In this study, we developed a distributed rainfall-runoff and sedimentation model based on one-dimensional kinematic wave equations. Physically-based rainfall-runoff and erosion-sediment processes were coupled and solved for each spatial grid, whilst the spatially distributed grids were connected to each other to allow for space-and-time movements of water and sediment. The model was applied to the Akatani River basin of the Chikugo River in Kyushu, Japan using a 10 m high-resolution digital elevation model and eXtended RAdar Information Network (XRAIN) data as a time-and-space distributed rainfall input of the northern Kyushu heavy rainfall event in July 2017. Our results indicate that the rainfall-runoff hydrograph and sediment flow results are in agreement with the collected field data, and elevation of the river bed after the disaster was successfully reproduced by applying a sediment theory to estimate river bed variation. In addition, we found that sediment transport results are sensitive to model spatial resolution. Our simulation model is intended for use with basins that feature steep slopes and are prone to erosion and shear strength reduction after heavy rainfall events. Hence, this model can be applied to give early warnings by identifying critical erosional areas during forecasted heavy rainfall events.

A distributed hydrologic model based on a kinematic wave approximation with surface and subsurface flow components is applicable to basins that have temperate climatic conditions similar to those in Japan. However, it is difficult to present long-term river discharge using the existing model structure in basins with different climatic conditions. This study aims to improve the model structure for better estimates of long-term discharge in the Nam Ngum River, the main tributary of the Mekong River, by incorporating bedrock aquifers as part of the slope flow component of the original model structure. Three bedrock groundwater structures are configured to incorporate the original model structure. The results show that a combination of the original model component and one unconfined aquifer structure are the best representations of the river flow regime from the original model structure, in which the rate of infiltration from the layer into the bedrock aquifer was calculated using vertical hydraulic conductivity. The Nash–Sutcliffe efficiency coefficient of the original and improved models increased from 0.80 to 0.86 during the calibration period and from 0.56 to 0.62 during the validation period. The results of this study show that the improved model structure is applicable for long-term hydrologic predictions in Southeast Asian catchments with distinct dry and rainy seasons.

To classify the aquifer type and to estimate the pneumatic diffusivity using harmonic analysis and analytical expressions, we measured groundwater levels and barometric pressure in an unconfined aquifer with a thick unsaturated zone at a southern part of the Beppu volcanic fan, Japan. The groundwater level was inversely related to the barometric pressure, with little or no time lag behind barometric pressure. The groundwater level and barometric pressure exhibit periodic changes with prominent spectra at K₁ (lunar-solar diurnal) and S₂ (main solar semidiurnal) tidal constituents but not at O₁ (main lunar diurnal) and M₂ (main lunar semidiurnal) tidal constituents, which are the motions of solar and lunar relative to the earth. It indicates that the aquifer is an unconfined aquifer type with no earth tide effect. Pneumatic diffusivity in the unsaturated zone was estimated as 7.6 × 10^–2 to 5.7 × 10^–3 m²/s using changes between the groundwater level and the barometric pressure. The pneumatic diffusivity and the unsaturated zone thickness strongly affect amplitude ratios and phase lags between the groundwater level and the barometric pressure in the unconfined aquifer.

Design rainfall depth, which is a fundamental index used in river planning, was estimated by rainfall obtained from super-ensemble simulations with bias correction, and the future change under 4 degree warming was projected. The modifications of existing bias correction methods were proposed to resolve the issue of overfitting and gap in size between reference and super-ensemble simulation data. A bias correction approach considering the bias between the historical experiment, the reference data, and the change between the historical and future experiments separately was defined as two-pass bias correction. The two-pass bias correction was performed with a moving window method that calculated moving average for time period and rank-order statistics. The result indicated that the approach proposed in this study estimates the design rainfall depth with a small error compared to that calculated without the moving window. The moving window method effectively resolves the issue of overfitting. The projection indicated that the range of projection among sea-surface temperature (SST) patterns is equivalent to 25% of the design rainfall depth for most basins and 60% for certain specific basins. The results indicate the importance of the appropriate bias correction and the consideration of range among the SST patterns for super-ensemble simulation data.