The African continent has a very low density of rain gauge stations, and long time-series for recent years are often limited and poorly available. In the context of global change, it is very important to be able to characterize the spatio-temporal variability of past rainfall, on the basis of datasets issued from observations, to correctly validate simulations. The quality of the rainfall data is for instance of very high importance to improve the efficiency of the hydrological modeling, through calibration/validation experiments. The HydroSciences Montpellier Laboratory (HSM) has a long experience in collecting and managing hydro-climatological data. Thus, HSM had initiated a program to elaborate a reference dataset, in order to build monthly rainfall grids over the African continent, over a period of 60 years (1940/1999). The large quantity of data collected (about 7000 measurement points were used in this project) allowed for interpolation using only observed data, with no statistical use of a reference period. Compared to other databases that are used to build the grids of the Global Historical Climatology Network (GHCN) or the Climatic Research Unit of University of East Anglia, UK (CRU), the number of available observational stations was significantly much higher, including the end of the century when the number of measurement stations dropped dramatically, everywhere. Inverse distance weighed (IDW) was the chosen method to build the 720 monthly grids and a mean annual grid, from rain gauges. The mean annual grid was compared to the CRU grid. The grids were significantly different in many places, especially in North Africa, Sahel, the horn of Africa, and the South Western coast of Africa, with HSM_SIEREM data (database HydroSciences Montpellier_Système d’Information Environnementales pour les Ressources en Eau et leur Modélisation) being closer to the observed rain gauge values. The quality of the grids computed was checked, following two approaches—cross-validation of the two interpolation methods, ordinary kriging and inverse distance weighting, which gave a comparable reliability, with regards to the observed data, long time-series analysis, and analysis of long-term signals over the continent, compared to previous studies. The statistical tests, computed on the observed and gridded data, detected a rupture in the rainfall regime around 1979/1980, on the scale of the whole continent; this was congruent with the results in the literature. At the monthly time-scale, the most widely observed signal over the period of 1940/1999, was a significant decrease of the austral rainy season between March and May, which has not earlier been well-documented. Thus, this would lead to a further detailed climatological study from this HSM_SIEREM database.

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.

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...