Investigation on the rainfall-runoff relationship for the Rompin river basin
Hydrological information derived from rainfall-runoff relationship are essential for efficient planning and management of watersheds. Flood and drought issues in a watershed are highly dependent on the hydrological analysis output for mitigation design purpose. Rainfall-runoff processes are unique f...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27864/ http://umpir.ump.edu.my/id/eprint/27864/ http://umpir.ump.edu.my/id/eprint/27864/1/Investigation%20on%20the%20rainfall-runoff%20relationship%20for%20the%20Rompin%20river.pdf |
| Summary: | Hydrological information derived from rainfall-runoff relationship are essential for efficient planning and management of watersheds. Flood and drought issues in a watershed are highly dependent on the hydrological analysis output for mitigation design purpose. Rainfall-runoff processes are unique for each basin and therefore needed to be modelled individually. Additionally, the raw hydrological data is also crucial and thus must be reliable to be applied in the modelling. This study aims to: 1) generate a rainfallrunoff relationship scheme for the Rompin River; 2) calibrate and validate the simulated rainfall-runoff streamflow by comparison with the observed data. The continuous rainfall-runoff modelling was carried out using HEC-HMS for both wet and dry period utilizing rainfall data from 6 gauging stations and streamflow data from 2 gauging stations within the Rompin River Basin. Clark Unit Hydrograph was selected as the transform method for this study with the Tc and R parameters estimated using Hydrological Procedure 27 which was developed by the Department of Irrigation and Drainage (DID) Malaysia. SCS Curve Number was used as the loss method to predict the direct runoff from precipitation. For the baseflow and lag routing, constant monthly was used. From the results obtained, Clark Unit Hydrograph showed better performances in modelling the wet period compared to the dry. For wet period, the RMSE and NSE value for the best model are 59.83 and -0.64. As for dry period, the RMSE and NSE value for the best model are 6.22 and -1.84. The model for wet period is producing similar flow pattern with overestimation of flow, however it is applicable if provided with soil map data. |
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