A PROPOSED TECHNIQUE FOR IMPROVING THE ACCURACY OF TIDAL MODELING OF RIVER NETWORKS CONNECTING TO THE DADAHUP IRRIGATION AREA
Keywords:Tidal modeling, Calibration, Barito – Kapuas Murung – Mengkatip River Network, HEC-RAS., Hydraulic modeling, HEC-RAS, River channel network chains, Manning roughness coefficient, Dadahup irrigation
AbstractOptimizing the operational management of tidal irrigation networks is a significant factor in the reactivation program of the swamp irrigation areas in Central Kalimantan Province. Appropriate water level and flow rate control in a tidal environment would provide a better solution for this effort. Monitoring water parameters and hydraulic modeling is an intelligent technique for evaluating irrigation canals' gate system operations. This paper focuses on providing more accurate hydraulic modeling that requires proper boundary conditions and calibration of the canal roughness coefficient. This study aims to obtain a more precise downstream boundary condition data calibration method with an appropriate river cross-sectional roughness coefficient value to improve the overall hydraulic modeling accuracy of the river network connecting to the Dadahup Irrigation Area. The hydraulic modeling utilized the HEC-RAS Software, where input data preparation used geometric data derived from the National Geospatial Agency's DEM in the form of river channel network chain, cross-section, and long-section data. Boundary condition data evaluation compared and selected data generated from references and the Tides Application Software. The results show that the tide prediction from the Tides Application Software provides the slightest difference between the predicted tide and the measured ones. The river network's channel roughness coefficient calibration utilized the generated boundary condition tide and simultaneously measured water level data at several locations. The results show that the most minor Root Mean Squared Error (RMSE) of the Manning roughness coefficient differences of the river channel network can reach 0.04 with a minimum RMSE value of 0.027.
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