River basins constitute a suitable natural territorial unit for the integrated management of natural resources. The El Fuerte River basin (EFRB) in northwestern Mexico is located on the western slope of the Sierra Madre Occidental. Its importance lies in its contribution to irrigation systems in the state of Sinaloa and to hydroelectric power generation, as well as its strong dependence on seasonal precipitation associated with the North American Monsoon (NAM). A reduction in NAM precipitation has been observed in northwestern Mexico. Using CHIRPS precipitation data and ERA5 reanalysis, we analyzed the relationship between precipitation trends and the variability of the Normalized Difference Vegetation Index (NDVI) in the El Fuerte River basin for the 2000–2025 period. Land vegetation cover in EFRB is dominated by Tropical Dry Forest (TDF). The results indicate that the TDF responds directly to precipitation conditions during both the dry and wet seasons. The exceptional drought and a cold wave in 2011 disrupted NDVI values in EFRB, causing an increase in greenness during the post-disturbance recovery period associated with a shift in the basin’s vegetation composition. The decline in NAM precipitation may be linked to multiple factors, including reduced moisture transport due to weakened low-level zonal winds and changes in land vegetation cover. The effects of these changes in precipitation trends and recurrent droughts could drive further changes in vegetation structure, thereby altering the basin’s hydrological cycle.
This study investigates changes in local groundwater carrying status and regional differences under the background of reductions in total groundwater abstraction, using Jiuquan, Jiayuguan, Zhangye, Jinchang, and Wuwei in the Hexi Corridor as study areas. The analysis was conducted from three dimensions: resource base, development pressure, and groundwater-level–storage change. The groundwater abstraction pressure index and net abstraction excess were jointly used to distinguish relative development intensity from the absolute volume exceeding allowable abstraction. Regional differences were further identified using groundwater-depth changes at 515 monitoring stations and shallow groundwater-storage changes. Residual correlations after controlling for month effects and linear trends, together with robustness tests, were used to examine the associations of precipitation, river runoff, and groundwater abstraction with groundwater depth. Results showed that total groundwater abstraction in the five cities decreased by 23.8% from 2018 to 2025, whereas the abstraction pressure index in key overexploitation areas remained between 1.12 and 2.08 in 2024, indicating that regional abstraction reduction and local overexploitation coexisted. Jinchang had the highest relative abstraction pressure, whereas Wuwei had the largest net abstraction excess, demonstrating that relative development intensity and excess abstraction volume correspond to different management priorities. During the study period, groundwater levels declined at 404 monitoring stations, and the cumulative shallow groundwater-storage change was −69.14 × 108 m3. Storage losses were concentrated mainly in Zhangye and Jiuquan and did not correspond to the regional ranking of development pressure, highlighting differences between current development intensity and cumulative storage change. After month effects and linear trends were controlled, the absolute values of 14 of the 15 correlations decreased, and 11 correlations changed from positive to negative, indicating that common seasonal cycles and long-term trends substantially affected the raw contemporaneous associations. By combining diagnosis of relative intensity and water-volume excess, comparison of groundwater-level and storage changes, and temporal-structure adjustment, this study refines regional identification of groundwater carrying status and provides a basis for spatially differentiated abstraction reduction and coordinated surface-water–groundwater management.
Effective water management requires accurate forecasting of dam inflows, rainfall, and actual electricity demand. In this paper, we propose a framework for intelligent control of Haditha Dam gates using the improved Harris Hawks Multi-Objective algorithm (IMOHHO) with Long Short-Term Memory (LSTM). The study aims to develop a framework for scheduling the operation of the gates that strikes a balance between maximizing hydropower generation, maintaining storage levels close to the target value, reducing unused discharge, and optimizing the operations of the gates. This ensures that a minimum net head of a 147 m water level in the dam reservoir is maintained. The proposed model was applied to the Haditha Dam using operational gates and a 48-step time horizon with an interval of 30 min that could be changed as needed. In the IMOHHO-LSTM algorithm, each filtered solution represents an integrated timeline for the opening of the six gates throughout the runtime; the algorithm relies on the concept of non-dominant solutions and creates an archive of optimal solutions using the Pareto interface to achieve a balance between different operational objectives. The final results showed the potential of the proposed algorithm to ensure the efficient operation of the dam, with a total power generation of 10,434.14 MWh, while the average output power was 434.76 MW. The maximum capacity reached 660 MW, which represents the maximum design capacity of a modern dam. Furthermore, the model kept the storage level within a very narrow range of 2199 to 2206 Mm3, centering around the target storage value of 2200 Mm3. This confirms the passing of the IMOHHO-LSTM algorithm in balancing energy production with maintaining the operational requirements of the dam lake’s water level.
The Ankang catchment, the primary water-producing area in the upper reaches of the Danjiangkou Reservoir catchment, experiences immense pressure on its water environmental carrying capacity. However, existing studies remain unable to differentiate between anthropogenic and natural inputs of riverine phosphorus (P), constraining catchment nutrient management. To address the issue of riverine P sources in the Ankang catchment, this study, based on data from three hydrological phases, applied a Bayesian phosphate oxygen isotope mixing model for the first time to quantify the natural and anthropogenic sources of riverine P. Subsequently, statistical methods were employed to reveal the mechanisms controlling riverine P export, thereby providing a scientific basis for riverine P management. Quantitative apportionment results showed that riverine P was primarily derived from natural sources (48.6%) in the normal phase. During the wet phase, the proportion of natural sources increased in some tributaries due to enhanced precipitation. The contribution of agricultural sources increased to 46.5% during the dry phase, driven by a new agricultural production cycle. Correlation analysis indicates that conserving water and increasing water use efficiency lower wastewater loading, and that decreasing fertilizer application or improving fertilizer use efficiency contributes to reducing agricultural inputs.
Reference evapotranspiration (ET0) is a standardized climatic index of atmospheric evaporative demand defined and calculated within the FAO-56 Penman–Monteith (PM) framework. When the required quality-controlled meteorological inputs are available, the standardized FAO-56 PM formulation remains the appropriate method for calculating ET0. This study does not seek to replace that formulation. Instead, it presents a preliminary, site-specific comparative investigation of how six machine learning architectures emulate an existing FAO-56 PM-derived ET0 target. Linear Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) were evaluated using 1827 daily records from ERA5-Land/Open-Meteo for Western Macedonia, Greece. The evaluation combined chronological data partitioning, time-series-aware cross-validation, hydrological performance metrics, Diebold–Mariano comparisons, and SHAP-based interpretation. Radiation- and temperature-related variables were the strongest statistical predictors. SVR achieved the lowest test-set RMSE (0.0862 mm day−1; R2 = 0.9967; PBIAS = 0.96%), although its predictive accuracy was not statistically distinguishable from XGBoost (DM = −1.8884, p = 0.0600). LSTM achieved substantially lower accuracy (R2 = 0.7636) under the present experimental configuration. These findings characterize the comparative statistical performance of the emulators within the sampled domain; they do not demonstrate physical equivalence, superior operational performance, or transferability beyond it. The Digital Twin perspective is conceptual: no operational Digital Twin or integrated hydrological system was developed or validated. The study provides preliminary methodological evidence for subsequent investigations into the roles and limitations of data-driven components in hydrological modelling, particularly meteorological data reconstruction and quality control in support of standardized FAO-56 PM calculations.
