[A-HW28] 水循環・水環境

[AHW28-P03] Synthesizing the predictive regression model for low flow in river basins

*Antonio Machava Junior¹、Andrew Charles Whitaker² (1.Graduate School of Science and Technology, Niigata University, Japan、2.Institute of Science and Technology, Niigata University, Japan)

キーワード：Recession constant, Low flow, Regression model, River basin, Basin characteristics

In the evaluation of low flow hydrology, low flow indices are crucial when considering water supply, water quality, and water resources planning in river basins. To evaluate a basin's low flow characteristics, the synthetic master recession curve (MRC), which is typically represented as an exponential function, has been used globally. Hydrologists have taken interest in predicting the baseflow recession rate or recession constant and the Q₇10flow (seven-day, 10-year low flow), and numerous regional studies have developed models to estimate baseflow recession characteristics from various catchment characteristics. However, accurately and easily deriving the MRC from streamflow records remains a challenge. This research aims to propose the synthetic regression model which can be widely applied for predicting low flow (Q₇10) (seven-day 10-year low flow) in order to evaluate sustainable water resources of river basin units.
The given locations (around 45 study basins) in Japan are adopted to use hydrological records for headwater catchments upstream of reservoirs, with the restriction that there are no regulations or diversion structures in the upstream reaches. Secondly, the results of regression analysis for each river basin will be compared, and parameters of the model generalized by means of using information on the dominant geology of the basins.
Ongoing results from 34 river systems indicate recession constants ranging from 0.0130 to 0.0746, showing considerable variability across basins. The results suggest how much baseflow recession rates might be influenced by basin characteristics, especially geology. The recession constants results provide initial insights into recession characteristics and their variability across basins. The ongoing research aims to finalize a synthetic regression model, which is expected to apply to other regions, such as Africa, for evaluating sustainable water resources.