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Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, including the Tassaoute watershed in central Morocco, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of the PERSIANN-CDR satellite-derived precipitation product for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in situ observations. Runoff is simulated using two hydrological models: GR2M (Génie Rural 2 parameters Mensuel) and the Thornthwaite water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation. The future water availability was assessed using 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030–2060 and 2061–2090. Results show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from −30% to −60% under RCP4.5 and −20% to −80% under RCP8.5. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, runoff is projected to decrease by 50–90%, with model and data-source differences highlighting the importance of multi-model and satellite-derived approaches in data-sparse regions. These results emphasize the utility of satellite precipitation datasets in guiding climate-adaptive water management strategies.

The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.

The Mediterranean Region is presumed to be one of the locations where climate change will have the most effect. This impacts natural resources and increases the extent and severity of natural disasters, in general, and soil water erosion in particular. The focus of this research was to assess how climate change might affect the rate of soil erosion in a watershed in the High Atlas of Morocco. For this purpose, high-resolution precipitation and temperature data (12.5 × 12.5 km) were collected from EURO-CORDEX regional climate model (RCM) simulations for the baseline period, 1976–2005, and future periods, 2030–2060 and 2061–2090. In addition, three maps were created for slopes, land cover, and geology, while the observed erosion process in the catchment was determined following field observations. The erosion potential model (EPM) was then used to assess the impacts of precipitation and temperature variations on the soil erosion rate. Until the end of the 21st century, the results showed a decrease in annual precipitation of −32% and −46% under RCP 4.5 for the periods 2030–2060 and 2061–2090, respectively, −28% and −56% under RCP 8.5 for the same periods, respectively, and a large increase in temperature of +2.8 °C and +4.1 °C for the RCP 4.5 scenario, and +3.1 °C and +5.2 °C for the RCP 8.5 scenario for the periods 2030–2060 and 2061–2090, respectively. The aforementioned changes are anticipated to significantly increase the soil erosion potential rate, by +97.11 m3/km2/year by 2060, and +76.06 m3/km2/year by 2090, under the RCP 4.5 scenario. The RCP 8.5 predicts a rise of +124.64 m3/km2/year for the period 2030–2060, but a drop of −123.82 m3/km2/year for the period 2060–2090.

Assessing and mapping the vulnerability of gully erosion in mountainous and semi-arid areas is a crucial field of research due to the significant environmental degradation observed in such regions. In order to tackle this problem, the present study aims to evaluate the effectiveness of three commonly used machine learning models: Random Forest, Support Vector Machine, and Logistic Regression. Several geographic and environmental factors including topographic, geomorphological, environmental, and hydrologic factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 191 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. The models’ performance was assessed by calculating the area under the ROC curve (AUC). The findings indicate that the RF model exhibited the highest performance (AUC = 89%), followed by the SVM (AUC = 87%) and LR (AUC = 87%) models. Furthermore, the results highlight those factors such as NDVI, lithology, drainage, and density were the most influential, as determined by the RF, SVM, and LR methods. This study provides a valuable tool for enhancing the mapping of soil erosion and identifying the most important influencing factors that primarily cause soil deterioration in mountainous and semi-arid regions.

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts.

The Tassaoute watershed, a significant tributary of the Oum Er-Rbia basin in central Morocco, is experiencing increasing water stress due to climate variability and declining precipitation trends. This study aims to assess the impacts of recent climatic changes on water resources in the watershed using the HBV (Hydrologiska Byrâns Vattenbalansavdelning) conceptual hydrological model. Monthly meteorological and hydrological data were collected and used to calibrate and validate the HBV model, enabling simulation of runoff and the evaluation of the watershed's hydrological response to observed climate variability. The results highlight a noticeable reduction in streamflow and changes in seasonal water availability, which are closely linked to variations in rainfall and temperature patterns over recent decades. These findings underline the growing vulnerability of the Tassaoute watershed to climatic fluctuations and provide a scientific basis for improving water resource management in the region.

Water erosion is one of the most widespread land degradation processes in arid and semi-arid mountainous regions, causing significant soil loss and severely impacting natural resources. This study aims to assess water erosion susceptibility in the Upper Tassaoute watershed (High Atlas, Morocco) using two machine learning models: Random Forest (RF) and Support Vector Machine (SVM). An inventory of approximately 200 eroded sites, established through the integration of field observations and satellite imagery, was used for model training (70%) and validation (30%). Twenty environmental conditioning factors were selected, encompassing topographic, geological, climatic, soil, and vegetation variables. The performance of both models was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC), showing satisfactory predictive accuracy for both RF and SVM. The analysis of variable importance revealed that NDVI, slope, curvature, soil properties, and lithology were among the most influential factors. The results confirm the effectiveness of machine learning approaches for mapping water erosion vulnerability and provide a robust scientific basis to support sustainable land management strategies in sensitive mountainous environments.

Hydrological process modelling consistency is associated with long available gauged data and reliable parameters, which is often limited. Multi-sensors and satellite-based products are considered as an alternative, especially dealing with ungauged basins. In this study, a HEC-HMS hydrological model is used to test two rainfall dataset reliability (Gauges, TRMM product). The testing period is fixed for 2000–2011 to simulate streamflow generated at upper Oum Er Rbia basin. Dealing with soil information lack in the study, we implemented SCS-CN approach in its spatially semi distributed manner, adjusting CN, and comparing streamflow obtained by interpolated gauged and average TRMM pixels values, the model was calibrated to determine its accurate CN for the study area. In the calibration phase, the analysis of CN sensitivity shows its importance on direct flow simulation and for HEC-HMS results. Moreover, the optimal value for the study area is ranging between 30 and 40. In addition, the elaborated model is showing slightly sufficient performance when using TRMM precipitation data at a daily time scale even though its overestimations during intense precipitation events. As a conclusion, the model validation results show an instability estimation for simulated daily streamflow at the upper Oum Er Rbia basin.

This study involves a loss of soil modeling in the catchment basin of Tessaoute upstream (Central High Atlas, Morocco), the integration of the universal soil loss equation in Wischmeier (USLE) in a Geographical Information System (GIS). The model allows a qualitative and quantitative assessment of water erosion. The various factors of water erosion (climate, topography, soil, land use and erosion control practices) were mapped, processed, sorted and merged in order to quantify the water erosion rate per unit area. The result is a quantitative map, spatial reference, with an average loss of about 15.44 t/ha/year by runoff sheet and rill. This allowed us to better understand the impact of each erosive factor, assess its contribution to soil loss and establish the decisive factors that control water erosion in the watershed in order of importance, slope, soil erodibility and vegetation cover.

The twenty-first century marks a significant shift in soil fertility evaluation, driven by advancements in pedometrics and Digital Soil Mapping (DSM). Pedometrics introduces quantitative methods to assess soil variability using statistical and geostatistical techniques, enhancing understanding of soil properties. DSM builds on this by creating high-resolution predictive maps, offering valuable data for researchers and practitioners. An in-depth bibliometric analysis on the Scopus platform (2000–2023) revealed 133 articles on pedometrics and an impressive 1,172 on DSM, underscoring growing interest in these technologies.The integration of Geographic Information Systems (GIS) and Remote Sensing (RS) has further advanced these fields, enabling extensive geospatial data collection and real-time monitoring. Machine Learning (ML) has also been transformative, facilitating complex pattern recognition and predictive analysis to improve soil fertility mapping and management. A review of 364 studies from 2000 to 2023 highlights the development and impact of these technologies, detailing their advantages and limitations. The surge in related publications and citations since 2000 reflects a rising interest in sustainable agriculture and environmental management. Significant milestones occurred in 2019 and 2022 with the introduction of new soil management technologies, while RS and GIS technologies surged in popularity in 2016 and 2020, driven by satellite advancements like Sentinel and Landsat. The capabilities of ML techniques were notably effective in 2019 and 2022. Countries like India, China, and Iran have been key adopters, transforming soil fertility mapping into a non-invasive, large-scale process that enhances agricultural decision-making.This transition emphasizes the value of specialized publications that advocate for GIS, RS, pedometrics, and DSM, which are crucial for addressing environmental challenges. In conclusion, integrating traditional and advanced methodologies provides a holistic, adaptable approach to sustainable land management, supporting data-driven decisions to enhance agricultural and environmental sustainability.