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GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed
Zihao Pan1, Shengtian Yang1, Xiaoyu Ren2
1College of Water Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Urban Water Cycle and Sponge City Technology, Beijing 100875, China.
The Science of the Total Environment
|January 29, 2023
Summary
Google Earth Engine (GEE) and remote sensing hydrological station (RSHS) technology significantly reduce uncertainties in remote sensing-driven distributed hydrological models (RS-DHMs). This improves data preparation, model parameter accuracy, and hydrological simulations for water resource management.
Area of Science:
- Hydrology and Remote Sensing
- Environmental Modeling
- Geospatial Data Analysis
Background:
- Remote sensing-driven distributed hydrological models (RS-DHMs) face uncertainties from multisource data coupling and lack of measured runoff.
- Google Earth Engine (GEE) is widely used for RS-DHMs, but its effectiveness in reducing uncertainties is not fully understood.
Purpose of the Study:
- To quantitatively analyze the contribution of GEE to improving hydrological model uncertainties.
- To evaluate the impact of GEE-based data preparation and remote sensing runoff inversion technology on RS-DHM performance.
Main Methods:
- Utilized twelve GEE remote sensing datasets to drive a typical RS-DHM (RS-DTVGM) and a remote sensing runoff inversion technology (RSHS).
- Analyzed the period from 2001 to 2020 to assess the contribution of GEE to reducing input data and model parameter uncertainties.
- Quantitatively evaluated operational efficiency improvements and model performance metrics like the Nash efficiency coefficient (NSE).
Main Results:
- GEE-based data preparation reduced input data uncertainty, enhancing spatial-temporal continuity and decreasing invalid grid areas by 6.20%.
- Operational efficiency was significantly improved, with reductions in image number (83.63%), memory size (99.53%), and processing time (98.73%).
- GEE-based RSHS technology reduced model parameter uncertainty, increasing the NSE from 0.67 to 0.87 (calibration) and 0.75 (validation).
- The calibrated RS-DTVGM demonstrated reliability and robustness, with simulated runoff and evapotranspiration aligning with statistical data.
Conclusions:
- GEE and RSHS technology effectively mitigate uncertainties in RS-DHMs, improving both data quality and model accuracy.
- Widespread adoption of GEE and RSHS can facilitate quicker and easier reliable hydrological process simulations.
- These advancements support integrated water resource management by enhancing efficiency and accuracy in hydrological modeling.
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