Rationale and Efficacy of Assimilating Remotely Sensed Potential Evapotranspiration for Reduced Uncertainty of Hydrologic Models

Abstract

The objective of this study is to systematically attribute sources of evapotranspiration uncertainty in a hydrologic model and accordingly propose a remote sensing‐based solution. Using Soil and Water Assessment Tool (SWAT) for three U.S. watersheds, representing different geophysical settings, this study first addresses the effects of parameter equifinality, energy‐related weather input uncertainty, and limited process representation on evapotranspiration simulation. Remotely sensed 8‐day total actual evapotranspiration (AET) from Moderate Resolution Imaging Spectroradiometer (MODIS) is used as a reference to evaluate the model outcome. Results indicate the likelihood of a pseudo‐accurate model showing high streamflow prediction skill despite severely erroneous spatiotemporal dynamics of AET. As a remedial measure, a hybrid daily potential evapotranspiration (PET) estimate, derived from MODIS, is directly ingested at each hydrologic response unit of the model to create a new configuration called SWAT‐PET. A key contribution is the modified SWAT source code that integrates the model (i.e., SWAT‐PET) with an automatic remote sensing data processor. The underlying notion is that remotely sensed PET works as a surrogate of actual vegetation dynamics, biophysical processes, and energy balance, without overruling the model's built‐in soil moisture accounting. Noticeably, increased accuracy of soil moisture, AET, and streamflow in SWAT‐PET, compared to independent sources of observations/reference estimates (i.e., field sensor, satellite, and gauge stations), approves the efficacy of the proposed approach toward improved physical consistency of hydrologic modeling. While the idea is tested for a past period, the ultimate goal is to improve near‐real‐time hydrologic forecasting once such PET estimates become available.

Differences among Evapotranspiration Products Affect Water Resources and Ecosystem Management in an Australian Catchment

“…In the MDB, it was modified from a linear relationship to a quadratic relationship [10]. The function between annual GPP and ET is given in Equation (14).…”

“…Generalisation of surface features (such as land use/cover and terrain) leads to the decreases of the resolution, thus the loss of details of surface features. The temporal resolutions of these five ET products vary from 3 h to one month, which could result in the representation of different meteorological processes and vegetation physiological processes in these ET products [14].…”

“…Some ET estimation methods, based on remotely sensed data like MODIS, can timely reflect the vegetation changes, but others cannot. If the vegetation growth process cannot be truly expressed, the evapotranspiration of vegetation, as well as its photosynthesis and other related hydrological processes (such as canopy interception and infiltration) will be affected [14].…”

“…They can be broadly classified into three categories: remote sensing (RS)-based ET products using vegetation index-based data or land surface temperature (LST) (e.g., the Moderate Resolution Imaging Spectroradiometer (MODIS) ET, Advanced Very High Resolution Radiometer (AVHRR) ET, Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) ET and Global Land Evaporation Amsterdam Model (GLEAM) ET), ET output from land surface models (LSMs) (e.g., Global Land Data Assimilation System (GLDAS) ET, Modern-Era Retrospective analysis for Research and Applications (MERRA) ET and European Centre for Medium-Range Weather Forecasts Re-analysis (ERA)-interim ET), and ET from a water budget with measured precipitation, runoff and total water storage change (e.g., TerraClimate ET and Gravity Recovery and Climate Experiment (GRACE)-inferred ET) [5,11,12]. These ET products have been used to a varying degree for irrigation water management [8], vegetation productivity estimation [9,10], calibration and optimization of hydrological models and prediction of runoff in the ungauged basins [13,14], and water management modes and water rights regimes [2,15,16]. However, these different ET products are of different spatial-temporal scales, and differ in accuracy or uncertainty [11,17,18].…”

Evapotranspiration Estimation for Tibetan Plateau Headwaters Using Conjoint Terrestrial and Atmospheric Water Balances and Multisource Remote Sensing

“…Advances in satellite remote sensing have largely reduced the limitations of traditional point-based observations, making remote sensing a promising approach to retrieving ET, particularly in poorly gauged basins (Famiglietti et al, 2015;Rajib et al, 2018;Samain et al, 2012). Visible and infrared bands of satellite sensors provide large-scale estimates of surface albedo, emissivity, leaf area index (LAI), normalized difference vegetation index (NDVI), and land surface temperature (LST), which are critical for deriving surface fluxes (e.g., net radiation, and sensible and latent heat fluxes).…”