GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

Zihao Pan; Shengtian Yang; Xiaoyu Ren; Hezhen Lou; Baichi Zhou; Huaixing Wang; Yujia Zhang; Hao Li; Jiekang Li; Yunmeng Dai

doi:10.1016/j.scitotenv.2023.161852

GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

Sci Total Environ. 2023 Apr 20:870:161852. doi: 10.1016/j.scitotenv.2023.161852. Epub 2023 Jan 26.

Authors

Affiliations

¹ College of Water Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Urban Water Cycle and Sponge City Technology, Beijing 100875, China.
² Beijing Weather Modification Office, Beijing Key Laboratory of Cloud, Precipitation, and Atmospheric Water Resources, Field Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, China.
³ College of Water Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Urban Water Cycle and Sponge City Technology, Beijing 100875, China. Electronic address: louhezhen@bnu.edu.cn.

PMID: 36709897
DOI: 10.1016/j.scitotenv.2023.161852

Abstract

The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatial-temporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively; (2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively; and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be widely adopted to drive the RS-DHM to more quickly and easily provide reliable hydrological processes simulation results for integrated water resource management, therefore achieving win-win results in terms of efficiency and accuracy.

Keywords: Distributed hydrological model; Efficiency and uncertainty; Google Earth Engine; Remote sensing; Remote sensing hydrological station technology.