Search Results (4,684)

Global water use efficiency (WUE) is a key indicator characterizing the carbon–water coupling relationship in terrestrial ecosystems. Elucidating its spatiotemporal evolution characteristics and driving mechanisms is of great significance for evaluating ecosystem carbon sink functions and formulating water resource management strategies under global change. Based on multi-source datasets integrating satellite remote sensing products and land surface model-derived evapotranspiration (ET) from 1982 to 2018, this study systematically analyzed the spatiotemporal evolution patterns of global WUE and identified the strongest statistical association factors and their spatial distributions, clarifying the key driving factors and spatial distribution characteristics of sustained increases and decreases in global WUE. The results were as follows: (1) From 1982 to 2018, WUE showed an increasing trend in approximately 62.24% of vegetated areas globally, with a significant increase in area accounting for 14.95% (slope ≥ 0.01). By trend type, monotonically increasing and monotonically decreasing areas accounted for 20.96% and 9.36% of global vegetated areas, respectively. (2) Under the combined influence of biotic and climatic factors, leaf area index (LAI) had the strongest correlation with areas of sustained WUE increase, covering the highest proportion (78.55%). When considering only climatic factors, temperature exerted the most significant influence on sustained WUE increase, covering an area of approximately 75.69%. (3) For areas of sustained WUE decrease, climatic factors exhibited the strongest correlation under the combined effects of biotic and climatic factors, accounting for approximately 55.28%, among which temperature contributed the most (approximately 54.09%). When considering only climatic factors, the impact of temperature on sustained WUE decrease rose to 92.42%. This study deepens the understanding of global vegetation carbon–water coupling mechanisms and provides a key scientific basis for identifying regionalized water resource management strategies that enhance ecosystem carbon sink capacity under climate change. Full article

Accurate estimation of crop evapotranspiration (ETc) is fundamental to irrigation planning and sustainable agricultural water management, particularly under increasing climate variability and water scarcity. Conventional flux measurement systems, including eddy covariance towers and lysimeters, provide high-quality observations but are costly, maintenance-intensive, and spatially constrained, limiting their scalability for precision water management. This study presents AquaVolt-AI, a physics-informed machine learning framework that integrates Sentinel-2 optical imagery, NASA ECOSTRESS thermal observations, and meteorological data with the FAO-56 dual crop-coefficient formulation to generate spatially explicit ETc estimates without requiring dedicated on-site sensing infrastructure for routine operation. The framework couples a dynamic residual neural network with physics-based constraints and an automated state-estimation mechanism designed to maintain inference during satellite data gaps and external data-service interruptions. AquaVolt-AI was evaluated over 36 days (28 June–3 August 2026) at the UC Davis Russell Ranch Sustainable Agriculture Facility using ground-based CIMIS observations for validation and ECOSTRESS thermal data as an auxiliary model input. ETc was represented across a 16 × 16 virtual sensing grid comprising 256 spatial sectors at 10 m resolution. The framework achieved a root mean square error of 0.3000 mm day⁻¹ and a mean absolute error of 0.2688 mm day⁻¹. During a consecutive 9-day satellite data gap, the physics-informed state estimator maintained continuous ETc predictions without detectable empirical drift in the evaluated period. These findings demonstrate the feasibility of integrating Earth observation, meteorological information, and physics-informed machine learning within a low-infrastructure computational framework for spatially resolved ETc monitoring. The approach provides a scalable foundation for precision irrigation assessment and data-driven agricultural water management, although broader multi-season and multi-site validation is required to establish transferability across cropping systems and agroclimatic environments. The main novelty of this study is in coupling a bounded residual neural correction to the FAO-56 dual crop coefficient model within a fully serverless architecture, eliminating on-site sensing hardware while preserving physical plausibility during data outages. Full article

Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET₀) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET₀. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET₀ was the dominant predictor, and RF and BT achieved the highest independent-test performance (R² = 0.875; RMSE = 0.398–0.399 mm day⁻¹). Excluding ET₀ reduced predictive performance, with RF, LSBoost, and BT achieving R² values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET₀ was unavailable: R² decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R² values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET₀ provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET₀ is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches. Full article

Frequent droughts seriously threaten rice productivity. Previous studies generally adopted uniform transplanting dates for rice pixels, with relatively limited attention to coexisting cropping systems. This study investigates drought responses of paddy rice ecosystems in the Jianghan Plain from 2000 to 2021 using satellite-derived solar-induced chlorophyll fluorescence (SIF), considering three cropping systems: conventional single-cropping rice (CSCR), double-cropping rice (DCR), and integrated farming of rice and aquaculture animals (IFRA). A pixel-specific phenological matching framework was developed to extract meteorological conditions and SIF values within the actual growing season for each rice pixel, with drought characterized by the standardized precipitation evapotranspiration index (SPEI). Results show that seasonal drought suppressed rice SIF, but the magnitude of decline was not linearly correlated with SPEI. DCR exhibited the greatest SIF reduction, possibly due to two consecutive growing seasons exposed to water stress, whereas IFRA showed the smallest reduction, potentially associated with persistent field water storage. The vegetative stage, particularly during tillering, was most sensitive to drought. Anomalously dry or wet pre-transplant moisture conditions may be associated with greater SIF reduction, while normal moisture conditions tend to produce milder SIF reduction. These findings highlight the importance of cropping system characteristics and phenological stages in modulating rice drought responses, providing insights into drought assessment and water resource management under diverse cropping systems. Full article

Accurate assessment of water status in woody crops is essential for optimizing irrigation management, particularly under Mediterranean conditions characterized by high spatial and temporal variability. Traditional field-based stem water potential measurements are reliable but limited for large-scale operational applications. In this study, a supervised machine learning approach based on Extreme Gradient Boosting was developed to estimate stem water potential in Mediterranean olive orchards by integrating high-resolution PlanetScope multispectral imagery with meteorological variables describing atmospheric evaporative demand. The model was trained and evaluated using a dataset comprising 1628 measurements, curated from 1856 measurements collected during 2021–2025 after temporal matching and quality-control filtering, and 128 predictive features: 44 PlanetScope-derived spectral features and 84 meteorological predictors evaluated across six temporal positions (t0–t5). Model performance was assessed using cross-validation. The model achieved a coefficient of determination of 0.84, a root mean square error of 0.27, and a mean absolute error of 0.21. Air temperature, solar radiation, and reference evapotranspiration were the most influential predictors, while spectral information captured complementary effects related to canopy structure, vegetative vigor, and accumulated physiological responses. Furthermore, the combined use of spectral indices and PlanetScope base-band reflectance values was associated with an approximately 30% lower RMSE than that reported in previous approaches. Full article

Soil moisture (SM) is a key variable in land–atmosphere interactions, but the coarse spatial resolution of existing SM products limits their applications in regional hydrological studies over heterogeneous terrains. This study developed and evaluated a progressive deep-learning (DL) downscaling framework (SE-ResNet) to downscale SMAP L4 SM from 9 km to 250 m over the Loess Plateau, using eight multi-source environmental predictors. The framework was systematically compared with traditional machine learning (ML) models (XGBoost and RF) and its standard counterparts (CNN, ResNet). Results showed that actual evapotranspiration, DEM, and precipitation were identified as the dominant factors controlling SM variability. DL models generally outperformed traditional ML models on the 9 km test dataset, with SE-ResNet achieving the highest accuracy (R = 0.902, RMSE = 0.026 m³/m³). However, in-situ validation showed that 250 m downscaled products did not consistently outperform the original 9 km SMAP SM, with substantial performance differences among stations, but SE-ResNet achieved the lowest Bias among all models. Monthly-scale error analysis further revealed pronounced temporal variations in model prediction uncertainty. Multi-resolution training further demonstrated that the architecture maintains consistently high but gradually decreasing performance when retrained at target resolutions (R = 0.797, 0.785, and 0.772 at 3 km, 1 km, and 250 m, respectively), confirming the presence of scale effects. These findings emphasize the need to consider environmental controls and scale-adaptive strategies when generating high-resolution SM products over complex terrains. Full article

Climate change poses a growing threat to global food security, with staple crop prices serving as a key transmission channel. This study systematically assesses the impacts of climate change on real maize price levels across 39 major producing countries over 1992–2023, using a two-way fixed-effects model with country-specific trends. Climate conditions are captured by growing-season mean temperature, accumulated precipitation, the share of extreme heat days (above 30 °C), and a drought indicator based on the Standardized Precipitation Evapotranspiration Index (SPEI). The benchmark results show a significant U-shaped relationship between growing-season temperature and maize price levels, with a turning point near 15.3 °C, implying that further warming raises prices in most producing countries. Extreme events emerge as the dominant driver: a one-standard-deviation increase in the share of extreme heat days is associated with approximately 11.9% (β = 0.610, p < 0.01) increase in real maize prices, and drier growing seasons are associated with modestly higher price. The heterogeneity analysis reveals that development level buffers the price effects of gradual temperature changes, whereas production capacity does not systematically moderate gradual climate shifts; extreme-heat effects are not moderated by either dimension; and drought effects strengthen with production scale. Extreme-heat price impacts are concentrated in net-exporting countries and thus propagate to world markets. These findings imply three policy pathways: coordinated global climate governance, multi-level price stabilization mechanisms, and country-specific support systems to contain climate-induced increases in maize price levels and safeguard global food security. Full article

Long-term Earth observation has reliably documented vegetation greening, biomass increases, and the mitigation of rocky desertification across the South China karst. However, “greening” alone neither demonstrates an incremental effect of ecological restoration interventions nor substitutes for the verification of functional recovery in hydrology, soils, carbon, biodiversity, and ecosystem resilience. Based on structured searches completed on 10 August 2026 in the Web of Science Core Collection, Scopus, and China National Knowledge Infrastructure (CNKI), which yielded 837 records in total, this review is organized around three evidence chains: the identification of state changes, the attribution of intervention effects, and the verification of ecological functions. The available evidence indicates that (1) optical, microwave, LiDAR, thermal infrared, and UAV observations have clear advantages for monitoring vegetation cover, canopy structure, aboveground biomass, evapotranspiration, and landscape patterns; (2) key functional variables, including soil thickness, soil physicochemical properties, groundwater processes, soil and dissolved carbon, and species composition, can generally only be constrained indirectly and must be jointly validated using field plots, sensor observations, laboratory analyses, and process models; and (3) existing quasi-experimental studies in karst regions show that, where project boundaries, implementation timing, and comparable controls are available, methods such as difference-in-differences can strengthen the credibility of intervention-effect identification, although such evidence remains limited. Future research should prioritize function-oriented multi-source Earth observation products, independent validation across years and catchments, the explicit representation of uncertainty, and testable designs such as BACI and DID, thereby advancing remote sensing from merely documenting surface greening to providing verifiable evidence for judging ecosystem functional recovery. Full article

This study investigates the spatiotemporal variability, spatial consistency, and non-stationarity of meteorological and hydrological droughts in three major water management basins (WMBs) of northern and central Kazakhstan: Nura–Sarysu, Esil, and Tobyl–Torgai. Long-term instrumental observations from meteorological stations and hydrological gauges were used to calculate the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Streamflow Drought Index (SDI). Structural changes were detected using the Pettitt, Buishand, and CUSUM tests, while spatial consistency was quantitatively assessed as the proportion of active meteorological stations simultaneously experiencing drought conditions. The results showed that major drought episodes represented spatially coherent regional events rather than isolated local anomalies; however, their spatial extent varied substantially among the basins. The highest synchronization of meteorological droughts was observed in the Esil WMB, whereas the Tobyl–Torgai WMB exhibited greater spatial heterogeneity. Statistically significant structural changes were identified in both meteorological and hydrological drought series; however, their timing and frequency differed among indices and basins, indicating pronounced temporal non-stationarity. A higher proportion of structural changes was identified for SPI-3 than for SPEI-3. However, this difference is interpreted as reflecting differences in the statistical behavior of the precipitation index and the climatic water balance index rather than direct evidence of the relative contributions of precipitation and atmospheric evaporative demand. SDI-12 exhibited more persistent and temporally smoothed hydrological drought dynamics compared with the short-term meteorological indices, while the timing of hydrological changes differed among river systems. The results demonstrate that drought development in Northern and Central Kazakhstan is characterized by spatial heterogeneity and temporal non-stationarity, while the joint interpretation of SPI, SPEI, and SDI provides a more comprehensive basis for basin-scale drought monitoring and adaptive water-resource management. Full article

Rainwater harvesting (RWH) may contribute to sustainable water management in Mediterranean regions, where water availability and demand are highly seasonal. This study examined the potential for RWH and the reliability of storage at the Kestel Campus of Alanya Alaaddin Keykubat University in Alanya, Türkiye, using monthly climate data from 2000 to 2024. The analysis considered variations in precipitation and air temperature, FAO-56 Penman–Monteith reference evapotranspiration (ET₀), the climatic water balance, campus land cover, potential runoff, non-potable water demand, and six storage-capacity scenarios. The campus area is 231,700 m², of which 35,700 m² represents the potential roof catchment area. The average annual precipitation was 1104.1 mm, and the mean annual ET₀ was 1241.6 mm, resulting in a mean climatic water deficit of 137.5 mm per year. The average theoretical roof runoff was 35,475 m³ per year, and the modelled annual non-potable water demand, including toilet flushing and irrigation of 1 hectare of green space, was about 19,804 m³ per year. With the assumed demand, runoff, and monthly operating conditions, the volumetric reliability rose from 72.3% for a storage capacity of 2500 m³ to 99.0% for 10,000 m³ and reached 100% at 12,500 m³; further increasing the storage to 15,000 m³ did not provide any additional benefit in terms of reliability. These storage capacities should be understood as planning scenarios only, not as design recommendations, since economic feasibility, event-based operation, and water quality were not assessed. The results show that a long-term seasonal water balance analysis is important for planning rainwater harvesting at the campus level in Mediterranean climates. Full article

Quantifying water availability at the national scale remains challenging, particularly where runoff observations are scarce or inconsistent. Hydrological modelling offers a practical solution, and monthly water balance models are especially attractive because of their simplicity and modest data requirements. In this study, the performance of the Thornthwaite–Mather Water Budget ($TM-WB$) model was evaluated for the period of 43 hydrologic years from 1 October 1980 to 30 September 2023, based on twelve watersheds spanning the north–south climatic gradient of mainland Portugal, using precipitation and potential evapotranspiration data derived from the ERA5-Land reanalysis database. Lumped and distributed modelling approaches were tested using contrasting potential evapotranspiration formulations: the data-intensive Penman–Monteith model and the temperature-based Thornthwaite model. The suitability of grid-based available water capacity ($AWC$) as a substitute for the $TM-WB$ model soil water storage conventional parameter ($Smax$) was also assessed. The results show that both evapotranspiration models successfully reproduce the spatial and temporal dynamics of monthly surface runoff; however, the choice of the model influences runoff magnitude at the national scale, with the Thornthwaite model generally overestimating runoff relative to Penman–Monteith. Overall, the Penman–Monteith formulation provided superior performance. Application of the $TM-WB$ model across mainland Portugal showed its ability to reproduce the characteristic north-to-southwest-to-east decrease in runoff and yielded mean annual runoff estimates of 317 or 371 mm/year for Penman–Monteith and Thornthwaite models, respectively, consistent with the Portuguese Environment Agency’s (APA) national estimate of 358 mm/year, although this estimate is now considerably outdated. Furthermore, the $AWC$ proved to be a robust and practical substitute for $Smax$, with a Monte Carlo synthetic generation procedure showing that the runoff estimates are largely insensitive to $AWC$ uncertainty, thus enabling a spatially consistent application of the $TM-WB$ model using globally available soil databases. These findings demonstrate the value of simple water budget models for national-scale water availability assessment, particularly in ground data-limited settings. The simplicity and consistency of the proposed methodology also enable its application at large spatial scales under climatic conditions different from those prevailing today, namely under climate change scenarios. Full article

The Huaihe River Basin (HRB) lies in the climatic transition zone between northern and southern China. Its precipitation shows obvious spatiotemporal heterogeneity, and frequent meteorological droughts seriously threaten regional food and water security. Clarifying the spatiotemporal variations, non-linear abrupt changes and multi-scale driving mechanisms of meteorological droughts in the basin is of great significance for regional drought risk prevention and control, as well as the optimized allocation of water resources. In this study, the one-month time scale Standardized Precipitation Evapotranspiration Index (SPEI-1) was adopted as the primary indicator for quantifying meteorological drought. An analytical workflow integrating Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST), MMK–Hurst coupling trend and persistence discrimination, Three-Threshold Run Theory and Partial Wavelet Coherence (PWC) is constructed. The framework systematically investigates drought spatiotemporal evolution, abrupt change features, persistent trend patterns and climatic driving effects over 1982–2024. The results indicate the following: (1) In 1982–2024, the drought in the whole basin showed a slight aggravating trend, with an average drought trend rate of −0.000387. Spatially, this trend varied, with faster progression in the west and slower in the east, and more severe conditions in the west and milder conditions in the east. (2) 1988 was the driest year within the study period, with three drought peaks occurring in April, June and November. Annual mean SPEI-1 values indicated the severest drought in the Yishu-Si River system (YSR, −0.62), followed by the Shandong Peninsula and Coastal River systems (SPCR, −0.56), Huai River Mainstream River system (HRMR, −0.55), and the Lixia River system (LR, −0.52). (3) The probability that the mutation point for the seasonal component of the SPEI occurred in March 2001 was 74%, whilst the probability that the potential mutation signal for the trend component occurred in September 1998 was 44.1%. (4) Within the HRB, droughts covering over 95% of the basin intensified in spring and autumn. A mean Hurst index of 0.70 implies overall persistent drought evolution. MMK–Hurst coupled analysis revealed that the basin was predominantly dominated by mild, non-significant, persistent drought. (5) The typical cross-seasonal drought event of 1998–1999 exhibited multi-stage fluctuations, with the central and western hilly regions constituting the core cluster of extreme droughts. During this event, areas experiencing moderate drought accounted for 41.79%, whilst those experiencing extreme drought accounted for only 1.47%, and drought intensity diminished progressively from west to east. (6) Air-specific humidity (AH) constitutes an Average Wavelet Coherence (AWC) of 0.94 and a Percentage of Significant Power (POSP) of 12.20%. AH achieves the highest total POSP with only a marginal advantage relative to SM. The multi-method coupled analysis framework established in this study provides theoretical support for regionalized drought early warning, water resource regulation, and disaster prevention and mitigation in the HRB. Full article

Water availability is typically a limiting factor in rainfed sub-Saharan farming systems. Smallholder farmers in this region grow neglected and underutilized crops (NUCs) such as taro to supplement food shortages. Understanding the water use of NUCs could contribute to improving productivity. Traditional methods of monitoring actual evapotranspiration (ETa) are point-based and do not capture the variability in smallholder fields. In contrast, empirical models that use vegetation indices derived from multispectral unmanned aerial vehicles (UAVs) have demonstrated the potential to provide reliable spatial variability of ETa. This study evaluated the viability of a VI-based empirical model to estimate the ETa of taro under different weed management practices in a smallholder farm using multispectral UAV imagery. Three treatments were tested: a consistently weeded plot, a plot that remained unweeded throughout the season, and an intermediate field that was partially managed for a short period after planting. A validated VI-based model that used the enhanced vegetation index (EVI2) as a proxy for the crop coefficient (Kc) was combined with reference evapotranspiration (ETo) to determine ETa on the three taro plots. The results demonstrated that the weeded plot used 27.0% less water, while the unweeded field used 35.5% more water than the intermediate field (representing the farmer’s typical integrated weed management practices throughout the growing season). Although ETa in the unweeded plot represents combined evapotranspiration from taro, weeds, and the soil surface rather than taro water use alone, the findings support an association between reduced crop–weed competition, lower evapotranspiration, and increased taro yield. The VI-based empirical models derived from multispectral UAV imagery showed great potential for promoting water use efficiency through integrated weed management (IWM) in smallholder farms. Full article

Land use/cover change (LUCC) can alter watershed water balance, while whole-watershed averages may obscure spatial heterogeneity among sub-basins. This study evaluated LUCC-induced hydrological responses in the northern region of Longyou County, Zhejiang Province, China, a subtropical humid hilly area. Land use maps for 2008, 2013, 2018, and 2023 were used to construct four SWAT scenarios under identical meteorological forcing, and partial least squares regression (PLSR) was applied to examine associations between land-cover changes and modeled hydrological responses. The classified maps indicated decreases in cropland and barren land and increases in forest and settlement between 2008 and 2023. Scenario simulations showed lower water yield and higher evapotranspiration under LU2023 than LU2008; these responses were robust, whereas surface runoff, lateral flow, and groundwater contribution were more model-sensitive. At the sub-basin scale, greater forest expansion was associated with larger decreases in surface runoff and water yield and greater increases in evapotranspiration. PLSR further identified forest as the most influential land use predictor for these three responses, although model explanatory power was moderate. These results highlight spatial heterogeneity in modeled LUCC responses and the value of sub-basin analysis for water management. Full article

Accurately characterizing groundwater storage variations and identifying their dominant drivers are essential for regional groundwater assessment and sustainable management. This study focuses on the Heilongjiang (Amur) River Basin and constructs a monthly groundwater storage anomaly (GWSA) series for 2003–2022 using GRACE/GRACE-FO, GLDAS, and multiple environmental datasets. The spatiotemporal evolution of GWSA was analyzed, and groundwater sustainability was further evaluated. On this basis, an XGBoost model with five-fold year-grouped cross-validation was developed, and SHAP and lagged correlation analyses were combined to quantify the model-based importance of the main environmental factors and characterize their nonlinear predictive relationships with GWSA, interaction patterns, and lagged responses. The results showed that basin-averaged GWSA exhibited a significant declining trend during the study period, with groundwater deficits becoming markedly more pronounced after 2017, while substantial spatial heterogeneity was observed among the sub-basins. The groundwater system exhibited relatively low overall sustainability and limited recovery capacity following groundwater deficits. The XGBoost model showed relatively stable performance in characterizing GWSA variations, with a correlation coefficient of 0.660 and an RMSE of 48.54 mm between the pooled predictions from the five test folds and the GRACE-derived GWSA. TreeSHAP analysis showed that precipitation had the highest relative SHAP importance (33.15%), while evapotranspiration, runoff, air temperature, land use, and snowmelt also exhibited varying degrees of SHAP importance and pronounced nonlinear patterns and interaction characteristics. Snowmelt and GWSA exhibited a seasonal timing offset, with a pronounced time–frequency association around the annual scale. GWSA remained elevated after spring thaw, and the annual frozen fraction decreased significantly during 2003–2022. The results provide scientific support for sustainable groundwater management and engineering planning in cold regions. Full article