Flood Inflow Estimation in an Ungauged Simple Serial Cascade of Reservoir System Using Sentinel-2 Multi-Spectral Imageries: A Case Study of Imjin River, South Korea

Abstract

The Imjin River is a representative transboundary river in the Korean Peninsula, originating from North Korea and flowing into South Korea. Upstream of Imjin River, on the North Korean side, is the Hwanggang Dam, with a 350 million m3 of storage capacity, which is in operation for power generation and water supply. The sharing of the operation information of Hwanggang Dam has been limited due to political and military tension. South Korea has constructed the Gunnam Flood Control Reservoir downstream of the Imjin River to prevent potential flood damage due to the urgent and unilateral release from the Hwanggang Dam. However, it is difficult to manage the flood of the Imjin River basin under the situation of limited shared real-time information on the Hwanggang Dam operation. In this study, a hydrological analysis system was established to estimate the inflow and release of the Hwanggang Dam by building a lumped hydrological model and an Auto ROM (Reservoir Operation Method)-based reservoir operation algorithm. To estimate the inflow of the Gunnam Flood Control Reservoir, the water level of the Hwanggang Dam was calculated using the Sentinel-2 multi-spectral images, and the hydrological analysis system was calibrated. The evaluation index of the water level of the ungauged Hwanggang Dam derived from January 2017 to August 2020 using the hydrological analysis system was shown to have a coefficient of determination of 0.76 and an RMSE of 3.97 m. In the case of the flood event in August 2020, the coefficient of determination of the flood inflow in the Gunnam Flood Control Zone was calculated to be 0.86.

Estimating the Impacts of Ungauged Reservoirs Using Publicly Available Streamflow Simulations and Satellite Remote Sensing

“…Instead, we directly utilized data from SRTM. In the study by Kim et al [36], an elevation of 110 m was estimated to correspond to an area of 15 km 2 , while in the study by JG Kim et al [54], it corresponds to 12 km 2 . In our study, the elevation of 110 m corresponded to an area of 14 km 2 , is closer to that of Kim et al [36], which used the SRTM DEM as well (Figure 3).…”

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

“…Remote Sensing (RS) imageries, especially those coming from satellites, have larger coverage and better spatial and temporal consistency than many other data resources, such as crowd-sourced data (Ali & Ogie, 2017;Li et al, 2022b) or other instrumentation efforts (Muste et al, 2017). In the research domain of flood inundation mapping, RS imagery has been widely used for cross-validating with hydrologic modeling (Kim et al, 2022;Thakur et al, 2020) and serves as an independent data source for water extent extraction (Gao et al, 2018;Sai et al, 2020;Li and Demir, 2023a). Additionally, RS imagery and its products have been widely adopted as image, map, and validation resources for other data-driven models, such as in machine learning and deep learning applications (Avand et al, 2021;Costache et al, 2019; for synthetic image generation (Gautam et al, 2022) and data augmentation (Sit et al, 2023).…”