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

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).

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

The soil and water assessment tool (SWAT) hydrological model has been used extensively by the scientific community to simulate varying hydro-climatic conditions and geo-physical environment. This study used SWAT to characterize the rainfall-runoff behaviour of a complex mountainous basin, the Budhigandaki River Basin (BRB), in central Nepal. The specific objectives of this research were to: (i) assess the applicability of SWAT model in data scarce and complex mountainous river basin using well-established performance indicators; and (ii) generate spatially distributed flows and evaluate the water balance at the sub-basin level. The BRB was discretised into 16 sub-basins and 344 hydrological response units (HRUs) and calibration and validation was carried out at Arughat using daily flow data of 20 years and 10 years, respectively. Moreover, this study carried out additional validation at three supplementary points at which the study team collected primary river flow data. Four statistical indicators: Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), ratio of the root mean square error to the standard deviation of measured data (RSR) and Kling Gupta efficiency (KGE) have been used for the model evaluation. Calibration and validation results rank the model performance as “very good”. This study estimated the mean annual flow at BRB outlet to be 240 m3/s and annual precipitation 1528 mm with distinct seasonal variability. Snowmelt contributes 20% of the total flow at the basin outlet during the pre-monsoon and 8% in the post monsoon period. The 90%, 40% and 10% exceedance flows were calculated to be 39, 126 and 453 m3/s respectively. This study provides additional evidence to the SWAT diaspora of its applicability to simulate the rainfall-runoff characteristics of such a complex mountainous catchment. The findings will be useful for hydrologists and planners in general to utilize the available water rationally in the times to come and particularly, to harness the hydroelectric potential of the basin.

Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing

An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.