Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Jeonghyeon Choi; Ungtae Kim; Sangdan Kim

doi:10.1016/j.scitotenv.2023.166617

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Sci Total Environ. 2023 Dec 10:903:166617. doi: 10.1016/j.scitotenv.2023.166617. Epub 2023 Aug 28.

Authors

Jeonghyeon Choi¹, Ungtae Kim², Sangdan Kim³

Affiliations

¹ Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang-Si, Gyeonggi-Do 10223, Republic of Korea. Electronic address: choijeonghyeon@kict.re.kr.
² Department of Civil and Environmental Engineering, Cleveland State University, Cleveland, OH 44115, USA. Electronic address: u.kim@csuohio.edu.
³ Division of Earth Environmental System Science (Major in Environmental Engineering), Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Republic of Korea. Electronic address: skim@pknu.ac.kr.

PMID: 37647955
DOI: 10.1016/j.scitotenv.2023.166617

Abstract

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

Keywords: Ecohydrologic model; Evapotranspiration; Leaf area index; Prediction in ungauged basins; Remotely sensed data; Soil moisture.