Search Results (275)

The Source Region of the Yangtze River is a high-altitude area with extensive permafrost on the Tibetan Plateau. While temperature, precipitation, and radiation significantly affect vegetation phenology, the influence of permafrost changes remains unclear. Using the daily Long-term Seamless NOAA AVHRR NDVI Dataset of China (2003–2022), we extracted the start (SOS) and end (EOS) of the growing season in the Source Region of the Yangtze River (SRYR). Soil thawing date (SOT) was obtained from freeze–thaw state products, while active layer thickness (ALT) was estimated using the Stefan model based on MODIS land surface temperature (LST). Partial least squares regression and mediation analysis quantified the direct and indirect effects of permafrost degradation. Results show: (1) The end of the growing season (EOS) became significantly earlier in 64.33% of the region, while the start of the growing season (SOS) showed little change. (2) The effect of SOT on SOS depends on moisture conditions. Earlier SOT leads to earlier SOS in wetter areas by supplying meltwater, but delays SOS in cold–dry areas by increasing soil water loss. (3) Thicker ALT strongly promotes earlier EOS, accounting for up to 42.61% of EOS variation in cold–dry zones, because a deeper active layer potentially promotes downward movement of water, which may further lead to the potential leaching of nutrients from the shallow root zone, limiting resources for shallow-rooted plants. (4) Alpine meadows respond more strongly to permafrost changes than alpine grasslands. Overall, water loss caused by permafrost degradation may reduce the potential lengthening of the growing season under climate warming, highlighting the key role of soil water in linking permafrost and vegetation dynamics. Full article

Rising human-caused nitrogen (N) deposition and increased rainfall variability threaten the capacity of temperate forests to sequester carbon. However, the combined effects of N enrichment and moisture changes on total soil respiration (Rs), including its autotrophic (Ra) and heterotrophic (Rh) components, remain poorly understood, especially in northern China’s warm-temperate forests. To explore this, a factorial field experiment was conducted at the Beijing Yanshan Earth Critical Zone National Research Station in Huairou District, Beijing. The experiment involved N addition (50 kg N ha⁻¹ yr⁻¹ as urea [CO(NH₂)₂]) and precipitation manipulation (±50% of ambient throughfall) during the 2024 growing season. Six treatments were implemented: control (CK), nitrogen addition (NA), 50% increased precipitation (W+50%), 50% decreased precipitation (W−50%), nitrogen addition with increased precipitation (NW+50%), and nitrogen addition with decreased precipitation (NW−50%). Under natural rainfall conditions, N addition increased Rs (+11.8%; p < 0.05). However, the effects of N largely depended on water availability: with increased rainfall, N addition significantly boosted Rs, Rh, and Ra by promoting fine root biomass and accelerating litter decomposition; under reduced rainfall, N addition still increased Rs, Rh, and Ra compared to drought alone (NW−50% vs. W−50%), though the extent of stimulation was considerably lower than under elevated precipitation, indicating that water availability influences the strength of N effects on forest soil respiration. Structural equation modelling (SEM; χ²/df = 1.8, RMSEA = 0.040, CFI = 0.97) revealed that water availability was a key mediator of the interaction between N addition and precipitation. These findings enhance understanding of how nitrogen supply and water availability interact in temperate forest soils, though further validation across other forest types and over longer periods remains necessary. Full article

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

Utilising highly mineralised mine water for drip irrigation of desert vegetation in mining areas represents a crucial approach to alleviating freshwater scarcity and achieving mine water resource utilisation. However, high salt inputs may pose risks of salt return to root zones and deep accumulation. To ensure the safe and effective utilisation of mine water, laboratory 45 cm soil column infiltration tests (freshwater, 8, 12, 16 g L⁻¹) were conducted in the heavily saline-affected desert vegetation zone of Dananhu, Hami, Xinjiang, alongside 2023–2024 field drip irrigation trials (8, 12, 16 g L⁻¹). This study established a ‘soil column inversion–field validation–scenario optimisation’ framework (16 g L⁻¹) and field drip irrigation trials (8, 12, 16 g L⁻¹) during 2023–2024. A multi-scale HYDRUS-1D/3D simulation framework—‘soil column inversion–field validation–scenario optimisation’—was established to quantify water–salt transport processes in the root zone and optimise emitter flow rates. HYDRUS-1D demonstrated excellent fitting for soil moisture content, wetting front, and salinity distribution (R² = 0.964–0.979, 0.995–0.998, 0.791–0.898). Following parameter migration, HYDRUS-3D achieved R² values of 0.834–0.949 for simulating field-scale stratified salinity. Overall desalination occurred in the 0–80 cm soil profile over two years. Within the 0–40 cm root zone, reduction rates decreased with increasing irrigation salinity: 45.77% (2023) and 59.64% (2024) under 8 g L⁻¹ treatment, significantly higher than the 24.24% and 30.91% reductions observed at 16 g/L (p < 0.05). During the high-temperature period of July–August, transient salt accumulation occurred in the 0–10 cm surface layer, while the 80–120 cm zone exhibited cumulative risk. Scenario simulations indicated that increased dripper flow rates expanded the wetted zone horizontally but weakened vertical leaching. The 2.0–2.4 L h⁻¹ range demonstrated superior overall performance in balancing root zone desalination rates and irrigation uniformity. The study recommends targeting root-zone salinity stability through a combination of moderate leaching, summer transpiration suppression, and seasonal flushing/natural leaching, alongside prioritising low-to-medium flow emitters. This approach synergistically reduces both surface salinity return and deep accumulation risks. Full article

Against the backdrop of accelerated terrestrial hydrological cycling and the increasing concurrence of drought-heatwave compound extremes under global warming, regional land-atmosphere coupling has emerged as a central mechanism shaping climate feedbacks and trajectories of ecosystem carbon uptake. However, prior studies spanning climatic regimes, observational scales, and data sources have often yielded contradictory conclusions. Here, we challenge these fragmented perspectives by constructing an integrated LST-SM-ET-GPP chain that jointly represents land surface temperature, soil moisture, evapotranspiration, and gross primary productivity, thereby linking water availability, surface energy balance, and plant physiological processes within a unified framework. We synthesize a conceptual diagnostic roadmap for interpreting land-atmosphere coupling across observations and models. When ecosystems operate in humid, energy-limited environments, radiative and advective controls should be prioritized to diagnose system forcing. By contrast, as the system becomes water-depleted, attribution must shift to a nonlinear regime transition framework governed by a critical soil moisture threshold. This threshold mechanism implies that, once the system enters the moisture-limited regime, even modest declines in soil moisture can trigger a rapid weakening of evaporative cooling, substantially amplifying LST anomalies and strongly suppressing GPP. The competitive regulation of stomatal conductance by atmospheric demand (vapor pressure deficit, VPD) and terrestrial supply (rootzone soil moisture) further explains why the “dominant” controlling factor can dynamically reverse across hydrothermal states, timescales, and stages of extreme-event evolution. Notably, the steady-state coupling assumption may break down under flux “flooring” during extreme drought, or when structural buffering such as deep root water uptake is present, delineating strict applicability bounds for existing diagnostic frameworks. Finally, current assessments remain constrained by multiple uncertainties, particularly the lack of ET partitioning constraints, representativeness biases arising from clear-sky observations and sampling-depth limitations, and systematic errors in Earth system model simulations during the warm season. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

This study examined the long-term effects of nanobubble-saturated water (NBSW) on silty clay loam and sandy loam soils, with an emphasis on soil moisture dynamics, water balance, compaction, and electrical conductivity (EC) over a 1.5-year experimental period. NBSW did not induce a significant long-term increase in soil moisture storage compared to conventional watering, though there were short-term changes. In both soils, NBSW caused higher cumulative evaporation and reduced leaching in water partitioning. Compaction increased over time, and this response was stronger under NBSW, with a greater increase in silty clay loam than in sandy loam. EC remained higher in both soils under NBSW treatment, with a greater temporal increase in sandy loam and permanently higher levels in silty clay loam, indicating more dissolved-ion retention in the soil profile and less leaching. Compaction and EC were normalized to the maximum measured soil moisture. This study reveals that NBSW has a greater long-term influence on water-loss partitioning and root-zone solute dynamics than it does on long-term soil water storage. Full article

The earlier onset of vegetation phenology and longer growing seasons resulting from global warming are widely recognized as beneficial for enhancing the carbon sink function of terrestrial ecosystems. However, significant uncertainty remains regarding whether the increased growth during the early growing season can be sustained and converted into growth benefits during the later season or even throughout the entire year. This study focuses on vegetation in northern China. Based on solar-induced chlorophyll fluorescence (SIF) data from 2001 to 2020, it establishes an analytical framework for assessing the “seasonal stability” of vegetation growth. The framework quantifies the evolutionary characteristics of early growth enhancement signals during the late growing season. Furthermore, structural equation modeling (SEM) is employed to elucidate the underlying climate-driven mechanisms. The results indicate: (1) Vegetation growth season stability in northern China has long been dominated by the Strong stabilizing type (accounting for 87.4%), suggesting that early growth enhancement signals are mostly attenuated or suppressed during seasonal progression rather than continuously amplified. (2) This stable pattern exhibits a distinct spatial structure at the interannual scale. The expansive and Weak stabilizing types undergo event-driven expansions during specific climatic years, with different vegetation functional types adopting differentiated regulatory strategies during this process. Shallow-rooted grasslands demonstrate higher growth elasticity, while forest vegetation exhibits stronger ecological inertia. (3) Mechanistic analysis reveals that in water-limited zones, enhanced early growth accelerates transpiration processes, thereby disrupting seasonal soil moisture continuity and exacerbating water deficits during the late growing season. This inhibits late-season photosynthesis, constituting a core hydrological–physiological regulatory mechanism that maintains the dominance of Strong stabilizing in the region. Conversely, in energy-limited zones, late-season temperature emerges as the dominant factor constraining sustained growth. This study examines the transmission and modulation mechanisms of early growth signals to the later growing season from the perspective of intra-seasonal dynamics, providing a new analytical approach for incorporating interseasonal processes into assessments of vegetation growth and carbon sink stability in northern China. Full article

Water resources in arid oases are extremely scarce, and the quality of irrigation water and groundwater depth are key factors affecting soil secondary salinization and maintaining high and stable crop yields. This study focuses on the oasis irrigation area of the 38th Regiment in Qiemo County, located in the extremely arid region at the southeastern edge of the Tarim Basin. For the first time, irrigation experiments with different water qualities, ranging from 0.5 to 3.0 g/L, were conducted under varying groundwater depths for multiple crops. Through indoor soil column experiments and numerical simulations of water and salt in the unsaturated zone, the study reveals the water and salt migration patterns in the root zones of watermelon, corn, jujube, and peanuts. It was found that the process of soil water and salt transport exhibits significant differentiation characteristics in the vertical direction, with the surface layer responding most rapidly to changes in moisture and salinity, while the middle and deep layers show certain lag and buffering effects. The study also examined the spatiotemporal distribution trends of soil water and salt under different water quality and quantity irrigation conditions, drawing nonlinear threshold response curves for groundwater depth and determining the optimal groundwater depth under various irrigation conditions. The results indicate: (1) for the four crops under freshwater (0.5 g/L) irrigation and actual irrigation water conditions, soil salinity is safe at groundwater depths of 1–2 m; (2) under slightly saline water (2.0 g/L) irrigation, the safe groundwater depth (GWD) ranges for corn, peanuts, watermelon, and jujube root zones are 3.5–4.2 m, 1.2–3.5 m, ≥2.9 m, and ≥1.6 m, respectively, with crop sensitivity ranking as “corn > peanuts > watermelon > jujube”; and (3) under saline water (3.0 g/L) irrigation, the salinity tolerance thresholds for corn and peanuts root zones are exceeded regardless of shallow or deep groundwater depths, while the upper limits of salinity tolerance thresholds for watermelon and jujube correspond to groundwater depths of 2.9 m and 2.1 m, respectively, with increased groundwater depth making soil salinity increasingly safe. The study proposes a “sensitive-suitable-reinforced” three-zone paradigm and constructs a threshold table for optimal crop layout in arid areas based on the synergistic dual factors of “water quality–water quantity,” providing a theoretical basis for crop layout considering the spatial heterogeneity of groundwater occurrence. This has guiding value for arid oases in addressing the dual stress of water quality deterioration and salinization. Full article

Accurate and real-time monitoring of root soil water content (RSWC) is key in optimizing field irrigation decisions and enhancing crop water productivity. However, relying only on the vegetation index as the input to the inversion model may result in lower inversion accuracy due to the canopy spectral saturation effect. To break through the limitation of a single data source, this study constructed an integrated network model (ATT-LSTM) incorporating the attention mechanism based on the long and short-term memory network (LSTM) to enhance the inversion performance by integrating heterogeneous data from multiple sources. The experiment used canopy spectral data based on UAV remote sensing and weather station monitoring data as input features. A control group was set up for cross-validation to realize the accurate inversion of RSWC in kiwifruit plants. The results show that the coefficient of determination (R²) of the ATT-LSTM model on the test set reaches 0.868. This study confirms that the multi-source data fusion framework effectively overcomes vegetation index saturation, improves rhizosphere moisture monitoring accuracy, supports precision irrigation decisions in kiwifruit orchards, and provides a reference for smart agriculture water management optimization. Full article

Under drip irrigation conditions, the transport pattern of soil water in the root zone directly affects the water use efficiency of crops. The type of soil matrix, initial moisture content, and irrigation water quality jointly determine the hydrodynamic process of water infiltration. However, as a special type of irrigation water, the water movement mechanism of biogas slurry under drip irrigation in soilless cultivation substrates still lacks systematic investigation. In this study, transparent soil column infiltration experiments were conducted using two types of cultivation substrates—organic (coconut coir) and inorganic (desert sand)—under controlled facility conditions. Three initial moisture contents (10%, 15%, and 20%) and two irrigation water qualities (tap water and diluted biogas slurry) were combined to form twelve treatment groups. Soil moisture sensors and visualization techniques were employed to quantitatively analyze the wetting front morphology, vertical and horizontal infiltration rates, wetting ratio, and soil moisture profile distribution under different treatments. The results showed that the initial moisture content significantly influenced the advancement pattern of the wetting front. Higher initial moisture levels promoted the transformation of the wetting front shape from a “semi-pear” form to a “hemispherical” one and reduced the rate of infiltration decline. The coconut coir substrate exhibited stronger vertical infiltration capacity and a central water aggregation characteristic, whereas the desert sand demonstrated a wider horizontal expansion range. Under low and moderate initial moisture conditions, the application of biogas slurry enhanced horizontal water diffusion and improved the uniformity of the wetted zone, with the wetting ratio increasing by more than 6% compared with high moisture conditions. In addition, the power function model provided an excellent fit for the cumulative infiltration process across all treatments (R² > 0.96), indicating its suitability for describing the water transport process in facility cultivation substrates. This study provides theoretical support for precise water and fertilizer management and the efficient utilization of biogas slurry in soilless cultivation systems. Full article

Precision field measurements were conducted to evaluate the mechanism of organic basin mulching on water and thermal dynamics in arid date palm orchards in central Saudi Arabia. Partly mulched zones (20 m radius) and fully mulched basins were compared with adjacent bare soil using micrometeorological sensors and microlysimeters. In partly mulched areas, soil heat flux (G) decreased by 68.3% while sensible heat flux (H) increased up to 86.9% during late spring, indicating enhanced energy redistribution. Bare soil exhibited slightly negative latent heat flux (λE) in early spring, reflecting vapor adsorption, whereas fully mulched basins substantially reduced evaporation, with Water Conservation Efficiency Index (WCEĪ) values of 0.33 in spring and 0.27 in summer, corresponding to 33% and 27% water savings, respectively. Root-zone thermal moderation, quantified by the Root-Zone Thermal Moderation Index (RTMI), confirmed effective buffering of subsurface temperatures by 6–7 °C across 2–10 cm depths, despite slightly elevated surface temperatures. These results demonstrate that basin mulching stabilizes soil moisture, moderates diurnal thermal fluctuations, and optimizes soil–atmosphere energy partitioning under arid conditions. By integrating direct lysimeter measurements with continuous energy flux observations and index-based analysis, this study provides novel, field-based insights into the dual role of organic mulching in enhancing water conservation and thermal regulation in arid date palm orchards. Full article

Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation–machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0–100 cm) for South Africa’s Vhembe District (2017–2022). Five algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)—were calibrated using ~50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R² = 0.96–0.97, RMSE < 0.025 cm³/cm³), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm³/cm³, field capacity = 0.35 cm³/cm³) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84–0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79–94% of maize areas required irrigation during dry years (2017–2019, 2021–2022) versus 32% in wet 2020–2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation. Full article

This study investigated land degradation sensitivity in Southern Tunisia’s Jeffara region and examined the effectiveness of water harvesting techniques (WHTs) as countermeasures. Land Degradation Sensitivity Index was calculated using a modified MEDALUS framework, in which thematic quality indices were derived from normalized indicators (climate, soil, vegetation, and management) and combined through a geometric mean within a GIS environment. The model is validated with field observations. The research found that almost the entire study area (≈99%) was classified as critically sensitive under the baseline scenario. Contributing factors include extreme aridity, limited vegetation cover, significant soil erosion, and human pressures. The most severely degraded areas are found in mountainous zones, desert plains, and mining areas, whereas regions dominated by olive orchards showed moderate sensitivity levels. This lower sensitivity is associated with the drought tolerance and deep root systems of olive trees, which enhance resistance to prolonged dry periods. This study modeled the impact of implementing traditional WHTs, notably Jessour and Tabias. Under this scenario, a clear qualitative improvement was observed, with the proportion of land classified as critical decreasing from 99% to 77.3%, indicating a measurable reduction in land degradation sensitivity associated with the implementation of WHTs. Despite their environmental benefits, such as enhancing soil moisture and stabilizing agricultural yields, the spatial expansion of WHTs remains limited. Full article