Hydrological modeling of spatial and temporal variations in streamflow due to multiple climate change scenarios in northwestern Morocco

Climate change is one of the most important factors impacting hydrological regimes. In this paper, climate change impact on streamflow of Loukkos basin (northwestern Morocco) is evaluated using SWAT model for three future periods: near (2021–2040), mid (2041–2070), and far (2071–2100), compared to baseline 1981–2020. A set of bias-corrected climate models was used: five regional climate models (EURO-CORDEX), four global climate models (CMIP6) and their ensemble mean, under two representative concentration pathways respectively (RCP 4.5; RCP 8.5) and (SSP2-4.5; SSP5-8.5). Furthermore, SUFI-2 algorithm in SWAT-CUP was performed to calibrate (1981–1997), validate (1998–2015), and analyze uncertainty for each dataset at ten hydrological stations. In most stations, statistical performance indicated a good simulation, with a Nash–Sutcliffe efficiency (NSE) greater than 0.77 and percent bias (PBIAS) within ±10% on a monthly basis. Overall, 82% of models indicated that future climate could decline streamflow. The largest decrease would be for 2071–2100 and under RCP 8.5/SSP5-8.5. Our findings could help planners and policymakers in developing reasonable water management policies and climate change adaptation measures

The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas

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

The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas

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

Surface Formations Salinity Survey in an Estuarine Area of Northern Morocco, by Crossing Satellite Imagery, Discriminant Analysis, and Machine Learning

The salinity of estuarine areas in arid or semi-arid environments can reach high values, conditioning the distribution of vegetation and soil surface characteristics. While many studies focused on the prediction of soil salinity as a function of numerous parameters, few attempted to explain the role of salinity and its distribution within the soil profile in the pattern of landscape units. In a wadi estuary in northern Morocco, landscape units derived from satellite imagery and naturalistic environmental analysis are compared with a systematic survey of salinity by means of apparent electrical conductivity (Eca) measurements. The comparison is based on the allocation of measurement points to an area of the estuary from Eca measurements alone, using linear discriminant analysis and four machine learning methods. The results show that between 57 and 66% of the points are well-classified, highlighting that salinity is a major factor in the discrimination of estuary zones. The distribution of salinity is mainly the result of the interaction between capillary rise and flooding by the tides and the wadi. The location of the misclassified points is analysed and discussed, as well as the possible causes of the confusions