Search Results (649)

Terrestrial evapotranspiration (ET) plays a vital role in the water cycle, comprising components such as transpiration, interception loss, bare-soil and open-water evaporation, etc. This study has validated the GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) product with eddy covariance ET data. The spatiotemporal variations in total ET and its components in China during 1980–2024, derived from the GLEAM model, and their relations with air temperature, precipitation and solar radiation in the context of climate change have been studied. During the study period, a significant increase in total ET was found over the southeast of China, especially in spring and summer. The different ET components showed somewhat different trends. While transpiration and interception losses increased significantly in humid and transitional zones, bare-soil evaporation declined markedly in humid regions but remained stable or increased slightly in the northwest and the Tibetan Plateau. Precipitation accounts for the largest share of total ET variability in arid regions, whereas transpiration in humid regions shows the strongest association with available energy. In transitional zones and the Tibetan Plateau, total ET reflects the synergistic regulation of both water and energy availability. Recent enhancements in total ET are primarily associated with rising precipitation in the Tibetan plateau and increasing air temperature in transitional zones. Full article

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the Berrechid plain represents a relevant case study illustrating both the practical and theoretical challenges of groundwater governance. The aquifer is heavily exploited to satisfy agricultural, industrial, and domestic needs. This study develops a hybrid “grey-box” modeling approach for predicting groundwater depth (GWD) fluctuations under climate change (CC). Unlike conventional black-box machine learning models, our framework combines a deterministic physical engine with a stochastic machine learning corrector. The physical component simulates aquifer mass balance using the Hargreaves method for evapotranspiration, linear drainage, climate memory via exponential decay, and an anthropogenic trend parameter (xi). The machine learning component—XGBoost with quantile regression—is trained exclusively on physical model residuals and predicts the 5th, 50th, and 95th percentiles, providing explicit 90% confidence intervals. Hydrological states (dry, normal, wet) are identified via K-means clustering for context-aware correction. The model is calibrated using historical data (1972–2019) and validated using blocked time-series cross-validation. Climate projections under the RCP 4.5 and RCP 8.5 scenarios were used to forecast GWD up to 2100. At piezometer 3933/20, the best performance was achieved, with an RMSE of 0.347 m and a KGE of 0.742 during the validation period. The proposed approach is suitable for seasonal GWD forecasting and offers practical value for water managers and decision-makers in the Berrechid region. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

Saline irrigation is increasingly practiced in semi-arid regions to cope with freshwater scarcity; however, it strongly affects crop growth, water use, and soil salinity. This study aims to calibrate and validate the AquaCrop model to simulate key growth parameters of winter wheat (cv. Achtar) under saline irrigation conditions in the Tadla Plain, Morocco, focusing on canopy cover (CC), actual evapotranspiration (ETa), soil water content (SWC), biomass (B), and grain yield (GY). The model was first calibrated using observed data from the 2023 growing season and subsequently validated using data from the 2022 growing season. Overall, AquaCrop effectively reproduced crop growth during both calibration and validation phases. During calibration, canopy cover was accurately simulated, with average RMSE values below 1%, while biomass and grain yield were also well reproduced, with low RMSE values (0.25 t ha⁻¹ for B and 0.10 t ha⁻¹ for GY), confirming the robustness of the calibrated parameters. The model also performed well in simulating ETa and SWC, capturing the seasonal dynamics of crop water use and soil moisture. During validation, ETa was satisfactorily reproduced, with an RMSE of approximately 0.80 mm day⁻¹, while SWC showed good agreement with observations, with NRMSE values ranging from 7.9 to 10.5%. Grain yield and biomass were reliably predicted, with NRMSE values below 4%. These results demonstrate that AquaCrop is a reliable tool for simulating winter wheat under saline irrigation and for assessing crop response under salt-affected conditions, providing an integrated evaluation of crop performance, water use, and soil salinity dynamics to support improved irrigation management and water-use efficiency under semi-arid conditions. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems. Full article

In the future, agriculture will need better irrigation management options to produce more food and decrease its air and water pollution contributions. Hydroponic systems conserve water over field production, but up to 50% of applied irrigation could be discharged from open-drain systems. TVPD is an evapotranspiration model developed for greenhouse production, particularly for hydroponics. In this study, we calibrate and evaluate TVPD on environmental and evapotranspiration data from hydroponic tomato production and compare predictions to those of random forest (RF) and K-nearest neighbors (KNN). Using five time-ordered data splits, we sought to gauge prediction accuracy for data-limited settings, where the model needs to be implemented with the least calibration time possible, and we evaluated TVPD, RF, and KNN with a 10-fold cross-validation to assess overall model robustness. Across the five data splits, TVPD produced more accurate predictions (r²: 0.86 to 0.90; RMSE: 0.1739 to 0.5796 L tray⁻¹) than RF (r²: 0.06 to 0.73; RMSE: 0.7354 to 2.0505 L tray⁻¹) and KNN (r²: 0.06 to 0.59; RMSE: 0.7694 to 1.7090 L tray⁻¹). With calibration on only the first five days of data, TVPD was able to produce acceptable predictions (r² = 0.87, RMSE = 0.5796 L tray⁻¹). The mean r² for a 10-fold cross-validation was 0.81 for TVPD, 0.88 for RF and 0.81 for KNN, and mean RMSE values were slightly better for the cross-validation for RF (0.4970 L tray⁻¹) and KNN (0.4968 L tray⁻¹) than for TVPD (0.5922 L tray⁻¹). Overall, TVPD could be a useful model to predict evapotranspiration for irrigation management and could decrease the volume of discharged hydroponic waste solution. Full article

Vegetation change in cryosphere-affected mountain basins reflects interacting climate and human pressures but their relative influence remains uncertain in the Upper Indus Basin. The novelty of this study is the integration of satellite vegetation, climate variables, human pressure indicators, residual attribution and diagnostic validation in a data-scarce high-mountain basin. We evaluated growing-season Normalized Difference Vegetation Index dynamics and associated drivers from 2001 to 2023 using trend analysis, correlation, Random Forest diagnostics, Sentinel 2 validation, and residual trend analysis. The results showed widespread greening across 96.59% of the basin, with stronger improvement in the lower and central areas. Significant greening covered 69.94% of the basin, while only 1.55% showed significant browning. Precipitation and temperature were predominantly positive drivers of vegetation change, whereas potential evapotranspiration and solar radiation were mostly negative. Soil moisture played a strong regulatory role along elevation gradients. Residual trend analysis provided approximate and method-dependent estimates of the possible anthropogenic influence on vegetation change at 73.09% and climatic drivers at 26.91% rather than direct causal decomposition. These values are approximate and method-dependent estimates, not direct causal decomposition. The findings highlight human-related greening in lower valleys and climate-controlled vegetation responses in high-mountain areas. Full article

The use of sophisticated software for greenhouse microclimate analysis often requires advanced modelling expertise and significant computational effort, which may not always be available to greenhouse designers. This study proposes an integrated and modular workflow aimed at supporting greenhouse design through coupled thermal and evapotranspiration simulations. The design methodology is based on three steps. In the initial phase, the greenhouse environmental conditions are evaluated through the implementation of a dynamic thermal analysis, which is conducted by the DesignBuilder software (version 4.2). Subsequently, a plant evapotranspiration model is employed in MATLAB/Simulink (version R2025b) to evaluate crop transpiration, moisture production, and irrigation water consumption. In the final phase, the simulated moisture production is used to estimate the required ventilation rates and to support the sizing of greenhouse systems, including irrigation and HVAC components. Plant moisture production is a crucial factor in determining the sizing of greenhouse subsystems, such as the irrigation system, the ventilation rate, and the HVAC system. Nonetheless, the implementation of the evapotranspiration model necessitates a bespoke calibration to a case study. Indeed, the proposed models are more generally applicable and must be adapted to real-world applications. The methodology was applied to a small greenhouse used for the cultivation of aeroponic lettuce (Lactuca sativa cv. Romana) in a Mediterranean environment. The aim of the study was to explore the potential of the proposed integrated modelling framework to estimate annual irrigation water demand and the minimum ventilation rate required to mitigate excess moisture production, using a coupled MATLAB/Simulink implementation. The proposed approach should be interpreted as an exploratory design-support methodology rather than a fully validated predictive model, intended to investigate system behaviour under the specific conditions of the case study. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

Summer maize (Zea mays L.) production on the North China Plain is highly dependent on variable seasonal rainfall, which increases the likelihood that inappropriate water and nitrogen allocation will cause yield fluctuations and ecological and environmental risks. Previous studies have mainly relied on single-site field comparisons or basic statistical evaluation methods, limiting the understanding of the dynamic response mechanisms of drought stress coupled with nitrogen application during the jointing and grain-filling stages. Based on field experiments conducted in 2024–2025, the DSSAT model was used to simulate aboveground dry matter accumulation (CWAM), grain yield, leaf area index (LAI), dry matter evapotranspiration productivity (DMPEM), and dry matter nitrogen productivity (DPNAM) of summer maize under different water–nitrogen treatments at different growth stages. Then, historical meteorological data for Henan Province from 2003 to 2023 were imported. The years were classified into three hydrological year types: wet years, normal years, and dry years. Subsequently, Principal Component Analysis (PCA), the TOPSIS method, and the Rank-Sum Ratio (RSR) method were employed to construct a multidimensional evaluation system for assessing water and nitrogen management strategies under different hydrological year types. The results showed that the nitrogen application rate had a significant regulatory effect on yield, DPNAM, and DMPEM. All three initially increased and then decreased as the nitrogen application rate rose, with the optimal performance observed under the normal nitrogen (N2) treatment. Under drought conditions during the same growth stage, the increase in the maximum yield under the N2 treatment was approximately 8.1% and 50% higher than that under the high-nitrogen (N1) and low-nitrogen (N3) treatments, respectively. Compared with drought during the grain-filling stage, drought during the jointing stage had a smaller negative effect on CWAM and LAI. A comprehensive evaluation with long-term meteorological data reflects that drought during the jointing stage combined with normal nitrogen (Q2) is the optimal water–nitrogen management strategy for wet years (with an RSR value of 0.994). The treatments of drought during the jointing stage combined with high nitrogen (Q1) and drought during the grain-filling stage combined with normal nitrogen (H2) reveal greater adaptability and favorable universality across different hydrological year types. The model’s reliability under various water–nitrogen coupling conditions was validated by integrating field experiments, DSSAT model simulations, and a multidimensional evaluation system. This study lays a scientific theoretical foundation for achieving high and stable yields in summer maize under different water–nitrogen coupling conditions and across various hydrological year scenarios. Full article

Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr⁻¹ from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution. Full article

The two-source energy balance model pyTSEB-PT was used to model latent heat fluxes from sunflower and wheat crops before senescence, grown on the same field in consecutive years. Input maps for the pyTSEB model were prepared using UAV-acquired multispectral/thermal imagery and ground control canopy leaf angle distribution (χ) and leaf area index (LAI) estimations based on canopy light transmission measurements by linear ceptometers. The modelled sensible and latent heat fluxes (H_pyTSEB, LE_pyTSEB) were validated against eddy covariance-measured respective fluxes (H_eddy, LE_eddy). Actual χ (χ_a) was estimated from 2 h courses of canopy light transmission values and ranged between 0.5 and 1.2 for wheat and between 2.8 and 5.8 for sunflower crops, respectively, affecting canopy light extinction coefficients (k) and LAI in both crops compared to the case of the generally assumed spherical leaf angle distribution (χ = 1). Vegetation cover fraction (f_c) was 3.4% smaller in wheat when using χ_a instead of χ₁, but this led to only minor—though significant—changes in modelled T_can, T_soil and canopy and surface resistances. The effect of leaf angle distribution on the combined validation of sensible and latent heat flux data was shown primarily in sunflower due to the decrease in sensible heat flux error, while validation improvement was not detectable in the case of wheat. Using field-calibrated thermal images instead of uncalibrated ones strongly improved validation results (fit of modelled vs. measured sensible and latent heat fluxes), showing the necessity of field calibration of the thermal camera when the data are used for vegetation energy balance modelling. Full article

Accurate hourly crop evapotranspiration (ETc) estimation is important for data-driven irrigation management support in tropical horticulture, yet existing approaches are constrained by data requirements and an inability to capture multi-scale temporal dynamics. This study proposes a Transformer encoder model for one-step-ahead hourly FAO-56 PM-derived ETc estimation in a durian orchard in Chanthaburi Province, Eastern Thailand, using 36,528 hourly meteorological observations obtained from the Visual Crossing Weather API for the orchard location over four years, with ETc computed from these inputs using the FAO-56 Penman–Monteith equation. The model employs a 168-h (7-day) look-back window, three stacked encoder blocks with multi-head self-attention ($h=8$, $d_{\mathrm{model}}=128$), and five meteorological input features (air temperature, relative humidity, solar radiation, wind speed, and ETc). A SARIMA$(2,1,2){(1,0,0)}_{24}$ model trained on the same dataset served as the statistical baseline. The Transformer achieved an RMSE of 0.0308 mm/h, MAE of 0.0188 mm/h, and $R^2$ of 0.9018 on the 168-h test set, outperforming SARIMA (RMSE = 0.0717, MAE = 0.0593, $R^2$ = 0.4688), representing a 57.0% reduction in RMSE, a 68.3% reduction in MAE, and a 92.4% improvement in $R^2$. The Transformer also achieved a daytime-only RMSE of 0.0414 mm/h vs. 0.0791 mm/h for SARIMA, and a daily cumulative ETc MAE of 0.1599 mm/day vs. 0.5901 mm/day, demonstrating superior accuracy during agronomically critical periods. The Transformer accurately reproduced both the 24-h diurnal cycle and the 7-day weekly pattern of ETc, whereas SARIMA exhibited a damped amplitude response. A recursive 168-h heuristic simulation demonstrated that the model generates physically plausible ETc patterns under an approximated meteorological scenario, suggesting the approach warrants further investigation as a component of future irrigation decision-support research. These results highlight the potential of Transformer-based deep learning for site-specific, proof-of-concept ETc estimation from meteorological inputs in tropical fruit production, pending validation across diverse sites and seasons. Full article

Agricultural producers need affordable tools to estimate reference evapotranspiration (ET₀) in field conditions, especially in regions with limited access to complete weather data. In this study, the ET₀ for an alfalfa (Medicago sativa) crop was estimated using the SEG-SRM-V1 electronic system, based on the surface renewal method and validated using the FAO-56 Penman–Monteith method. The estimated ET₀ values for alfalfa ranged from approximately 4.0 to 8.5 mm day⁻¹ under the prevailing climatic conditions: periods of high temperature (21 °C to 33 °C), as measured by the system in the experimental area, with cloud cover, wind (1 to 8 m/s) and net radiation of 664 W/m² to 910 W/m². Comparisons between the two methods yielded determination coefficients of between 0.65 and 0.85, and the values of the errors (MSE, RMSE and MAE) tend to 0, which indicates that the estimates of ET₀ measured by the system (SEG-SRM-V1) are close to those obtained using the Penman–Monteith method. Similarly to the performance of open-field systems operating under atmospheric and vegetation cover conditions, these results demonstrate that high-frequency (10 Hz) air temperature measurements provide sufficient physical information to support the estimation of ET₀ in alfalfa, while the open architecture of the SEG-SRM-V1 system allows for flexibility and scalability for irrigation management applications in other crop types. Full article