Improve Evapotranspiration Estimates 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.

Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data

Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.

DNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information

Accurate estimates of the spatiotemporal distribution of evapotranspiration (ET) are essential for understanding terrestrial energy, carbon and water cycles. Station-based observations are limited for their spatial coverage whereas satellite-derived ET products exhibit large discrepancies and uncertainties. Here we presented a Deep Neural Networks based Merging ET (DNN-MET) framework that combines information from satellite-derived ET products, eddy covariance (EC) observations and ancillary surface properties to improve the representation of the spatiotemporal distribution of ET, especially in data-sparse regions. DNN-MET was implemented over the Heihe River Basin (HRB) from 2008 to 2015, and the performance of DNN-MET and eight input state-of-the-art satellite-derived ET products (i.e., MOD16, ET-SEMI, ET-JPL, ET-MS, ET-HF, GLEAM, ETMonitor and EB-ET) was evaluated against observations from 19 EC flux tower sites. The results showed that DNN-MET improved ET estimates over HRB, and decreased the RMSE by 0.13 to 1.02 mm/day (14%-56%) when compared with eight products. DNN-MET also yielded superior performance compared to the products derived by other merging methods (i.e., Random Forest, Bayesian model averaging and a simple averaging method). When DNN-MET was validated for data-scarce regions, its performance remained better even when the training samples were decreased to 20% of the available EC sites. An innovation of our approach is by building a multivariate merging model with ancillary surface properties, DNN-MET incorporated geographical proximity effects and spatial autocorrelations into merging procedure, which can be used as a “spatial knowledge engine” to improve ET predictions. The approach can be readily and effectively applied elsewhere to improve the spatiotemporal representation of various hydrometeorological variables.

Improving terrestrial evapotranspiration estimation across China during 2000–2018 with machine learning methods

Estimating terrestrial evapotranspiration (ET) accurately at various temporal and spatial scales is crucial for understanding the hydrological cycle and water resource management. The currently available ET estimates have some uncertainties and need to be further improved. In this study, six machine learning methods including the random forests, support vector machine, Gaussian process regression (GPR), ensemble trees, general regression neural network, and Bayesian Model Averaging, are applied and evaluated to improve China terrestrial ET estimation by integrating five process-based ET algorithms including SEMI-PM, RS-PM, RRS-PM, MOD16, and PMLv2. Then evaluations are conducted with the eddy covariance flux observations at 14 China flux tower sites distributing in forest, shrub, wetland, grassland, and cropland, as well as with water balance-based ET at basin scale. According to the multiple training, validation, and testing, the GPR method is superior to all the other methods. Compared with the individual algorithms, the GPR method can reduce the root mean square error (RMSE) by 0.45 mm day−1 (for SEMI-PM) ~0.81 mm day−1 (for PMLv2), improve the coefficientofdetermination (R2) by 0.061 (for PMLv2) ~ 0.33 (for MOD16), and decrease the absolute relative percent error (RPE) by 8.32% (for RS-PM) ~42.47% (for PMLv2) for all the test data. At basin scale, the results demonstrate that the annual GPR-merged China terrestrial ET is reliable (R2 = 0.88, RMSE = 57.18 mm year−1, RPE = −0.26%) and has higher accuracy than the currently available eight high-resolution ET products and the estimates from the other five machine learning methods and the five single ET models. The annual average terrestrial ET across China for 2000–2018 estimated by the GPR method is approximately 397.65 mm year−1. More ground-based observations of terrestrial ET covering various land types should be collected to update the integrating methods and improve ET estimates. The resultant China terrestrial ET product with a spatial and temporal resolution of 1 km and 10 days (ChinaET1km10days) produced by the GPR method is available at https://doi.org/10.6084/m9.figshare.12278684.v5.

On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient

Accurately mapping of regional-scale evapotranspiration (ET) from the croplands using remote sensing is currently challenged by limited spatial information on crop and field management to properly characterize the biophysical constraints on ET. A multi-model ensemble can potentially address this challenge, however, conventional ensemble models using the simple average (MEAN) or Bayesian Model Average (BMA) assign a fixed weight to each model and may not fully utilize the strengths of individual models. To this end, we developed four ensemble ET Models (EEMs) that use different machine learning (ML) classifiers, namely K-nearest neighbors, random forest, support vector machine, and multi-layer perception neural network (MLP), to assign varying weights to assemble six physically-driven remote sensing-based ET models. These ML-based EEMs were compared against the six individual ET models and two conventional ensemble methods (MEAN and BMA) using latent heat fluxes (λE) observations from 47 cropland eddy covariance flux sites covering diverse environments across the globe. Results suggested that while MEAN and BMA can reduce some uncertainties in the individual models, ML-based EEMs can better integrate the capabilities of multiple biophysical constraints on ET used across the individual models. The four ML-based EEMs yielded daily λE for training, validation, and testing datasets with the coefficient of determination (R2) and root mean squared error (RMSE) within 0.75 – 0.83 and 18 – 21 W m−2, respectively, among which the MLP algorithm was found to be the most efficient with respect to accuracies and costs. These performance metrics were much better than those from the conventional ensemble models (R2 = 0.69 – 0.71, RMSE = 23 – 25 W m−2) and six individual ET models (R2 = 0.53 – 0.69, RMSE = 26 – 35 W m−2). Results suggested that ML-based EEMs perform much better than the conventional approaches and hence can be viable tools for mapping cropland ET across a wide environmental gradient.

Evaluation of Simplified Surface Energy Balance Index (S-SEBI) Method for Estimating Actual Evapotranspiration in Kangsabati Reservoir Command Using Landsat 8 Imagery

Evapotranspiration (ET) is an important hydrological variable for better irrigation management, water budgeting, and runoff estimation which should be estimated as precisely as possible both in space and time. However, most of the available crop coefficient-based ET computation methods provide point-scale estimates which need upscaling to apply at the catchment or command area scale. This study evaluates the applicability of the simplified surface energy balance index (S-SEBI) method to compute the spatially distributed daily ET in the Kangsabati reservoir command in eastern India considering the crop coefficient-based coupled Hargreaves–Samani (ETc_HG) method as the benchmark. The study is based on two major crops of paddy and potato in the Rabi season of 2015 at 100 surveyed ground truth locations in the selected command area having different crop growth stages and using the site-specific Landsat 8 images on three cloud-free dates. The S-SEBI method shows improved ET estimates during the crop development stage characterized by higher canopy cover than that during the initial crop development stage with lesser canopy cover that traps less radiation. Results revealed that S-SEBI-based ET estimates correlated well with ETc_HG with r and RMSE value of 0.06 and 1.13 mm/day (initial stage), 0.71 and 0.52 mm/day (development stage) and 0.77 and 0.52 (maturity stage) for paddy. The r and RMSE value for potato is found to be better during the development stage (0.43, 0.69 mm/day) than the initial stage (0.17, 0.64 mm/day) in a similar trend with paddy. Therefore, the crop coefficient-based method could be advantageous at point-scale with adequate data availability conditions, whereas the S-SEBI method could be used in data-scarce areas to estimate the spatially distributed ET values.

SSII-Evap: Simplified scheme to incorporate improved evapotranspiration estimates into the streamflow elasticity framework

The streamflow elasticity concept based on the Budyko framework is widely used in hydrological impact assessment studies. However, in landscapes transformed by human activities, identification of climate contributions to runoff change is difficult due to changing surface properties of river basins. Here, a method is proposed to quantify the effects of changing vegetation cover and included them in the calculus. The simplified scheme to incorporate improved evapotranspiration estimates into the streamflow elasticity framework is introduced and named as “SSII-Evap” method. SSII-Evap allows for calculating runoff changes induced by climate taking into account: 1) changes in two climatic variables (precipitation and potential evapotranspiration) and 2) changes in land surface conditions responsible for varying actual evapotranspiration. The six-step procedure provides a focused guide for enhancing the original method.•The SSII-Evap method introduces a bias correction to the original bivariate framework of streamflow elasticity to climate change.•In contrast to the original method, SSII-Evap accounts for the influence of vegetation changes on actual evapotranspiration that is estimated from satellite-derived data.•The elaborated customization is helpful for discriminating between climatic and human induced changes in mean annual runoff and is applicable to heavily modified river basins.

Estimating evapotranspiration using the complementary relationship and the Budyko framework

Abstract Several models have been developed to estimate evapotranspiration. Among those, the complementary relationship has been the subject of many recent studies because it relies on meteorological data only. Recently, the modified Granger and Gray (GG) model showed its applicability across 34 diverse global sites. While the modified GG model showed better performances compared to the recently published studies, it can be improved for dry conditions and the relative evaporation parameter in the original GG model needs to be further investigated. This parameter was empirically derived from limited data from wet environments in Canada – a possible reason for decreasing performance with dry conditions. This study proposed a refined GG model to overcome the limitation using the Budyko framework and vegetation cover to describe relative evaporation. This study used 75 eddy covariance sites in the USA from AmeriFlux, representing 36 dry and 39 wet sites. The proposed model produced better results with decreasing monthly mean root mean square error of about 30% for dry sites and 15% for wet sites compared to the modified GG model. The proposed model in this study maintains the characteristics of the Budyko framework and the complementary relationship and produced improved evapotranspiration estimates under dry conditions.

Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally

AbstractUncertainty in ground heat flux (G) means that evaluation of the other terms in the surface energy balance (e.g., latent and sensible heat fluxes (LE and H)) remains problematic. Algorithms that calculate LE and H require available energy, the difference between net radiation, RNET, and G. There are a wide range of approaches to model G for large‐scale applications, with a subsequent wide range of estimates and accuracies. We provide the largest review of these methods to date (N = 6), evaluating modeled G against measured G from 88 FLUXNET sites. The instantaneous midday variability in G is best captured by models forced with net radiation, while models forced by temperature show the least error at both instantaneous and daily time scales. We produce global decadal data sets of G to illustrate regional and seasonal sensitivities, as well as uncertainty. Global model mean midmorning instantaneous G is highest during September, October, and November at 63.42 (±16.84) Wm−2, while over December, January, and February G is lowest at 53.86 (±18.09) Wm−2 but shows greater intermodel uncertainty. Results from this work have the potential to improve evapotranspiration estimates and guide appropriate G model selection and development for various land uses.

Evaluation of Clear-Sky Incoming Radiation Estimating Equations Typically Used in Remote Sensing Evapotranspiration Algorithms

Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave components of net radiation are obtained from remote sensing data, while the incoming shortwave (RS) and longwave (RL) components are typically estimated from weather data using empirical equations. This study evaluates the accuracy of empirical equations commonly used in remote sensing ET algorithms for estimating RS and RL radiation. Evaluation is carried out through comparison of estimates and observations at five sites that represent different climatic regions from humid to arid. Results reveal (1) both RS and RL estimates from all evaluated equations well correlate with observations (R2 ≥ 0.92), (2) RS estimating equations tend to overestimate, especially at higher values, (3) RL estimating equations tend to give more biased values in arid and semi-arid regions, (4) a model that parameterizes the diffuse component of radiation using two clearness indices and a simple model that assumes a linear increase of atmospheric transmissivity with elevation give better RS estimates, and (5) mean relative absolute errors in the net radiation (Rn) estimates caused by the use of RS and RL estimating equations varies from 10% to 22%. This study suggests that Rn estimates using recommended incoming radiation estimating equations could improve ET estimates.

Peach orchard evapotranspiration in a sandy soil: Comparison between eddy covariance measurements and estimates by the FAO 56 approach

The evapotranspiration from a 3 to 4 years old drip irrigated peach orchard, located in central Portugal, was measured using the eddy covariance technique during two irrigation seasons, allowing the determination of crop coefficients. These crop coefficient values differed from those tabled in FAO Irrigation and Drainage Paper 56. In order to improve evapotranspiration estimates obtained from FAO tabled crop coefficients, a dual crop coefficient methodology was adopted, following the same guidelines. This approach includes a separation between the plant and soil components of the crop coefficient as well as an adjustment for the sparse nature of the vegetation. Soil evaporation was measured with microlysimeters and compared with soil evaporation estimates obtained by the FAO 56 approach. The FAO 56 method, using the dual crop coefficient methodology, was also found to overestimate crop evapotranspiration. During 2 consecutive years, measured and estimated crop coefficients were around 0.5 and 0.7, respectively. The estimated and measured soil evaporation components of the crop coefficient were similar. Therefore, the overestimation in evapotranspiration seems to result from an incorrect estimate of the plant transpiration component of the crop coefficient. A modified parameter to estimate plant transpiration for young, yet attaining full production, drip irrigated orchards is proposed based on field measurements. The method decreases the value of basal crop coefficient for fully developed vegetation. As a result, estimates of evapotranspiration were greatly improved. Therefore, the new approach seems adequate to estimate basal crop coefficients for orchards attaining maturity established on sandy soils and possibly for other sparse crops under drip irrigation conditions.