Estimation Of Actual Evapotranspiration Research Articles

Estimation of Actual Evapotranspiration and Water Stress in Typical Irrigation Areas in Xinjiang, Northwest China

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas.

Different Vegetation Covers Leading to the Uncertainty and Consistency of ET Estimation: A Case Study Assessment with Extended Triple Collocation

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas.

Different Vegetation Covers Leading to the Uncertainty and Consistency of ET Estimation: A Case Study Assessment with Extended Triple Collocation

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas.

Estimates of net primary productivity and actual evapotranspiration over the Tibetan Plateau from the Community Land Model version 4.5 with four atmospheric forcing datasets

Abstract The accuracy of simulation of carbon and water processes largely relies on the selection of atmospheric forcing datasets when driving land surface models (LSM). Particularly in high-altitude regions, choosing appropriate atmospheric forcing datasets can effectively reduce uncertainties in the LSM simulations. Therefore, this study conducted four offline LSM simulations over the Tibetan Plateau (TP) using the Community Land Model version 4.5 (CLM4.5) driven by four state-of-the-art atmospheric forcing datasets. The performances of CRUNCEP (CLM4.5 model default) and three other reanalysis-based atmospheric forcing datasets (i.e., ITPCAS, GSWP3, and WFDEI) in simulating the net primary productivity (NPP) and actual evapotranspiration (ET) were evaluated based on in-situ and gridded reference datasets. Compared with in-situ observations, simulated results exhibited determination coefficients (R2) ranging from 0.58–0.84 and 0.59–0.87 for observed NPP and ET, respectively, among which GSWP3 and ITPCAS showed superior performance. At the plateau level, CRUNCEP-based simulations displayed the largest bias compared to the reference NPP and ET. GSWP3-based simulations demonstrated the best performance when comprehensively considering both the magnitudes and change trends of TP-averaged NPP and ET. The simulated ET increase over the TP during 1982–2010 based on ITPCAS was significantly greater than in the other three simulations and reference ET, suggesting that ITPCAS may not be appropriate for studying long-term ET changes over the TP. These results suggest that GSWP3 is recommended for driving CLM4.5 in conducting long-term carbon and water processes simulations over the TP. This study contributes to enhancing the accuracy of LSM in water-carbon simulations over alpine regions.

Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management

Accurate estimations of actual evapotranspiration (ETa) are essential to various environmental issues. Artificial intelligence-based models are a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANNs) are proven to be suitable for predicting reference evapotranspiration (ETo) and ETa based on RS data. This study aims to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables in a large irrigation catchment. The ANN model has been applied to the two different scenarios. Data from only the 38 days of satellite overpass dates was selected in Scenario I, while in Scenario II all datasets, i.e., the 769-day data were used. An irrigation scheme, located in the Mediterranean region of Turkiye, was selected, and a total of 38 Landsat images and local climatic data collected in 2021 and 2022 were used in the ANN model. The ETa results by the ANN model for Scenarios I and II showed that the R2 values for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84) were all remarkably high. Moreover, the results of the new ANN model in two scenarios showed an acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential for obtaining daily ETa using limited climatic data and RS imagery. As a result, the suggested ANN model for daily ETa computation offers a trustworthy way to determine crop water usage in real time for sustainable water management in agriculture. It may also be used to assess how crop evapotranspiration in drought-prone areas will be affected by climate change in the 21st century.

Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms

AbstractEvapotranspiration (ET) and actual evapotranspiration (AET) serve as critical parameters in the water vapour exchange between terrestrial surfaces and the atmosphere. ET denotes the theoretical maximum evapotranspiration achievable under ideal conditions, whereas AET represents the actual evapotranspiration observed, factoring in the constraints imposed by available water resources. Precise estimation of AET is imperative for the optimization of water resource management and the advancement of sustainable development initiatives. In recent years, deep learning techniques have been extensively utilized in AET estimation. However, traditional deep learning models often lack the incorporation of essential physical constraints. We proceeded to enhance the loss function of the temporal convolutional network (TCN) by taking into account the physical relationships that exist among soil water content (SWC), potential evapotranspiration (PET) and AET, thereby introducing a novel physically coupled deep learning model (AET, SWC after kernel principal component analysis, PET, TCN and AKP‐TCN), and checked the rationality of the model with the FLUXNET 2015 dataset. These findings underscore that the AKP‐TCN model exhibits heightened sensitivity to peak fluctuations in AET under the imposition of physical constraints. This approach notably enhances the precision of AET simulations in areas marked by complex and variable climatic conditions, such as the Mediterranean climate zone and Oceania, achieving determination coefficient (R2) values surpassing the threshold of 0.900. Compared to traditional models, which include long short‐term memory (LSTM), convolutional neural networks (CNN) and TCN, the AKP‐TCN delivers substantial R2 improvements of 16%, 16% and 9%, respectively. This advancement offers a novel perspective for coupling deep learning with physical mechanisms.

Estimation of actual evapotranspiration from different ecosystems on the Tibetan Plateau based on a generalized complementary evapotranspiration theory model

AbstractActual evapotranspiration constitutes a vital component of the exchange of energy and water vapour between the soil‐vegetation and atmospheric systems on terrestrial terrain. Nevertheless, the Tibetan Plateau, owing to its austere environmental conditions, harbours a scarcity of terrestrial monitoring stations. This circumstance presents a formidable challenge in attaining precise estimations of actual evapotranspiration. The complementary relationship method is a potential approach because it requires only routine meteorological data to estimate actual evapotranspiration on a regional or global scale. However, the suitability of the complementary relationship model across diverse ecosystems on the Tibetan Plateau necessitates further investigation. In this study, we scrutinized the simulation of daily and monthly actual evapotranspiration across 18 observation sites spanning eight distinct land use categories on the Tibetan Plateau. We employed the polynomial generalized complementary function introduced by Brutsaert (B2015), alongside its enhanced rendition proposed by Szilagyi (S2017) and Crago (C2018). The outcomes reveal that all three models adeptly replicate the fluctuations in actual evapotranspiration, irrespective of land use category or temporal scale—whether daily or monthly. This is true regardless of whether original or calibrated parameter values are applied. However, there exist significant variations in the performance of these models. In general, the C2018 model demonstrates superior performance across most ecosystems when original parameters are employed. Following parameter calibration, the simulation efficacy of the models experienced marked enhancement. Post parameter calibration, the B2015 model outperforms the other two models notably in desert and wetland environments. Furthermore, the simulation outputs from all three models display heightened sensitivity to parameter α, particularly in the context of the C2018 and S2017 models. These findings suggest that accurate estimation of parameter values is critical to improving the accuracy of estimating actual evapotranspiration. Calibrated parameter values, contingent on a fusion of vegetation, meteorology and surface roughness, exhibit variability across diverse ecosystems.

Correction to: Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

Correction to: Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

Agricultural water accounting: Complementing a governance monitoring schema with remote sensing calculations at different scales

Water use requires monitoring and quantification at different spatial scales to enhance water security, especially in regions facing water scarcity and threats to food security. Consequently, water metering has been implemented in various countries as part of water governance frameworks. This study aims to evaluate the implementation of a water metering network within the Chilean water governance system, which is based on the commoditisation of water through water rights. Additionally, it assesses the potential of supplementing the water metering network with remote sensing-based estimates of actual evapotranspiration (AET) and discusses the need to integrate these estimates into an appropriate water governance scheme. To conduct this study, publicly available water use reports were obtained from the Water Resources Directorate and subsequently processed to eliminate anomalies in the withdrawal time series. Water withdrawal data was supplemented with information on granted water rights to provide additional insights and contrast water allocations with actual withdrawals. AET estimates from the Mapping EvapoTranspiration at high Resolution with Internalised Calibration (METRIC) model using Landsat scenes were also acquired for the period from 2019 to 2022 to compare withdrawals and water demand in the agricultural sector. It was found that only a small fraction of water rights ( ∼ 2%) is currently being metered. Actual reported withdrawals, on average, amount to approximately one fifth to one fourth of the volumes granted through water rights. However, water extractions vary depending on geographical locations and usage categories. Remote sensing-based AET demonstrates a good correlation with withdrawals, suggesting its potential in auditing water withdrawal records provided by water users and calculating water availability and withdrawals at aggregated scales within an adaptive water governance framework. While different applications were explored within the Chilean context, these have a broader application in global water governance, particularly in regions experiencing similar challenges in water resource management.

Evapotranspiration Partitioning and Estimation Based on Crop Coefficients of Winter Wheat Cropland in the Guanzhong Plain, China

Accurate estimation and effective portioning of actual evapotranspiration (ETa) into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on ETa rates measured using the eddy covariance (EC) technique in three winter wheat growing seasons from October 2020 to June 2023. The variation in the crop coefficients (Kc, α, and KHc) were quantified by combining the ETa and reference evapotranspiration rates using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations. In addition, the application of models based on the modified crop coefficient (Kc, α, and KHc) was proposed to estimate the ETa rates. According to the obtained results, the average cumulative ETa, T, and E rates in the three winter wheat growth seasons were 471.4, 265.2, and 206.3 mm, respectively. The average T/ETa ratio ranged from 0.16 to 0.72 at the different winter wheat growth stages. Vapor pressure deficit (VPD) affected the ETa rates at a threshold of 1.27 KPa. The average Kc, α, and KHc values in the middle stage were 1.34, 1.54, and 1.21, respectively. The measured ETa rates and ETa rates estimated using the adjusted Kc, α, and KHc showed regression slope coefficients of 0.96, 0.99, and 0.96, and coefficients of determination (R2) of 0.92, 0.93, and 0.90, respectively. Therefore, the Priestley–Taylor-equation-based adjusted crop coefficient is recommended. The adjusted crop-coefficient-based models can be used as valuable tools for local policymakers to effectively improve water use.

Average monthly recharge, surface runoff, and actual evapotranspiration estimation using WetSpass-M model in Low Folded Zone, Iraq

Abstract The evaluation of the spatial and temporal distribution of groundwater recharge is required to develop the regional groundwater model for more precise simulations of various management scenarios. WetSpass-M (Water and Energy Transfer between Soil, Plants, and Atmosphere under Steady-State Conditions), which is a GIS-based spatially distributed water balance model, is deployed to evaluate monthly groundwater recharge, surface runoff, and actual evapotranspiration in the Low Folded Zone from 2000 to 2019. ArcGIS software prepares the essential relevant input data for the Wetspass-M model as grid maps. They include monthly climatological measurements (precipitation, temperature, and wind speed), land cover distribution, soil map, groundwater depth, topography, and slope. The mean annual groundwater recharge, evapotranspiration, and runoff were found to be 128, 131, and 72 mm, respectively. Accordingly, recharge accounts for 39% of the precipitation, while the rest, 40 and 21%, become evapotranspiration and surface runoff, respectively. WetSpass-M model results are meant to enable integrated groundwater modeling. The study of simulation data demonstrates that the WetSpass-M model accurately simulates the hydrological water budget components of the Low Folded Zone. In addition, a better understanding of the simulated spatial and temporal distribution of water balance components is beneficial for managing and planning the available water resources of the Low Folded Zone in Iraq, which faces water scarcity threats.

Global evapotranspiration models and their performance at different spatial scales: Contrasting a latitudinal gradient against global catchments

Actual evapotranspiration (AET) is a key variable in the global water balance, driving agricultural production and ecosystem health. It is a complex hydrologic process that depends on vegetation, climate, and available water conditions. Different moderate resolution global AET models have been developed to quantify water resources at large scales. In this work we evaluate five of these products, including MODIS, PML, SSEBop, TerraClimate, and a Synthesis AET using point and catchment-scale datasets based on flux towers. We also contrast water balance changes with total water storage (TWS) products. These comparisons cover different radiation and precipitation regimes over catchments around the world and along a strong climatic gradient in north-central Chile. We rank the models, contrast TWS datasets, and study differences related to scale in validation and the effect of rainfall and radiation on simulated values. Additionally, we use a Budyko framework to evaluate the AET products in terms of their agreement with expected water budgets. At different evaluation scales, AET estimates and observations agreed reasonably well, with the largest mean R2 of about 0.7 and errors of approximately 15% of the magnitude of the observed variables. MODIS and Synthesis AET had the highest R2 at the point (0.62) and at the catchment scales (0.71 and 0.59 for regional and global catchments), respectively, but were closely followed by PML. PML and TerraClimate led to the lowest magnitude errors at the point (RMSE = 0.78 mm day−1) and catchment scales (mean RMSE = 1.5 mm day−1), respectively. The rainfall gradient is reflected in a performance gradient. PML, MODIS, and TerraClimate gave consistent behaviour based on the Budyko curve, with a few arid catchments exceeding the water limit. The major conclusion is that remotely sensed AET outperforms flux tower AET extrapolation for water balance calculations at the catchment scale, which means that errors in satellite-based AET products tend to cancel out at larger spatial scales, which makes them viable alternatives for regional water balance studies. However, flux data integrated into AET models, such as the FluxCom model, leads to the lowest errors. The assimilation and downscaling of Gravity Recovery and Climate Experiment (GRACE) into the Global Land Data Assimilation System (GLDAS) leads to an improvement in regional results compared with other TWS products.

A Multi-sensor Analysis of Selected Reflectance-Based Crop Coefficient Models for Daily Maize Evapotranspiration Estimation

This study evaluated three reflectance-based crop coefficient models (RBCC) for daily maize actual evapotranspiration (ETa) estimates, using multispectral data from spaceborne, airborne, and proximal platforms. The goal was to identify the optimal multispectral sensor that gives more accurate daily ETa estimates. The remote sensing (RS) multispectral platforms included Landsat-8, Sentinel-2, Planet CubeSat, handheld multispectral radiometer (MSR), and unmanned aerial system or UAS, spatial resolution from 30 m to 0.03 m. Three RBCC models that use different vegetation indices as input variables were evaluated in the study. One RBCC uses the normalized difference vegetation index (NDVI). The second model uses the soil-adjusted vegetation index (SAVI), and the third model uses canopy cover (fc). The data for this study were from two maize research sites in Greeley and Fort Collins, Colorado, USA, collected in 2020 and 2021. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Daily maize ETa predictions were compared with observed daily maize ETa data from an Eddy Covariance system installed at each research site. Results indicated that, depending on the RS of ETa algorithm and platform, the optimal input RS data was different. The MSR sensor (1 m) provided the best remote sensing data (input) for the SAVI-based RBCC ETa model, with a maize ETa error (MBE±RMSE) of -0.13 (-3%)±0.67 (16%) mm/d. Sentinel-2 was the best sensor for the remaining two RBCC daily maize ETa algorithms, since the errors for the NDVI-based and fc-based RBCC models for maize ETa were 0.21 (5%)±0.78 (18%) mm/d and 0.59 (14%)±1.07 (25%) mm/d, respectively. These results indicate the need for methods to improve the spectral quality of the remote sensing data to improve spatial ETa estimates and advance sustainable irrigation water management.

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.

Using a crop water stress index to determine water use efficiency in a raspberry crop in the Mediterranean Central Chile

AbstractWater availability is projected to decrease in Mediterranean Central Chile, necessitating sustainable production strategies based on improved irrigation management. This study focuses on estimating water use efficiency (WUE) in raspberry crops at two validation sites using remote sensing and soil irrigation data. By employing the crop water stress index (CWSI), we demonstrate the potential of this tool in enhancing irrigation management and establishing sustainable production practices. The results demonstrate the successful estimation of actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model (coefficient of determination [R2] = 0.92; root mean square error [RMSE] = 0.97 mm day−1), while the CWSI indicated high stress levels after 5 days of irrigation. Moreover, validation at two sites reveals significant differences in applied irrigation, with sites A and B receiving 17,097 and 3760 m3 ha−1, respectively, while the average water demand is close to 5300 m3 ha−1. These variations result in discrepancies in WUE, with values of 0.79 and 3.64 kg m−3. By integrating remote sensing indices and soil data, this study proposes that maintaining an 85% ETa rate during noncritical periods can enhance WUE. This work demonstrates the potential use of a water stress index to monitor crops in the Chilean central zone for efficient water resource use under future scarcity scenarios.

A Multi-Framework of Google Earth Engine and GEV for Spatial Analysis of Extremes in Non-Stationary Condition in Southeast Queensland, Australia

The frequency and severity of extremes, including extreme precipitation events, extreme evapotranspiration and extreme water storage deficit events, are changing. Thus, the necessity for developing a framework that estimates non-stationary conditions is urgent. The aim of this paper is to develop a framework using the geeSEBAL platform, Generalised Extreme Value (GEV) models and spatiotemporal analysis techniques that incorporate the physical system in terms of cause and effect. Firstly, the geeSEBAL platform has enabled the estimation of actual evapotranspiration (ETa) with an unprecedented level of spatial-temporal resolution. Following this, the Non-stationary Extreme Value Analysis (NEVA) approach employs the Bayesian method using a Differential Evolution Markov Chain technique to calculate the frequency and magnitude of extreme values across the parameter space. Station and global climate datasets have been used to analyse the spatial and temporal variation of rainfall, reference evapotranspiration (ETo), ETa and water storage (WS) variables in the Lockyer Valley located in Southeast Queensland (SEQ), Australia. Frequency analysis of rainfall, ETa, and water storage deficit for 14 stations were performed using a GEV distribution under stationary and non-stationary assumptions. Comparing the ETa, ETo and ERA5 rainfall with station data showed reasonable agreement as follows: Pearson correlation of 0.59–0.75 for ETa, RMSE of 45.23–58.56 mm for ETa, Pearson correlation of 0.96–0.97 for ETo, RMSE of 73.13–87.73 mm for ETo and Pearson correlation of 0.87–0.92 for rainfall and RMSE of 37.53–57.10 mm for rainfall. The lower and upper uncertainty bounds between stationary and non-stationary conditions for rainfall station data of Gatton varied from 550.98 mm (stationary) to 624.97 mm (non-stationary), and for ERA5 rainfall datasets, 441.30 mm (stationary) to 450.77 mm (non-stationary). The results demonstrate that global climate datasets underestimate the difference between stationary and non-stationary conditions by 9.47 mm compared to results of 73.99 mm derived from station data. Similarly, the results demonstrate less variation between stationary and non-stationary conditions in water storage, followed by a sharp variation in rainfall and moderate variation in evapotranspiration. The findings of this study indicate that neglecting the non-stationary condition in some hydrometeorological variables can lead to underestimating their amounts. This framework can be applied to any geographical area for estimating extreme conditions, providing valuable insights for infrastructure planning and design, risk assessment and disaster management.

A remote sensing approach to estimate variable crop coefficient and evapotranspiration for improved water productivity in the Ethiopian highlands

Proper and reasonable estimation of actual evapotranspiration is critical for the design, operation, and management of irrigation systems. However, the density of climatic stations is low in many parts of Ethiopia to estimate spatially reasonable reference evapotranspiration (ETo), and the lack of spatial variability of crop coefficient (Kc) is clear barrier to the proper management of irrigation water. Therefore, the main objective of this study was to estimate reasonable crop evapotranspiration (ETc) by deriving spatially and temporally varying crop coefficients using remote sensing products in 10 wheat plots at the Koga irrigation scheme. The moderate-resolution imaging spectroradiometer (MODIS) potential evapotranspiration was calibrated at two class-I climate stations (Bahir Dar and Dangila) based on Penman–Monteith estimates and Normalized Difference Vegetation Index (NDVI) was derived from Sentinel 2B, which was later used to derive Kc. The correlation between reference evapotranspiration from MODIS Penman Monteith was acceptable for both Dangila (R2 = 0.64) and Bahir Dar (R2 = 0.74) stations. The MODIS calibration constant for Koga irrigation schemes was 0.27 and 1.99 (regression slope and constant) on an 8-day basis. Similarly, a strong correlation (R2 = 0.95) was found between Sentinel-based NDVI and FAO crop coefficient, which indicated an alternative pathway for estimating crop coefficient. The value of Kc varies in space (across the 10 plots) from 0.16 to 0.42 at the initial stage and from 1.20 to 1.32 at the mid-stage. Similarly, the mean value of Kc varies in time from 0.29 at the initial stage to 1.26 at the mid-stage. On the other hand, evaluation of MODIS and WaPOR ETc found a significant difference (p < 0.05) with the calibrated MODIS-Sentinel 2B derived ETc. This indicated the need to calibrate both MODIS and WaPOR for the proper estimation of crop water needs. Underestimation of ET was observed from MODIS, and the reverse is true for WaPOR. Our findings showed that calibrating and integrating MODIS with Sentinel 2B would be a feasible approach to estimate Kc and hence ETc that varies in time and space. This would assist water managers in estimating crop water needs for better productivity in the region.

Calibration and assessment of evapotranspiration methods for cucumber plants in a Venlo‐type greenhouse

AbstractAccurate estimation of actual crop evapotranspiration (ETc) is a necessary step in water resource management. Several ETc models have been developed and applied to estimate ETc in open fields. To date, there is no standard equation to estimate ETc under greenhouse conditions due to the structural and environmental differences between greenhouses and open fields. Therefore, an experiment was conducted in a Venlo‐type greenhouse in south‐east China during four planting seasons of cucumber plants in 2018 and 2019. Four methods, the pan evaporation method (Kp‐method), crop coefficient model (Kc‐method), Penman–Monteith model (rc‐method) and Priestley–Taylor model (αPT‐method), were employed and parameterized to estimate the ETc of cucumber in the Venlo‐type greenhouse. The performances of the four models were evaluated using ETc measured by lysimeters. The Kp‐method and αPT‐method underestimated and overestimated ETc values with root mean square errors (RMSEs) equal to 0.79 and 0.49 mm day−1 for the spring‐summer planting season (SSPS) and 0.35 and 0.51 mm day−1 for the autumn‐winter planting season (AWPS), respectively. The Kc‐method and rc‐method showed comparatively good performances in estimating cucumber ETc, with RMSEs equal to 0.53 and 0.34 mm day−1 for SSPS and 0.35 and 0.47 mm day−1 for AWPS, respectively.

Estimation of actual crop evapotranspiration using artificial neural networks in tomato grown in closed soilless culture system

Estimation of actual crop evapotranspiration using artificial neural networks in tomato grown in closed soilless culture system

Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network

Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network