Remotely Sensed Evapotranspiration Research Articles

SWAT Model Calibration and Validation with Remotely Sensed Evapotranspiration data in Maklang-Tuyungbi-Taret lok Ungauged Basin in Manipur, India

SWAT Model Calibration and Validation with Remotely Sensed Evapotranspiration data in Maklang-Tuyungbi-Taret lok Ungauged Basin in Manipur, India

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Identification of Actual Irrigated Areas in Tropical Regions Based on Remote Sensing Evapotranspiration

Identification of Actual Irrigated Areas in Tropical Regions Based on Remote Sensing Evapotranspiration

Uncertain Benefits of Using Remotely Sensed Evapotranspiration for Streamflow Estimation—Insights From a Randomized, Large-Sample Experiment

AbstractRemotely sensed evapotranspiration (ETRS) shows promise for enhancing hydrological models, especially in regions lacking in situ streamflow observations. However, model calibration studies showed conflicting results regarding the ability of ETRS products to improve streamflow simulation. Rather than relying on model calibration, here we produce the first randomized experiment that explores the full streamflow–ET skill distribution, and also the first probabilistic assessment of the value of different global ETRS products for streamflow simulation. Using 280,000 randomized SWAT (Soil and Water Assessment Tool) model runs across seven catchments and four ETRS products, we show that the relationship between ET and streamflow skills is complex, and simultaneous improvement in both skills is only possible in a limited range. Parameter sensitivity analysis indicates that the most sensitive parameters can have opposite contributions to ET and streamflow skills, leading to skill trade-offs. Conditional probability assessment reveals that models with good ET skills are likely to produce good streamflow skills, but not vice versa. We suggest that randomized experiments such as ours should be performed before model calibration to determine whether using ETRS is worthwhile, and to help in interpreting the calibration results.

A ground-independent method for obtaining complete time series of in situ evapotranspiration observations

In situ evapotranspiration (ET) observations play an important role in validating remotely-sensed ET models and products and analyzing eco-hydrological processes. The eddy covariance technique has been widely utilized to obtain ground truths. However, due to power failures, sensor malfunction, and unreasonable observation, ET measured by the eddy covariance is temporally discontinuous at the hourly and daily scales, which limits the application of the observed ET data. We cannot fill the gaps using ground-based auxiliary measurements because of their discontinuity due to similar reason. To solve this “double-discontinuous trouble” issue and obtain a complete time series of in situ ET, we proposed a ground-independent (GI) method based on multi-source data and machine learning (ML) algorithm. First, reference variables were selected from reanalysis and remote sensing data according to the Penman-Monteith (PM) equation and processed to form the temporally continuous data. Then, the ML algorithm was trained to establish the relationship between ET and reference variables. Finally, the gaps were filled by the well-trained ML model to obtain temporally continuous ET. This study took the random forest (RF) algorithm as an example, and the method was tested at 6 sites. Results show that the proposed GI method has excellent overall performance (averaged R2 = 0.85, RMSE = 39.88 W/m2) and showing better performance than previous studies at cropland and forested sites, with the averaged R2 of 0.85 and 0.82, respectively. Using reanalysis data as reference data causes 3.5 % decrease for R2 compared with the ground-dependent (GD) method, but the GI method is more widely applicable. Furthermore, the GI method can effectively fill gaps under different gap length scenarios, even for very long gaps scenario (R2 = 0.75). We also found that the RF outperforms the other three ML algorithms, and it is essential to include at least radiation, normalized difference vegetation index, and air temperature in the reference data. In conclusion, we provided an accurate and efficient gap-filling method independent of ground-based auxiliary observations. This proposed GI method could be valuable in supporting hydro-ecological research and the validation of ET models and products.

Calibrating a remote sensing evapotranspiration model using the Budyko framework

Remote sensing (RS) evapotranspiration (ET) models are commonly used to estimate ET at a large scale. These models often include spatially varying parameters that need to be determined. Typically, these parameters are either held as constants or obtained from a pre-specified lookup table, potentially introducing bias into ET estimates. This study aimed to evaluate the feasibility of calibrating a RS ET model using Budyko-derived mean annual ET. We applied this approach to calibrate the single parameter of the Penman-Monteith-Leuning (PML) model. Instead of using the default Budyko-Fu parameter ω (ω=2.6), we developed a data-driven model that relates the basin-calibrated ω and its primary controls. Subsequently, we mapped the spatial pattern of ω across China. The calibrated PML model was evaluated using flux observations from 12 ChinaFlux sites and water balance-based ET estimates from 80 basins in China. Results indicate that the Budyko-based calibration significantly improved the performance of the PML model at both site and basin scales compared to the uncalibrated model. This study highlights the potential of leveraging the Budyko framework to constrain the parameter estimates of RS ET models, thereby enhancing the accuracy of large-scale ET estimates.

Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

Accurate terrestrial evapotranspiration (ET) estimation over complex surfaces with spatial heterogeneity is crucial for local, regional, and global applications. Currently, a variety of global ET products with different spatiotemporal resolutions have been developed and evaluated. However, little is known about their performance in capturing the heterogeneity of ET over complex surfaces. Focusing on the Heihe River Basin (HRB), a typical arid and semi-arid region that is hydrologically vulnerable, this study compared ET from eleven global ET products (six remotely- sensed ET, two land surface model ET, one hydrological model-based ET, one reanalysis ET, and one synthesis ET) and one regional ET dataset against dense eddy covariance observations in terms of the magnitude, seasonal cycle, and spatial pattern. In general, the remotely-sensed ET and synthesis ET outperformed other categories, with the operational Simplified Surface Energy Balance (SSEBop), Penman-Monteith–Leuning (PML), Moderate Resolution Imaging Spectroradiometer (MOD16) and Global LAnd Surface Satellite (GLASS) performing relatively better (root mean square error ranging from 1.22 to 1.57 mm d-1). Across all the land cover types, ET products reproduced a relatively feasible ET over the desert steppe, meadow, and barren, but substantially underestimated ET over ecosystems with high ET values but a low land area fraction over HRB (cropland, wetland, and forest). This highlights the importance of accurately representing the heterogeneity and local climates in the complex land surfaces for ET quantification in regional water resource management.

Climate change rather than vegetation greening dominates runoff change in China

China leads the world in vegetation greening and accounts for one-fourth of the global net increase in leaf area over the past two decades. However, it remains elusive on the relative importance of vegetation greening and climate change on China’s hydrological cycle due to the lack of observational-based constraints on notorious model uncertainties. Here, we developed a process-based distributed hydrological model that couples a nonlinear runoff generation mechanism with a remotely-sensed evapotranspiration (ET) module. This model could well capture the spatiotemporal patterns of the main hydrological components (runoff, ET, and soil moisture) at grid scale and streamflow at watershed scale during 1982–2012 over the mainland China. We show that the changes in climatic factors (precipitation and potential ET) dominated hydrologic change at the national scale, with climate induced runoff decrease by 7.6 mm year−1 compared to 0.6 mm year−1 caused by vegetation change. Vegetation effect was primarily notable in water-limited regions as a higher correlation between vegetation contribution to runoff change and absolute leaf area index (LAI) trend in water-limited regions (r = 0.52, p < 0.01) than energy-limited regions (r = 0.19, p < 0.01). Our results highlight the significance of region-dependent differential measures for sustainable water resources management and climate change adaptation under a changing climate.

Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

AbstractEstimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys. Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient (r) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

A two-step calibration framework for hydrological parameter regionalization based on streamflow and remote sensing evapotranspiration

• We proposed a two-step calibration based parameter regionalization method. • This method combines spatial proximity method and RS ET based parameter calibration method. • The proposed regionalization method achieves the overall best model performance. • The accuracy of RS ET is the main factor affecting the performance of the proposed method. Runoff prediction in ungauged catchments is an important and challenging research topic in the field of hydrology. However, traditional parameter regionalization methods may not be applicable in regions with sparsely distributed hydrological stations. Remote sensing (RS) evapotranspiration (ET) products provide valuable information for model parameter estimations. This study aimed to improve the accuracy of hydrological modeling in ungauged catchments by combining the traditional spatial proximity (SP) method with the RS ET-based parameter calibration method to form a two-step calibration-based regionalization method (SP-bcET method using bias-corrected RS ET data and SP-rawET method using raw RS ET data). The proposed regionalization scheme is demonstrated by using the variable infiltration capacity model in the Yangtze River Basin, China, and its performance was evaluated and compared with the SP method and two RS ET-based parameter calibration schemes (bcET method using bias-corrected RS ET data and rawET method using raw RS ET data). The results indicate that the bcET method performs better than the rawET method, the SP method outperforms the RS ET-based parameter calibration method, and the SP-bcET and SP-rawET methods achieve the best overall performance among the five methods. Taking the simulated streamflow at a daily scale in the calibration period as an example, the average Nash-Sutcliffe coefficient of efficiency for the SP, bcET, rawET, SP-bcET, and SP-rawET methods were 0.69, 0.61, 0.56, 0.71, and 0.71, respectively. Moreover, the SP-bcET and SP-rawET methods improved the simulation of the temporal dynamics and spatial variability of soil moisture anomalies. The accuracy of the RS ET data was one of the most dominant factors affecting the performance of the proposed parameter regionalization scheme. The SP-bcET method is expected to perform better than the SP method in warmer catchments or when gauged catchments are sparely distributed. The findings demonstrate the great potential of combining the traditional parameter regionalization and RS ET-based parameter calibration methods in hydrological simulations in ungauged catchments.

Remote sensing-based evapotranspiration determination: A review of single-source energy balance models

Remote Sensing evapotranspiration models are critical in order to understand the cycling of water in the environment. Initially, an outline of the concepts related to evapotranspiration, as well as the shortcomings of land-based methods, is presented. The aim of the study was based on reviewing remote sensing evapotranspiration models which provide an alternative data source. These models have proved to be a cheaper alternative to mapping and estimating spatiotemporal evapotranspiration measurements across local and regional scales. This paper reviews the single-source energy balance model, which differs from the two-source model, for estimating spatiotemporal measurements of evapotranspiration. The single-source energy balance model is underpinned by mathematical equations which differentiate the various single-source evapotranspiration models (Surface Energy Balance Systems, Simplified Surface Energy Systems, Surface Energy Balance Algorithm, and Mapping Evapotranspiration at high Resolution and with Internalised Calibration). The soil surface and forest canopy components were observed to be the major difference between the single and dual-source models. Further advice was discussed on the implementation of the OpenET tool, which provides an open and accessible satellite-based estimation of evapotranspiration for improved water management.

Modeling Evapotranspiration at Larger Temporal Scales: Effects of Temporal Aggregation and Data Gaps

Evapotranspiration (ET) at weekly and monthly time scales is often needed for various applications. When using remote sensing (RS)-based models, this can be achieved either by averaging all the required input variables to the intended time scale and simulating ET using models (input aggregation), or by estimating daily ET from the models and averaging to weekly or monthly ET (output aggregation). It is not clear if both these aggregation approaches yield the same outcome when using RS-based models for the estimation of ET. Another issue in obtaining ET at longer time scales is the lack of enough satellite observations to estimate ET with reasonable accuracy. This study aimed to compare the input and output aggregation approaches to obtain ET at weekly and monthly time scales using three RS ET models, namely, Priestley–Taylor Jet Propulsion Lab (PT-JPL), Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE), and Surface Temperature Initiated Closure (STIC) models. The study was conducted using in situ data over six sites of different agro-climatic conditions in India, Tunisia, and France. The results indicate that the input aggregation provided relatively better results for monthly and weekly ET values than the output aggregation, having a lower RMSE (1–40%). Further, it was found that at least seven to eight satellite observations per month are required to obtain reliable ET estimate when using RS-based models.

Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces

Validation of remotely sensed evapotranspiration (RS_ET) products is important because their accuracy is critical for various scientific applications. In this study, an integrated validation framework was proposed for evaluating RS_ET products with coarse spatial resolution extending from homogenous to heterogeneous land surfaces. This framework was applied at the pixel and river basin scales, using direct and indirect validation methods with multisource validation datasets, which solved the spatial mismatch between ground measurements and remotely sensed products. The accuracy, rationality of spatiotemporal variations, and error sources of RS_ET products and uncertainties during the validation process were the focuses in the framework. The application of this framework is exemplified by validating five widely used RS_ET products (i.e., GLEAM, DTD, MOD16, ETMonitor, and GLASS) in the Heihe River Basin from 2012 to 2016. Combined with the results from direct (as the priority method) and indirect validation (as the auxiliary method), DTD showed the highest accuracy (1-MAPE) in the vegetation growing season (75%), followed by ETMonitor (71%), GLASS (68%), GLEAM (54%), and MOD16 (44%). Each product reasonably reflected the spatiotemporal variations in the validation dataset. ETMonitor exhibited the highest consistency with the ground truth ET at the basin scale (ETMap) (R = 0.69), followed by GLASS (0.65), DTD (0.63), MOD16 (0.62), and GLEAM (0.57). Error sources of these RS_ET products were mainly due to the limitations of the algorithms and the coarse spatial resolution of the input data, while the uncertainties in the validation process amounted to 15–28%. This work is proposed to effectively validate and improve the RS_ET products over heterogeneous land surfaces.

Assessment of an extended SPARSE model for estimating evapotranspiration from directional thermal infrared data

The spatial distribution of evapotranspiration is often obtained from dual source energy balance models forced by surface temperature data. The use of multi-angular remotely-sensed thermal data in such methods makes them susceptible to directional-anisotropy/thermal-radiation directionality effects that may result from the satellite's position, relative to the Sun, at overpass time. It is therefore important to have these effects accounted for to ensure realistic flux retrievals irrespective of sensor viewing position. At present, dual source models generally interpret surface temperature according to two sources, representing the soil surface and the vegetation. This may be insufficient to adequately represent the limiting temperature conditions that not only depend on the source type but also on their exposure to the Sun. Here, we present a modified version of the SPARSE (Soil Plant Atmosphere Remote Sensing Evapotranspiration) model, wherein the original SPARSE is modified to incorporate sunlit/shaded soil/vegetation elements and coupled with a radiative transfer model that links these four component emissions to out-of-canopy directional radiances as observed by remote sensors. An initial evaluation is carried out to check the model's capability in retrieving surface fluxes over diverse environments instrumented with in-situ thermo-radiometers. When run with nadir-acquired thermal data, both algorithms show no observable difference in their retrieval of total fluxes. We nonetheless show that by incorporating the solar direction and discriminating between sunlit and shaded elements, the partitioning of these overall fluxes between the soil and vegetation can be improved especially in water-stressed environments. We also test the sensitivity of flux and component temperature estimates to the viewing direction of the thermal sensor by using two sets of TIR data (nadir and oblique) acquired simultaneously to force the models and show that sensitivity to viewing direction is significantly reduced. This is an important aspect particularly when using high resolution spatial and temporal data from Earth observation missions that inherently have to consider a wide-range of viewing angles in their design.

Estimating Spatially Explicit Irrigation Water Use Based on Remotely Sensed Evapotranspiration and Modeled Root Zone Soil Moisture

AbstractEstimation of irrigation water use (IWU) is critical to assessing water use efficiency and optimizing water resource allocation. However, diversity in irrigation scheduling, irrigation system, and crop type has imposed restrictions on deriving the accurate spatial distribution of IWU. Existing approaches of deriving IWU may have large uncertainty, due mostly to methodological limitations, inadequate inputs, and the mismatch in spatiotemporal scale between IWU estimates and in situ counterparts. Remote sensing has the potential to provide valuable information on moisture transport, which could unravel water consumption by crops. First, we developed a robust relationship between root zone soil moisture (RZSM) and the ratio of actual evapotranspiration (ETa) to reference ET from the land components of European ReAnalysis‐5 (ERA5L) without an irrigation module across the Contiguous United States (CONUS). Second, RZSM under irrigation was derived from the developed relationship and remotely sensed ETa from the operational Simplified Surface Energy Balance (SSEBop) model during 2000–2020. Third, IWU over the two decades was derived based on increases in RZSM and ETa due to irrigation using remotely sensed and model‐based variables, along with our derived formulas considering groundwater recharge originating from irrigation. The IWU estimates were fairly reliable compared with reported IWU from the Farm and Ranch Irrigation Survey at the state‐level in four years, with the coefficient of determination ranging from 0.74 to 0.84, root mean square error ranging from 1.90 to 2.33 km3/yr, and bias ranging from −0.11 to 0.15 km3/yr. Integration of remotely sensed ETa with ERA5L output resulted in IWU estimates of much higher accuracy than published studies. The IWU estimates across the CONUS reflected an increasing trend (∼2.71 km3/yr2) during 2000–2007 but a decreasing trend (∼0.90 km3/yr2) during 2008–2020, due to efficient irrigation practices and increased precipitation.

Evaluating the added value of multi-variable calibration of SWAT with remotely sensed evapotranspiration data for improving hydrological modeling

Hydrological processes in a watershed consist of multiple sub-processes (such as plant growth, evapotranspiration, water yield, and soil–water balance) that have complex interactions. The common practice of calibrating hydrological models against only a single variable (e.g., streamflow) can lead to parameter uncertainty (also known as equifinality), resulting in significant uncertainties in the representation and simulation of sub-processes. As multi-variable calibration can be a potential solution to this issue, we tested the integration of spatially and temporally gridded remotely sensed evapotranspiration (RS-ET) data with the Soil and Water Assessment Tool (SWAT) hydrological model. This approach was intended to reduce equifinality by enhancing related hydrological sub-processes in both space and time rather than improving the evaluation metrics at the streamflow outlet. We further introduced the principle of repeated measure design in the calibration process, where the SWAT was calibrated under two different schemes: Scheme1 (using only streamflow data) and Scheme2 (using both RS-ET and streamflow data). The model's performance was evaluated using the concept of stability at multiple spatial scales (basin outlet, sub-basins, and hydrological response units) and aspects (different model outputs and most sensitive calibrated parameters). The significance of the difference between the stabilities produced by the two schemes was estimated using the Mann–Whitney U test. Testing this approach in Meichuan Basin (China) showed that Scheme2 substantially reduced equifinality for calibrated parameters and model outputs compared to Scheme1. In addition, the model solutions and outputs for Scheme2 were significantly different from Scheme1. Our results demonstrate the added value of using increasingly available open-access RS-ET data for improving hydrological model calibration.

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

Aerodynamic resistance (hereafter ra) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of ra plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates.The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019.Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low ra (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of ra on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on ra for modelling LE (VIP < 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of ra.

Evapotranspiration in the Amazon: spatial patterns, seasonality, and recent trends in observations, reanalysis, and climate models

Abstract. Water recycled through transpiring forests influences the spatial distribution of precipitation in the Amazon and has been shown to play a role in the initiation of the wet season. However, due to the challenges and costs associated with measuring evapotranspiration (ET) directly and high uncertainty in remote-sensing ET retrievals, the spatial and temporal patterns in Amazon ET remain poorly understood. In this study, we estimated ET over the Amazon and 10 sub-basins using a catchment-balance approach, whereby ET is calculated directly as the balance between precipitation, runoff, and change in groundwater storage. We compared our results with ET from remote-sensing datasets, reanalysis, models from Phase 5 and Phase 6 of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6 respectively), and in situ flux tower measurements to provide a comprehensive overview of current understanding. Catchment-balance analysis revealed a gradient in ET from east to west/southwest across the Amazon Basin, a strong seasonal cycle in basin-mean ET primarily controlled by net incoming radiation, and no trend in ET over the past 2 decades. This approach has a degree of uncertainty, due to errors in each of the terms of the water budget; therefore, we conducted an error analysis to identify the range of likely values. Satellite datasets, reanalysis, and climate models all tended to overestimate the magnitude of ET relative to catchment-balance estimates, underestimate seasonal and interannual variability, and show conflicting positive and negative trends. Only two out of six satellite and model datasets analysed reproduced spatial and seasonal variation in Amazon ET, and captured the same controls on ET as indicated by catchment-balance analysis. CMIP5 and CMIP6 ET was inconsistent with catchment-balance estimates over all scales analysed. Overall, the discrepancies between data products and models revealed by our analysis demonstrate a need for more ground-based ET measurements in the Amazon as well as a need to substantially improve model representation of this fundamental component of the Amazon hydrological cycle.

Analyzing the Suitability of Remotely Sensed ET for Calibrating a Watershed Model of a Mediterranean Montane Forest

The ability to spatially characterize runoff generation and forest health depends partly on the accuracy and resolution of evapotranspiration (ET) simulated by numerical models. A possible strategy to increase the accuracy and resolution of numerically modeled ET is the use of remotely sensed ET products as an observational basis for parameter estimation (model calibration) of those numerical models. However, the extent to which that calibration strategy leads to a realistic representation of ET, relative to ground conditions, is not well understood. We examined this by comparing the spatiotemporal accuracy of ET from a remote sensing product, MODIS MOD16A2, to that from a watershed model (SWAT) calibrated to flow measured at an outlet streamgage. We examined this in the upper Kings River watershed (3999 km2) of California’s Sierra Nevada, a snow-influenced watershed in a Mediterranean climate. We assessed ET accuracies against observations from three eddy-covariance flux towers at elevations of 1160–2700 m. The accuracy of ET from the stream-calibrated watershed model surpassed that of MODIS in terms of Nash-Sutcliffe efficiency (+0.36 versus −0.43) and error in elevational trend (+7.7% versus +81%). These results indicate that for this particular experiment, an outlet streamgage would provide a more effective observational basis than remotely sensed ET product for watershed-model parameter estimation. Based on analysis of ET-weather relationships, the relatively large errors we found in MODIS ET may be related to weather-based corrections to water limitation not representative of the hydrology of this snow-influenced, Mediterranean-climate area.