Streamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin in Morocco, where water resources are facing a severe drought and the demand is continuously increasing. The present analysis focuses on evaluating Machine Learning techniques, namely support vector regression (SVR) and Random Forest (RF) against the multiple linear regression (MLR) for daily streamflow forecasting in the mountainous sub-basin of Rheraya between 2003 and 2016. The results show that SVR performed best, followed by RF and MLR. In measurable terms and regarding mean performance, SVR exhibited the higher Nash–Sutcliffe efficiency score (NSE = 0.59) and a lower root mean squared error (RMSE = 1.18 $$\text {m}^3\,\text {s}^{-1}$$m3s-1 ) compared to RF (NSE = 0.53, RMSE = 1.18 $$\text {m}^3\,\text {s}^{-1}$$m3s-1 ) and MLR (NSE = 0.54, RMSE = 1.01 $$\text {m}^3\,\text {s}^{-1}$$m3s-1 ). Furthermore,the available time series was too short to properly capture the full range of streamflow variability, which reduced the prediction performance outside of the calibration conditions. These findings suggest that ML algorithms, particularly SVR, can provide accurate streamflow estimation useful for water resources management when trained on a representative period. The results highlight the capacity of Machine Learning algorithms, specifically SVR, to augment streamflow prediction for enhanced water resource management in arid regions.

Daily hydrological modelling is among the most challenging tasks in water resource management, particularly in terms of streamflow prediction in semi-arid areas. Various methods were applied in order to deal with this complex phenomenon, but recently data-driven models have taken a better space, given their ability to solve prediction problems in time series. In this study, we have employed the Long Short-Term Memory (LSTM) network to simulate the daily streamflow over the Ait Ouchene watershed (AIO) in the Oum Er-Rbia river basin in Morocco, based on a temporal sequence of in situ and remotely sensed hydroclimatic data ranging from 2001 to 2010. The analysis adopted in this work is based on three-dimension input required by the LSTM model (1); the input samples used three splitting approaches: 70% of the dataset as training, splitting the data considering the hydrological year and the cross-validation method; (2) the sequence length; (3) and the input features using two different scenarios. The prediction results demonstrate that the LSTM performs poorly using the default data input scenario, whereas the best results during the testing were found in a sequence length of 30 days using approach 3 (R² = 0.58). In addition, the LSTM fed with the lagged data input scenario using the Forward Feature Selection (FFS) method provides high performance accuracy using approach 2 (R² = 0.84) in a sequence length of 20 days. Eventually, in applications related to water resources management where data are limited, the use of the deep learning technique is able to create high predictive accuracy, which can be enhanced with the right combination subset of features by using FFS.

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability.

This study thoroughly compared two types of neural networks, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks (RNNs), within the context of hydrological modeling for predicting runoff in the Oum Er Rabia sub-basins. By assessing their performance on both daily and monthly scales, we aimed to understand the dynamics of runoff, which is crucial in water resources management. The combined analysis of predictions at both time scales provides a comprehensive perspective, using daily outputs for short-term decisions and monthly predictions for longer-term planning.Using a hydroclimatic time series dataset from 2000 to 2019, incorporating key factors such as snow cover area, temperature, and rainfall that influence hydrological processes and significantly impact flow patterns, the research evaluates the predictive accuracy of the models at both scales. The results reveal nuanced differences in predictive accuracy, with average Kling-Gupta Efficiency (KGE) values for LSTM and GRU at daily and monthly scales, respectively, being 0.64, 0.52, and 0.46, 0.54. These findings provide insights into the strengths and limitations of each architecture in the mountainous region of Morocco.The study enhances our understanding of the applicability of LSTM and GRU architectures in hydrological modeling, aiding practitioners in selecting models tailored to specific needs. By establishing a robust framework for short-term decision-making and long-term planning in water resource management, this research contributes to advancing predictive modeling and promoting sustainable water use while mitigating flood risks. The knowledge acquired paves the way for improved decision support in the critical area of water resource management.