For better water resources management, we proposed a method to estimate basin-scale seasonal rainfall over selected areas of the Chao Phraya River Basin, Thailand, from existing climate indices that represent variations in the Asian summer monsoon, the El Niño/Southern Oscillation, and sea surface temperatures (SST) in the Pacific Ocean. The basin-scale seasonal rainfall between 1965 and 2015 was calculated for the upper Ping River Basin (PRB) and the upper Nan River Basin (NRB) from a gridded rainfall dataset and rainfall data collected at several gauging stations. The corresponding climate indices, i.e., the Equatorial-Southern Oscillation Index (EQ-SOI), Indian Monsoon Index (IMI), and SST-related indices, were examined to quantify seasonal rainfall. Based on variations in the rainfall anomaly and each climate index, we found that IMI is the primary variable that can explain variations in seasonal rainfall when EQ-SOI is negative. Through a multiple regression analysis, we found that EQ-SOI and two SST-related indices, i.e., Pacific Decadal Oscillation Index (PDO) and SST anomalies in the tropical western Pacific (SST NW ), can quantify the seasonal rainfall for years with positive EQ-SOI. The seasonal rainfall calculated for 1975 to 2015 based on the proposed method was highly correlated with the observed rainfall, with correlation coefficients of 0.8 and 0.86 for PRB and NRB, respectively. These results suggest that the existing indices are useful for quantifying basin-scale seasonal rainfall, provided a proper classification and combination of the climate indices are introduced. The developed method could forecast seasonal rainfall over the target basins if well-forecasted climate indices are provided with sufficient leading time.

Large floods occurred frequently in the upper Ping River basin and their potential damages are increasing due to the growth of cities and economical activities. Therefore, the information for flood protection is needed to reduce the damages, especially for city areas. This study attempts to establish a model to simulate flood and inundation against extreme rainfalls in the upper Ping River basin in Thailand for analyzing causal factors of inundation and proposing countermeasures. CASC2D model, a two dimensional hydrologic model was applied for this purpose. The model was calibrated and verified with flood discharge and inundated areas observed during the devastating floods in 2011. The simulation results were evaluated using two indices: Nash-Sutcliffe efficiency (NSE) and coefficient of determination (r 2) to show acceptable model performance. The model performance of inundated areas was improved by modifying topographic conditions. It showed predicted areas of local inundation along rivers and in the agricultural fields after storm events.

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.

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In 2011, massive flooding and inundation in the Chao Phraya River basin, in Thailand, caused serious damage to various activities for a prolonged period of time. Although snapshot images of the inundated area are available, detailed information including temporal changes of the inundated areas and the relationship with meteorological and hydrological conditions are not well documented, particularly for the middle and upper sections of the basin. Therefore, we conducted an analysis using two types of satellite data, HJ-1A and Envisat, to better understand behavior of the large-scale inundation occurred in 2011, focusing on the middle section of the Chao Phraya River basin. In the analysis, water surface in selected domains was extracted using the NDWI value calculated from HJ-1A data. The threshold value of the Envisat ASAR image was then adjusted so that the inundated area estimated from Envisat gives the closest possible match with that estimated from HJ-1A. Finally, the inundated area was estimated for the whole study domain based on the same threshold value from the Envisat data. Results indicated that the inundated area began to extend along the Yom and Nan rivers in early August and continued to spread down to the Nakhon Sawan city area until October. A significant increase in inundated areas occurred between September 2 and September 13, during which higher rainfall intensity was observed. Even after the water level in rivers receded below the bank-full elevation, large areas were left inundated along rivers, particularly over low-lying marsh and paddy fields. In addition, several areas located far from rivers were also inundated, which was likely a consequence of water ponding in paddy fields.