing all hydrological processes across different climatic zones. These findings align with previous studies in the sense that there is no rainfall dataset that is the best everywhere (Beck et al., 2017b; Sylla et al., 2013). For datasets providing both rainfall and temperature data, the combination of the two variables as model input is not necessarily the best option for obtaining the highest performance in modelling a given hydrological state or flux variable. The best rainfall–temperature combinations for the spatio-temporal representation of each hydrological flux and state variable are provided in the Supplement (Fig. S15).
The results are primarily valid for the study region in West Africa, while a
wider generalization of the findings should be made with caution and after
repeating similar evaluation studies at other places. Nevertheless, the key
message is that
Moreover, when comparing the results of this study to the findings of Satgé et al. (2020) based on a point-to-pixel evaluation of gridded rainfall datasets in West Africa, it is noticeable that the ground evaluation might lead to different results as compared to the hydrological evaluation adopted in the current study. The skill of a rainfall product in reproducing ground measurements well under a point-to-pixel evaluation does not necessarily correlate with its performance for hydrological modelling, particularly in large and complex hydroclimatic environments such as the VRB.
Despite the efforts to produce a comprehensive evaluation of the meteorological datasets, the results obtained might be subject to uncertainties related to the potential model structural deficiencies as well as errors in the observational datasets used for the model evaluation (McMillan et al., 2010; Renard et al., 2010; Gupta and Govindaraju, 2019). The distribution of the final model parameters (Figs. S79–S80) highlights the possibility of obtaining equally good model performances for different parameter sets (i.e. equifinality), which can be a justification for model recalibration. Moreover, it can be noticed that most of the model parameters are sensitive to the change in meteorological input datasets (Fig. S79). A detailed analysis of parameter variability as a function of input data is beyond the scope of the current study but could build the basis of future research, namely to identify data errors by analysing parameter patterns (e.g. rooting depth) and resolve potential structural deficiencies of the mHM model. However, the mHM is chosen because of its adequacy for the experiment of this study (for model selection, see Addor and Melsen, 2019). The structure of mHM allows the representation of seamless spatial patterns of hydrological processes through the MPR scheme (Samaniego et al., 2017). In addition, mHM facilitates parameter regionalization and is therefore convenient for large-scale modelling, and it harnesses the full potential of the forcing datasets as it is a fully distributed model that has performed well in previous studies including those in the VRB (e.g. Poméon et al., 2018; Dembélé et al., 2020b). Regarding the model evaluation, the comparison between the observed and modelled hydrological processes is made only with regard to their temporal dynamics and spatial patterns using bias-insensitive metrics, except for streamflow, which limits the potential impact of satellite data uncertainty.
The model is calibrated only on
Finally, the importance of regional evaluation is emphasized by this study because some region-tailored datasets (e.g. TAMSAT and ARC) which are not included in global-scale studies (e.g. Beck et al., 2017b; Mazzoleni et al., 2019; Essou et al., 2016) outperform global datasets. The decision to use a given dataset is motivated not only by the availability or the accuracy of the data but also by data accessibility (e.g. storage platforms, openness, format and pre-processing requirement). The findings of this study provide further awareness for the data users and improvement avenues for data producers in their quest of the most accurate products (e.g. Massari et al., 2020; Contractor et al., 2020; Berg et al., 2018; Brocca et al., 2014; Cucchi et al., 2020; Beck et al., 2017a).
This modelling study evaluates the ability of multiple combinations of
rainfall–temperature datasets to reproduce plausible hydrological processes
and patterns. The experiment is done in the Volta River basin with the fully
distributed mesoscale Hydrologic Model (mHM) over a 10-year period
(2003–2012), using 17 rainfall and 6 temperature datasets from satellite and
reanalysis sources. The spatial and temporal representation of streamflow,
terrestrial water storage, soil moisture and actual evaporation are
evaluated using in situ and satellite remote-sensing observational datasets.
The key findings are as follows:
No rainfall dataset consistently outperforms all the others in reproducing
the highest model performance for all hydrological processes, and the best
dataset for the temporal dynamics is not necessarily the best for the
spatial patterns. Rainfall datasets have a higher impact on the spatio-temporal representation
of hydrological processes than temperature datasets, but the latter have a
greater influence on the spatial patterns of soil moisture. The large-scale performance for the meteorological datasets is not always
valid for sub-regions in the same basin.
The findings of this study give a critical insight on the performance for
several meteorological datasets in the challenging hydroclimatic environment
of West Africa. They are expected to foster further research initiatives on
improving the gridded meteorological datasets and further draw users'
attention to the contrasting performances of these datasets in modelling
hydrological fluxes and state variables. Efforts should be devoted to
reporting on the impact of data uncertainties on process representation in
hydrological modelling, especially when model outputs are used for
decision-making.
Future studies can test the transferability of the model's global parameters across different input datasets, i.e. how reliable a parameter set obtained with a given input dataset is for running the same model with a different input dataset. The answer to this research question will shed light on the necessity of model recalibration when using different meteorological forcing. Furthermore, the predictive skill of the model can be improved with a parameter sensitivity analysis to determine parameters that affect the spatio-temporal representation of each hydrological flux and state variable.
Mean annual rainfall totals over the period 2003–2012 for 17 rainfall datasets in the Volta River basin.
Mean annual air temperature (average
Model performance for streamflow (
The meteorological and modelling datasets used in this study are freely
available via the web links provided in Table 1 and
Table 2. More information on satellite-based
precipitation datasets can be found at
The supplement related to this article is available online at:
MD performed the analyses and drafted the manuscript. All authors contributed to the writing, review and editing process that led to the final manuscript.
The authors declare that they have no conflict of interest.
We thank the providers of the datasets used in this study (see Tables 1 and 2). We are grateful to the developers of mHM at the Department of Computational Hydrosystems at the Helmholtz Centre for Environmental Research (CHS/UFZ, Germany) for their open-source model. We thank the providers of the streamflow data obtained from the Volta Basin Authority (VBA), the Direction Générale des Ressources en Eau (DGRE) of Burkina Faso, the Hydrological Services Department (HSD) of Ghana and the Direction Générale de l'Eau et de l'Assainissement (DGEA) of Togo. We thank the reviewers for their useful comments.
This research has been supported by the Swiss Confederation (grant no. 2016.0533/Burkina Faso/OP) and the Swiss National Science Foundation (grant nos. SNF, P1LAP2_178071 and SNF, PP00P2_157611). Moctar Dembélé was supported by the Swiss Government Excellence Scholarship (2016.0533/Burkina Faso/OP) and the Doc.Mobility fellowship (SNF, P1LAP2_178071) of the Swiss National Science Foundation. Bettina Schaefli was supported by a research grant of the Swiss National Science Foundation (SNF, PP00P2_157611).
This paper was edited by Albrecht Weerts and reviewed by Nadav Peleg and one anonymous referee.