Search Results (149)

Reference evapotranspiration (ET₀) is essential for irrigation management, but its spatial estimation is limited by sparse meteorological observations. Satellite products such as MOD16 potential evapotranspiration (PET) provide spatial continuity, yet they are not directly comparable to FAO-56 ET₀ due to structural differences in model parameterization. This study proposes a topography-constrained framework to convert MOD16 PET into FAO-consistent ET₀. The approach was tested in two heterogeneous regions of northern Italy (Piemonte and Veneto) using ground-based ET₀ derived from the FAO Penman–Monteith equation (2010–2022). PET–ET₀ transformation coefficients, estimated via station-wise linear regression, showed no significant temporal drift over the study period and strong spatial structure. Among the tested topographic predictors, elevation was retained as the main topographic proxy for modelling the spatial variability of the correction coefficients. The locally calibrated correction reduced the systematic overestimation of raw MOD16 PET and improved agreement with station-based ET₀ in both regions. Its performance was comparable to an IDW interpolation benchmark, although IDW slightly outperformed the topography-based model in Piemonte. A cross-region test showed that the correction reduced MOD16 PET errors when transferred between Piemonte and Veneto, but residual bias remained. The proposed framework should therefore be interpreted as a parsimonious regional topographic correction approach that requires local calibration and validation before application to other areas. Full article

Hydrological processes in dryland urban lakes are jointly shaped by landscape pattern dynamics and water resource scarcity, yet the spatial correspondence between landscape fragmentation and lake ecological water demand remains poorly understood. This study took Yuehai Lake, a typical dryland urban lake in Northwest China, as a case study. Landscape pattern analysis was integrated with a water balance model to quantify ecological water demand and its spatial correspondence with landscape metrics. The model coupled the Penman–Monteith equation, a depth-modified evaporation model, and a Darcy’s Law-based zonal seepage calculation. Results showed that: (1) the landscape structure remained highly stable over 2014–2022, with the Aggregation Index ranging from 95.07% to 95.28% and the Largest Patch Index from 90.20% to 90.70%; (2) the annual ecological water demand for maintaining ecosystem integrity was estimated at 2036.97 × 10⁴ m³, comprising inherent lake water volume of 1138.02 × 10⁴ m³ (55.9%), evapotranspiration of 659.72 × 10⁴ m³ (32.4%), and lakebed seepage of 239.23 × 10⁴ m³ (11.7%); and (3) evapotranspiration was concentrated between May and August, accounting for 80.5% of annual losses, with water surface evaporation dominating the flux at 91.5%. These findings suggest a spatial correspondence between landscape metrics and ecological water demand components, providing quantitative support for differentiated water supplementation strategies in dryland urban lakes. Full article

Incomplete spatio-temporal (ST) data from sensor networks in precision agriculture often limits environmental modeling and decision-making accuracy. To address this, we propose the Spatio-Temporal Conditional Diffusion Framework (ST-CDF), a generative approach for high-fidelity data reconstruction. The framework’s core is a deep denoising network that integrates a Graph Attention Network (GAT) to explicitly model non-Euclidean spatial correlations, a Differential Attention Transformer to capture abrupt temporal dynamics, and an Inverse Discrete Wavelet Transform (IDWT) module to preserve multi-scale signal details. The generative process is constrained by a physics-informed training objective, which injects known physical laws (i.e., the Penman–Monteith equation for reference evapotranspiration, $E{T}_0$) as an inductive bias, ensuring the imputed data maintains physical consistency. For privacy-preserving deployment on resource-constrained IoT devices, we extend the framework with a Federated Cluster-Guided Distillation (Fed-CGD) strategy. We conducted extensive experiments against established methods on two real-world agricultural datasets. ST-CDF demonstrated improved imputation accuracy across evaluated metrics. Its efficacy was most pronounced in the physically-demanding $E{T}_0$ calculation task, where data imputed by ST-CDF at an 80% missing rate achieved a Root Mean Square Error (RMSE) of 0.3485 and a Coefficient of Determination ($R^2$) of 0.7558, outperforming the baseline models. Furthermore, we explore ST-CDF as an explainable (XAI) framework for active agricultural decision support, demonstrating its utility in performing counterfactual simulations of “what-if” interventions, such as irrigation. The findings highlight ST-CDF as an effective, physically-grounded, and interpretable tool for data-driven scientific computation and precision agriculture. 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

Reference evapotranspiration (ET₀) is most commonly estimated using the FAO-56 Penman–Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET₀, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET₀ using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET₀ prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations were developed and tested. The random forests, gradient boosting and k-nearest neighbors models were used as a benchmark, and model performance was evaluated using R², NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R² = 0.97, NSE = 97.07%, RMSE = 0.23 mm/day, and MAE = 0.21 mm/day. The results showed that a limited number of input variables can be used to estimate ET₀ with high accuracy, achieving an R² value of 0.95 using only three input variables. Therefore, the findings of this study may contribute to more accurate and cost-effective irrigation scheduling and water balance estimation, providing practical benefits for agricultural water management and farmers in Serbia. Full article

The aim of this research was to quantify the capacity of different shrub species to remove atmospheric CO₂, to adsorb particulate matter and to dissipate latent heat through transpiration. A total of 308 established plants comprising Deutzia scabra, Elaeagnus × ebbingei, Euonymus japonicus, Forsythia × intermedia, Laurus nobilis, Ligustrum vulgare, Pittosporum tobira, Prunus laurocerasus and Viburnum tinus were selected in Lugano (Switzerland) and Bolzano (Italy). Stem diameter, crown radius, Leaf Area Index, net CO₂ assimilation per unit leaf area (A_leaf), transpiration, and stomatal conductance (g_s) were measured during spring, summer, and fall. The net CO₂ assimilation per unit of crown projection area and per plant were calculated by upscaling A_leaf using a multilayer model. Latent heat dissipation was calculated using the Penman–Monteith equation. The amount of PM trapped on leaves was measured using a gravimetric method. Differences in leaf area and leaf gas exchange among species affected their capacity to deliver specific ecosystem services. Forsythia, Pittosporum, Elaeagnus and Deutzia removed about 40% more CO₂ per unit crown projection area than Laurus, Ligustrum, and Euonymus. Latent heat dissipation by shrubs was, on average, 130 W m⁻², which is comparable to that of tree species. PM removal per unit leaf area was higher in species with sparse canopies and rough leaf surfaces. Full article

In this study, we evaluated the use of five different ML algorithms (CatBoost, XGBoost, random forest, gradient boosting, and support vector regression [SVR]) to estimate daily ET₀ based only on four independent variables: 2 m air temperature, vapor pressure deficit, 10 m wind speed, and sunshine duration. We used a total of 9132 daily values (2000–2025) from the Open-Meteo Historical Weather API (2000–2025) at 10 stations in the Tlemcen province of northwest Algeria. The dataset was divided into training, validation, and testing sets using a chronological split of 70/15/15. We estimated the performance of each algorithm by using several statistics (RMSE, MAE, R², NSE, RSR, and Willmott Index) as well as some statistics to evaluate the potential of overfitting and the ability to reproduce the behavior observed during the training phase. CatBoost had the highest overall accuracy and the most generalized performance, with an RMSE of approximately 0.292 mm day⁻¹, MAE of approximately 0.208 mm day⁻¹, R² of 0.971, and NSE of 0.971 in the test set, suggesting an extremely low risk of overfitting. The optimal CatBoost model was also used to estimate the spatial and temporal variations of monthly ET₀. The results showed high interannual variability (changes from year to year from −12.815 to +8.707 mm month⁻¹) in the semi-arid region of Tlemcen but no significant long-term trends (cumulative net change of approximately −0.021 mm month⁻¹ over 2000–2025). Therefore, the use of CatBoost is recommended as a robust, efficient, and reliable emulator of the FAO-56 Penman–Monteith equation (ET₀) for estimating ET₀ in semi-arid environments with limited climate data availability, and could be particularly useful in northwestern Algeria and other semi-arid Mediterranean regions. Full article

Against the backdrop of ongoing climate change, the Qinghai–Tibet Plateau, a region highly sensitive to climatic variation, exhibits intricate spatiotemporal patterns in reference crop evapotranspiration (ET_O), with significant implications for regional water-resource planning. This study selected four agro-climatic zones across the plateau region (TSA, TSH, TAZ, and WCH). Long-term daily observations from 28 meteorological stations were used to estimate ET_O via the FAO 56 Penman–Monteith equation. This extensive dataset enabled robust trend analysis using the Mann–Kendall test, alongside a cloud-model framework, and analyses of sensitivity and contributions to evaluate ET_O’s spatiotemporal evolution, its distributional uncertainty, and the underlying drivers. Results reveal pronounced regional heterogeneity in the interannual variability of ET_O. Annual ET_O declined in TSH and TSA (trend rates of −1.12 and −6.58 mm·10a⁻¹, respectively) and increased in TAZ and WCH (15.76 and 10.74 mm·10a⁻¹, respectively). At monthly and seasonal timescales, ET_O exhibited an unimodal pattern, with the greatest stability in winter and spring and lower stability in summer and autumn. The cloud-model parameter He indicates that ET_O stability is greatest in TSH and weakest in WCH, with He values of 7.15 and 12.29 mm, respectively. Contribution-rate analyses identify T_max and T_mean as the principal determinants of rising ET_O across all study zones, reflecting the largest individual contributions. Temperature-related factors together account for the majority of ET_O variability across the regions, with their absolute contributions ranging from 5.61% to 8.63%, well above those of aerodynamic factors (0.62–1.78%). Stability assessments indicate that ET_O is generally more unstable than its meteorological drivers, with substantial regional disparities, implying that ET_O evolution cannot be explained by a single controlling factor. Overall, the study characterizes the uncertainty in ET_O variations under complex terrain, highlights the value of the cloud model for refined hydrological assessments, and provides a scientific basis for adaptive agricultural water-resource management in the region. Full article

The Three-River Connectivity Zone in the lower Tarim River Basin (TRCZ) is a typical area that has experienced decades of river cut-off, followed by artificial ecological water transfers and vegetation restoration. However, the long-term patterns of ecological water requirements and their response mechanisms to ecosystem services in this region remain unclear. This study aims to quantify the spatiotemporal dynamics and driving factors of ecological water requirements in the TRCZ from 1990 to 2020. We integrated multi-temporal remote sensing land cover data with the FAO Penman–Monteith equation to estimate vegetation evapotranspiration (as a proxy for ecological water requirement) and coupled the InVEST model with Random Forest modeling to identify key climatic and hydrological drivers. Unlike previous studies that focused primarily on precipitation inputs, our approach explicitly considers the ecosystem’s water yield function alongside water demand, offering new insights into the constraints on ecosystem services. Key findings reveal: (1) During the period of 2005–2010, the land cover types underwent significant changes, characterized by a marked expansion of sparse forest (14–21%) and a pronounced decline in forest land, which fundamentally reconfigured the ecosystem’s water demand structure. (2) Accordingly, the multi-year average ecological water requirement quota in the study area is 2.95 × 10⁷ m³, and the total ecological water requirement exhibited a fluctuating decline at a rate of −1.39 × 10⁵ m³/yr, yet sparse forest persisted as the dominant water-consuming component. (3) The Random Forest model (R² = 0.942) identified water yield (importance: 0.527) and precipitation (0.255) as the primary drivers, establishing the ecosystem’s water yield function rather than precipitation input alone as the critical constraint. (4) A widespread increase in the unit area ecological water requirement across vegetation types signaled escalating pressures from climate change. This research provides a quantitative framework and a transferable methodology for adaptive water resource management and ecological restoration in arid regions, emphasizing the balance between ecosystem water demand and supply functions. Full article

The three-temperatures (3T) method is a robust approach to estimating evapotranspiration (ET), requiring relatively few measurable, physical parameters and an imitation surface, making it potentially suited for estimating ET from sustainable drainage systems (SuDS) and green infrastructure (GI) in urban environments. However, limited 3T-ET data from SuDS and/or GI makes it difficult to assess the conditions that affect its accuracy. The purpose of this study was to determine whether reasonable ET estimates could be achieved using the 3T method with a plastic imitation surface for a small, homogenous vegetated surface. The 3T-ET estimates were produced at an hourly timestep and compared to reference ET ($E{T}_o$) derived using the Penman–Monteith equation. The 3T-ET estimates were consistently higher than $E{T}_o$ (mean absolute error of 0.05 to 0.15 mm·h⁻¹), which may indicate systematic overestimation of ET or that the actual ET was greater than $E{T}_o$. Unrealistic 3T-ET estimates are produced when the air temperature and the imitation surface temperature converge, limiting the method’s application to between mid-morning and late afternoon. Further work to validate and refine the 3T method is required before it can be recommended for deployment in the field for spot-sampling ET rates from urban SuDS/GI. Full article

Reference crop evapotranspiration (ET₀) is crucial for water management. Although the FAO56-PM method is widely used to estimate ET₀, its input variables of solar radiation (R_s) and net longwave radiation (R_nl) are not readily available. Currently, mere calibration of the formula for R_s is assumed to be effective in improving FAO56-PM’s performance. To test this hypothesis, all input variables for FAO56-PM were measured in Lhasa, Linzhi, and Bange over the Tibetan Plateau (TP) to assess how different calculation methods affect ET₀ estimates. Compared to the original FAO56-PM, calibration of both R_s and R_nl formulas yielded the best model performance. Mere calibration of the formula for R_nl notably improved ET₀ estimation accuracy, while mere calibration of the formula for R_s reduced its accuracy. The general calibration model was slightly less effective than calibration of both R_s and R_nl formulas but obviously outperformed the original FAO56-PM. This model showed that ET₀ increased from east to west, ranging from 569.4 mm/year to 1118.5 mm/year. Trend analysis indicated rapid increases in ET₀ in the eastern region and significant decreases in the western region of the TP over recent decades. The findings are useful for the regional application of FAO56-PM to achieve sustainable development in Tibet. Full article

The Penman–Monteith formula (P-M) is a well-established indirect method for estimating reference evapotranspiration (ET₀). The key input for this equation is global solar radiation (H). When real data are unavailable, other weather parameters are used to estimate H. In this study, sixteen years’ worth daily registers of H, sunshine duration (S), and air temperature (t) from 10 sites across Poland were used to determine coefficients for the Angström–Prescott (A-P) and Hargreaves–Sammani (H-S) equations. The H values obtained with locally calibrated, general Polish and global A-P and H-S equations were applied to the P-M formula. The ET₀ results thus obtained were compared to those derived with the P-M method and measured solar radiation data. The method of determination of the radiation component had a significant but sometimes unexpected impact on the ET₀ values. The better predictive power of the solar radiation model usually resulted in better accuracy of the evapotranspiration estimation; however, there were exceptions to this rule. Full article

Reference evapotranspiration (ET₀) is a fundamental variable for irrigation scheduling and water management. Conventional estimation methods, such as the FAO-56 Penman–Monteith equation, are of limited use in developing regions where meteorological data are scarce. This study evaluates the potential of machine learning (ML) approaches to estimate ET₀ in Paraguay, using only geographical and temporal predictors—latitude, longitude, altitude, and month. Five algorithms were tested: artificial neural networks (ANNs), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGB), and adaptive neuro-fuzzy inference systems (ANFISs). The framework consisted of ET₀ calculation, baseline model testing (ML techniques), ensemble modeling, leave-one-station-out validation, and spatial interpolation by inverse distance weighting. ANFIS achieved the highest prediction accuracy (R² = 0.950, RMSE = 0.289 mm day⁻¹, MAE = 0.202 mm day⁻¹), while RF and XGB showed stable and reliable performance across all stations. Spatial maps highlighted strong seasonal variability, with higher ET₀ values in the Chaco region in summer and lower values in winter. These results confirm that ML algorithms can generate robust ET₀ estimates under data-constrained conditions, and provide scalable and cost-effective solutions for irrigation management and agricultural planning in Paraguay. Full article

Potential evaporation (PE) from saturated bare surfaces is the basis for estimating actual evaporation (E_s) in agricultural and related disciplines. Most models estimate PE using meteorological data. Thus, the dependence of soil temperature (T) on PE is often simplified in applications. To address this gap, we conducted an in situ lysimeter experiment in the Guanzhong Basin, China, continuously measuring PE, T, and soil heat flux (G) at high temporal resolution over three fully saturated sandy soils. Results show that annual PE over fine sand was 7.1% and 11.0% higher than that of coarse sand and gravel. The observed PE differences across textures can be quantitatively explained using the surface energy balance equation and a radiatively coupled Penman-Monteith equation, accounting for the dependence of T on net radiation (R_n) and G. In contrast, PE estimates diverged from observations when R_n and G were assumed to be independent of T. We further evaluated the influence of T and other influencing variables on PE. The random forest model identified that near-surface heat storage variations (∆S) contribute most significantly to PE estimation (relative importance = 0.37), followed by surface temperature (0.24) and sensible heat flux (0.23). These findings highlight the critical role of near-surface temperature in PE estimation. Full article

Accurate estimations of reference evapotranspiration (ETo) are critical for hydrologic studies, efficient crop irrigation, water resources management and sustainable development. The evaluation of an empirical method was carried out to estimate hourly ETo, utilizing short-wave radiation and relative humidity as a surrogate of vapor pressure deficit (VPD), calibrated under semi-arid conditions and validated for different climatic regimes (hyper-arid, arid, subhumid) using American Society of Civil Engineers Penman–Monteith (ASCE PM) (2005) values as a standard, for the state of California. For hyper-arid climatic conditions, the empirical method resulted in underestimation and had coefficient of determination (R²) values of 0.88–0.95 and root mean square error (RMSE) values of 0.062–0.115 mm h⁻¹. Hyper-arid climatic conditions correspond to lower R² and different relations between the vapor pressure deficit (VPD) and the relative humidity function (1/lnRH) that the empirical method utilizes. For the other climatic regimes (arid, semi-arid, subhumid), the empirical method performed satisfactorily. The RMSE was calculated for groups of empirical estimates corresponding to various wind velocity values, and it was satisfactory for >99% of wind speed values (u₂). The RMSE was also calculated for grouped values of the estimates of the empirical method corresponding to observed VPDs and was satisfactory for >97% of all observed values of VPD, except for hyper-arid stations (59% of u₂ and 60% of all observed values of VPD). Full article