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Water is one of the fundamental resources of economic prosperity, food security, human habitats, and the driver of many global phenomena, such as droughts, floods, contaminated water, disease, poverty, and hunger. Therefore, its deterioration and its inadequate use lead to heavy impacts on environmental resources and humans. Thus, we argue that to address these challenges, one can rely on hydrological management strategies. The objective of this study is to simulate and quantify water balance components based on a hydrologic model with available data at the R’Dom watershed in Morocco. For this purpose, the hydrologic model used is the Soil and Water Assessment Tool + (SWAT+) model. The streamflow model simulations were run at the monthly time step (from 2002 to 2016), during the calibration period 2002–2009, the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) values were 0.84 and 0.70, respectively, and 0.81 and 0.65, respectively, during the validation period 2010–2016. The results of the water balance modeling in the watershed during the validation period revealed that the average annual precipitation was about 484 mm, and out of this, 5.75 mm came from the development of irrigation in agricultural lands. The evapotranspiration accounted for about 72.28% of the input water of the watershed, while surface runoff (surq_gen) accounted for 12.04%, 11.90% was lost by lateral flow (latq), and 4.14% was lost by groundwater recharge (perco). Our approach is designed to capture a real image of a case study; zooming into other case studies with similar environments to uncover the situation of water resources is highly recommended. Moreover, the outcomes of this study will be helpful for policy and decision-makers, and it can be a good path for researchers for further directions based on the SWAT model to simulate water balance to achieve adequate management of water resources.

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.