FOSS4G-Asia 2023 Seoul

Enhancing Hydrological Modeling with Remote Sensing Evapotranspiration Products: Impacts of Dataset Qualities and Aggregation Scales
11-29, 14:30–14:50 (Asia/Seoul), Online Talks

Remote sensing evapotranspiration (RS-ET) products have been the ubiquitous datasets of echo-hydrology, increasingly employed in precise hydrological modeling. However, these diverse ET products often offer considerable uncertainties, posing substantial challenges when incorporating them into hydrological models. This study aims to investigate the effects of integrating remote sensing evapotranspiration (RS-ET) products into the calibration of hydrological models. Specifically, we address two main questions: (1) among those RS-ET products considered, which offers the highest hydrologic predictability for hydrological modeling? (2) What is the optimal scale for incorporating RS-ET datasets into the hydrological model to achieve maximum benefits for enhancing hydrological modeling accuracy?
We conducted an in-depth exploration of these questions by implementing a hydrological modeling experiment using the Soil and Water Assessment Tool (SWAT) within the Meichuanjiang Basin in Jiangxi Province, China. This research employed four distinct RS-ET products: MOD16, GLASS, SSEBop, and ETMonitor, along with a merged product. Additionally, we examined three distinct scales for aggregating RS-ET datasets: subbasin (SUB), land use (LU), and hydrological response unit (HRU).
Our findings revealed discernible variations in the evapotranspiration (ET) simulations of SWAT models across the study area, upon the calibration with five distinct products. Notably, GLASS and the fusion datasets demonstrated superior ET simulation performance with best scores, evaluated by a Kling-Gupta Efficiency (KGE) both surpassing 0.6. However, SSEBop yielded the lowest KGE of approximately 0.5. This study further highlighted that while the accuracy of ET simulations was enhanced, this improvement incurred a marginal cost in terms of a slight reduction in streamflow precision. However, this compromise was deemed acceptable due to its role in contributing to a more accurate spatial representation of catchment behavior while mitigating equifinality.
Similarly, at different scales of the same ET dataset used in calibration, although the differences in simulation results might be minimal, each dataset seems to have a specific optimal input scale. Notably, during the simulation of the evapotranspiration hydrological component, there is a particularly significant observation: the land use (LU) or subbasin (SUB) scale often proves to be more effective. The rationale for selecting the LU scale lies in the strong correlation between ET and various land use types. By analyzing each land use type separately, we can more accurately capture their distinct ET patterns, leading to improved simulation outcomes. Conversely, the enhanced effectiveness of the SUB scale can be attributed to the scale aggregation effect.
Considering that different RS-ET datasets and aggregation scales introduce uncertainty in observation data for calibration, we infer that the uncertainty in observation data gives rise to simulation uncertainty in inversing SWAT, subsequently backpropagating into parameter uncertainty. These findings shed light on the critical significance of carefully considering the quality of RS-ET datasets and the chosen aggregation scale in the context of hydrological modeling.

PhD student from the College of Resources and Environment at the University of Chinese Academy of Sciences (UCAS), specializing in remote sensing hydrology.

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Master's student from the College of Resources and Environment at the University of Chinese Academy of Sciences (UCAS), specializing in remote sensing hydrology.

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