Optimasi Model Natural Rural Electrical Cooperation Agency Untuk Memprediksi Debit Aliran Bulanan di Sub DAS Lesti

Ery Suhartanto; Ussy Andawayanti; Rahmah Dara Lufira; Rizki Tri Utami

doi:10.21107/rekayasa.v18i3.31267

Optimasi Model Natural Rural Electrical Cooperation Agency Untuk Memprediksi Debit Aliran Bulanan di Sub DAS Lesti

Ery Suhartanto, Ussy Andawayanti, Rahmah Dara Lufira, Rizki Tri Utami

Abstract

Climate change and land use change in the Lesti Sub-DAS increase the risk of flooding and land degradation, requiring reliable flow predictions to support water resource management. However, the performance of the NRECA model in predicting monthly flows in this region is still not optimal because calibration-validation strategies and the use of environmental parameters have not been systematically studied. This study optimizes the NRECA model to predict monthly discharge for the period 2011-2020 in the Lesti sub-watershed by calibrating the GWF and PSUB parameters based on rainfall, evapotranspiration, and watershed morphometry data in three data division scenarios (70:30, 80:20, and 90:10 for calibration:validation). The results show that all scenarios produce excellent performance with calibration Nash-Sutcliffe Efficiency (NSE) values between 0.99491-0.99561 and correlation coefficients (R) between 0.99746-0.99785, while validation yielded NSE values between 0.89112-0.97227 and R between 0.49959-0.81520. The best scenario was obtained with a combination of 8 years of calibration and 2 years of validation, with NSE = 0.99561 and R = 0.99785 at the calibration stage, and NSE = 0.97227 and R = 0.81512 at the validation stage, indicating the model's ability to consistently represent monthly discharge variations. The similarity between the model discharge pattern and observations during the base and peak flow periods indicates that GWF optimization specifically improves the representation of base flow response. This study contributes by presenting an optimization-based calibration-validation scheme for the NRECA model, which can be used as a reference in conservation planning and reservoir operation management in watersheds with limited data.

Keywords

monthly discharge, model calibration, NRECA, parameter optimization, Lesti sub-watershed

Full Text:

PDF (Bahasa Indonesia)

References

Anaba, L. A., Banadda, N., Kiggundu, N., Wanyama, J., Engel, B., & Moriasi, D. (2017). Application of SWAT to Assess the Effects of Land Use Change in the Murchison Bay Catchment in Uganda. Computational Water, Energy, and Environmental Engineering, 06(01), 24–40. https://doi.org/10.4236/cweee.2017.61003.

Anonim. (2013). Standar Perencanaan Irigasi, Kriteria Perencanaan, Bagian Perencanaan Jaringan Irigasi KP-01. Kementerian Pekerjaan Umum, Direktorat Jenderal Sumber Daya Air, Direktorat Irigasi dan Rawa.

Asdak, C. (2020). Hidrologi dan Pengelolaan Daerah Aliran Sungai. In Gadjah Mada University Press.

Billah, K., Le, T. B., & Sharif, H. O. (2021). Data-and Model-Based Discharge Hindcasting over a Subtropical River Basin. Water (Switzerland), 13(2560), 1–20. https://doi.org/10.3390/w13182560.

Chiew, F. H. S., Zheng, H., & Potter, N. J. (2018). Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and Under Climate Change. Water (Switzerland), 10(1319), 1–19. https://doi.org/10.3390/w10101319.

Dangol, S., Zhang, X., Liang, X. Z., Anderson, M., Crow, W., Lee, S., Moglen, G. E., & McCarty, G. W. (2023). Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets. Remote Sensing, 15(2417), 1–22. https://doi.org/10.3390/rs15092417.

Dhungel, S., Tarboton, D. G., Jin, J., & Hawkins, P. (2016). Potential Effects of Climate Change on Ecologically Relevant Streamflow Regimes: River Research and Applications (pp. 1–14). John Wiley & Sons, Ltd. https://doi.org/10.1002/rra.

Duan, Y., Xiong, J., Cheng, W., Li, Y., Wang, N., Shen, G., & Yang, J. (2022). Increasing Global Flood Risk in 2005-2020 from a Multi-Scale Perspective. Remote Sensing (MDPI), 14(5551), 1–21. https://doi.org/doi.org/10.3390/rs14215551.

Fattahi, P., Ashrafzadeh, A., Pirmoradian, N., & Vazifedoust, M. (2022). Integrating IHACRES with a Data-Driven Model to Investigate the Possibility of Improving Monthly Flow Estimates. Water Supply, 22(1), 360–371. https://doi.org/10.2166/ws.2021.267.

Hidayat, D. P. A., Darsono, S. L. W., & Farid, M. (2021). Modelling of Potential Recharge Zone Using Geographical Information System Based on Indonesia Regulation and Water Balance Model. International Journal of GEOMATE, 20(79), 147–154. https://doi.org/10.21660/2021.79.j2037.

Huang, S., Shah, H., Naz, B. S., Shrestha, N., Mishra, V., Daggupati, P., Ghimire, U., & Vetter, T. (2020). Impacts of Hydrological Model Calibration on Projected Hydrological Changes Under Climate Change—A Multi-Model Assessment in Three Large River Basins. Climatic Change, 163(3), 1143–1164. https://doi.org/10.1007/s10584-020-02872-6.

Indarto. (2018). Hidrologi: Dasar Teori dan Contoh Aplikasi Model Hidrologi. Bumi Aksara, Jakarta.

Jabbari, A., & Bae, D. H. (2018). Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin. Water (Switzerland), 10(1626), 1–20. https://doi.org/10.3390/w10111626.

Jin, X., & Jin, Y. (2020). Calibration of a Distributed Hydrological Model in a Data-Scarce Basin Based on GLEAM Datasets. Water (Switzerland), 12(897), 1–14. https://doi.org/10.3390/w12030897.

Kazemi, H., Hashemi, H., Maghsood, F. F., Hosseini, S. H., Sarukkalige, R., Jamali, S., & Berndtsson, R. (2021). Climate vs. Human impact: Quantitative and Qualitative Assessment of Streamflow Variation. Water (Switzerland), 13(2404), 1–24. https://doi.org/10.3390/w13172404.

Lehner, F., Wood, A. W., Llewellyn, D., Blatchford, D. B., Goodbody, A. G., & Pappenberger, F. (2017). Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest. Geophysical Research Letters, 44(24), 12,208-12,217. https://doi.org/10.1002/2017GL076043.

Leta, M. K., Ebsa, D. G., & Regasa, M. S. (2022). Parameter Uncertainty Analysis for Streamflow Simulation Using SWAT Model in Nashe Watershed, Blue Nile River Basin, Ethiopia. Applied and Environmental Soil Science, 2022(1826942), 1–19. https://doi.org/10.1155/2022/1826942.

Li, H., Huang, G., Li, Y., Sun, J., & Gao, P. (2021). A C‐Vine Copula‐Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability (Switzerland), 13(4627), 1–22. https://doi.org/10.3390/su13094627.

Moriasi, D. N., Arnold, J. G., Liew, M. W. V, Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153.

Moriasi, D. N., Zeckoski, R. W., Arnold, J. G., Baffaut, C. B., Malone, R. W., Daggupati, P., Guzman, J. A., Saraswat, D., Yuan, Y., Wilson, B. W., Shirmohammadi, A., & Douglas-Mankin, K. R. (2015). Hydrologic and Water Quality Models: Key Calibration and Validation Topics. Transactions of the ASABE, 58(6), 1609–1618. https://doi.org/10.13031/trans.58.11075.

Myers, D. T., Ficklin, D. L., Robeson, S. M., Neupane, R. P., Botero-Acosta, A., & Avellaneda, P. M. (2021). Choosing An Arbitrary Calibration Period for Hydrologic Models: How Much Does It Influence Water Balance Simulations? Hydrological Processes, 35(e14045), 1–17. https://doi.org/10.1002/hyp.14045.

Nagireddy, N. R., Keesara, V. R., Venkata Rao, G., Sridhar, V., & Srinivasan, R. (2023). Assessment of the Impact of Climate Change on Streamflow and Sediment in the Nagavali and Vamsadhara Watersheds in India. Applied Sciences (Switzerland), 13(7554), 1–22. https://doi.org/10.3390/app13137554.

Nelson, A. M., Moriasi, D. N., Talebizadeh, M., Steiner, J. L., Confesor, R. B., Gowda, P. H., Starks, P. J., & Tadesse, H. (2017). Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model. Journal of the American Water Resources Association, 53(5), 1164–1177. https://doi.org/10.1111/1752-1688.12564.

Pambudi, A. S., Moersidik, S. S., & Karuniasa, M. (2021a). Analisis Sebaran Limpasan Permukaan pada Sub DAS Lesti Sebagai Pertimbangan Konservasi Hulu DAS Brantas. Jurnal Teknik Pengairan, 12(2), 104–115. https://doi.org/doi.org/10.21776/ub.pengairan.2021.012.02.03 1.

Pambudi, A. S., Moersidik, S. S., & Karuniasa, M. (2021b). Analysis of Recent Erosion Hazard Levels and Conservation Policy Recommendations for Lesti Subwatershed, Upper Brantas Watershed. Jurnal Perencanaan Pembangunan, V(1), 71–93. https://doi.org/10.36574/jpp.v5i1.167.

Putri, A. S., Suhartanto, E., & Andawayanti, U. (2025). Genetic Algorithm-Based NRECA Parameter Calibration for Rainfall-Discharge Modeling in Rejoso Watershed. Jurnal Penelitian Pendidikan IPA, 11(6), 734–742. https://doi.org/10.29303/jppipa.v11i6.11091.

Scanlon, B. R., Zhang, Z., Rateb, A., Sun, A., Wiese, D., Save, H., Beaudoing, H., Lo, M. H., Müller-Schmied, H., Döll, P., van Beek, R., Swenson, S., Lawrence, D., Croteau, M., & Reedy, R. C. (2019). Tracking Seasonal Fluctuations in Land Water Storage Using Global Models and GRACE Satellites. Geophysical Research Letters, 46(10), 5254–5264. https://doi.org/10.1029/2018GL081836.

Subrata, I. B., Hartana, & Setiawan, E. (2020). Analisis Ketersediaan Air Menggunakan Model Rain Run NRECA dan Tangki di DAS Babak. Jurnal Teknik Pengairan, 11(2), 73–82. https://doi.org/doi.org/10.21776/ub.pengairan.2020.011.02.01.

Sugiyono. (2007). Statistika Untuk Penelitian. Alfabeta.

Suhartanto, E., Andawayanti, U., Lufira, R. D., Darmawan, A. A., & Putri, A. S. (2025). Model Prototipe Alih Ragam Hujan Ke Debit Menggunakan Data Satelit TRMM Dan Jaringan Syaraf Tiruan. Teras Jurnal : Jurnal Teknik Sipil, 15(1), 161–173. https://doi.org/10.29103/tj.v15i1.1219.

Sun, N., Wigmosta, M., Zhou, T., Lundquist, J., Dickerson-Lange, S., & Cristea, N. (2018). Evaluating the Functionality and Streamflow Impacts of Explicitly Modelling Forest–Snow Interactions and Canopy Gaps in a Distributed Hydrologic Model. Hydrological Processes, 32(13), 2128–2140. https://doi.org/10.1002/hyp.13150.

Tabari, H. (2020). Climate Change Impact on Flood and Extreme Precipitation Increases with Water Availability. Scientific Reports, 10(13768), 1–10. https://doi.org/10.1038/s41598-020-70816-2.

Trilita, M. N., Suryandari, W., & Handajani, N. (2021). Water Availability Study using NRECA Model (Embung Sei Fatimah North Kalimantan Case Study). IOP Conference Series: Materials Science and Engineering, 1125(012095), 1–6. https://doi.org/10.1088/1757-899x/1125/1/012095.

Wang, W., Dong, Z., Zhu, F., Cao, Q., Chen, J., & Yu, X. (2018). A Stochastic Simulation Model for Monthly River Flow in Dry Season. Water (Switzerland), 10(1654), 1–16. https://doi.org/10.3390/w10111654

Xiong, L., & Zeng, L. (2019). Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation. Water (Switzerland), 11(666), 1–20. https://doi.org/10.3390/w11040666.

Yao, L., Libera, D. A., Kheimi, M., Sankarasubramanian, A., & Wang, D. (2020). The Roles of Climate Forcing and Its Variability on Streamflow at Daily, Monthly, Annual, and Long-Term Scales. Water Resources Research, 55(e2020WR027111), 1–23. https://doi.org/10.1029/2020WR027111.

Zhou, Z., Gao, X., Yang, Z., Feng, J., Meng, C., & Xu, Z. (2019). Evaluation of Hydrological Application of CMADS in Jinhua River Basin, China. Water (Switzerland), 11(138), 1–22. https://doi.org/10.3390/w11010138.