Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin.

In India, the per capita availability of water is projected to be 1465 m³ and 1235 m³ by the years 2025 and 2050, respectively, and hence, India would be a water-stressed country as per the United Nations’ standard of less than 1700 m³ per capita water availability. India is predominantly an agricultural-dominant country. Rainfed agriculture in the country contributes 40% of food grain production and supports half of the human population and two-thirds of the livestock population. The country has 15 different agro-climatic zones, and each agro-climatic region has its own constraints of water availability and management along with the potential for their optimum utilization. Such situations warrant the formulation of regional-level strategies. Efforts were made to integrate and evaluate the feasibility of water harvesting and its utilization at twelve different sites representing six different agro-climatic conditions spanning pan India. It was found that water harvesting through tanks/ponds is a feasible approach and can increase the crop production as well as diversification. The results reveal that the range of crop diversification index increased from 0.49–0.85 to 0.65–0.98; the crop productivity index increased from 0.28–0.66 to 0.66–0.90; the cultivated land utilization index increased from 0.05–0.69 to 0.34–0.84; and the crop water productivity index increased from 0.20–0.51 to 0.56–0.96, among other production and diversification indices, due to additional water availability through rainwater harvesting intervention. Moreover, the gross return increased from INR 43,768–704,356 to INR 220,840–1,469,108 ha⁻¹, representing a 108 to 400% increase in the returns due to the availability of water. The findings of this study suggest that the water harvesting in small ponds/tanks is economical and feasible, requires less technological intervention, and increases crop diversification in all the studied agro-climatic conditions, and hence, the same needs to be encouraged in the rainfed areas of the country.

The present study predicts the future evaporation losses by applying novel hybrid Machine Learning Algorithms (MLA). Water resources management is achieved by covering the reservoir water surface with floating semitransparent polymer solar cells. The energy produced by these panels will be used in the irrigation activities. The study is applied for the mass water body of Nasser Lake, Egypt and Sudan. Five MLAs namely additive regression (AR), AR-random subspace (AR-RSS), AR-M5Pruned (AR-M5P), AR-reduced error pruning tree (AR-REPTree), and AR- support vector machine (AR-SVM) were developed and evaluated for predicting future evaporation losses in the years 2030, 2050, and 2070. The study concludes that the hybrid AR-M5P ML model was not only superior to the AR model alone but also outperformed other hybrid models such as AR-RSS and AR-REPTree. The expected total annual water saving are projected to reach 3.47 billion cubic meters (BCM), 3.68 and 3.90 BCM, while the total annual power production is observed to be 1389 × 10⁹ Megawatt (MW), 1535 × 10⁹ MW and 1795 × 10⁹ MW in the years 2030, 2050 and 2070, respectively. These results were achieved by covering the shallow water depths from contour level 0 m to 10 m below the surface water level. Additionally, this study shows the ability of using MLAs in the estimation of reservoir evaporation and addressing the water shortages in high stress regions. Graphical Abstract

The degradation of land and increasing water scarcity are existing challenges for agricultural sustainability, necessitating the implementation of improved soil-conservation practices at the watershed scale. The identification and selection of critical/prone areas based on erosion-governing criteria is essential and helps in the execution of the management process for determining priority. This study prioritizes erosion-prone sub-watersheds (alternatives) based on morphometric parameters (multiple criteria) via five Multi-Criteria Decision Analysis (MCDA) approaches, i.e., AHP: Analytical Hierarchy Process; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; VIKOR: VIseKriterijumska Optimizacija I Kompromisno Resenje; SAW: Simple Additive Weighting; and CF: Compound Factor. Based on their priority score, 19 sub-watersheds were classified into four priority classes: low priority (0–0.25), moderate priority (0.25–0.50), high priority (0.50–0.75), and very high priority (0.75–1). The results revealed that about 8.34–30.15% area of the Burhanpur watershed is critically prone to erosion, followed by 23.38–52.05% area classed as high priority, 7.47–49.99% area classed as moderate priority, and 10.33–18.28% area classed as low priority. Additionally, four indices—percentage of changes (∆P), intensity of changes (∆I), the Spearman rank correlation coefficient test (SCCT), and the Kendall tau correlation coefficient test (KTCCT)—were employed to compare the models. This study confirms the efficacy of morphometric parameters for prioritizing sub-watersheds to preserve soil and the environment, particularly in areas for which limited information is available.