Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

peer reviewedWater availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area

Moroccan groundwater resources and evolution with global climate changes

International audienceIn semi-arid areas, many ecosystems and activities depend essentially on water availability. In Morocco, the increase of water demands combined to climate change induced decrease of precipitation put a lot of pressure on groundwater. This paper reports the results of updating and evaluation of groundwater datasets with regards to climate scenarios and institutional choices. The continuous imbalance between groundwater extraction and recharge caused a dramatic decline in groundwater levels (20 to 65 m in the past 30 years). Additionally, Morocco suffers from the degradation in groundwater quality due to seawater intrusion, nitrate pollution and natural salinity changes. Climate data analysis and scenarios predict that temperatures will increase by 2 to 4 ◦C and precipitation will decrease by 53% in all catchments over this century. Consequently, surface water availability will drastically decrease, which will lead to more extensive use of groundwater. Without appropriate measures, this situation will jeopardize water security in Morocco. In this paper, we zoom on the case the Souss-Massa basin, where management plans (artificial recharge, seawater desalination, and wastewater reuse) have been adopted to restore groundwater imbalance or, at least, mitigate the recorded deficits. These plans may save water for future generations and sustain crop production

Isotopic and Chemical Tracing for Residence Time and Recharge Mechanisms of Groundwater under Semi-Arid Climate: Case from Rif Mountains (Northern Morocco)

Karstic aquifers play an important role for drinking and irrigation supply in Morocco. However, in some areas, a deeper understanding is needed in order to improve their sustainable management under global changes. Our study, based on chemical and isotopic investigation of 67 groundwater samples from the karst aquifer in the Rif Mountains, provides crucial information about the principal factors and processes influencing groundwater recharge and residence time. The δ18O and δ2H isotopic values indicate that the recharge is derived from meteoric water at high, intermediate, and low elevations for Lakraa Mountain, North of Lao River, and Haouz and Dersa Mountain aquifers, respectively. All samples show an isotopic signature from Atlantic Ocean except for those from the Lakraa Mountain aquifer, which shows Mediterranean Sea influence. Groundwater age determined by radiocarbon dating using the IAEA model indicates that the ages range from modern to 1460 years. This short residence time is consistent with the detectable tritium values (>2.7 TU) measured in groundwater. These values are similar to those of precipitation at the nearest GNIP stations of Gibraltar and Fez-Saiss, situated around 100 km north and 250 km south of the study area, respectively. This evidence indicates that groundwater in the Rif Mountains contains modern recharge (<60 years), testifying to significant renewability and the vulnerability of the hydrological system to climate variability and human activities. The results also indicate the efficiency of isotopic tracing in mountainous springs and would be helpful to decision makers for water in this karstic zone

Isotopic and Chemical Tracing for Residence Time and Recharge Mechanisms of Groundwater under Semi-Arid Climate: Case from Rif Mountains (Northern Morocco)

Karstic aquifers play an important role for drinking and irrigation supply in Morocco. However, in some areas, a deeper understanding is needed in order to improve their sustainable management under global changes. Our study, based on chemical and isotopic investigation of 67 groundwater samples from the karst aquifer in the Rif Mountains, provides crucial information about the principal factors and processes influencing groundwater recharge and residence time. The &delta;18O and &delta;2H isotopic values indicate that the recharge is derived from meteoric water at high, intermediate, and low elevations for Lakraa Mountain, North of Lao River, and Haouz and Dersa Mountain aquifers, respectively. All samples show an isotopic signature from Atlantic Ocean except for those from the Lakraa Mountain aquifer, which shows Mediterranean Sea influence. Groundwater age determined by radiocarbon dating using the IAEA model indicates that the ages range from modern to 1460 years. This short residence time is consistent with the detectable tritium values (&gt;2.7 TU) measured in groundwater. These values are similar to those of precipitation at the nearest GNIP stations of Gibraltar and Fez-Saiss, situated around 100 km north and 250 km south of the study area, respectively. This evidence indicates that groundwater in the Rif Mountains contains modern recharge (&lt;60 years), testifying to significant renewability and the vulnerability of the hydrological system to climate variability and human activities. The results also indicate the efficiency of isotopic tracing in mountainous springs and would be helpful to decision makers for water in this karstic zone

Assessing land use /cover variation effects on flood intensity via hydraulic simulations: A case study of Oued El Abid watershed (Morocco)

Despite the semi-arid to arid nature of its climate, Morocco is exposed, like all the Mediterranean countries to inundations, which can be very damaging for public and private infrastructure, and cause many victims. On Monday 10 August 2015, one of Oued El Abid tributary raised in a remarkable way, causing five deaths and serious material damage in Iminwargue village (Azilal province). This study comes to assess the impact of land use/cover variation in the multiplication of floods frequency via the hydraulic simulations. For this purpose, relying on land use/cover variation from 1987 to 2017, flow and precipitation database, we made a hydraulic simulation of the 2015 event and the 100 years recurrence inundation. As results, the land use/cover decrease is always followed by an increase in flows peak even that all recorded precipitations are more or less similar. The case study hundred years inundation prediction shows a 5.03 m increase in the river and all population near banks, agricultural land and other useful infrastructure such as roads and bridges are invaded. These results show that more catastrophic events could be reproduced and oblige the decision makers in Oued El Abid watershed to be aware of the critical situation

Robustness of Optimized Decision Tree-Based Machine Learning Models to Map Gully Erosion Vulnerability

Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%