Search Results (6)

Morocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated by climate change, has underscored the critical role of dams as essential water reservoirs. These dams serve multiple purposes, including flood management, hydropower generation, irrigation, and drinking water supply. Accurate estimation of reservoir flow rates is vital for effective water resource management, particularly in the context of climate variability. The prediction of monthly runoff time series is a key component of water resources planning and development projects. In this study, we employ Machine Learning (ML) techniques—specifically, Random Forest (RF), Support Vector Regression (SVR), and XGBoost—to predict monthly river flows in the Bouregreg basin, using data collected from the Sidi Mohamed Ben Abdellah (SMBA) Dam between 2010 and 2020. The primary objective of this paper is to comparatively evaluate the applicability of these three ML models for flow forecasting in the Bouregreg River. The models’ performance was assessed using three key criteria: the correlation coefficient (R²), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results demonstrate that the SVR model outperformed the RF and XGBoost models, achieving high accuracy in flow prediction. These findings are highly encouraging and highlight the potential of machine learning approaches for hydrological forecasting in semi-arid regions. Notably, the models used in this study are less data-intensive compared to traditional methods, addressing a significant challenge in hydrological modeling. This research opens new avenues for the application of ML techniques in water resource management and suggests that these methods could be generalized to other basins in Morocco, promoting efficient, effective, and integrated water resource management strategies. Full article

Water supply for drinking and agricultural purposes in semi-arid regions is confronted with severe drought risks, which impact socioeconomic development. However, early forecasting of drought indices is crucial in water resource management to implement mitigation measures against its consequences. In this study, we attempt to develop an integrated approach to forecast the agricultural and hydrological drought in a semi-arid zone to ensure sustainable agropastoral activities at the watershed scale and drinking water supply at the reservoir scale. To that end, we used machine learning algorithms to forecast the annual SPEI and we embedded it into the hydrological drought by implementing a correlation between the reservoir’s annual inflow and the annual SPEI. The results showed that starting from December we can forecast the annual SPEI and so the annual reservoir inflow with an NSE ranges from 0.62 to 0.99 during the validation process. The proposed approach allows the decision makers not only to manage agricultural drought in order to ensure pastoral activities “sustainability at watershed scale” but also to manage hydrological drought at a reservoir scale. Full article

Sediment transport in basins disturbs the ecological systems of the water bodies and leads to reservoir siltation. Its evaluation is crucial for managing water resources. The practical application of the process-based model can confront some limitations noticed in the lower accuracy during the validation process due to the lack of reliable physical datasets. In this study, we attempt to apply machine-learning-based modeling (ML) to predict the suspended sediment load, using hydro-climatic data as input variables in the semi-arid Bouregreg basin, Morocco. To that end, data for the years 2016 to 2020 were used for the training process, and the validation was performed with 2021 data. The results showed that most ML models have good accuracy, with a Nash–Schiff efficiency (NSE) ranging from 0.47 to 0.80 during the validation phase, which indicates satisfactory performances in predicting the SSL. Furthermore, the models were ranked against their generalization ability (GA), which revealed that the developed models are good to excellent in terms of GA. Overall, the present study provides new insight into predicting the SSL in a semi-arid environment, such as the Bouregreg basin. Full article

The management of water resources requires as a first step the modeling of rainfall-runoff. It allows simulating the hydrological behavior of the basin for a good evaluation of the potentiality of this in terms of water production. There are different hydrological models used for water resource assessment, but conceptual models are still the most used due to their simple structure and satisfactory performance. In this study, t he performances of the conceptual model of rainfall and runoff (GR4J) modeled under R with the AirGR package, are used to Aguibat Ezziar the subbasin of the Bouregreg basin in Morocco. The enormous amount of data required and the uncertainty of some of the m makes these models of limited usefulness. The GR4J model allows evaluation of the runoff rates and describes the hydrological behavior of the Aguibat Ezziar watershed, which presents the aim behind writing this paper. A period from 2003 to 2017 has been selected. This period has been divided into two parts: one for calibration (2003–2006), and one for validation (2013–2016). After the calibration of the model and following the performance obtained (Nash higher than 0.72) we can say that the GR4J model behaves well in the Aguibat Ezziar catchment area. Full article

Understanding the spatiotemporal distribution of future droughts is essential for effective water resource management, especially in the Mediterranean region where water resources are expected to be scarcer in the future. In this study, we combined meteorological and hydrological drought indices with the Soil and Water Assessment Tool (SWAT) model to predict future dry years during two periods (2035–2050and 2085–2100) in a typical Mediterranean watershed in Northern Morocco, namely, Bouregreg watershed. The developed methodology was then used to evaluate drought impact on annual water yields and to identify the most vulnerable sub-basins within the study watershed. Two emission scenarios (RCP4.5 and RCP8.5) of a downscaled global circulation model were used to force the calibrated SWAT model. Results indicated that Bouregreg watershed will experience several dry years with higher frequency especially at the end of current century. Significant decreases of annual water yields were simulated during dry years, ranging from −45.6% to −76.7% under RCP4.5, and from −66.7% to −95.6% under RCP8.5, compared to baseline. Overall, hydrologic systems in sub-basins under the ocean or high-altitude influence appear to be more resilient to drought. The combination of drought indices and the semi-distributed model offer a comprehensive tool to understand potential future droughts in Bouregreg watershed. Full article

The evaluation and quantification of solids transport in Morocco often uses the Universal Soil Loss Model (USLE) and the revised version RUSLE, which presents a calibration difficulty. In this study, we apply the MUSLE model to predict solid transport, for the first time on a large river basin in the Kingdom, calibrated by two years of solid transport measurements on four main gauging stations at the entrance of the Sidi Mohamed Ben Abdellah dam. The application of the MUSLE on the basin demonstrated relatively small differences between the measured values and those expected for the calibrated version, these differences are, for the non-calibrated version, +5% and +102% for the years 2016/2017 and 2017/2018 respectively, and between −33% and +34% for the calibrated version. Besides, the measured and modeled volumes that do not exceed 1.78 × 10⁶ m³/year remain well below the dam’s siltation rate of 9.49 × 10⁶ m³/year, which means that only 18% of the dam’s sediment comes from upstream. This seems very low because it is calculated from only two years. The main hypothesis that we can formulate is that the sediments of the dam most probably comes from the erosion of its banks. Full article