In this paper, we attempted to review the erosion in the Ouergha watershed by applying two spatial approaches. The Ouergha watershed has an area of around 7300 km² representing approximately 18.2% of the Sebou basin of which it is the main tributary. In order to develop the erosion map using the SWAT model, it was important to prepare a large spatial database describing basin proprieties, furthermore, the daily hydro-climatic data. This model integrates MUSLE equation for the estimation of specific degradation. In addition, the estimation of erosion through SWAT was consolidated by constructing an erosion mapping through RUSLE method. This method was applied following an approach based on the use of remote sensing data and GIS tools to produce the major factors involved in the erosive process and their integration into RUSLE. The results obtained, in cartographic form, make it possible to target areas that require priority action for a larger-scale analysis, with a view to finding appropriate solutions to fight against erosion and protect the natural environment. Soil degradation in the Ouergha watershed is around 27 ton/ha/year (SWAT_MUSLE) and 25 ton/ha/year (RUSLE). Average sediment yield was estimated for Al Wahda dam of 10.4 Million tons.

Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981–2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed.

Understanding the spatiotemporal distribution of past and future climate change impact is essential for effective water resource management. This study aims to reveal the impact of temperature and precipitation change on hydrological streamflow of Ouergha watershed and on the inflow regime of Al Wahda dam. Initially, historical climate trend was assessed using Mann Kendall tests and Sen’s slope. Then, regional Climate Models (Cordex-Africa) were used to project future precipitation and temperature data under two emission scenarios (RCP4.5 as realistic and RCP8.5 as pessimistic). After correcting the biases in climatic variables using three different methods, the calibrated and validated SWAT model was forced to project the hydrological simulations under both scenarios. The study shows a clear decreasing in precipitation and augmentation in annual mean temperature over the past decades. In addition, projected climate variables expected severe change in future precipitation (decreasing) and mean temperature (Increasing). The impact of this climatic alteration is expected to extremely affect rivers discharge and reservoir inflows in both magnitude and timing.

Streamflow modelling is crucial for developing successful long-term management, soil conservation planning, and water resource management strategies. The current work attempts to develop a robust hydrological model that simulates streamflow with the slightest uncertainty in the calibration parameters. A physical-based and semidistributed hydrological SWAT model was employed to assess the hydrological simulation of the Ouergha watershed. The monthly simulation of the SWAT model achieved in the time frame from 1990 to 2013 has been split into warm-up (1990-1996), calibration (1997-2005), and validation (2006-2013). The SUFI-2 algorithm's preliminary sensitivity and uncertainty analysis was done to calibrate the model using 11 hydrologic parameters. The model's performance and robustness findings are promising. To evaluate the model, the coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), and percent of bias (PBIAS) were utilized. The value of R 2 , NSE, and PBIAS ranged from 0.45-0.77, 0.6-0.89, and +12.72 to +21.89% during calibration and 0.51-0.85, 0.64-0.88, and +8.82 to +22.19% during validation period, respectively. A high correlation between the observed and simulated streamflow was recorded during the calibration and validation periods. More than 68% of the observation data are encompassed by the 95PPU across both the calibration and validation intervals, which is excellent in terms of the P-factor and R-factor uncertainty criterion. The projected streamflow matches the observed data well graphically. According to the total hydrological water balance study, 29% of precipitation is delivered to streamflow as runoff, whereas 54% of precipitation is lost through evapotranspiration. The recharge to the deep aquifers is 8%, whereas the lateral flow is 10%. The findings of this study will help as a roadmap for the anticipated water management activities for the basin since the management and planning of water resources require temporal and spatial information.

<p class="Parag">Typically, hydrological models are calibrated using observed streamflow at the outlet of the watershed. This approach may fail to mimic landscape characteristics, which significantly impact runoff generation because the streamflow incorporates contributions from several hydrological components. However, remotely sensed evapotranspiration (AET) products are commonly used as additional data with streamflow to better constrain model parameters. Several researchers demonstrated the efficacy of AET products in reducing the degree of equifinality and predictive uncertainty, resulting in a significant enhancement in hydrological modelling. Due to the variety of publicly available AET datasets, which vary in their methods, parameterization, and spatiotemporal resolution, selecting an appropriate AET for hydrological modelling is of great importance. The purpose of this study is to investigate the difference in simulated hydrologic responses resulting from the inclusion of different remotely sensed AET products in a single and multi-objective calibration with observed streamflow data. The GLEAM_3.6a, GLEAM_3.6b, MOD16A2, GLDAS, PML_V2, TerraClimate, FLDAS, and SSEBop datasets were downloaded and incorporated into the calibration of the SWAT hydrological model. The findings indicate that the incorporation of remotely sensed AET data in multi-objective calibration tends to improve model performance and decrease predictive uncertainty, as well as significantly improves parameter identification. Furthermore, AET single-variable calibration results show that the model would have performed well in simulating streamflow even without streamflow data. Moreover, each dataset included in this investigation responded differently. GLEAM_3.6b and GLEAM_3.6a performed the best, followed by FLDAS and PML_V2, while MOD16A2 was the least performing dataset. Thus, this research supports the use of remotely sensed AET in the calibration of hydrological models as a best practice.</p> <p> </p>