Estimation Of Evapotranspiration Research Articles

Satellite-Based PT-SinRH Evapotranspiration Model: Development and Validation from AmeriFlux Data

The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RHVPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R2) of 0.55, root-mean-square error (RMSE) of 17.5 W/m2, bias of −1.2 W/m2 and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R2 of 0.49, an RMSE of 20.3 W/m2, a bias of −8.6 W/m2 and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R2 and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m2. This simple but accurate method permits us to investigate the decadal variation in regional ET over the land.

CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060

The “Future Global Aridity Index and PET Database” provides high-resolution (30 arc-seconds) average annual and monthly global estimates of potential evapotranspiration (PET) and aridity index (AI) for 22 CMIP6 Earth System Models for two future (2021-2041; 2041-2060) and two historical (1960-1990; 1970–2000) time periods, for each of four shared socio-economic pathways (SSP). Three multimodel ensemble averages are also provided (All; Majority Consensus, High Risk) with different level of risks linked to climate model uncertainty. An overview of the methodological approach, geospatial implementation and a technical evaluation of the results is provided. Historical results were compared for technical validation with weather station data (PET: r 2 = 0.72; AI: r 2 = 0.91) and the CRU_TS v 4.04 dataset (PET: r 2 = 0.67; AI: r 2 = 0.80). Within the context of projected significant change in the near- and medium-term, the “Future_Global_AI_PET Database” provides a set of data projections and tools available for a variety of scientific and practical applications, illustrating trends and magnitude of predicted climatic and eco-hydrological impacts on terrestrial ecosystems. The Future_Global_AI_PET Database is archived in the ScienceDB repository and available online at: https://doi.org/10.57760/sciencedb.nbsdc.00086

Effect of Different Fertigation Scheduling Methods on the Yields and Photosynthetic Parameters of Drip-Fertigated Chinese Chive (Allium tuberosum) Grown in a Horticultural Greenhouse

This study investigated the performance of four different fertigation scheduling methods in greenhouse-grown, drip-fertigated Chinese chive (Allium tuberosum) cultivation. These methods were based on (1) the use of a timer (control), (2) accumulated radiation (AR), (3) estimated evapotranspiration (ET), and (4) measured soil moisture (SM), with fertilizer application proportional to the supplied water. These methods caused considerable variations in the amount of fertigation water (I), soil volumetric water content (θ), and bulk soil electrical conductivity, leading to variations in the harvested fresh weight (FW). The SM-based method maintained the target θ and achieved the highest irrigation water productivity (WP; the ratio of FW to ΣI), while the ET-based method led to insufficient I and FW loss. The AR-based method over-fertigated, but no FW loss was observed. Compared to the WP of the control, those of the SM-, ET-, and AR-based methods varied by +1%, −14%, and −57%, respectively. Different fertigation methods did not significantly affect leaf photosynthetic capacity, but under-fertigation caused a significant decline in stomatal conductance. Compared to the ET- and AR-based methods, the SM-based method seemed to have a lower risk of under-/over-fertigation because I in the SM-based method could be adjusted according to θ.

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.

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.

The effects of drought and salinity on KS and RAW managerial coefficients in the efficient water management in maize farms

This study aimed to investigate the simultaneous effects of drought and salinity on irrigation management coefficients in maize farms. A three-year field research was conducted in the form of a 3 × 3 factorial experiment with a randomized complete block design and three replications from 2020 to 2022 in a maize farm, in Aliabad Fashafoye, Qom province, Iran. The applied treatments included three levels of salinity (S0 = 1.8, S1 = 5.2, and S2 = 8.6 dS/m) and three levels of irrigation (W0 = 100%, W1 = 75%, and W2 = 50% of field capacity). Evapotranspiration stress coefficient (KS) due to W0S1 and W0S2 treatments was (0.975 and 0.934), (0.974 and 0.932), and (0.962 and 0.935) in 2020, 2021, and 2022, respectively. According to the results, KS decreased by increasing the salinity level of irrigation water, so a 1-unit increase in salinity level above the tolerance threshold of the crop to salinity decreased KS by 0.78 and 1.76% for S1 and S2, respectively. Moreover, each percent of volumetric moisture decrease from field capacity decreased KS by 5.9 and 13.3% in W1 and W2, respectively. Also, with the increase in the intensity of the stresses, the readily available water (RAW) of treatments decreased. The sole application of salinity stress decreased the decreasing slope of RAW by 3.2%, while the application of both stresses resulted in the decreasing slopes of 4.9, 5.7, and 7.8% at the salinity levels of S0, S1, and S2, respectively, compared to the control. The findings of this study show that the accurate estimation of crop evapotranspiration and RAW can help to improve the irrigation schedule, and the amount of irrigation water used is less than in non-stress conditions due to the reduction of total evapotranspiration and less water uptake in environmental stresses in maize farms.

Spatial-temporal variations in evapotranspiration across the continental United States: An atmospheric water balance perspective

Accurate estimation of terrestrial evapotranspiration (ET) is crucial for understanding land–atmosphere interactions, but is challenging on the regional scales. The water balance approach, including terrestrial and atmospheric water balance (TWB and AWB), provides a simple tool for estimating regional ET. This study estimated ET values based on the AWB approach (ETAWB) over the continental United States (CONUS) for the period 1979–2021. Validations using TWB-based ET estimates (ETTWB) suggest that ETAWB is accurate. ETAWB demonstrates comparable interannual variability with the other three long-term ET products over the CONUS and is consistent with ETTWB in the majority of basins. The 43-year CONUS averaged ETAWB is 548 ± 26 mm/year, with higher values in the eastern region and the coastal regions in the western CONUS, and lower values in the western arid regions. During 1979–2021, increasing trends of ETAWB are observed in the eastern CONUS, while decreasing trends occurred in the western regions. The intercomparison between ETAWB, GLEAM, Noah, and ERA5 illustrates similar spatial patterns and linear trends. In the water-limited arid basins, ETAWB anomalies over time show strong agreements with precipitation anomalies. The results of this study indicate that the AWB approach provides reasonable regional-to-continental terrestrial ET estimates over the CONUS, serving as a reference for hydrological and climatic modeling.

Comparative analysis of actual evapotranspiration values estimated by METRIC model using LOCAL data and EEFlux for an irrigated area in Northern Sinaloa, Mexico

Landsat images combined with the METRIC model have been used in applications such as EEFlux to estimate actual evapotranspiration (ETa) in irrigated areas, with uncertainty as to whether the results are sufficiently accurate at local scales. This work compares temporally and spatially ETa estimates using Landsat imagery and the METRIC model with LOCAL measured weather data and EEFlux for an irrigated area in northern Sinaloa, Mexico, from 1995 to 2018. A regression analysis and error metrics such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and slope were used to compare both models. The temporal analysis results confirm that ETa differences are closely related to crop growth and that the daily mean absolute error was 1.17 mm/day. Spatial analysis showed that when using only cropland pixels without non-cropland pixels in EEFlux, the R2 increases from 0.36 to 0.73, and the RMSE decreases from 2.52 to 1.98 mm/day. Therefore, EEFlux should be limited to cropland areas, and it is suggested to exclude Landsat images close to the planting and harvesting season to reduce the differences.

Precision modelling of leaf area index for enhanced surface temperature partitioning and improved evapotranspiration estimation

Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Ts). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIred-edge) considering the hysteresis between them. Considering the distinct characteristics, modeling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Ts, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Ts, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Ts became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R2), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.

Tibetan Plateau Runoff and Evapotranspiration Dataset by an observation-constrained cryosphere-hydrology model

Runoff and evapotranspiration (ET) are pivotal constituents of the water, energy, and carbon cycles. This research presents a 5-km monthly gridded runoff and ET dataset for 1998–2017, encompassing seven headwaters of Tibetan Plateau rivers (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, and Indus) (hereinafter TPRED). The dataset was generated using the advanced cryosphere-hydrology model WEB-DHM, yielding a Nash coefficient ranging from 0.77 to 0.93 when compared to the observed discharges. The findings indicate that TPRED’s monthly runoff notably outperforms existing datasets in capturing hydrological patterns, as evidenced by robust metrics such as the correlation coefficient (CC) (0.944–0.995), Bias (−0.68-0.53), and Root Mean Square Error (5.50–15.59 mm). Additionally, TPRED’s monthly ET estimates closely align with expected seasonal fluctuations, as reflected by a CC ranging from 0.94 to 0.98 when contrasted with alternative ET products. Furthermore, TPRED’s annual values exhibit commendable concordance with operational products across multiple dimensions. Ultimately, the TPRED will have great application on hydrometeorology, carbon transport, water management, hydrological modeling, and sustainable development of water resources.

Estimation of irrigated crop artificial irrigation evapotranspiration in China

Agriculture water use accounts for 70% of the total water withdrawal worldwide. The evapotranspiration during crop growth is one of the important hydrological processes in the agricultural water cycle. This study proposed the concept of artificial irrigation evapotranspiration of irrigated crops to describe that the evapotranspiration caused by irrigation water use. Irrigated crops rely on two kinds of water sources: precipitation and irrigation water. With the construction of irrigation schemes, the artificial irrigation evapotranspiration plays an increasingly important role in the dualistic water cycle system of irrigated cropland. To reveal the amount of artificial irrigation evapotranspiration of 17 categories of irrigated crops in China, this study proposed a new quantitative model system which was established based on traditional evapotranspiration models and soil water balance models. Based on the new model system, we calculated the annual artificial irrigation evapotranspiration of irrigated crops for the period 2013 to 2017 in China. The results showed that the proportion of artificial irrigation evapotranspiration to the total evapotranspiration of irrigated crops was 41.3%, whose value was 228.1 km3 a−1. The artificial irrigation evapotranspiration in different agricultural water management regions were 90.0 km3 a−1 in the northeast region, 86.0 km3 a−1 in the southeast region, and relatively low 52.2 km3a−1 in the west region. The results of this study can provide methods for water management and policy–making in agricultural irrigated areas, and it can also provide a preliminary understanding of the influence of human activities on the dualistic water cycle in cropland.

Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators.

Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

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.

Drought evaluation using various evapotranspiration models over semi-arid river basins

ABSTRACT Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Improved remote sensing reference evapotranspiration estimation using simple satellite data and machine learning

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (i.e., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies

Estimating crop coefficients and water use of a full-bearing mango orchard in north-eastern South Africa using the fraction of vegetation cover and a dual source evapotranspiration model

In semi-arid countries like South Africa, commercially produced mango (Mangifera Indica L.) are grown entirely under irrigation. However, there is little accurate quantitative information on the water use of mango orchards, and few accurate water use models currently exist. In this study, we evaluated the performance of the FAO 56 method of estimating crop evapotranspiration (ETc) and a dual source water use model against measurements of actual consumptive water use of a commercial mango orchard. The FAO approach calculates ETc as the product of a crop coefficient (Kc) and the reference evapotranspiration (ETo). We derived Kc for well-watered orchards from readily available data including the fraction of vegetation cover, average tree height, and a stomatal control coefficient. The dual-source model calculated orchard evapotranspiration as the sum of tree transpiration and orchard floor evaporation derived from the modified Shuttleworth and Wallace model. Actual evapotranspiration was measured using an open path eddy covariance system, while tree transpiration was measured using the heat ratio method of monitoring sap flow. Data were collected in a mature ‘Tommy Atkins’ mango orchard grown with micro-sprinkler irrigation in north-eastern South Africa. Results show that the reference evapotranspiration explained most of the variation in orchard transpiration (R2 ∼ 0.78) compared to the solar radiation (R2 ∼ 0.66) and the vapour pressure deficit of the air (R2 ∼ 0.66). Compared with field measured water use data, the FAO method gave relatively accurate estimates of transpiration (R2 = 0.74, NRMSE = ±18.00 %, NMAE = ±15.00 %, NSE = 0.70) and evapotranspiration (R2 = 0.58, NRMSE = ±19.00 %, NMAE = ±16.00 %, NSE = 0.55). NSE is the Nash-Sutcliffe Efficiency. The dual-source model yielded slightly less accurate estimates of transpiration (R2 = 0.61, NRMSE = ±22.00 %, NMAE = ±20.00 %, NSE = 0.39) and evapotranspiration (R2 = 0.52, NRMSE = ±23.00 %, NMAE = ±25.00 %, NSE = 0.51). The basal crop coefficient (Kcb) showed small seasonal fluctuations, varying between 0.40 and 0.60 throughout the year. In contrast, Kc showed clear seasonality varying between 0.59–0.64 at flowering and fruit set and rising to 0.81–0.90 during the fruit growing phase. Estimates of the annual total transpiration (686 mm) from the FAO method were within 5 % of the actual measured values (677 mm) while the dual source model estimated 612 mm of transpiration, which was almost 10 % lower than the measured values. The results highlight that even though the FAO method requires fewer input parameters, it is potentially more accurate in estimating water use in mango orchards than the dual-source model.

Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology.

A dataset of surface evapotranspiration in the Three-River Headwaters Region of Qinghai Province based on Bayesian model averaging from 2003 to 2015

Evapotranspiration (ET) plays an important in the exchange of water, carbon and energy on land. The ET estimates based on different models and remote sensing data have different degrees of uncertainty. Bayesian model averaging (BMA) provides a way to reduce the uncertainty. Based on water and heat flux observation data in the Three-River Headwaters Region of China, evapotranspiration simulated by ARTS and PT-JPL models, and internationally shared MOD16 and SSEBop remote sensing evapotranspiration products, we conducted a BMA integration study, resulting in a dataset of surface evapotranspiration in the Three-River Headwaters Region of Qinghai Province based on Bayesian model averaging from 2003 to 2015. By verifying the results of each input model and BMA integrated model, it is found that the correlation between ET based on BMA and flux observation data is 0.94, which can explain 89% of the seasonal variation data, outperforming the performance of a single model. The results show that BMA model integration can integrate the inherent advantages of different models and reduce the uncertainty of result estimation for more reliable estimation results. This dataset can provide more accurate data for studying hydrothermal changes and evaluating ecosystem regulation functions in the Three-River Headwaters Region.

Estimating crop evapotranspiration of wheat-maize rotation system using hybrid convolutional bidirectional Long Short-Term Memory network with grey wolf algorithm in Chinese Loess Plateau region

Accurate estimation of crop evapotranspiration (ET) is essential for the efficient utilization of agricultural water resources, crop production enhancement, and sustainable agricultural development. However, direct measurement of ET is highly expensive, intricate, and time-consuming, highlighting the imperative of establishing a novel model to accurately estimate ET in agricultural ecosystems. To address the above problems, this study proposed a novel model (GWA-CNN-BiLSTM), which incorporates Grey Wolf Algorithm (GWA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as a hyperparameter adjuster, feature extractor, and regression component, respectively, to estimate ET built upon various input combinations comprising net solar radiation (Rn), vapor pressure deficit (VPD), average air temperature (Ta), soil water content (SWC), and leaf area index (LAI) about winter wheat-spring maize rotation system during 2012–2020 in the Loess Plateau. Besides, following a comparative assessment within GWA-CNN-BiLSTM, Convolutional Bidirectional Long Short-Term Memory network (CNN-BiLSTM), BiLSTM, Long Short-Term Memory network (LSTM), and Shuttleworth-Wallace (SW) models, the results revealed that GWA-CNN-BiLSTM under varied inputs obtained the superior performance, ranging from 0.562 to 0.957 in determination coefficient (R2), 8.4–41.5 % in relative root mean square error (RRMSE), 0.349 mm d−1 to 1.521 mm d−1 in mean absolute error (MAE), −3.26 % to 14.11 % in percent bias (PBIAS), and 0.820–1.091 in regression coefficient (b0), respectively. Moreover, while the accuracy of BiLSTM over LSTM was evident, its performance was notably improved by the incorporation of the CNN module. Additionally, LSTM-type models under complete input combination present better precision than SW by 29.7−51.4 % in R2, 44.2−76.1 % in RRMSE, and 33.6−63.4 % in MAE, respectively. Furthermore, the accuracy of all models under varied inputs exhibited excellence in winter wheat compared to spring maize, and corresponding improvements ranged 1.4−4.3 % in R2, 5.1−20.1 % in RRMSE, and 3.1−17.9 % in MAE, respectively. Besides, the meteorological factors (Rn, VPD, Ta) proved to be the most important inputs for ET estimation in winter wheat and spring maize. Wherein the importance of SWC exceeded that of LAI in winter wheat, while the opposite trend was observed in spring maize. In brief, GWA-CNN-BiLSTM is the highly recommended model to estimate ET of winter wheat-spring maize rotation system under diverse input data scenarios in the Loess Plateau, which can facilitate to offer valuable assistance in regional agriculture water management decisions.

Aspect Differences in Vegetation Type Drive Higher Evapotranspiration on a Pole‐Facing Slope in a California Oak Savanna

AbstractQuantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.