Satellite-based Evapotranspiration Research Articles

Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather

Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.

Assessing Evapotranspiration Changes in Response to Cropland Expansion in Tropical Climates

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale.

Satellite-based near-real-time global daily terrestrial evapotranspiration estimates

Abstract. Accurate and timely global evapotranspiration (ET) data are crucial for agriculture, water resource management, and drought forecasting. Although numerous satellite-based ET products are available, few offer near-real-time data. For instance, products like NASA's ECOsystem Spaceborne Thermal Radiometer Experiment mounted on the International Space Station (ECOSTRESS) and MOD16 face challenges such as uneven coverage and delays exceeding 1 week in data availability. In this study, we refined the Variation of the Standard Evapotranspiration Algorithm (VISEA) by fully integrating satellite-based data, e.g., European Centre for Medium-Range Weather Forecasts ERA5-Land shortwave radiation (which includes satellite remote sensing data within its assimilation system) and MODIS land surface data (which include surface reflectance, temperature and/or emissivity, land cover, vegetation indices, and albedo as inputs). This enables VISEA to provide near-real-time global daily ET estimates with a maximum delay of 1 week at a resolution of 0.05°. Its accuracy was assessed globally using observation data from 149 flux towers across 12 land cover types and comparing them with five other satellite-based ET products and Global Precipitation Climatology Centre (GPCC) data. The results indicate that VISEA provides accurate ET estimates that are comparable to existing products, achieving a mean correlation coefficient (R) of about 0.6 and an RMSE of 1.4 mm d−1. Furthermore, we demonstrated VISEA's utility in drought monitoring during a drought event in the Yangtze River basin in 2022 in which ET changes correlated with precipitation. The near-real-time capability of VISEA is, thus, especially valuable in meteorological and hydrological applications for coordinating drought relief efforts. The VISEA ET dataset is available at https://doi.org/10.11888/Terre.tpdc.300782 (Huang, 2023a).

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Toward field-scale groundwater pumping and improved groundwater management using remote sensing and climate data

Groundwater overdraft in the western United States has prompted water managers to develop groundwater management plans that include mandatory reporting of groundwater pumping (GP). However, most irrigation systems in this region are not equipped with irrigation water flow meters to record GP and performing quality control of the available metered GP data is difficult due to the scarcity of reliable secondary GP estimates. We hypothesize that Landsat-based actual evapotranspiration (ET) estimates from OpenET can be used to predict GP and aid in quality control of the metered GP data. The objectives of this study are to: 1) pair OpenET estimates of consumptive use (Net ET, i.e., actual ET less effective precipitation) and metered annual GP data from Diamond Valley, Nevada, and Harney Basin, Oregon; 2) evaluate linear regression and machine learning models to establish the GP vs Net ET relationship; and 3) compare GP estimates at the field- and basin-scales. Results from using a bootstrapping technique showed that the mean absolute errors and root mean square errors for field-scale GP depth are ∼11 % and ∼14 % across Diamond Valley and Harney Basin based on the OpenET ensemble mean, which showed the highest skill among all the OpenET ET models. Moreover, the regression models explained 50 %-70 % variance in GP depth and ∼90 % variance in GP volumes. Our GP volume estimates are also within 7 % and 17 % of the total reported and measured volumes in Diamond Valley and Harney Basin, respectively, and the estimated average irrigation efficiency of 87 % aligns with known center-pivot system efficiencies. Additionally, the OpenET ensemble proves to be useful for identifying discrepancies in metered GP data, which are subsequently flagged as outliers. Results from this study illustrate usefulness of satellite-based ET estimates for estimating GP and metered GP data quality control and have the potential to help estimate historical GP.

Estimating Hydrological Regimes from Observational Soil Moisture, Evapotranspiration, and Air Temperature Data

Abstract Evapotranspiration has long been understood to vary with soil moisture in drier regions and to be relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability. Significance Statement When a soil is somewhat dry, an increase in soil moisture can lead to an increase in evapotranspiration E. In contrast, when a soil is wet, E is limited instead by the availability of energy. Determining where E is water limited, energy limited, or some combination of both is important because it tells us where accurate soil moisture initialization in a forecast system might contribute to more accurate forecasts of E and thus air temperature. Here we use a combination of independent datasets (satellite-derived estimates of soil moisture and E as well as air temperature measurements from weather stations) to provide new monthly maps of the water-limited, energy-limited, and combination regimes over the continental United States and across the world.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

Integrated framework for hydrologic modelling in data-sparse watersheds and climate change impact on projected green and blue water sustainability

Climate and hydrologic hazards pose a threat to the distribution of watersheds’ water resources in time and space, necessitating planning for sustainable resilience and adaptation. Hydrologic modelling has emerged as a potential solution for understanding watershed responses to projected climate change, and a prediction model that can deliver actionable information is necessary, although it requires basin-scale observations to calibrate the model to reliably predict basin-scale water resources hazards. Such luxury is not always tenable in watersheds with inadequate ground-based observation. However, satellite-based evapotranspiration (ET) data coupled with a machine learning feature selection as a data refinement process has made integrated water balance modelling widely regarded as a viable alternative for improving the capability of watershed modelling processes in data-sparse regions. This study developed a convincing hydrologic model framework to sufficiently calibrate and provide accurate behavioural solutions for all model responses. The framework was applied to four sub-basins that form the larger Lake Chad basin. The model results were applied to assess the dynamic changes in projected blue and green water resource sustainability in response to climate change in one of the sub-basins. Study findings indicate that hydrologic fluxes can be simulated accurately with varying degrees of acceptability, with R2 and NSE values in the range of 0.69–0.88 and 0.45–0.77 for calibration and 0.69–0.79 and 0.34–0.63 for validation, respectively, and captured within a satisfactory uncertainty range of P-factor and R-factor values of 0.68–0.93 and 0.73–1.31, respectively, in 83%, 67%, 85.7%, and 81.3% of the sub-watersheds based on multi-site simulation despite distinct watershed morphology, although there are significant trade-offs in parameter sensitivity. Whilst green water is the dominant freshwater component across the basin relative to blue water, climate change may be a significant factor influencing changes in the projected green water sustainability status, and the combination of socioeconomic drivers and climate change may significantly impact the projected blue water sustainability status across the basin. Projected changes in the green and blue water sustainability status have shown that more than 50% of the watershed will become ecologically fragile. In addition, the identified freshwater geographic sustainability hotspots may be beyond restoration without adequate long-term river basin water resource plans.

The divergence of energy- and water-balance evapotranspiration estimates in humid regions

Evapotranspiration (ET) calculated as the residual of catchment water balance (ETWB) has often been used as a benchmark to evaluate satellite-based ET retrievals that use the energy-balance approach (ETEB). However, errors from water balance components will accrue in ETWB, leading to considerable disparities with ETEB. In this study, we set out to investigate whether ETEB from multiple sources (MOD16, GLEAM, PT-JPL, and PT-hybrid) can capture the spatiotemporal variability of ETWB across 53 catchments in central-western Europe with a humid climate. Using ET retrievals from the Budyko framework that accounts for the control of energy demand on water supply and upscaled ET from FLUXCOM as references, we explored the causes of discrepancies between ETWB and ETEB at long-term, annual, and monthly scales. We found that (1) ETEB significantly diverged from ETWB at the mean annual scale (r = 0.35), particularly for energy-limited catchments, but Budyko-simulated ET considering energy limit correlated well with ETEB (r > 0.86); (2) neither ETEB nor upscaled ET can reproduce annual ETWB time series (r < 0.40), and the closure errors in water budgets closely follow excess precipitation beyond energy demand; (3) monthly ETWB exhibited better correspondences with ETEB (r = 0.73), presumably because of similarity in seasonal patterns. Our results demonstrate that errors from precipitation and terrestrial water storage anomalies introduce large uncertainties in ETWB, thereby complicating water balance validation in humid regions across multiple timesteps. To improve the application of ETWB for benchmarking ETEB in humid regions, high-quality input data should be used or – like the Budyko framework – energy constraints should be considered.

The combined impact of shallow groundwater and soil salinity on evapotranspiration using remote sensing in an agricultural alluvial setting

Study regionThe Nile Delta, Egypt Study focusShallow groundwater (GW) and soil salinity are major issues for irrigated agriculture, particularly in arid and semi-arid regions, but more research is needed to link both issues with evapotranspiration. Satellite-based evapotranspiration from Landsat images (ETLS) has the potential to be an efficient method of estimating evapotranspiration (ET), which can integrate ETLS with groundwater and soil salinity, particularly in data-scarce areas. This study examines shallow GW and soil salinity effects on crop water use in the North Nile Delta during the summer season of 2017 and winter season of 2017/2018. New hydrological insights for the regionThe ETLS was moderately affected by groundwater depth (GWD), decreasing from 4.3 to 4.0 mm day−1 when GWD was reduced from 75 to 120 cm, then increasing to 4.4 mm day−1 when GWD was increased to 140 cm. The study also highlighted a significant negative correlation between ETgw and GWD; which increased with shallower GW (>75 cm) and then decreased with deeper GW. The shallower the GW, the greater the contribution to crop water requirements, with GW contributing 1.6 and 1.7 mm day−1 for seed melon and cotton, respectively, while GW contributed 0.9 mm day−1 for sugar beet and 1.3 mm day−1 for wheat and clover. The study's findings highlight the importance of remote sensing and GIS techniques for quickly and cheaply assessing the impact of shallow GW and soil salinity on evapotranspiration over large geographic areas.

Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau

More accurate and process-based satellite evapotranspiration (ET) estimation for the Tibetan Plateau (TP)—the Third Pole of the world—have long been of major interest in hydrometeorology. Combining recent advances in satellite-based ET mechanistic algorithms and data-oriented methods allows ET hybrid modeling by coupling physical constraints with machine learning (ML). Specifically, we developed two hybrid models, a surface conductance-based ML model (ML-Gs) and a soil evaporation-based ML model (ML-Es), to estimate regional ET on the TP. These hybrid models have biophysical framework, under which one of the parameters or components is modeled using ML. Hybrid models make ML complementary to the process-based ET framework, which to find an optimal junction between well physical mechanism and high model performance. The daily ET estimates were evaluated at 28 eddy covariance flux tower sites, as well as by comparison with two process-based ET algorithms (a Penman–Monteith-based ET-PM algorithm and a Priestley-Taylor-based ET-PT algorithm) and a data-oriented pure ML method. The hybrid models decreased the root-mean-square-error (RMSE) of two physical algorithms (1.11 mm/day for ET-PM, 1.09 mm/day for ET-PT) to 0.50 mm/day, and increased the Kling-Gupta efficiency (KGE) (0.35 for ET-PM, 0.36 for ET-PT) to 0.92. Our hybrid models also showed improved performance (KGE of 0.65) than pure ML (KGE of 0.62) at data-sparse regions as well as for the responses to extreme weather events. It indicates that our approach does not only boost the ET simulation accuracy, but also improve the physical understanding of ML-based ET estimation. More importantly, ML-Es focuses on the ET components on the TP and is more well-defined than ML-Gs. An innovation of our approach is that for data-sparse regions and extreme cases, the more robust physical mechanism was coupled, the better generalization performance of hybrid model could achieve. The spatiotemporal ET patterns based on our hybrid models were consistent with the variations in local climatic regions and could provide critical information on the understanding of hydrological processes under the global and regional climate changes.

Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.

Evaluating the accuracy of gridded water resources reanalysis and evapotranspiration products for assessing water security in poorly gauged basins

Abstract. Achieving water security in poorly gauged basins is critically hindered by a lack of in situ river discharge data to assess past, current, and future evolution of water resources. To overcome this challenge, there has been a shift toward the use of freely available satellite and reanalysis data products. However, due to inherent bias and uncertainty, these secondary sources require careful evaluation to ascertain their performance before being applied in poorly gauged basins. The objectives of this study were to evaluate river discharge and evapotranspiration estimates from eight gridded water resources reanalysis (WRR), six satellite-based evapotranspiration (ET) products, and ET estimates derived from complimentary relationship (CR–ET) across eight river basins located in Central–West Africa. Results highlight strengths and weaknesses of the different WRR in simulating discharge dynamics and ET across the basins. Likewise, satellite-based products also show some strength and weaknesses in simulating monthly ET. Our results further revealed that the performance of the different models in simulating river discharge and evapotranspiration is strongly influenced by model structure, input data, and spatial resolution. Considering all hydrological model evaluation criteria, FLDAS-Noah, Lisflood, AWRAL, and Terra were among the best performing WRR products while for ET estimates, FLDAS-Noah, Terra, GLEAM3.5a and 3.5b, and PMLV2 outperformed the rest of the products. Given the plethora of WRR and ET products available, it is imperative to evaluate their performance in representative gauged basins to identify products that can be applied in each region. However, the choice of a particular product will depend on the application and user requirements. Taking this together, results from this study suggest that gridded WRR and ET products are a useful source of data for assessing water security in poorly gauged basins.

Application of a multiple model integration framework for mapping evapotranspiration with high spatial–temporal resolution in the Haihe River Basin, China

Evapotranspiration (ET) is a key component of the water and carbon cycles. Because it cannot be observed directly on large regional scales at present, many satellite-based ET datasets have been widely used for different purposes. However, their use has been limited at regional and field scales because of their coarse spatial and temporal resolution. In this study, the Bayesian model averaging (BMA) method was used to simulate daily ET values with 500 m spatial resolution in the Haihe River Basin (HRB) from 2000 to 2019. Validation performed with the in-situ observations showed that the BMA ET values had higher accuracy (R2 = 0.69, RMSE = 1.15 mm/day) than those found with individual models. The BMA ET values also had higher accuracy and more credibility based on a water balance equation validation in the HRB. We used interpolated meteorological datasets, reanalysis net radiation products, and remote-sensing datasets to drive the BMA ET model. The mean annual BMA ET in the HRB from 2000 to 2019 was about 601.8 mm/year. The monthly change characteristics of this BMA ET product reflected water consumption characteristics and irrigation regularity of the winter wheat and summer maize rotation system. The BMA ET in the HRB showed a significant increasing trend from 2000 to 2019 of 3.39 mm/y2. The BMA ET values showed a strong positive trend at 80 % of HRB areas. The increasing trend of the BMA ET values was mainly attributed to an increase in the leaf area index. The high spatiotemporal resolution product showed variations in ET trends more accurately than the coarse resolution products. This high spatiotemporal product has great potential for applications in studies of regional microclimate change, interactions between human activities and climate change, drought disaster monitoring, agricultural policy making, and water security.

Multi-Variable SWAT Model Calibration Using Satellite-Based Evapotranspiration Data and Streamflow

In this study, monthly streamflow and satellite-based actual evapotranspiration data (AET) were used to evaluate the Soil and Water Assessment Tool (SWAT) model for the calibration of an experimental sub-basin with mixed land-use characteristics in Athens, Greece. Three calibration scenarios were performed using streamflow (i.e., single variable), AET (i.e., single variable), and streamflow–AET data together (i.e., multi-variable) to provide insights into how different calibration scenarios affect the hydrological processes of a catchment with complex land use characteristics. The actual evapotranspiration data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration was achieved with the use of the SUFI-2 algorithm in the SWAT-CUP program. The results suggested that the single variable calibrations showed moderately better performance than the multi-variable calibration. However, the multi-variable calibration scenario displayed acceptable outcomes for both streamflow and actual evapotranspiration and indicated reasonably good streamflow estimations (NSE = 0.70; R2 = 0.86; PBIAS = 6.1%). The model under-predicted AET in all calibration scenarios during the dry season compared to MODIS satellite-based AET. Overall, this study demonstrated that satellite-based AET data, together with streamflow data, can enhance model performance and be a good choice for watersheds lacking sufficient spatial data and observations.

Evaluation of daily gridded meteorological datasets for hydrological modeling in data-sparse basins of the largest lake in Southeast Asia

Study regionTonle Sap Lake (TSL) Basin in Cambodia. Study focusThe development and application of hydrological models for data-sparse basins are hindered by the limited hydro-meteorological data. Although gridded meteorological products are alternatively considered in many studies, the validation of the products with limited point observations overlooks the original spatiotemporal characteristics, thus leading to a selection of datasets with high uncertainty. Here, we evaluated seven gridded meteorological datasets of rainfall and air temperature covering the data-sparse Tonle Sap Lake Basin by employing the statistical approach based on the bilinear-interpolation method and hydrological approach using the SWAT model, which ensures the reliable estimates of streamflow and evapotranspiration. New hydrological insights for the regionThe results of the statistical approach indicate that APHRODITE, ERA5, TRMM and IMERG-based precipitation and CPC and SA-OBS-based air temperature performed comparably well (R ≥ 0.75) with the gauged data. However, ERA5-based streamflow performed relatively poor, while SWAT driven by APHRODITE underestimated satellite-based evapotranspiration, indicating the underestimation of basin-wide precipitation by APHRODITE. Although TRMM and IMERG provide more reliable estimation of streamflow and evapotranspiration, slightly better performance and a higher spatial resolution of IMERG dataset suggest that IMERG precipitation is superior for basin-wide hydrological modeling and impact assessment. These findings showed that statistical comparisons with gauge-data and hydrological evaluation of streamflow are not enough to justify the reliability of each gridded dataset.

Historical and future Palmer Drought Severity Index with improved hydrological modeling

With the ongoing climate warming, changes in drought and the adverse effects on water resources, food production and ecosystem functioning have been key research topics of ever-increasing interest. The Palmer Drought Severity Index (PDSI) is among the most widely used indicators for drought monitoring and research. However, the two-layer bucket water balance model embedded in the original PDSI model has been criticized for being over-simplified to accurately quantify the surface water balance and therefore raising uncertainties in the subsequent PDSI estimates (PDSIoriginal). Here we improve the water balance calculations in the PDSI model by using direct hydrological outputs from physically-based, more sophisticated global hydrological models (GHMs) participated in the Inter-Sectoral Impact Model Inter-Comparison Project (ISIMIP). Validation results show that the estimated runoff (Q) and evapotranspiration (ET) from ISIMIP GHMs perform much better than those from the original PDSI two-layer bucket model in capturing the long-term trend and monthly variabilities of Q and ET, especially in cold regions and relatively dry areas, using observed Q (at 2191 catchments) and an independent satellite-based ET product (the Global Land Evaporation Amsterdam Model, GLEAM; over the entire terrestrial environment) as the reference. In addition, the new PDSI estimates with improved hydrological modeling (PDSIISIMIP) exhibit a significantly stronger correlation with observed Q than PDSIoriginal in nearly all studied catchments, suggesting that PDSIISIMIP is superior to PDSIoriginal in capturing hydrological droughts. We further compare the long-term PDSI trends and changes in drought using PDSIoriginal and PDSIISIMIP under both historical climate (1900–2005) and future climate change scenarios (2006–2099). We find that PDSIoriginal and the PDSIoriginal-identified land areas under drought generally show a larger trend than those based on PDSIISIMIP. For future climate change scenarios, the PDSIoriginal-projected increasing trend of land proportion under drought is about two times larger than that assessed with PDSIISIMIP, implying that PDSIoriginal may largely overestimate future drought increases, as commonly done in existing studies. In this light, our approach of directly using hydrological outputs from physically-based, more sophisticated GHMs provide an effective, yet relatively simple approach to reduce uncertainties in PDSI estimates thereby achieving a better prediction of drought changes under warming.

Estimation of bell pepper evapotranspiration using two-source energy balance model based on high-resolution thermal and visible imagery from unmanned aerial vehicles

Crop evapotranspiration (ET) is a crucial component of energy and water budgets. The accurate determination of ET is vital for agricultural water management. Several satellite-based ET models have been developed to map ET at the field to regional scales. The spatial resolution of the satellite observations, particularly thermal-infrared imagery, is insufficient to estimate ET for small (<1 ha) agricultural fields. With unmanned aerial vehicle (UAV) technology advancement, high spatial and temporal images can be acquired with UAVs to monitor ET for small fields or even at a canopy scale. The aim of this study was the evaluation of the two-source energy balance (TSEB) model to estimate daily and seasonal crop (bell pepper) ET (ETTSEB) using high-resolution visible and thermal UAV imagery. Also, the impact of using different pixel resolutions (40, 50, 60, and 70 cm) with TSEB is compared with ET values derived using a soil water budget approach (ETSWD) with a profile soil water content. The results of this study showed that there is a high correlation between ETTSEB and ETSWD values (R2 = 0.73 for daily, R2 = 0.98 for seasonal). The root mean square error values for daily and seasonal ETTSEB are 0.62 mm day − 1 and 11.46 mm season − 1, respectively. The sensitivity of TSEB output to the spatial resolution indicated that different pixel resolutions do not significantly impact ET estimates. We suggest that the TSEB model has a real potential for agricultural water management applications for small agricultural fields using high-resolution UAV multispectral and thermal images.

Development of a three-source remote sensing model for estimation of urban evapotranspiration

Estimation of urban evapotranspiration (ET) is meaningful for evaluating the urban heat island effect and promoting urban water conservation. While multi-source satellite-based ET models for farmland and natural ecosystems have been developed, these models lack the object-oriented solutions for the urban areas. In view of this, the present study proposed a Three-source Remote sensing model for Urban areas (TRU) to simulate the urban ET, which can discriminate between urban soil evaporation, vegetation transpiration, and impervious surface evaporation. The new parameterization scheme proposed herein is characterized by the theory of complementary relationship to estimate the urban soil evaporation. Evaporation losses in the impervious areas were independently estimated in accordance with the distribution of urban land use and land cover (LULC). The developed TRU model was evaluated for 63 cloud-free test days in Tianjin, China by using 30 m Landsat Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM+) images. The outcomes of this study revealed a root-mean-square-error (RMSE) of 40.2 W/m2 and a bias of 10.8 W/m2 compared with the two eddy correlation (EC) observations for the instantaneous latent heat flux (LE) simulation. Moreover, the RMSE and bias were 0.092 and −0.023, respectively in simulating the vegetation latent heat flux fraction (LEv/LE) when verified with respect to the stable water isotope measurements. Due to limited water availability, the impervious urban surface evaporation magnitude was found quite low with minimal variation on different test days. The results emphasize the importance of coupling LULC information in the urban ET modeling conceptualization. Certainly, the TRU model improved the ET simulation accuracy in urban areas and could facilitate the mapping of urban ET components at high spatial resolution.

Determining satellite-based evapotranspiration product and identifying relationship with other observed data in Punjab, Pakistan

Determining satellite-based evapotranspiration product and identifying relationship with other observed data in Punjab, Pakistan