Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

Abstract

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021

“…Many scholars have widely applied this flood potential index to assess the flood potential of many river basins around the world. Its performance of indicating large-scale floods was confirmed, such as in the Niger Basin [16], the Pearl River Basin [17], and the Yangtze River Basin [18], [19], [20]. Sun, et al [21] analyzed the hydrological state in Yangtze River Basin and found that FPI, calculated from the GRACE-derived TWSA and Tropical Rainfall Measuring Mission data, can identify flood events in the Yangtze River Basin.…”

Improvement and Evaluation of CLM5 Application in the Songhua River Basin Based on CaMa-Flood

“…Discharge is one of the most comprehensive indicators of the overall impact of various factors in basin-scale hydrology [8][9][10], and accurately modeling discharge is key to understanding the water cycle, water resource management, and climate change. There are various research methods to simulate discharge, such as using the water balance equation combined with precipitation, evapotranspiration, and terrestrial water storage anomaly (TWSA) measured by GRACE Follow-On (GRAFO) to derive the production and sinking process; this method has been validated in the Yangtze River Basin, but the timeliness of the monthly GRAFO for TWSA monitoring is lacking [11,12]. Land surface models (LSMs) driven by meteorological forcing data can simulate discharge at multiple time scales (from monthly to interannual) [13], and LSMs require more meteorologically forcing data than traditional distributed hydrologic models (DHMs), such as precipitation, solar radiation, near-surface air pressure, near-surface wind speed, near-surface air moisture content, and near-surface air temperature data.…”

“…We used CLM5, which has been updated from the development of CLM4 [17] and CLM4.5 [18], but the improvements in CLM5 still do not simulate changes in flooded areas on time scales, and most of the previous work on flood characterization has been conducted using runoff data rather than flooded areas [19]. The catchment-based macroscale floodplain model (CaMa-Flood) is a global river hydrodynamic model that can perform high-precision simulations of confluence processes and flooded areas in large basins and has been validated in the Amazon Basin and the Yangtze River Basin [12,20]. In this study, we coupled the model with CLM5 to compensate for the shortcomings of CLM5 in runoff simulation.…”