Evaluating the Contribution of Remote Sensing Data Products for Regional Simulations of Hydrological Processes in West Africa using a Multi-Model Ensemble

Water is a crucial resource for human health, agricultural production and economic development. This holds especially true in West Africa, where large parts of the population work as self-sustaining farmers. Accurate knowledge of available water resources is therefore essential to properly manage this valuable commodity. Hydrologic modeling is seen as a key aspect in generating predictions of available resources. However, the overall availability of in situ data for model parametrization in West Africa has been steadily declining since the 1990s. When observations are available, they often contain errors and gaps. This lack of data severely hinders the application of hydrologic models in the region. Nowadays, many global and regional remote sensing and reanalysis data products exist which may be used to overcome these problems. A thorough analysis of the contribution of these products to regional simulations of hydrologic processes in West Africa has so far not been conducted. The purpose of this study is to close this gap. The study area spans from 3 to 24° latitude and -18 to 16° longitude and encompasses, among others, the Niger, Volta, and Senegal river basins. This study focuses on three key aspects, namely how the performance of remotely sensed and reanalyzed products can be validated without the availability of in situ data for the region; to what extent semi-distributed hydrologic models of the region can be parameterized and validated using these data; and how a fully distributed, grid-based model can be set up, calibrated and validated for sparsely-gauged river basins using multivariate data inputs. Comparisons of remote sensing and reanalysis precipitation products for the region show strong variability. A hydrologic evaluation was conducted, during which the skill of each precipitation dataset to accurately reproduce observed streamflow in HBV-light simulations was tested. Best results are achieved by products which include satellite infrared and microwave measurements as well as bias-correction based on in situ observations. Averaged Nash-Sutcliffe Efficiencies (NSE) of 0.66 were reached during the calibration of the CMORPH CRT and PERSIANN CDR products over six subbasins. In a next step, three SWAT models were set up for the region using multiple remote sensing and reanalysis data products and then calibrated and validated against observed river discharge with global and local approaches. While streamflow results differ within models and model regions, they are mostly satisfactory with coefficient of determination (R2) values of 0.52 and 0.51 for calibrations and 0.63 and 0.61 for validations. In a multivariate validation framework, the skill of the model in simulating variables not included in the calibration is further evaluated against remote sensing observations of actual evapotranspiration, soil moisture dynamics, and total water storage anomaly. Here, it has been shown that the models perform robustly and reach a good agreement in relation to observations. Furthermore, the grid-based mHM model was applied to several river basins in the south of the study area. After the quality of precipitation and evapotranspiration inputs was tested, a multivariate calibration was conducted. Models were calibrated using discharge observations (Q) and, to further constrain model boundary conditions, discharge in combination with remote sensing actual evapotranspiration observations (Q/ET). Finally, the quality of the simulations was tested against streamflow data as well as against remote sensing actual evapotranspiration, soil moisture, and total water storage anomaly data. Streamflow simulations performed well with averaged Kling-Gupta Efficiencies (KGE) of 0.53 for the first (Q) and 0.49 for the second (Q/ET) calibration. Further variables tested during the multiobjective validation were within good predictive ranges, especially during the Q/ET calibration. When SWAT and mHM model results are compared against each other and against external data products, results show that while both models perform robustly, mHM predictions outperform SWAT results. This study furthers the understanding of the contribution of remote sensing, reanalysis and global data products in regional simulations of hydrologic processes in West Africa. Specific modeling strategies and routines were developed to further increase predictive capabilities of hydrologic models of the region using these freely-available datasets

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Evaluation of MOD16 algorithm over irrigated rice paddy using flux tower measurements in Southern Brazil

Evapotranspiration (ET) is an important component of the hydrological cycle. Understanding the ET process has become of fundamental importance given the scenario of global change and increasing water use, especially in the agricultural sector. Determining ET over large agricultural areas is a limiting factor due to observational data availability. In this regard, remote sensing data has been used to estimate ET. In this study, we evaluated the Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface ET product estimates (hereafter MOD16 ET - MODIS Global Terrestrial Evapotranspiration Product) over two rice paddy areas in Southern Brazil, through the ET measured using the eddy covariance technique (hereafter EC). The energy balance components were evaluated during fallow and flooded seasons showing latent heat flux dominates in both seasons. The results showed that MOD16 ET underestimated EC measurements. Overall, the RMSE (root mean square error) ranged between 13.40 and 16.35 mm 8-day-1 and percent bias (PBIAS) ranged between -33.7% and -38.7%. We also assessed the ET (measured and estimated) main drivers, with EC yielding higher correlation against observed net radiation (Rn) and global radiation (Rg), followed by air temperature (Temp) and vapor pressure deficit (VPD), whilst MOD16 ET estimates yielded higher correlation against leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR). The MOD16 algorithm was forced with meteorological measurements but the results did not improve as expected, suggesting a low sensitivity to meteorological inputs. Our results indicated when a water layer was present over the soil surface without vegetation (LAI around zero), the largest differences between EC measurements and MOD16 ET were found. In this period, the expected domain of soil evaporation was not observed in MOD16 ET physical processes partition, indicating the algorithm was not able to detect areas with high soil moisture. In general, the MOD16 ET product presented low accuracy when compared against experimental measurements over flooded rice paddy, suggesting more studies are necessary, in order to reduce uncertainties associated to the land cover conditions