cc_byEl Ouali, AnasEl Hafyani, MohammedRoubil, AllalLahrach, AbderrahimEssahlaoui, AliHamid, Fatima EzzahraMuzirafuti, AnselmeParaforos, Dimitrios S.Lanza, StefaniaRandazzo, Giovanni2024-11-062024-11-062021https://hohpublica.uni-hohenheim.de/handle/123456789/16887https://doi.org/10.3390/app11199297With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R2, the root mean square error and p-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 µg/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions.engZiz basinWater qualitySatellite image analysisModeling approachNitrateDissolved oxygenChlorophyll aClimate changeTime series analysisEnvironmental monitoring630Article1775114007