The performance of the well-known Soil and Water Assessment Tool (SWAT) and the new SWAT+ for streamflow simulation in a paddy- field-dominated basin was compared. The Lam Sioa River Basin, northeast Thailand (drainage area of 3,394 km2) was selected. The data inputs consisted of DEM, land use, soil, and climate (rainfall, temperature, sunshine hour, wind speed and humidity). The model parameters used the default values from SWAT database and daily simulation was conducted from 2005 to 2017. The division of sub-basins into “landscape units” is one of new features of SWAT+. The total number of HRUs defined from SWAT+ were higher than those from SWAT because the sub-basins derived from SWAT+ contained two landscape units (floodplain and upslope). With the default model parameters, the model performance indicators were found below the satisfactory rating. Both models simulated relatively high streamflow at the beginning of rainy season, while the observed streamflow was still not occurred. In paddy field, rainfall excess become ponding water, not surface runoff. The appropriate representation of paddy field in SWAT model should be further investigated.
Reservoir characteristics are the essential information for water management planning and reservoir operation. Regular monitoring and assessment of the reservoir characteristics can reduce risks associated with the reservoir operation. This research assessed the reservoir characteristics (water surface, volume) of Vajiralongkorn Dam using remote sensing. Reservoir water surface was classified using the Normalized Difference Water Index (NDWI) derived from the Landsat 8 data, and validated using the streamline matching rate (SMR) and the streamline matching error (SME) techniques for shoreline accuracy assessment. The volume between two water levels was calculated using the prismatic equation. The storage capacity curve was constructed from the reservoir water level and cumulative volume. The accuracy of NDWI technique was satisfactory in identifying reservoir water surface with a good accuracy of shoreline delineation (SMR>95% and SME=11.7 m). The water surface has decreased on the average of 8.2 km 2 (2.8%) compared with the original data in 1980. The storage capacity has decreased 495.3 million m 3 (MCM) over 38 years from 1980 to 2018, an annual capacity loss of 13 MCM. Finally, sustainable service of the reservoir needs better knowledge of the effects of storage loss, the erosion and sediment-transport processes, and conservation measures.
Data on soil properties are indispensable for process-based hydrological modeling. Soil information of Thailand is primarily provided by the Land Development Department (LDD), nevertheless soil property data are available only in arable land whose slope is less than 35%. The steep-slope land was generally labeled as Slope Complex (SC), there is no information available. This paper demonstrated the application of soil-landscape evaluation approach for predicting the missing properties of soil which resulted on enhancement of model performance in streamflow estimation in Krasioa Basin by the Soil and Water Assessment Tool (SWAT) model. The physical properties of soil-soil thickness, fraction of soil particles (clay, sand, organic matter) were predicted using the Soil-Landscape Estimation and Evaluation Program (SLEEP). The additional properties of soil including bulk density, hydraulic conductivity, and available water content were estimated using the pedo-transfer functions (ROSETTA). It was found that SLEEP model could provide consistent information on physical properties of soil. The SWAT model performance in streamflow simulation at the Krasioa Reservoir was improved using the proposed approach. Appropriate model inputs can generate reasonable output. Model performance can further be improved by calibration.
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