This study investigates the robustness of the physically-based hydrological model WaSiM (water balance and flow simulation model) for simulating hydrological processes in two data sparse small-scale inland valley catchments (Bankandi-Loffing and Mebar) in Burkina Faso. An intensive instrumentation with two weather stations, three rain recorders, 43 piezometers, and one soil moisture station was part of the general effort to reduce the scarcity of hydrological data in West Africa. The data allowed us to successfully parameterize, calibrate (2014–2015), and validate (2016) WaSiM for the Bankandi-Loffing catchment. Good model performance concerning discharge in the calibration period (R2 = 0.91, NSE = 0.88, and KGE = 0.82) and validation period (R2 = 0.82, NSE = 0.77, and KGE = 0.57) was obtained. The soil moisture (R2 = 0.7, NSE = 0.7, and KGE = 0.8) and the groundwater table (R2 = 0.3, NSE = 0.2, and KGE = 0.5) were well simulated, although not explicitly calibrated. The spatial transposability of the model parameters from the Bankandi-Loffing model was investigated by applying the best parameter-set to the Mebar catchment without any recalibration. This resulted in good model performance in 2014–2015 (R2 = 0.93, NSE = 0.92, and KGE = 0.84) and in 2016 (R2 = 0.65, NSE = 0.64, and KGE = 0.59). This suggests that the parameter-set achieved in this study can be useful for modeling ungauged inland valley catchments in the region. The water balance shows that evaporation is more important than transpiration (76% and 24%, respectively, of evapotranspiration losses) and the surface flow is very sensitive to the observed high interannual variability of rainfall. Interflow dominates the uplands, but base flow is the major component of stream flow in inland valleys. This study provides useful information for the better management of soil and scarce water resources for smallholder farming in the area.
Modeling and nowcasting of flash floods remains challenging, mainly due to uncertainty of high-resolution spatial and temporal precipitation estimates, missing discharge observations of affected catchments and limitations of commonly used hydrologic models. In this study, we present a framework for flash flood hind- and nowcasting using the partial differential equation (PDE)-based ParFlow hydrologic model forced with quantitative radar precipitation estimates and nowcasts for a small 18.5 km2 headwater catchment in Germany. In the framework, an uncalibrated probabilistic modeling approach is applied. It accounts for model input uncertainty by forcing the model with precipitation inputs from different sources, and accounts for model parameter uncertainty by perturbing two spatially uniform soil hydraulic parameters. Thus, sources of uncertainty are propagated through the model and represented in the results. To demonstrate the advantages of the proposed framework, a commonly used conceptual model was applied over the same catchment for comparison. Results show the framework to be robust, with the uncalibrated PDE-based model matching streamflow observations reasonably. The model lead time was further improved when forced with precipitation nowcasts. This study successfully demonstrates a parsimonious application of the PDE-based ParFlow model in a flash flood hindcasting and nowcasting framework, which is of interest in applications to poorly or ungauged watersheds.
Water scarcity for smallholder farming in West Africa has led to the shift of cultivation from uplands to inland valleys. This study investigates the impacts of climate and land use/land cover (LULC) change on water resources in an intensively instrumented inland valley catchment in Southwestern Burkina Faso. An ensemble of five regional climate models (RCMs) and two climate scenarios (RCP 4.5 and RCP 8.5) was utilized to drive a physically-based hydrological model WaSiM after calibration and validation. The impact of climate change was quantified by comparing the projected period (2021–2050) and a reference period (1971–2000). The result showed a large uncertainty in the future change of runoff between the RCMs. Three models projected an increase in the total runoff from +12% to +95%, whereas two models predicted a decrease from −44% to −24%. Surface runoff was projected to show the highest relative change compared to the other runoff components. The projected LULC 2019, 2025, and 2030 were estimated based on historical LULC change (1990–2013) using the Land Change Modeler (LCM). A gradual conversion of savanna to cropland was shown, with annual rates rom 1 to 3.3%. WaSiM was used to simulate a gradual increase in runoff with time caused by this land use change. The combined climate and land use change was estimated using LULC-2013 in the reference period and LULC-2030 as future land use. The results suggest that land use change exacerbates the increase in total runoff. The increase in runoff was found to be +158% compared to the reference period but only +52% without land use change impacts. This stresses the fact that land use change impact is not negligible in this area, and climate change impact assessments without land use change analysis might be misleading. The results of this study can be used as input to water management models in order to derive strategies to cope with present and future water scarcities for smallholder farming in the investigated area.
The prediction of freshwater resources remains a challenging task in West Africa, where the decline of in situ measurements has a detrimental effect on the quality of estimates. In this study, we establish a series of modeling routines for the grid-based mesoscale Hydrologic Model (mHM) using Multiscale Parameter Regionalization (MPR). We provide a computationally efficient application of mHM-MPR across a diverse range of data-scarce basins using in situ observations, remote sensing, and reanalysis inputs. Model performance was first screened for four precipitation datasets and three evapotranspiration calculation methods. Subsequently, we developed a modeling framework in which the pre-screened model is first calibrated using discharge as the observed variable (mHM Q), and next calibrated using a combination of discharge and actual evapotranspiration data (mHM Q/ET). Both model setups were validated in a multi-variable evaluation framework using discharge, actual evapotranspiration, soil moisture and total water storage data. The model performed reasonably well, with mean discharge KGE values of 0.53 (mHM Q) and 0.49 (mHM Q/ET) for the calibration; and 0.23 (mHM Q) and 0.13 (mHM Q/ET) for the validation. Other tested variables were also within a good predictive range. This further confirmed the robustness and well-represented spatial distribution of the hydrologic predictions. Using MPR, the calibrated model can then be scaled to produce outputs at much smaller resolutions. Overall, our analysis highlights the worth of utilizing additional hydrologic variables (together with discharge) for the reliable application of a distributed hydrologic model in sparsely gauged West African river basins.
Predicting freshwater resources is a major concern in West Africa, where large parts of the population depend on rain-fed subsistence agriculture. However, a steady decline in the availability of in-situ measurements of climatic and hydrologic variables makes it difficult to simulate water resource availability with hydrological models. In this study, a modeling framework was set up for sparsely-gauged catchments in West Africa using the Soil and Water Assessment Tool (SWAT), whilst largely relying on remote sensing and reanalysis inputs. The model was calibrated using two different strategies and validated using discharge measurements. New in this study is the use of a multi-objective validation conducted to further investigate the performance of the model, where simulated actual evapotranspiration, soil moisture, and total water storage were evaluated using remote sensing data. Results show that the model performs well (R2 calibration: 0.52 and 0.51; R2 validation: 0.63 and 0.61) and the multi-objective validation reveals good agreement between predictions and observations. The study reveals the potential of using remote sensing data in sparsely-gauged catchments, resulting in good performance and providing data for evaluating water balance components that are not usually validated. The modeling framework presented in this study is the basis for future studies, which will address model response to extreme drought and flood events and further examine the coincidence with Gravity Recovery and Climate Experiment (GRACE) total water storage retrievals.
Abstract. The Niger River represents a challenging target for deriving discharge from spaceborne radar altimeter measurements, particularly since most terrestrial gauges ceased to provide data during the 2000s. Here, we propose deriving altimetric rating curves by “bridging” gaps between time series from gauge and altimeter measurements using hydrological model simulations. We show that classical pulse-limited altimetry (Jason-1 and Jason-2, Envisat, and SARAL/Altika) subsequently reproduces discharge well and enables continuing the gauge time series, albeit at a lower temporal resolution. Also, synthetic aperture radar (SAR) altimetry picks up the signal measured by earlier altimeters quite well and allows the building of extended time series of higher quality. However, radar retracking is necessary for pulse-limited altimetry and needs to be further investigated for SAR. Moreover, forcing data for calibrating and running the hydrological models must be chosen carefully. Furthermore, stage–discharge relations must be fitted empirically and may need to allow for break points.
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