Search Results (521)

In order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), based on the Fourier transform and the Random Forest algorithm was developed to predict winter wheat yield. Matrix multiplication in Fourier space was performed to predict yield, while the Random Forest algorithm was employed to quantify the contribution of various yield factors to winter wheat yield. The combined model effectively captured the dynamic dependencies between yield factors and time series, improving predictive accuracy by 5.00%, 10.00%, and 27.00%, and reducing the root mean square error by 26.26%, 29.31%, and 88.20%, respectively, compared to the StemGNN, Informer, and Random Forest models. The predicted outputs ranged from 520 to 720 g/m², with an average error of 2.69% compared to the actual measure outputs. Under the insufficient real-time irrigation mode, winter wheat yield was highest at 90% irrigation upper limit and 70% irrigation lower limit, with a medium fertilization level (850 mg/kg). The yield showed an overall decreasing trend as both irrigation limits and fertilizer application decreased. Rainfall and soil moisture were the most significant factors influencing winter wheat yield, followed by air temperature and evapotranspiration. Solar radiation and sunshine duration had the least impact. The results of this study provide a valuable reference for accurately predicting winter wheat yield. Full article

In arid regions, water scarcity necessitates reliance on surface runoff as a vital water source. Studying the impact of climate change on surface runoff can provide a scientific basis for optimizing water use and ensuring water security. This study investigated runoff patterns in the upper-middle Amu Darya River (UADR) from 1960 to 2015. Special emphasis was placed on the effects of climatic factors and the role of major atmospheric circulation indices, such as the Eurasian Zonal Circulation Index (EZI), Niño 3.4, and the Indian Ocean Dipole (IOD). The results show a significant linear decreasing annual trend in runoff at a rate of 2.5 × 10⁸ m³/year, with an abrupt change in 1972. Runoff exhibited periodic characteristics at 8–16 and 32–64 months. At the 8–16-month scale, runoff was primarily influenced by precipitation (PRE), actual evapotranspiration (AET), and snow water equivalent (SWE), and, at the 32–64-month scale, Niño 3.4 guided changes in runoff. In addition, El Niño 3.4 interacted with the EZI and IOD, which, together, influence runoff at the UADR. This study highlights the importance of considering multiple factors and their interactions when predicting runoff variations and developing water resource management strategies in the UADR Basin. The analysis of nonlinear runoff dynamics in conjunction with multiscale climate factors provides a theoretical basis for the management of water, land, and ecosystems in the Amu Darya Basin. Full article

The ecosystems of the Yarlung Tsangpo River Basin (YTRB) are fragile and sensitive to climate change, so an in-depth analysis of the relationship between the vegetation dynamics in the YTRB and climate change is crucial to understand regional and global climate change. This study quantified the spatial and temporal characteristics of the vegetation cover and meteorological elements in the YTRB over the past four decades. The evapotranspiration data were corrected by combining the characteristics of the vegetation in the region in order to systematically explore the relationship between the vegetation change and climate change response in the YTRB. The results indicated that the fractional vegetation cover (FVC), air temperature (ATEM) and precipitation (PRE) showed a significant increase during 1981–2020, with a variable speed of 0.05/10a, 0.38 °C/10a, and 13.3 mm/10a. The actual evapotranspiration (AET) decreased significantly (32.8 mm/10a). There were positive effects of the increased ATEM and decreased AET on the increase in FVC, with ATEM as the leading factor of influence. After excluding the influence of other factors, the degree of influence of PRE on FVC increased to 2.5 times of the original, and the AET increased by 28.57%. The three climate factors synergistically contribute to the positive development of FVC in 47.43% of the upper and middle sections of the YTRB. Full article

How to quickly and accurately obtain the basal crop coefficient is the key to estimating evapotranspiration in sparse vegetation. To enhance the accuracy of vineyard evapotranspiration estimation in the subhumid region of Northwest China, this study utilized the actual evapotranspiration (ET_c) measured by the Bowen ratio system as the reference standard. The reference crop evapotranspiration (ET_o) was calculated using the Penman formula, and the grape crop coefficient (K_c) was subsequently derived. The FAO-56 dual crop coefficient method was then employed to determine the soil evaporation coefficient (K_e) and the water stress coefficient (K_s), leading to the acquisition of the basal crop coefficient (K_cb). Concurrently, multispectral remote sensing images captured by unmanned aerial vehicle (UAV) were used to gather grape spectral data, from which the reflectance of multiple bands was extracted to compute four vegetation indices: the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Ratio Vegetation Index (RVI), and the Difference Vegetation Index (DVI). Relationship models between the grape basal crop coefficient (K_cb) and these vegetation indices were established using univariate linear regression, polynomial regression, and multiple linear regression. These models were then used to estimate vineyard evapotranspiration and validate the accuracy of the UAV multispectral remote sensing in estimating the grape K_cb. The results indicated that: (1) The growth stage, type of vegetation index, and modeling method were three significant factors influencing the fitting accuracies of the relationship models between the grape basal crop coefficient (K_cb) and vegetation indices. These model fitting accuracies had a notable impact on the estimation accuracies of evapotranspiration. (2) The application of UAV-based multispectral remote sensing to estimate the grape basal crop coefficient in the subhumid region of Northwest China was feasible. Compared to the K_cb values recommended by the FAO-56, the K_cb values derived from the UAV data improved the estimation accuracies of evapotranspiration by more than 11% in 2021 and 13% in 2022. Full article

This study investigated the relative contributions of natural and anthropogenic factors to the nutrient status of 33 representative lakes and reservoirs in the Yangtze River Basin. Using national water quality monitoring data, remote sensing imagery, Geographic Information System, (GIS), Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and Redundancy Analysis (RDA), we analyzed the Spatiotemporal differences of total nitrogen (TN), total phosphorus (TP), the ratio of TN to TP (TN/TP), trophic level index (TLI), and habitat quality (HQ). Results revealed significant spatial heterogeneity in lake nutrient status, with upstream reservoirs exhibiting better water quality than their midstream and downstream counterparts. Over time, there is a decreasing trend in nutrient loads in lakes and reservoirs, yet the risk of eutrophication remains high. The middle and lower reaches of lakes and reservoirs face more severe eutrophication pressure. The contribution rates of natural factors and human activities to TN and TP in lakes and reservoirs are 19.1% and 35.0%, respectively. The main driving factors are livestock and poultry breeding volume, habitat quality, and urbanization, with contribution rates of 13.0%, 9.8%, and 0.2%, respectively. The contribution rates of natural factors and human activities to TN/TP and TLI of lakes and reservoirs are 19.8% and 15.5%, respectively. Actual Evapotranspiration (7.8%), habitat quality (7.3%), and hydraulic retention time (3.1%) were key drivers for the shifts of TN/TP and TLI. Management strategies should therefore control agricultural nitrogen fertilizer inputs upstream, industrial and agricultural non-point source pollution in the midstream, and enhanced wastewater treatment alongside population density and economic development control in the downstream areas. This research provides a crucial scientific basis for the ecological environment protection and sustainable utilization of water resources in the Yangtze River Basin. Full article

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R²) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture. Full article

Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to practical constraints. This paper introduces geeSSEBI, a Google Earth Engine implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, for deriving ET from Landsat data and ERA5-land radiation. The source code and a graphical user interface implemented as a Google Earth Engine application are provided. The model ran on 871 images, and the estimates were evaluated against multiyear data of four eddy covariance towers belonging to the ICOS network, representative of both forests and agricultural landscapes. The model showed an RMSE of approximately 1 mm/day, and a significant correlation with the observed values, at all the sites. A procedure to upscale the data to monthly is proposed and tested as well, and its accuracy evaluated. Overall, the model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales. This implementation is particularly valuable for mapping evapotranspiration in data-scarce environments by utilizing Landsat archives and ERA5-land radiation estimates. Full article

The cultivation of Japanese plums (Prunus salicina Lindl.) in South Africa has increased over the years, yet their water use is unknown. Their cultivation in the Western Cape Province of South Africa is highly dependent on supplementary irrigation, indicating their high water use demand. This study used remote sensing techniques to estimate the actual evapotranspiration (ET_{c act}) of the Japanese plums to assess their water use on a large scale. The accuracy of the procedure had to be validated before getting to tangible conclusions. The eddy covariance was used to measure ET_c act in an African Delight plum orchard to validate the FruitLook remote sensing data for the 2023–2024 hydrological year and irrigation season. The seasonal and annual plum crop water requirements measured using the eddy covariance system were 751 and 996 mm, while those estimated by FruitLook were 744 and 948 mm, respectively. Although FruitLook slightly underestimated plum ET_{c act} by a Pbias of −6.15%, it performed well with a Nash–Sutcliffe efficiency (NSE) of 0.91. FruitLook underestimated evapotranspiration mainly during the peak summer season with full vegetation cover when the model may inaccurately represent irrigation impacts, soil moisture availability, and localized advection effects, better captured by the eddy covariance system. Based on the results, FruitLook proved to be sufficiently accurate for large-scale applications to estimate evapotranspiration in Japanese plum orchards in the Western Cape. Full article

Water scarcity, exacerbated by climate change and increasing agricultural water demands, highlights the necessity for efficient irrigation management. This study focused on estimating actual evapotranspiration (ETa) in watermelons under semi-arid Mediterranean conditions by integrating high-resolution satellite imagery and agro-meteorological data. Field experiments were conducted in Rutigliano, southern Italy, over a 2.80 ha area. ETa was measured with the eddy covariance (EC) technique and predicted using machine learning models. Multispectral reflectance data from Planet SuperDove satellites and local meteorological records were used as predictors. Partial least squares, the generalized linear model and three machine learning algorithms (Random Forest, Elastic Net, and Support Vector Machine) were evaluated. Random Forest yielded the highest predictive accuracy with an average R² of 0.74, RMSE of 0.577 mm, and MBE of 0.03 mm. Model interpretability was performed through permutation importance and SHAP, identifying the near-infrared and red spectral bands, average daily temperature, and relative humidity as key predictors. This integrated approach could provide a scalable, precise method for watermelon ETa estimation, supporting data-driven irrigation management and improving water use efficiency in Mediterranean horticultural systems. Full article

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions. Full article

Accurate and precise prediction of actual evapotranspiration (AET) on a large scale is a fundamental issue in natural sciences such as forestry (especially in species selection and planning), hydrology, and agriculture. With the estimation of AET, controlling dams, agriculture, and irrigation and providing potable and utility water supply for industry would be possible. Gathering reliable AET data is possible only with a sufficient weather station network, which is rarely established in many countries like Turkey. Therefore, climate models must be developed for reliable AET data, especially in countries with complex terrains. This study aimed to generate spatiotemporal AET surfaces using the Australian National University spline (ANUSPLIN) model and compare the results with the maps generated by the inverse distance weighting (IDW) and co-kriging (KRG) interpolation techniques. Findings from the interpolated surfaces were validated in three ways: (1) some diagnostics from the surface fitting model include measures such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and the estimated standard deviation of noise in the spline; (2) a comparison of common error statistics between the interpolated surfaces and withheld climate data; and (3) evaluation by comparing model results with other interpolation methods using metrics such as mean absolute error, mean error, root mean square error, and adjusted R² (R²_adj). The correlation between AET and normalized difference vegetation index (NDVI) was also evaluated. ANUSPLIN outperformed the other techniques, accounting for 73 to 94% (RMSE: 3.7 to 26.1%) of the seasonal variation in AET with an annual value of 83% (RMSE: 10.0%). The correlation coefficient between observed and predicted AET based on NDVI ranged from 0.49 to 0.71 for point-based and 0.62 to 0.83 for polygon-based data. The generated maps at a spatial resolution of 0.005° × 0.005° could provide valuable insights to researchers and practitioners in the natural resources management domain. Full article

In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the Qinglongshan Irrigation Area (QLS). We aimed to assess the impacts of climate and land use (LULC) changes between 1980 and 2020 on several hydrological parameters in the QLS, including actual evapotranspiration (ET), soil water (SW), soil recharge to groundwater (PERC), surface runoff (SURQ), groundwater runoff (GWQ), and lateral runoff (LATQ). We predicted the trends in hydrological factors from 2021 to 2050. Based on the S1 scenario, the precipitation and the paddy field area decreased by 42.28 mm and 1717.65 km², respectively; hydrological factors increased by 91.53, 104.28, 50.66, 21.86, 55.93, and 0.79 mm, respectively, in the QLS. Climate changes contributed 6.10%, −7.58%, −54.11%, 26.90%, −121.17%, and −31.66% to changes in hydrological factors, respectively; LULC changes contributed −2.19%, 3.63%, 11.61%, −2.93%, 25.89%, and 16.86%, respectively; and irrigation water volume changes contributed 96.09%, 103.95%, 142.50%, 76.03%, 195.28%, and 114.80%, respectively. Irrigation and water intake were the main factors affecting the changes in hydrological elements. This was followed by climatic changes and LULC. In natural development scenarios, the QLS is anticipated to face challenges, including increased actual ET, reduced seepage and groundwater contribution, and declining groundwater levels. Full article

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies. Full article

The effective characterization of landscape water balance components—evapotranspiration, runoff, recharge, and soil storage—is critical for understanding the integrated effects of the water balance on vegetation dynamics, water availability, and associated environmental responses to climate change. An improved parameterization of these components can improve assessments of landscape stress and provide useful insights for predicting and managing vegetation responses to climate change. Hydrology models typically are not able to address water availability below the mapped soil profile, but we refined a landscape hydrology model, the Basin Characterization Model, by balancing measures of actual evapotranspiration (AET) with modeled subsurface soil water holding capacity, including bedrock storage. The purpose of this study was to characterize the effective rooting depth (the depth of soil and bedrock storage required to support AET) for 35 native vegetation types in California in order to quantify soil and bedrock water use, which ranged from 0 to 3.1 m for most vegetation types, exceeding mapped soil depths. This resulted in the quantification of bedrock water use, increasing available water 67% over that calculated by mapped soils alone. We found that mid-elevation vegetation types with lower water and energy limitations have the highest evapotranspiration rates and deepest effective rooting depth. We also evaluated the resilience to drought with this more spatially realistic characterization of water and vegetation interactions. Full article

The Gravity Recovery and Climate Experiment (GRACE) enables large-scale monitoring of terrestrial water storage changes, significantly contributing to hydrology and related fields. However, the coarse resolution of groundwater storage anomaly (GWSA) data limits local-scale research utilizing GRACE and GRACE-FO missions. In this study, we develop a regional downscaling model based on the linear regression relationship between GWSA and environmental variables, reducing the grid resolution of GWSA obtained from GRACE from approximately 25 km to 1 km. First, we estimate the missing values of monthly continuous terrestrial water storage anomaly (TWSA) for the period from 2003 to 2020 using interpolated multi-channel singular spectrum analysis (IMSSA). Next, we apply the water balance equation to separate GWSA from TWSA, which is provided jointly by the Global Land Data Assimilation System (GLDAS) and the distributed ecohydrological model ESSI-3. We then employ a partial least squares regression (PLSR) model to identify the most significant environmental variables related to GWSA. Precipitation (Prec), normalized difference vegetation index (NDVI), and actual evapotranspiration (AET), with variable importance in projection (VIP) values greater than 1.0, are recognized as effective variables for reconstructing long-term, high-resolution groundwater storage changes. Finally, we downscale and reconstruct the long-term (2003–2020), high-resolution (1 km × 1 km) monthly GWSA in the Songhua River Basin using fused and supplemented GRACE/GRACE-FO data, employing either geographically weighted regression (GWR) or random forest (RF) models. The results demonstrate superior performance of the GWR model (CC = 0.995, NSE = 0.989, RMSE = 2.505 mm) compared to the RF model in downscaling. The downscaled GWSA in the Songhua River Basin not only achieves high spatial resolution but also exhibits improved accuracy when compared to in situ groundwater observation records. This research enhances understanding of spatiotemporal variations in regional groundwater due to local agricultural and industrial water use, providing a scientific basis for regional water resource management. Full article