This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R²). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R² > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.

This paper analyzed the evolution of climate data in the Rio Bravo Basin in Mexico from 1980–2009 and projects future climate conditions in this region. Then, the potential impacts of climate change on water resources for crops in the nine irrigation districts (IDs) of the Rio Bravo Basin were evaluated. Specifically, climate data on precipitation, maximum and minimum temperatures, and evapotranspiration from the baseline period of 1980–2009 were compared with projected climate conditions for 2015–2039, 2045–2069, and 2075–2099. The projections were based on two representative concentration pathways (RCPs) of greenhouse gases in the atmosphere (RCP4.5 and RCP8.5). Patterns in the behavior of the analyzed climate variables over the past ten decades were examined and compared to the projected evolution of these variables through to the end of the century. Overall, in the future, temperatures, rates of evapotranspiration, and crop water demand are expected to increase. Also, the future precipitation patterns of all IDs were modified under the considered scenarios. Finally, the IDs of Acuña-Falcón and Delicias will be the most impacted by climate changes, while Palestina will be the least affected.

The present work proposes the use of green infrastructure (GI) called sustainable urban agriculture drainage systems with water reuse (SUADS-WR) to manage percolated water sustainably in urban agricultural areas (f.i. golf courses). The substrate of the system is commonly used in golf courses and includes a subsurface reservoir for water that exceeds the edaphic zone. Data obtained from a lysimeter, installed in a golf course in Spain, are used to validate the methods employed in developing hydro-informatics tools based on daily water balance, which estimates the water requirement for crops, reservoir height, and capacity for unused water reuse. Reference evapotranspiration can be estimated using the Penman–Monteith or Hargreaves–Samani method. The results were compared with experimental data, revealing that the estimated irrigation depths were lower than the supplied ones and that the estimated percolation was consistent with the measured field drainage. The applicability of the proposed methods for determining the reservoir height and irrigation depth for any type of crop in urban agricultural areas is confirmed. With the implementation of SUADS-WR, the harvested water depth can cover more than 38% of the annual water demand for the crop and utilize leached fertilizers, thus preventing pollution of the receiving surface water body or groundwater.

The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins.

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography.