This study evaluated the accuracy of gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP_Gauge version V5.222.1, hereafter G_Gauge) data in Japan's Tone River basin during [2006][2007][2008][2009]. Specifically, the accuracy of a gauge non-adjusted product, GSMaP Moving Vector with Kalman Filter (GSMaP_MVK, hereafter G_MVK), was also evaluated. Both products were also evaluated against ground observation data from rain gaugeradar combined product Radar-Automated Meteorological Data Acquisition System (Radar-AMeDAS) in terms of temporal and spatial variability. Temporal analyses showed that G_Gauge had better accuracy than G_MVK at sub-daily time scales (1, 3, 6, 9, 12, and 24 h) within any range of precipitation intensity and better detection capabilities of rainfall event. Linear regressions with Radar-AMeDAS showed better performance for G_Gauge than G_MVK at any time scales in terms of Pearson's correlation coefficient and the slope of regression. At an hourly scale, in particular, Pearson's correlation coefficient for G_Gauge (0.84) was higher than that for G_MVK (0.72) as well as the slope of linear regression (0.87 and 0.65, respectively). The probability of detection (POD) improved from 0.48 (G_MVK) to 0.70 (G_Gauge) when gauge-adjusted data were used. However, spatial analysis detected that G_Gauge still underestimated the precipitation intensity in high-elevation regions and slightly overestimated it in low elevation regions. The POD and false alarm ratio had a linear relationship with log-transformed elevation data, and the relationships were stronger in the winter seasons than in the summer seasons. At any spatial and temporal scale, the evaluation of these products should consider seasonal changes (especially in winter) and the topographic effects. For further improvements of G_Gauge, we suggest including higher resolution gauge-based network data than the Climate Prediction Center unified gauge-based analysis of global daily precipitation, which is used for G_Gauge.

Abstract. Soil erosion and sediment transport have been modeled at several spatial and temporal scales, yet few models have been reported for large river basins (e.g., drainage areas > 100 000 km2). In this study, we propose a process-based distributed model for assessment of sediment transport at a large basin scale. A distributed hydrological model was coupled with a process-based distributed sediment transport model describing soil erosion and sedimentary processes at hillslope units and channels. The model was tested on two large river basins: the Chao Phraya River Basin (drainage area: 160 000 km2) and the Mekong River Basin (795 000 km2). The simulation over 10 years showed good agreement with the observed suspended sediment load in both basins. The average Nash–Sutcliffe efficiency (NSE) and average correlation coefficient (r) between the simulated and observed suspended sediment loads were 0.62 and 0.61, respectively, in the Chao Phraya River Basin except the lowland section. In the Mekong River Basin, the overall average NSE and r were 0.60 and 0.78, respectively. Sensitivity analysis indicated that suspended sediment load is sensitive to detachability by raindrop (k) in the Chao Phraya River Basin and to soil detachability over land (Kf) in the Mekong River Basin. Overall, the results suggest that the present model can be used to understand and simulate erosion and sediment transport in large river basins.

Flood simulation by using a distributed hydrological model (DHM) has been implemented to support dam operation. This study aims to investigate the applicability of Satellite Based Precipitation (SBP) combined with local rain gauge network as input for flood forecast at Chao Phraya basin, Thailand. DHM was set-up at contributing area to Bhumipol dam for inflow forecast in order to protect lower region. As input data, SBP was combined with local gauge network targeting effect of monsoon. DHM was run during 2007-2010 to validate the spatial and temporal accuracy of corrected SBP. The approach showed reduction of the large overestimations of SBP. Then, the accuracy of discharge simulation was successfully improved. The results indicate feasibility to estimate inflow of Bhumipol dam using corrected SBP with local gauge network. Moreover, this method can be useful not only for risk assessment of flooding, but also support proper dam operation.

Soil loss and its transport processes were coupled with an existing distributed hydrological model to assess the effects of land use change on stream flow and suspended sediment load in the Chao Phraya River basin, Thailand. The simulation period spanned from 2001 to 2010. The results indicate that the Nash–Sutcliffe efficiency of upper sub-basins fluctuated in the range 0.51–0.72, indicating the applicability of the model for long-term simulation at the monthly scale. Land use change during 2001–2010 caused a 1.6% increase in suspended sediment load based on the present trend. The changes were particularly pronounced in the Wang River basin, where the delivery ratio was highest. Moreover, the urbanization and conversion of farm land from paddy fields exerted negative effects on sediment runoff in Chao Phraya River basin. The proposed model has the ability to quantitatively evaluate the heterogeneity of sediment runoff in the basin, demonstrating the benefits and trade-offs of each land use change class. The results of this study can support basin and local land development policy to control sediment losses during development.

This study aims to investigate the applicability of Satellite Based Precipitation (SBP) combined with rain gauges as input to a distributed hydrological model (DHM) for flood forecast. SBP was evaluated at different scales in upper Chao Phraya basin against available local gauge network in Thailand. The procedure includes the usage of Root Mean Square Error (RMSE) to assess the accuracy of SBP and Relative Error (RE) to evaluate the degree of estimation. Furthermore, RE values were utilized to obtain correction factors at each rain gauge per season. DHM was run during 2007-2010 to validate spatial and temporal accuracy of improved SBP. The river discharge simulations using corrected SBP could reduce the overestimation gaps when compared to observed discharge in target period. It was noticed that SBP can enhance precipitation’s pattern by using local gauge network. The obtained results show possibility to apply this procedure to other humid vegetated basins and also using other SBP dataset. We believe that this method can be useful not only for flooding risk assessment but also to support enhanced dam operation.