Reliable Estimates Of Evapotranspiration Research Articles

Analysis of Spatial and Temporal Variations in Evapotranspiration and Its Driving Factors Based on Multi-Source Remote Sensing Data: A Case Study of the Heihe River Basin

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

An evolutionary parsimonious approach to estimate daily reference evapotranspiration

The reference evapotranspiration (ETo) is an essential component in hydrological and ecological processes. The objective of this research is to develop an explicit model to estimate ETo only using commonly measurable meteorological parameters such as relative humidity, air temperature, and wind speed, where the measurements corresponding to solar radiation are omitted. The model was generated using Genetic Programming (GP), evaluated, and validated with reference data ETo using FAO56-PM. This reference data was obtained from different climates (warm-temperate and arid-warm) and latitudes, acquired from CIMIS stations in the state of California, United States, and the El Porvenir station in the state of Coahuila, located in north-central Mexico. After applying the proposed methodology, a total of 3754 results were generated, demonstrating a significant improvement in the estimation of ETo compared to the Hargreaves–Samani model. A particularly noteworthy result revealed that our approach outperformed the Hargreaves–Samani model in the training phase by 27%, and in the testing phase by 16%, on average. In order to achieve a generalized model, a dataset encompassing meteorological stations in two different climates (warm-temperate and arid-warm) and various latitudes was utilized. The obtained outcome unveiled a highly effective model for estimating ETo in diverse climatic contexts, eliminating the need for local adjustments. This model significantly surpassed the Hargreaves–Samani model, exhibiting superior performance by 17% during the training phase and 18% during the testing phase. These results conclusively underscore the capability of our approach to provide more accurate and reliable ETo estimates. These results conclusively underscore the capability of our approach to provide more accurate and reliable ETo estimates. Finally, to validate the model, four different datasets with climates similar to those used for model creation (warm-temperate, warm-arid) and different latitudes were employed. The validation stage results clearly indicate the superiority of our reference evapotranspiration ETo11 model over the Hargreaves–Samani model by 51% in warm-temperate climates. For the dataset with arid-warm climate, our model continued to show satisfactory results, surpassing the Hargreaves–Samani model by 8%. GP emerges as an innovative and effective alternative for simplified model development. This approach introduces a novel paradigm that facilitates the efficient development of models, standing out for its simplicity and effectiveness in generating solutions.

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.

Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3‐ha lysimeter and Landsat normalized difference vegetation index data

AbstractIn arid and semiarid environments, evapotranspiration (ET) is the primary discharge component in the water balance, with potential ET exceeding precipitation. For this reason, reliable estimates of ET are needed to construct accurate water budgets in these environments. Remote sensing affords the ability to provide fast, accurate, field‐scale ET estimates, but these methods have largely been restricted to deep rooted (phreatophytic) plant communities underlain by shallow groundwater. We used 13 years of data from a 3‐ha drainage lysimeter in a semiarid sagebrush steppe and Landsat normalized difference vegetation index (NDVI) data to calibrate a generalized least squares model capable of predicting vadose zone ET in a high elevation upland ecosystem. Annual precipitation was the best predictor of annual ET, as they were nearly balanced every year analysed (mean difference = 3 mm). We incorporated reference crop ET and a linear combination of NDVI and precipitation to capably predict ET on a subannual, lag‐determined interval of 48 days, with a mean error of only 9.92% across all observations. To our knowledge, this is the first vegetation index‐ET algorithm calibrated in a semiarid upland plant community using field‐scale lysimetry. Vadose zone ET is particularly important at waste disposal sites in the Desert Southwest, where accurate and spatially explicit ET estimates are needed for monitoring potential mobilization and transport of contaminants past the root zone into local aquifers and for monitoring and modelling effects of recharge on flow and transport of contaminants in underlying aquifers.

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Abstract. Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (Reco). However, accurate estimation of ET and Reco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and Reco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and Reco variables, as well as providing reliable estimation of ET and Reco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and Reco and enhances process understanding at sparsely monitored watersheds.

A global assessment of PT-JPL soil evaporation in agroecosystems with optical, thermal, and microwave satellite data

Evapotranspiration (ET) accounts for water movements from land to air and plays a vital role in the terrestrial water, energy, and carbon cycles. Reliable estimates of ET for agricultural landscapes can facilitate water resources management and food security analysis. The widely used Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model has the most potential to operationally simulate ET over large areas, but its inability to fully track soil evaporation dynamics using atmospheric humidity limits its application in agroecosystems. In this study, we isolated the uncertainties resulting from soil evaporation and assessed three Earth observation-based alternatives - apparent thermal inertia (ATI), microwave soil moisture (SM), and optical spectral indices based on shortwave infrared (SWIR) to formulate soil evaporation. Our results illustrate that the incorporation of the SWIR-based soil moisture divergence index (SMDI) and microwave-based SM into monthly soil evaporation led to 6% and 5% increase in explained ET variances and reduced RMSE by 23.2% and 13.1% for cropland and grassland, respectively, as compared to PT-JPL using atmospheric reanalysis data only. Further analyses demonstrated that PT-SMDI explained more observed ET variances than PT-JPL using in-situ measurements of atmospheric humidity during the crop growing season, particularly for irrigated cropland (R2=0.65 for PT-SMDI; R2=0.62 for PT-JPL). On the other hand, the use of microwave SM outperformed other indices for ET assessment in grasslands but had lower performance in croplands. Our results suggest that a combination of optical SWIR and microwave SM has good potential to improve the PT-JPL model accuracy for agricultural landscapes.

Adopting regional calibration scenarios for ensuring reliable ETo estimates in semi-arid regions: assessing the ancillary data supply method

ABSTRACT Reference evapotranspiration (ETo ) is an essential parameter of the hydrologic cycle that plays a key role in the water resources management, especially in arid and semi-arid regions where the total amount of annual precipitation is low. Accurate information on ETo values would be essential for irrigation scheduling and water resources planning as well as water allocation policies. Due to the difficulties involved in the precise direct measuring of this parameter, indirect methods should be adopted and validated in different data completeness scenarios. Although local estimation of ETo using indirect methods is a way to achieve reliable estimations of this parameter, assessing external/regional scenarios for evaluating these methods would be necessary, since lots of regions suffer from the lack of necessary meteorological records for the ETo estimation. The present paper evaluated different spatial (external) data management scenarios for adopting the most commonly used empirical equations under ancillary data supplanting in semi-arid regions. The obtained results revealed that when designed properly, the external data supplying structure for ETo estimations in a target point would be a suitable way to obtain reliable estimates.

Relationships between riparian evapotranspiration and groundwater depth along a semiarid irrigated river valley

AbstractEvapotranspiration (ET) from riparian vegetation can be difficult to estimate due to relatively abundant water supply, spatial vegetation heterogeneity, and interactions with anthropogenic influences such as shallower groundwater tables, increased salinity, and nonpoint source pollution induced by irrigation. In semiarid south‐eastern Colorado, reliable ET estimates are scarce for the riparian corridor that borders the Arkansas River. This work investigates relationships between the riparian ecosystem along the Arkansas River and an underlying alluvial aquifer using ET estimates from remotely sensed data and modelled water table depths. Results from a calibrated, finite‐difference groundwater model are used to estimate weekly water table fluctuations in the riparian ecosystem from 1999 to 2009, and estimates of ET are calculated using the Operational Simplified Surface Energy Balance (SSEBop) model with over 200 Landsat scenes covering over 30 km2 of riparian ecosystem along a 70‐km stretch of the river. Comparison of calculated monthly SSEBop ET to estimated alfalfa reference ET from local micrometeorological station data indicated statistically significant high linear correspondence (R2 = .87). Daily calculated SSEBop ET showed statistically significant moderate linear correspondence with data from a local weighing lysimeter (R2 = .59). Simulated monthly SSEBop ET values were larger in drier years compared with wetter years, and ET variability was also larger in drier years. Peak ET most commonly occurred during the month of June for all 11 years of analysis. Relationships between ET and water table depth showed that peak monthly ET was highest when groundwater depths were less than about 3 m, and ET values were significantly lower for groundwater depths greater than 3 m. Negative sample Spearman correlation highlighted riparian corridor locations where ET increased as a result of decreased groundwater depths across years with different hydroclimatic conditions. This study shows how a combination of remotely sensed riparian ET estimates and a regional groundwater model can improve our understanding of linkages between riparian consumptive use and near‐river groundwater conditions influenced by irrigation return flow and different climatic drivers.

Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data

Accurate estimation of reference evapotranspiration (ETo) is required in many fields, e.g. irrigation scheduling design, agricultural water management, crop growth modeling and drought assessment. Nevertheless, reliable estimation of ETo is difficult when lack of complete or long-term meteorological data at the target station. This study evaluated the efficiency of a new tree-based soft computing model, Light Gradient Boosting Machine (LightGBM), for estimating daily ET0 using limited local (target-station) and external (cross-station) meteorological data from 49 weather stations in humid subtropical region of China, including 16 in Jiangxi Province and other 33 in the region. The performance of LightGBM was compared with the tree-based M5 Model Tree (M5Tree) and Random Forests (RF) as well as four empirical models (Hargreaves-Samani, Tabari, Makkink and Trabert). Eight input combinations of daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (Hr), wind speed at 2 m height (U2), extraterrestrial solar radiation (Ra) and global solar radiation (Rs) calculated from sunshine duration (n) for the period 2001–2015 were used to test the models. The results showed that LightGBM was superior to M5Tree and RF in local applications under all input combinations during testing, with average root mean square error (RMSE) of 0.08–0.58 mm d−1, 0.11–0.62 mm d−1and 0.13–0.60 mm d−1, respectively. M5Tree performed slightly better than RF under input combinations 6–8, whereas RF outperformed M5Tree under the other input combinations. However, all three soft computing models produced much better daily ETo estimates than the corresponding empirical models with the same input variables. Rs was the most influential meteorological variable for daily ETo estimation in this region, followed by Tmax and Tmin, Hr and finally U2. In external applications, LightGBM also generally performed better than the RF, M5Tree and empirical models. Soft computing models developed with meteorological data from Station 57894, having the most similar climatic characteristics to the other stations, gave satisfactory ETo estimates for the 15 cross stations in Jiangxi Province, even for the other 33 stations across the humid subtropical region of China. LightGBM was proved to be efficient and exhibit good generalization capability in both local and external applications, which was thus recommended as an alternative model for daily ETo estimation.

A comparative assessment of SWAT-model-based evapotranspiration against regional-scale estimates

We attempted to assess the actual evapotranspiration (ETa) rates estimated by the Soil and Water Assessment Tool (SWAT) model in an arid region by comparing other regional-based ETa estimates: the Operational Simplified Surface Energy Balance (SSEBop) products, the MODerate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration product (MOD16), and the Generalized Complementary Relationship (GCR) model. The results by the SWAT model clearly showed that ETa rates in the SWAT model are limited by soil water availability. From the comparison of the annual ETa estimates, the GCR model among those ETa estimation methods simulated slightly higher values than the water-balance derived ETa rates (i.e., the difference between annual precipitation and annual stream flow rates). The annual ETa rates of the SSEBop products showed the closest slope of the regression line to 1.0, while the highest R2 (coefficient of determination) value was found from the annual ETa estimates by the SWAT model. The monthly ETa by the GCR model had the highest linear relationship with those of the SSEBop products. However, the SWAT model did not have a strong linear relationship with the monthly ETa of the SSEBop products unlike the comparison against the MOD16 products. This study recommends a further study on more reliable ETa estimates in arid or semi-arid regions by the SWAT model.

Uso de datos satelitales MODIS y balance de energía para estimar la evapotranspiración


 
 
 La evapotranspiración (ET) es un factor importante para el desarrollo y producción de los cultivos agrícolas, su valor a nivel local y regional es determinante para la planeación en el manejo de los recursos hídricos; algunas técnicas como covarianza de vórtices miden evapotranspiración de manera puntual, mientras que otras, como las basadas en percepción remota y scintilometría lo hacen a escala regional. El objetivo fue estimar la ET utilizando datos del sensor MODIS y un balance de energía, y compararla contra datos obtenidos con scintilómetro y sistema de covarianza de vórtices. El trabajo se implementó en el Valle de Culiacán, México, sobre una superficie de chile Bell, durante 81 días de desarrollo del cultivo. Se utilizaron datos de índice de diferencia normalizada (NDVI) y temperatura de superficie obtenidos del sensor MODIS, un scintilómetro BLS450 para estimar el flujo de calor sensible, y un sistema de covarianza de vórtices IRGA EC-150 para medir el flujo de calor latente. La ET total obtenida fue de 255.4, 275.2 y 262.0 mm para MODIS, scintilómetro y covarianza de vórtices, respectivamente; el mejor ajuste estadístico se obtuvo comparando los datos obtenidos con MODIS y scintilómetro, donde la raíz media del error cuadrático (RMSE), BIAS, coeficiente de concordancia de Willmott (d) y coeficiente de correlación (R) fueron de 0.44 mm d-1, -0.245 mm d-1, 0.8 y 0.75, respectivamente. El uso de datos satelitales combinado con el balance de energía sobre la superficie terrestre permite estimar de manera confiable la evapotranspiración a escala regional.
 
 

Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

Abstract. Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month−1, 10.55 mm month−1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

Automated Chamber System to Measure Field Evapotranspiration Rates

Reliable estimation of evapotranspiration is important for many hydrological applications, including agriculture, climatology, and assessment of stormwater control measures. An automated chamber system was constructed to measure field evapotranspiration. The system consists of six chambers, sampled consecutively by means of a dynamic closed-chamber design, and is fully automated to allow replicable long-term measurements. However, the use of such a system is subject to measurement errors due to vapor adsorption along the sampling tubes and inappropriate internal ventilation. Because these issues are rarely addressed in the literature on flux chambers, a laboratory calibration was performed to estimate errors in evapotranspiration rates measured by the system. The effects of the sampling tube length and air mixing were investigated. A correction factor for evapotranspiration rates relative to the tube length was established. Appropriate air mixing induced by small fans ensured that the measured rates were representative of the internal conditions of the chambers. With appropriate internal ventilation and correction factors, the relative error of measurements was less than 5% for high evapotranspiration rates.

基于遥感和Penman-Monteith模型的内陆河流域不同生态系统蒸散发估算

The terrestrial water cycle is of critical importance to a wide array of earth systems. Evapotranspiration( ET) is an important component of the terrestrial water cycle,which controls the distribution of plant communities and the net primary production of ecosystems. Considerable efforts have been made by scientists to use remotely sensed data to estimate the spatial and temporal distribution of evaporation rates. Compared with other methods of estimation based on remotely sensed data,the Penman-Monteith equation has proven effective at both the point scale and the kilometer scale but the effectiveness of the surface conductance model in some specific areas such as arid regions still requires validation.Evaporation processes are complex in arid regions and the processes and patterns of ET under different vegetation types in arid regions are poorly researched. Therefore the Heihe River Basin was chosen as a study area because it is a typical arid area in China. The aim of this study was to evaluate the Penman-Monteith equation for estimating daily evaporation fluxes in arid regions using MODIS LAI data and meteorological data. A simple biophysical model using leaf area index( LAI) from remotely sensed data and the Penman-Monteith equation was used to calculate the daily ET of grassland and farmland at various sites in the middle and upper reaches of the Heihe River. The Penman-Monteith equation is a biophysically basedequation for calculating land surface evaporation and has been used extensively. The model for computing the surface conductance contains six parameters requiring local calibration. The shuffled complex evolution( SCE) algorithm was used to optimize the parameters in the surface conductance model for each site. The Nash-Sutcliffe efficiency( NSE) was selected as the optimized objective function. Optimal parameters in the surface conductance model were obtained by minimizing the cost function NSE with the SCE algorithm. Excellent agreement was obtained between the measured mean daily evaporation rates and those calculated using remotely sensed LAI data and the Penman-Monteith equation. The NSE at the A' rou( grassland) and Yingke( farmland) sites were 0.84 and 0.85,respectively and the root mean square error( RMSE) were1.25 and 1.66 MJ m-2d-1,respectively. With regard to grassland and farmland ecosystems in this study,the transpiration was much stronger than the soil evaporation during the growing seasons,however,the opposite situation was found during the non-growing seasons. From this study it can be concluded that the Penman-Monteith equation combined with LAI data can provide reliable estimates of ET at daily time scales in different ecosystems of arid and cold regions. However,there are still some uncertainties in ET estimations of the ecosystems in this study area,especially the cropland at the Yingke station.These biases are possibly because the model cannot simulate the impact of the extreme soil moisture changes caused by irrigation on ET. The soil evaporation factor is a constant in this study,whereas in reality it is a variable that depends on the moisture status of the soil near the surface. Thus,the effect of soil moisture variation on surface conductance was considered,and it improved the precision of the equation in the simulation of farmland ET.

Evapotranspiration mapping for agricultural water management: An overview

Evapotranspiration (ET) is an essential component of the water balance. Any attempt to improve water use efficiency must be based on reliable estimates of ET, which includes water evaporation from land and water surfaces and transpiration by vegetation. ET varies regionally and seasonally according to weather and wind conditions. Remote sensing based agro-meteorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. The use of remote sensing to estimate ET is presently being developed along two approaches: (a) land surface energy balance (EB) method and (b) Reflectance based crop coefficient and reference ET approach. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. Automated contours are not confined to specific pre-determined geographic areas (as in MLRA), require less time and cost. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (~10 to 200 ha). However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from K1VHRR/Landsat/ASTER/MODIS images using same/other-sensor high resolution multi-spectral images.

Reliable estimation of evapotranspiration on agricultural fields predicted by the Priestley–Taylor model using soil moisture data from ground and remote sensing observations compared with the Common Land Model

Evapotranspiration (ET) is a crucial factor in understanding the hydrological cycle and is essential to many applications in hydrology, ecology and water resources management. However, reliable ET measurements and predictions for a range of temporal and spatial scales are difficult. This study focused on the comparison of ET estimates using a relatively simple model, the Priestley–Taylor (P-T) approach, and the physically based Common Land Model (CLM) using ground and remotely sensed soil moisture data as input. The results from both models were compared directly with hourly eddy covariance measurements at two agricultural field sites during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in the corn soybean production region in the Upper Midwest, USA. The P-T model showed a significant overestimation of the potential ET compared to the measurements, with a root mean square error (RMSE) between 115 and 130 W m–2. Actual ET was better predicted by the CLM, with the RMSE ranging between 50 and 75 W m–2. However, actual ET from the P-T model constrained with a soil moisture dependency parameterization showed improved results when compared to the measurements, with a significantly reduced bias and RMSE values between 60 and 65 W m–2. This study suggests that even with a simple semi-empirical ET model, similar performance in estimating actual ET for agricultural crops compared to more complex land surface–atmosphere models (i.e. the CLM) can be achieved when constrained with the soil moisture function. This suggests that remote sensing soil moisture estimates from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and others such as the Soil Moisture and Ocean Salinity (SMOS) mission may be effective alternatives under certain environmental conditions for estimating actual ET of agricultural crops using a fairly simple algorithm.

Performance of Penman-Monteith FAO56 in a Semiarid Highland Environment

Reliable estimates of evapotranspiration are essential for irrigation and water resources planning and management. Although several methods are available for computing reference evapotranspiration (ETo), the provision of complete and accurate climate data is often a problem. Therefore, weighing lysimeter data from a semiarid highland environment were used to evaluate the performance of six commonly used reference evapotranspiration estimation methods with different data requirements (Penman-Monteith-FAO56, Priestley-Taylor, Radiation-FAO24, Hargreaves, Blaney-Criddle, Class A pan). The lysimeter experiments were conducted at Ankara Research Institute of Rural Services in Turkey, during the April–October cropping seasons of the years 2000–2002. The average ETo for the three seasons, computed from the lysimeter data, was 964 mm. The Penman-Monteith-FAO56 method was also evaluated for cases where relative humidity, wind speed, solar radiation, or all three parameters would be missing. This resulted in a total of 10 different methods. The RMS errors (RMSE) and index of agreement for the daily data and the monthly averages as well as the mean absolute error (MAE) for the seasonal totals were computed to compare these methods. The methods were ranked based on the sum of the ranks for all five evaluation criteria. The Penman-Montheith-FAO56 method with the full data set, with replacement of wind speed, and with replacement of relative humidity took the top three spots, with MAEs for the seasonal totals ranging between 40 and 70 mm. The Hargreaves method came in fourth (MAE 54 mm), followed by the Penman-Montheith-FAO56 method with replacement of all three parameters (MAE 57 mm). The RMSE for the monthly average ETo was 0.43 and 0.50 mm⋅days−1 for the Penman-Monteith-FAO56 without and with replacement of all three parameters and 0.48 mm⋅days−1 for Hargreaves. Thus, if only temperature data would be available, the much easier to use Hargreaves method would be preferred above the Penman-Montheith-FAO56 equation with replacement of humidity, radiation, and wind speed data, for this semiarid highland environment.

Special issue on evapotranspiration measurement and modeling

Water availability for irrigation throughout the world has been reduced in recent years due to a combination of frequent droughts and competition for water resources among agricultural, industrial, and urban users. In addition, some major agricultural areas face moderate to significant reductions of rainfall, or changes in timing of stream flow due to changes in timing of snowmelt, as a result of global climate change. Under such conditions, sophisticated irrigation water management will be required to optimize water use efficiency and maintain sufficient levels of crop productivity and quality. A key factor to achieve these targets is the estimation of actual evapotranspiration (ET). Accurate determination of ET can be a viable tool in better utilization of water resources through well-designed irrigation management programs. Reliable estimates of ET are also vital to develop criteria for in-season irrigation management, water resource allocation, long-term estimates of water supply, demand and use, design and management of water resources infrastructure, and evaluation of the effect of land use and management changes on the water balance. ET is commonly calculated using grass or alfalfareference evapotranspiration (ETo) multiplied by grass or alfalfa-reference-based crop-specific coefficients (Kc). The Penman–Monteith combination equation is widely accepted as the best-performing method for reference evapotranspiration estimates from a well-watered hypothetical grass or alfalfa surface having a fixed crop height, albedo, and surface canopy resistance. The Kc is basically the ratio of ET to ETo where ET can be measured using a lysimeter, soil water balance approach, eddy covariance method, Bowen ratio energy balance system, or surface renewal method. Advances over the last two to three decades in instrumentation, data acquisition systems, remote data access, and the off-the-shelf availability of aforementioned ET measurement tools have significantly enhanced our understanding of ET and its relation to microclimatic conditions. Advances also enabled the availability and affordability of data for practitioners for use in irrigation management. While the reference ET and Kc approach provides a simple and convenient way to estimate crop water requirements for a variety of crops and climatic conditions, a major uncertainty in this approach is that many Kc values reported in the literature are empirical and often not adapted to local conditions. This is due to the fact that ratios of ET to ETo depend on nonlinear interactions of soil, crop and atmospheric conditions, and irrigation management practices. This consideration is especially Communicated by R. Evans.

Surface Energy Balance-Based Model for Estimating Evapotranspiration Taking into Account Spatial Variability in Weather

Reliable estimates of evapotranspiration (ET) from vegetation are needed for many types of water-resource investigations. How well models can estimate ET from vegetation varies, depending on the capabilities of the model as well as the nature of the targeted vegetation. Model accuracy also depends heavily on the quality and quantity of the data used. Several ET models have been developed that use an energy balance approach in which the data used by the models are derived from satellite imagery. This research introduces an enhanced surface energy balance-based model, the remote sensing of evapotranspiration or ReSET model, for estimating ET. ReSET is an ET estimation model that takes into consideration the spatial variability in weather parameters, which makes it particularly applicable for calculating regional scale ET. ReSET also has the capability of interpolating between the available weather stations in time and space. The model's accuracy at daily and seasonal time scales is evaluated in several case studies.

Utility of thermal image sharpening for monitoring field‐scale evapotranspiration over rainfed and irrigated agricultural regions

The utility of a thermal image sharpening algorithm (TsHARP) in providing fine resolution land surface temperature data to a Two‐Source‐Model for mapping evapotranspiration (ET) was examined over two agricultural regions in the U.S. One site is in a rainfed corn and soybean production region in central Iowa. The other lies within the Texas High Plains, an irrigated agricultural area. It is concluded that in the absence of fine (sub‐field scale) resolution thermal data, TsHARP provides an important tool for monitoring ET over rainfed agricultural areas. In contrast, over irrigated regions, TsHARP applied to kilometer‐resolution thermal imagery is unable to provide accurate fine resolution land surface temperature due to significant sub‐pixel moisture variations that are not captured in the sharpening procedure. Consequently, reliable estimation of ET and crop stress requires thermal imagery acquired at high spatial resolution, resolving the dominant length‐scales of moisture variability present within the landscape.