Search Results (297)

The residues of rice production could be used as a mulch to reduce the effects of rill erosion on long and steep hillslopes. However, there is a need to identify the most effective size of this residue to apply as a countermeasure of rill erosion, exploring its effect on hydraulic variables. Several investigations have focused on the anti-erosive effects of other crop residues, while experiences on rice straw applications to reduce rill erosion are still lacking. To fill this gap, this study has measured the variability in flow velocity, stream power and the resulting soil loss in a rill covered by rice straw. Flume experiments simulating rill erosion have been carried out comparing soil loss among treatments with rice straw (dose of 3 tonnes ha⁻¹ and lengths between 20 and 70, 80 and 130, or 140 and 190 mm) and a non-mulched control. Moreover, a multiple regression model that predicts soil loss for a rill cover with rice straw of a given length has been proposed. The application of rice straw reduced the soil loss by at least 20% compared to bare soils. The most suitable size of the applied straw was 90 to 130 mm, which reduces soil loss by 45%. Finer straw (20 to 70 mm) did not significantly improve the soil’s resistance to rill erosion. The beneficial effects of straw must be ascribed to the reduction in flow velocity due to the presence of straw, as shown by accurate power equations regressing the soil loss to this variable. In spite of some limitations (small experimental scale, local environmental conditions, and low incorporation level of the substrate), the results are useful for land managers and hydrologists for soil conservation in hillslopes subjected to intense rill erosion and with similar climatic and hydrological and geomorphological conditions as the case study. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

Under the combined influence of global climate change and intensified human activities, ecosystem services (ESs) are undergoing substantial transformations. Identifying their nonlinear driving mechanisms is crucial for promoting regional sustainable development. Taking Anhui Province as a case study, this research evaluates the spatial patterns and temporal dynamics of six key ecosystem services from 2000 to 2020—namely, biodiversity maintenance (BM), carbon fixation (CF), crop production (CP), net primary productivity (NPP), soil retention (SR), and water yield (WY). The InVEST and CASA models were employed to quantify service values, and the XGBoost–SHAP framework was used to reveal the nonlinear response paths and threshold effects of dominant drivers. Results show a distinct “high in the south, low in the north” spatial gradient of ES across Anhui. Regulatory services such as BM, NPP, and WY are concentrated in the southern mountainous areas (high-value zones > 0.7), while CP is prominent in the northern and central agricultural zones (>0.8), indicating a clear spatial complementarity of service types. Over the two-decade period, areas with significant increases in NPP and CP accounted for 50% and 64%, respectively, suggesting notable achievements in ecological restoration and agricultural modernization. CF remained stable across 98.3% of the region, while SR and WY exhibited strong sensitivity to topography and precipitation. Temporal trend analysis indicated that NPP rose from 395.83 in 2000 to 537.59 in 2020; SR increased from 150.02 to 243.28; and CP rose from 203.18 to 283.78, reflecting an overall enhancement in ecosystem productivity and regulatory functions. Driver analysis identified precipitation (PRE) as the most influential factor for most services, while elevation (DEM) was particularly important for CF and NPP. Temperature (TEM) and potential evapotranspiration (PET) affected biomass formation and hydrothermal balance. SHAP analysis revealed key threshold effects, such as the peak positive contribution of PRE to NPP occurring near 1247 mm, and the optimal temperature for BM at approximately 15.5 °C. The human footprint index (HFI) exerted negative impacts on both BM and NPP, highlighting the suppressive effect of intensive anthropogenic disturbances on ecosystem functioning. Anhui’s ES exhibit a trend of multifunctional synergy, governed by the nonlinear coupling of climatic, hydrological, topographic, and anthropogenic drivers. This study provides both a modeling toolkit and quantitative evidence to support ecosystem restoration and service optimization in similar transitional regions. Full article

Non-point source pollution (NPS) from agriculture is a primary driver of water eutrophication, necessitating effective control for regional water ecological security and sustainable agricultural development. This study focuses on the Chenzhuang village watershed, a typical green agricultural demonstration area in Jiangsu Province, using the HYPE model to analyze hydrological processes and Total Nitrogen (TN) and Total Phosphorus (TP) migration patterns. The model achieved robust performance, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.7 for daily runoff and 0.35 for monthly TN and TP simulations, ensuring reliable predictions. A multi-scenario simulation framework evaluated the synergistic control effectiveness of Best Management Practices (BMPs), including agricultural production management, nutrient management, and landscape configuration, on TN and TP pollution. The results showed that crop rotation reduced annual average TN and TP concentrations by 11.8% and 13.6%, respectively, by shortening the fallow period. Substituting 50% of chemical fertilizers with organic fertilizers decreased TN by 50.5% (from 1.92 mg/L to 0.95 mg/L) and TP by 68.2% (from 0.22 mg/L to 0.07 mg/L). Converting 3% of farmland to forest enhanced pollutant interception, reducing TN by 4.14% and TP by 2.78%. The integrated BMP scenario (S13), combining these measures, achieved TN and TP concentrations of 0.63 mg/L and 0.046 mg/L, respectively, meeting Class II surface water standards since 2020. Economic analysis revealed an annual net income increase of approximately 15,000 CNY for a 50-acre plot. This was achieved through cost savings, increased crop value, and policy compensation. These findings validate a “source reduction–process interception” approach, providing a scalable management solution for NPS control in small rural watersheds while balancing environmental and economic benefits. Full article

Soil moisture is a very important parameter influencing many hydrological and climatic processes. It is also a key factor in agriculture, determining crop yields and thus influencing food security. It is crucial to model this variable for large areas with high spatial and temporal resolution and good accuracy. The aim of this study is to develop a soil moisture model for bare soils from Sentinel-1 SAR data that would be characterized by high spatial resolution and would be universal enough to be applicable to large areas of various soil types, textures, and large ranges of roughness. Over 800 soil moisture measurements from five study areas located in different parts of Poland were used. The work was performed on Sentinel-1 data registered between March 2024 and March 2025 using both backscattering and polarimetric analysis. The soil data were obtained from a 1:5000 scale soil map available online for Poland through the soil-agricultural geoportal. The results of machine learning modeling of soil moisture based on backscattering were relatively poor, with R² = 0.49 and 6.65% accuracy of volumetric water content in the soils. In the case of polarimetric channels, results were more or less the same. The best results were obtained by taking the silt and clay content (particles < 0.02 mm) in the soil into account. Volumetric water content accuracy of 5.27% with R² = 0.69 was thus achieved. The proposed solution seems to be a good alternative to soil moisture studies that take soil roughness into account due to its simplicity, good accuracy, and relatively easy availability of data necessary for model inversion. The analyses carried out showed that it can be used for exposed soils of very diverse roughness. Full article

Climate change is expected to reduce water availability during cropping season, while growing populations and rising living standards will increase the global water demand. This creates an urgent need for national water management tools to optimize water allocation. In particular, agriculture requires targeted approaches to improve efficiency. Alongside field measurements and remote sensing, agro-hydrological models have emerged as a particularly valuable resource for assessing and managing agricultural water demand. This study introduces WaterCROPv2, a state-of-the-art agro-hydrological model designed to estimate national-scale irrigation water demand while effectively balancing accuracy with practical data requirements. WaterCROPv2 incorporates innovative features such as hourly time-step computations, advanced rainwater canopy interception modeling, detailed soil-dependent leakage dynamics, and localized daily evapotranspiration patterns based on meteorological data. Through comprehensive analyses, WaterCROPv2 demonstrates significantly enhanced reliability in estimating irrigation water needs across various climatic regions, particularly under contrasting dry and wet conditions. Validation against independent data from the Italian National Institute of Statistics (ISTAT) for maize cultivation in Italy in 2010 confirms the model’s accuracy and underscores its potential for broader international applications. A spatial analysis further reveals that the estimation errors align closely with regional precipitation patterns: the model tends to slightly underestimate irrigation needs in the wetter northern regions, whereas it somewhat overestimates demand in the drier southern areas. WaterCROPv2 has also been used to analyze irrigation water requirements for maize cultivation in Italy from 2005 to 2015, highlighting its significant potential as a strategic decision-support tool. The model identifies optimal cultivation areas, such as the Pianura Padana, where the irrigation requirements do not exceed 200 mm for the entire maize growing period, and unsuitable regions, such as Salentino, where over 500 mm per season are required due to the local climatic conditions. In addition, estimates of the water volumes required for the current extent of maize cultivation show that the Pianura Padana region demands nearly three times the amount of water used in the Salentino area. The model has also been used to identify regions where adopting efficient irrigation technologies could lead to substantial water savings. With micro-irrigation currently covering less than 18% of irrigated land, simulations suggest that a complete transition to this system could reduce the national water demand by 21%. Savings could reach 30–40% in traditionally water-rich regions that rely on inefficient irrigation practices but are expected to be increasingly exposed to temperature increases and precipitation shifts. The analysis shows that those regions currently lacking adequate irrigation infrastructure stand to gain the most from targeted irrigation system investments but also highlights how incentives where micro-irrigation is already widespread can provide further 5–10% savings. Full article

This study investigates the combined impact of climate change and land use changes on water resources and soil conditions in the Litani River Basin (LRB) in Lebanon. The Mediterranean region, including the LRB, is highly vulnerable to climate change. This study utilizes the WiMMed (Water Integrated Management for Mediterranean Watersheds) model to assess hydrological variables such as infiltration, runoff, and soil moisture for the years 2009, 2014, and 2019. It considers 2019 climate conditions to project the 2040 scenarios for Representative Concentration Pathways (RCPs) 2.6 and 8.5, incorporating the unique characteristics of the Mediterranean watershed. Results indicate a concerning trend of declining infiltration, runoff, and soil moisture, particularly under the more severe RCP 8.5 scenario, with the most significant reductions occurring during summer. Land use changes, such as deforestation and urban expansion, are identified as key contributors to reduced infiltration and increased runoff. This study highlights the critical role of soil moisture in crop productivity and ecosystem health, showing how land cover changes and climate change intensify these effects. Soil moisture is highly sensitive to precipitation variations, with a 20% reduction in precipitation and a 5 °C temperature increase leading to substantial decreases in soil moisture. These findings highlight the urgent need for sustainable land management practices and climate mitigation strategies in the Litani River Basin (LRB) and similar Mediterranean watersheds. Protecting forests, implementing soil conservation measures, and promoting responsible urban development are crucial steps to maintain water resources and soil quality. Furthermore, this research offers valuable insights for policymakers, farmers, and environmentalists to prepare for potential droughts or flooding events, contributing to the preservation of this vital ecosystem. The data from this study, along with the recommended actions, can play a crucial role in fostering resilience at the national level, addressing the challenges posed by climate change. Full article

The Cauto River Basin (CRB), the heartland of Cuban agriculture, has been hit hard by drought and water shortages. In response to this pressing issue, this study provides a comprehensive assessment of the water supply, demand and balance within the Cauto River Basin, considering the baseline and projected socio-economic and climatic conditions by coupling SWAT and WEAP models. The obtained results revealed that the annual flow in the CRB is projected to slightly decrease (2.5%), in which, the reduction in the rainy season (3.1%) will be higher than that in the dry season (1.3%). The total water demand in the baseline scenario is around 1.194 billion m³, dominated by agriculture (96%), with rice crops requiring nearly half. For the future scenario of 2050, the study showed a 16.6% surge in demand to 1.394 billion m³, driven by climate change and agricultural expansion. However, domestic use will decrease by 10% due to population reduction. The water deficit in the future is projected to increase by 52% from 172.4 to 262.7 million m³ due to a rising water demand and declining water supply. This study shows that integrating a hydrological model into a water allocation model is a promising approach to estimate the water supply, demand and balance, which is a crucial component of water resources management. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

Soil moisture serves as a critical factor in the hydrological cycle, affecting plant growth, ecosystem health, and groundwater reserves. Current methods for monitoring and predicting it fail to account for the complexities introduced by climatic variations and other influencing factors, such as the effects of atmospheric interference and data gaps, leading to reduced prediction accuracy. To address these challenges, this study introduces a novel soil moisture prediction model based on remote sensing and deep learning, utilizing the Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) optimized by the Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and incremental learning (IL) techniques. The proposed method for monitoring soil moisture utilizes hyperspectral and soil moisture data from a 2017 campaign in Karlsruhe, encompassing variables such as datetime, soil moisture percentage, soil temperature, and remote sensing spectral bands. The proposed methodology begins with comprehensive preprocessing of historical remote sensing data to fill gaps, reduce noise, and correct atmospheric disturbances. It then employs a unique seasonal mapping and grouping technique, enhanced by the AdaK-MCC method, to analyze the impact of climatic changes on soil moisture patterns. The model’s innovative feature selection approach, using HCSWO, identifies the most significant predictors, ensuring optimal data input for the AGRL-RBFN model. The model achieves an impressive accuracy of 98.09%, a precision of 98.17%, a recall of 97.24%, and an F1-score of 98.95%, outperforming existing methods. Furthermore, it attains a mean absolute error (MAE) of 0.047 in gap filling and a Dunn Index of 4.897 for clustering. Although successful in many aspects, the study did not investigate the relationship between soil moisture levels and specific crops, which presents an opportunity for future research aimed at enhancing smart agricultural practices. Furthermore, the model can be refined by integrating a wider range of datasets and improving its resilience to extreme weather conditions, thereby providing a reliable tool for climate-responsive agricultural management and water conservation strategies. Full article

Effective water resource management plays a crucial role in achieving sustainability in agriculture, hydrology, and environmental protection, particularly under growing water scarcity and climate-related challenges. Soil moisture (θ), matric potential (h), and hydraulic conductivity (K) are critical parameters influencing water availability for crops and regulating hydrological, environmental, and ecological processes. To address the need for accurate, real-time soil monitoring in both laboratory and open-field conditions, we proposed an innovative IoT-based monitoring system called SHYPROM (Soil HYdraulic PROperties Meter), designed for the simultaneous estimation of parameters θ, h, and K at different soil depths. The system integrates capacitive soil moisture and matric potential sensors with wireless communication modules and a cloud-based data processing platform, providing continuous, high-resolution measurements. SHYPROM is intended for use in both environmental and agricultural contexts, where it can support precision irrigation management, optimize water resource allocation, and contribute to hydrological and environmental monitoring. This study presents recent technological upgrades to the proposed monitoring system. To improve the accuracy and robustness of θ estimates, the capacitive module was enhanced with an integrated oscillator circuit operating at 60 MHz, an upgrade from the previous version, which operated at 600 kHz. The new system was tested (i.e., calibrated and validated) through a series of laboratory experiments on soils with varying textures, demonstrating its improved ability to capture dynamic soil moisture changes with greater accuracy compared to the earlier SHYPROM version. During calibration and validation tests, soil water content data were collected across a θ range from 0 to 0.40 cm³/cm³. These measurements were compared to reference θ values obtained using the thermo-gravimetric method. The results show that the proposed monitoring system can be used to obtain predictions of θ values with acceptable accuracy (R² values range between 0.91 and 0.96). To further validate the performance of the upgraded SHYPROM system, evaporation experiments were also conducted, and the θ(h) and K(θ) relationships were determined among soils. Retention and conductivity data were fitted using the van Genuchten and van Genuchten–Mualem models, respectively, confirming that the device accurately captures the temporal evolution of soil water status (R² values range from 0.97 to 0.99). Full article

In river-connected lake regions, both land use and hydrological regime changes may affect the ecosystem services; however, few studies have attempted to elucidate their complex influences. In this study, the spatiotemporal dynamics of eight ecosystem services (crop production, aquatic production, water yield, soil retention, flood regulation, water purification, net primary productivity, and habitat quality) were investigated through remote-sensing images and the InVEST model in the Dongting Lake Region during 2000–2020. Results revealed that crop and aquatic production increased significantly from 2000 to 2020, particularly in the northwestern and central regions, while soil retention and net primary productivity also improved. However, flood regulation, water purification, and habitat quality decreased, with the fastest decline in habitat quality occurring at the periphery of the Dongting Lake. Land-use types accounted for 63.3%, 53.8%, and 40.3% of spatial heterogeneity in habitat quality, flood regulation, and water purification, respectively. Land-use changes, particularly the expansion of construction land and the conversion of water bodies to cropland, led to a sharp decline in soil retention, flood regulation, water purification, net primary productivity, and habitat quality. In addition, crop production and aquatic production were higher in cultivated land and residential land, while the accompanying degradation of flood regulation, water purification, and habitat quality formed a “production-pollution-degradation” spatial coupling pattern. Furthermore, hydrological fluctuations further complicated these dynamics; wet years amplified agricultural outputs but intensified ecological degradation through spatial spillover effects. These findings underscore the need for integrated land-use and hydrological management strategies that balance human livelihoods with ecosystem resilience. Full article

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 10⁷ m² study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors of the groundwater environment in the context of soil salinity–alkalinity improvement, as well as the impact of irrigation on the ionic characteristics of groundwater. Furthermore, based on this analysis, a groundwater numerical model and a prediction model for the study area were developed using Visual MODFLOW Flex 6.1 software to forecast the future groundwater levels in the study area and evaluate the effects of varying irrigation scenarios on these levels. The key findings are as follows: (1) The groundwater depth stabilized at 1.63 ± 0.15 m (0.4 m increase) post-improvement measures, maintaining equilibrium under current irrigation but increasing with reductions in water supply. The groundwater salinity increased by 0.59–1.2 g/L across the crop growth period. (2) Spring irrigation raised the groundwater total dissolved solids by 15.6%, as influenced by rock weathering (38.2%), evaporation (31.5%), and cation exchange (30.3%). (3) Maintaining current irrigation systems and planting structures could stabilize groundwater levels at 1.60–1.65 m over the next decade, confirming the sustainable hydrological effects of soil improvement measures. Reducing irrigation to 80% of the current water supply of the Yellow River enables groundwater level stabilization (2.05 ± 0.12 m burial depth) within 5–7 years. This approach decreases river water dependency by 20% while boosting crop water efficiency by 18.7% and reducing root zone salt stress by 32.4%. Full article