DOI : https://doi.org/10.18517/ijaseit.14.2.19810

An Advanced Empirical NRCS-CN Model Estimation for Ungauged Catchment Insufficient Data

Martheana Kencanawati (1), Data Iranata (2), Mahendra Andiek Maulana (3)
(1) Civil Engineering Department, University of Balikpapan, Balikpapan, 76114, Indonesia
(2) Department of Civil Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
(3) Department of Civil Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
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How to cite (IJASEIT) :
Kencanawati, Martheana, et al. “An Advanced Empirical NRCS-CN Model Estimation for Ungauged Catchment Insufficient Data”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 761-7, doi:10.18517/ijaseit.14.2.19810.
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The calculation of runoff is continuously considered a difficult analysis to define specific prediction methods in ungauged catchments. This leads to the implementation of an observed model to determine curve number (CN) and estimate peak discharge from ungauged basins. Therefore, this study aimed to modify an experimental NRCS model through fieldwork and conduct sensitivity analysis for relevant modeling procedures. In the analysis, the assessment of CN required land use, soil, infiltration in situ measurement, and Hydrological Soil Group (HSG) parameters for the catchment area. The determination of infiltration rate was also initially carried out using the Horton method (double-ring infiltrometer), accompanied by the evaluation of CN through soil classes and land use parameters. Based on infiltration rate and soil classification, HSG was significantly defined for the catchment area. The results showed that the analytical parameters in rainfall-runoff modeling included the Composite Curve Number (SCS-CN ( ⁄S) with a ratio value of 0.2. This was accompanied by the potential retention maximum (S) of 264.37 mm, with the initial proportional abstraction ( ) being 52.87 at an assumed preliminary coefficient of 0.2. Therefore, the CN composite estimation was 65.5, and the correlation between P and Q was evaluated using graph analysis. The trial CN ranging from 39-74 were also significantly considered to optimize the development models of HEC HMS for the best performance, proving that the improvement of CN was interrelated with discharge.

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