Gridded Evapotranspiration Research Articles

Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method

This study presents a Bayesian-driven ensemble learning framework to minimize errors in evapotranspiration (ET) data assimilation, thereby generating a continuous-scale ET estimation with improved accuracy for the data-scarce arid Central Asia (CA). Five state-of-the-art gridded ET products were extensively tested and utilized to reconstruct new long-term ET data at grid scale, including two Global Land Data Assimilation System (GLDAS)-Land Surface Models (LSM) (Catchment and NOAH), two reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications – ver. 2 (MERRA2)), along with the Global Land Evaporation Amsterdam (GLEAM) satellite ET product ver. 3.6b. Taylor Skill Score was employed to evaluate the input gridded ET datasets, using in situ measurements across four arid biomes — cropland, shrubland, farmland, and grassland —as benchmarks. Bayes-Markov Chain Monte Carlo (Bayes-MCMC)-derived logarithmically transformed predictors from the posterior ensemble anomalies were standardized to resolve the mismatches in the cumulative distribution functions of ET data relating to the in situ observations. We found that the constrained residual errors were more profound between 45th and 95th percentiles for the rescaled total ET anomalies across all the input ET datasets at 95 % confidence interval. Moreover, the weighted ensemble estimates derived from the Bayes-MCMC were instrumental in correcting ET anomalies, leading to the robust reconstruction of a more reliable ensemble mean ET data for Central Asia (CAETens) at monthly temporal and 0.25° spatial resolutions, spanning from 2003 to 2020. Overall, CAETens exhibited superior performance, achieving the highest averaged Kling-Gupta Efficiency of 0.75 and registering the lowest Root Mean Square Error of 6.85 mm/m compared to the standalone gridded ET products. Comparisons of spatial distributions of fine-scale features, and evaluations using standard metrics revealed that all ET products varied significantly (p < 0.05) by biome types. Remarkably, only CAETens exhibited a reasonable low positive bias and pronounced ability to resolve the complex spatiotemporal patterns associated with the input gridded ET products. The study bridges the gap in continuous ET monitoring for sustainable water resources management in CA.

Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region

Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results.

Water balance for gaged watersheds in the Central Sierra Nevada, California and Nevada, United States

Watershed managers require accurate, high-spatial-resolution evapotranspiration (ET) data to evaluate forest susceptibility to drought or catastrophic wildfire, and to determine opportunities for enhancing streamflow or forest resilience under climate warming. We evaluate an easily calculated product by using annual gridded precipitation (P) and measured discharge (Q), together with a gridded ET product developed from ET and P measured at flux towers plus Landsat NDVI (normalized difference vegetation index) to evaluate uncertainties in water balances across 52 watersheds with stream-gauge measurements in the Central Sierra Nevada. Watershed areas ranged from 5 to 4823 km2, and the study-area elevation range was 52–3302 m. Study-area P, ET, and Q averaged 1263, 634, and 573 mm yr–1 respectively, with precipitation at higher elevations up to five times that at lower elevations. We assessed uncertainty in water-balance components by applying a multiplier to P or Q values across the period of record for each watershed to align annual P-ET and Q values, resulting in average P-ET-Q = 0. Most year-to-year values of annual change in storage (ΔS), calculated as P-ET-Q for watersheds with well-constrained water balances, were within about + 300 mm. Across the study area we found that for each of 37 watersheds, applying a constant multiplier to either annual P or Q resulted in well-constrained water balances (average annual P-ET-Q = 0). Multiplicative adjustment of ET values for each watershed did not improve average water balances over the period of record, and would result in inconsistent values across adjacent and nested watersheds. For a given watershed, ET was relatively constant from year to year, with precipitation variability driving both interannual and spatial variability in runoff. These findings highlight the importance of evapotranspiration as a central metric of water-balance change and variability, and the strength of using high-confidence spatial- evapotranspiration estimates to diagnose uncertainties in annual water balances, and the components contributing to those uncertainties.

Can Indirect Evaluation Methods and Their Fusion Products Reduce Uncertainty in Actual Evapotranspiration Estimates?

AbstractAccurately estimating actual evapotranspiration (ET) across global land surface is one of the key challenges in terrestrial hydrological cycles and energy flux balance studies. Gridded ET products have the potential for application in ungauged basins, but their uncertainties are possibly large and it remains unclear which one is best for a given basin. The water balance (WB) method provides a direct estimate of basin scale ET, but it cannot be used in ungauged basins where streamflow data are unavailable. Here, we first assess the performance of ET from 10 global ET products against WB ET estimates in 43 large river basins. The paper then uses three indirect evaluation methods [Three Cornered Hat (TCH), Arithmetic Average (AA), and Bayesian Three Cornered Hat] to identify the optimal ET products without the need of prior information, and to generate fusion products combining the ET from multiple products. Using the evaluation results derived from the WB method as the reference, the results show that the three methods have great success in identifying poorer products, suggesting that they are useful in filtering poor ET products in applications. However, the ability of such methods in identifying better ET products degrades slightly. The AA fusion product, which combines ET outputs from multiple products, is marginally better than the best single ET product in many of the 43 basins. Because of its simplicity, it could be used to reduce the uncertainty in ET estimates from multiple products for ungauged basins and regions.

Evapotranspiration and energy partitioning of a typical alpine wetland in the central Tibetan Plateau

The alpine wetlands in the Tibetan Plateau (TP) account for more than 30% of the wetlands in China. As an ecosystem that is particularly sensitive to the changing climate, the rapid warming of the TP may have a significant impact on the growth of vegetation in these cold wetlands. However, due to the lack of ground observations, it remains unclear how the land-atmosphere exchanges of water and energy develop in such special alpine ecosystems. This limits the understanding of how eco-hydrological processes respond to environmental changes. In this study, we used ground measured data to investigate the dynamics of energy partitioning and water budget over a typical wetland in the central TP over two hydrological years. The results demonstrate that the seasonal variations in land-atmosphere energy exchange were significant during the observation period. In the growing season, the available energy was mainly consumed by latent heat flux (LE), with the mean ratio of LE to net radiation (Rn) exhibiting values of 0.68 and 0.76 in 2019 and 2020, respectively. During the freezing period, the available energy was primarily consumed by sensible heat flux (H), with mean ratio of H to Rn values of 0.69 and 0.68 in 2019 and 2020, respectively. The total annual evapotranspiration (ET) (include sublimation) in 2019 and 2020 were 652.5 mm and 706.6 mm, respectively. Annual ET was found to be higher than precipitation (P), indicating that soil water was supplied via lateral flow from the melting of upstream glaciers and snow, supporting ET from the wetland. During the growing season, ET was mainly controlled by solar radiation but became more strongly coupled with soil water during the freezing period. Evaluations of four typical gridded ET products suggest that the Penman–Monteith–Leuning (PML) model showed the best performance with the highest Nash-Sutcliffe Efficiency (NSE) when compared with the ground-measured ET rates, indicating that this product may be appropriate for use in similar data-scarce regions. The land-atmosphere energy and water exchanges in the alpine wetland are important for an improved understanding of the hydrological cycle of the Asian Water Tower and could be used as a reference for further ET model parameterizations and validations.

Spatial Patterns in Actual Evapotranspiration Climatologies for Europe

Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.

EVAPOTRANSPIRATION ESTIMATION USING SSEBop METHOD WITH SENTINEL -2 AND LANDSAT-8 DATA SET

Abstract. Estimation of evapotranspiration (ET) parameters is essential for understanding crop water requirements and to find out the ground water recharge. In situ data collection procedures are generally adopted to measure the parameters required to find ET. Latest remote sensing technologies accompanied by newly launched satellite datasets can supplement the field data collection and analysis by finding out some of the parameters such as land surface temperature, normalized difference vegetation index (NDVI), albedo, emissivity, etc. The Upper Ganga Canal Command Area (UGC) lying between two rivers Ganga and Yamuna situated between two states, namely Uttarakhand and Uttar Pradesh in North India is selected as the study area for this research work. Operational Simplified Surface Energy Balance (SSEBop) method is used to derive high resolution (10m) ET map for the Upper Ganga Canal Command Area. Sentinel-2 multi spectral images were used to derive land use, land cover (LU/LC) maps, NDVI, albedo, etc. Downscaled Landsat 8 images were used to derive land surface temperature of the command area. Meteorological data retrieved from the Indian Meteorological Department (IMD) was used to calculate reference evapotranspiration. ET map of the study area was generated using the above estimated parameters. Further, validation of the obtained ET values was accomplished by gridded ET data obtained from IMD.

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Abstract. Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

Development of an Evapotranspiration Data Assimilation Technique for Streamflow Estimates: A Case Study in a Semi-Arid Region

Streamflow estimates are substantially important as fresh water shortages increase in arid and semi-arid regions where evapotranspiration (ET) is a significant contribution to the water balance. In this regard, evapotranspiration data can be assimilated into a distributed hydrological model (SWAT, Soil and Water Assessment Tool) for improving streamflow estimates. The SWAT model has been widely used for streamflow estimations, but the applications combining SWAT and ET products were rare. Thus, this study aims to develop a SWAT-based evapotranspiration data assimilation system. In particular, SWAT is gridded at Hydrologic Response Unit (HRU) level to incorporate gridded ET products acquired from the remote sensing-based ETMonitor model. In the modeling case, Gridded SWAT (GSWAT) shows a good agreement of streamflow modeling with the original SWAT. Such a scant margin between them is due to the modeling domain mismatch caused by different HRU delineations. In the ET assimilation case, we carry out a synthetic data experiment to illustrate the state augmentation Direct Insertion (DI) method and a real data experiment for the upper Heihe River Basin. The results demonstrate the benefits of the ET assimilation for improving hydrologic processes representations. In the future, more remotely sensed data can be assimilated into the data assimilation system to provide more reliable hydrological predictions.

Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins

AbstractAccurately predicting regional-scale water fluxes and states remains a challenging task in contemporary hydrology. Coping with this grand challenge requires, among other things, a model that makes reliable predictions across scales, locations, and variables other than those used for parameter estimation. In this study, the mesoscale hydrologic model (mHM) parameterized with the multiscale regionalization technique is comprehensively tested across 400 European river basins. The model fluxes and states, constrained using the observed streamflow, are evaluated against gridded evapotranspiration, soil moisture, and total water storage anomalies, as well as local-scale eddy covariance observations. This multiscale verification is carried out in a seamless manner at the native resolutions of available datasets, varying from 0.5 to 100 km. Results of cross-validation tests show that mHM is able to capture the streamflow dynamics adequately well across a wide range of climate and physiographical characteristics. The model yields generally better results (with lower spread of model statistics) in basins with higher rain gauge density. Model performance for other fluxes and states is strongly driven by the degree of seasonality that each variable exhibits, with the best match being observed for evapotranspiration, followed by total water storage anomaly, and the least for soil moisture. Results show that constraining the model against streamflow only may be necessary but not sufficient to warrant the model fidelity for other complementary variables. The study emphasizes the need to account for other complementary datasets besides streamflow during parameter estimation to improve model skill with respect to “hidden” variables.

Using uncertainty of Penman and Penman–Monteith methods in combined satellite and ground-based evapotranspiration estimates

Abstract Satellite data are often used for their ability to fill in temporal and spatial patterns in data-sparse regions. It is also known that global satellite products generally contain more noise than ground-based estimates. Data validation of satellite data often treats ground-based estimates as the ‘gold standard’: without error or uncertainty. In the estimation of evapotranspiration (ET) however, ground-based estimates have considerable uncertainty, caused by the input components of the ET equations. This research presents an analysis of uncertainty of reference ET (ET 0 ) caused by these input components. A dataset of correlated random variables is generated for a country with a diverse climate and diverse density of ground observations: New Zealand. The uncertainty analysis shows that: ET 0 is most sensitive to temperature, followed by solar radiation, relative humidity, and cloudiness ratio; and that uncertainty varies between 10% and 40% of ET 0 , and depends on the ET 0 value. Using this uncertainty analysis, a set of correlated random variables, and a Monte-Carlo fitting approach, MOD16 satellite PET data becomes a ‘soft interpolator’ between ground-based ET 0 estimates. The resulting 1 km × 1 km monthly nation-wide dataset has the advantage of: taking into account land cover and vegetation characteristics through the use of satellite data; still abiding to local climate diversity and locally used standards through the use of ground-based estimates; and containing an uncertainty estimate. Further comparison suggests that original MOD16 satellite PET could estimate real PET better than using ground-based estimates of ET 0 . Further research recommends combination with other existing gridded ET estimates, and further validation of real PET estimates.

Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign

Robust spatial information about environmental water use at field scales and daily to seasonal timesteps will benefit many applications in agriculture and water resource management. This information is particularly critical in arid climates where freshwater resources are limited or expensive, and groundwater supplies are being depleted at unsustainable rates to support irrigated agriculture as well as municipal and industrial uses. Gridded evapotranspiration (ET) information at field scales can be obtained periodically using land–surface temperature-based surface energy balance algorithms applied to moderate resolution satellite data from systems like Landsat, which collects thermal-band imagery every 16days at a resolution of approximately 100m. The challenge is in finding methods for interpolating between ET snapshots developed at the time of a clear-sky Landsat overpass to provide complete daily time-series over a growing season. This study examines the efficacy of a simple gap-filling algorithm designed for applications in data-sparse regions, which does not require local ground measurements of weather or rainfall, or estimates of soil texture. The algorithm relies on general conservation of the ratio between actual ET and a reference ET, generated from satellite insolation data and standard meteorological fields from a mesoscale model. The algorithm was tested with ET retrievals from the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model and associated DisALEXI flux disaggregation technique, which uses Landsat-scale thermal imagery to reduce regional ALEXI maps to a finer spatial resolution. Daily ET at the Landsat scale was compared with lysimeter and eddy covariance flux measurements collected during the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment of 2008 (BEAREX08), conducted in an irrigated agricultural area in the Texas Panhandle under highly advective conditions. The simple gap-filling algorithm performed reasonably at most sites, reproducing observed cumulative ET to within 5–10% over the growing period from emergence to peak biomass in both rainfed and irrigated fields.