River discharge prediction for ungauged mountainous river basins during heavy rain events based on seismic noise data

Abstract

Several mountainous river basins in Japan do not have a consistent hydrological record due to their complex environment and remoteness, as discharge measurements are not economically feasible. However, understanding the flow rate of rivers during extreme events is essential for preventing flood disasters around river basins. In this study, we used the high-sensitivity seismograph network (Hi-net) of Japan to identify the time and peak discharge of heavy rain events. Hi-net seismograph stations are distributed almost uniformly at distance intervals of approximately 20 km, while being available even in mountainous regions. The Mogami River Basin in Northeastern Japan was selected as a target area to compare the seismic noise data of two Hi-net stations with the hydrological response of a nearby river. These stations are not located near hydrological stations; therefore, direct comparison of seismic noise and observed discharge was not possible. Therefore, discharge data simulated using a hydrological model were first validated with gauging station data for two previous rain events (10–23 July 2004 and 7–16 September 2015). Then, the simulated river discharge was compared with Hi-net seismic noise data for three recent events (10–23 July 2004, 7–16 September 2015, and 10–15 October 2019). The seismic noise data exhibited a similar trend to the time series of simulated discharge in a frequency range of 1–2 Hz for the selected events. Discharge values predicted from the noise data effectively replicate the simulated discharge values in many cases, especially the timing and amount of peak discharge.Simulated and predicted discharge near NIED Hi-net seismic stations in the Mogami River Basin for the event of October 2019 (Typhoon Hagibis).

Seismic Signatures and Site Characterization of an Intermittent Stream in Dry and Flood Conditions: An Implication for Soil Losses and Landslide Triggering

“…ANb monitoring techniques can provide high temporal and spatial resolutions of the landscapes, and their demands are increasing in bedload monitoring because of their low cost and non-invasiveness [23] . In the past decades, an increasing number of contributions have been developed using the variations in ambient noise as the base of the study of the river flow system [25,29,[34][35][36] . It was also found that the river discharge vs noise power followed a seasonal hysteresis trend consistent with the regional sediment transport rates in the river [37] .…”