The Reliability of W-flow Run-off-Rainfall Model in Predicting Rainfall to the Discharge

D. Riyadi Tama, Lily M. Limantara, E. Suhartanto, Y. Padma Devia

Abstract


This research intends to predict the discharge (run-off) from rainfall for which the model is built using W-flow. The research location is in the Gajah Mungkur reservoir (Wonogiri) in Indonesia. The estimation of reservoir inflow has an important role, mainly in the scheme of reservoir operation and management. However, the heterogeneity of complex spatial and temporal patterns of rainfall and also the physiographic context of a watershed cause the development of a model of real-time run-off and rainfall that can accurately predict the reservoir inflow to become a challenge in the development of water resources. In relation to the analysis and prediction of rainfall, the constraint and problem that is still often faced is the minimal availability of observed rainfall data spatially as well as temporally; the time series of rainfall data is not long and complete enough; and the number of rainfall stations is less evenly distributed. The methodology consists of carrying out the literature study, collecting as much rainfall data as possible to build a W flow model, then carrying out the model calibration and analyzing the prediction of real-time reservoir inflow for operation. The result shows that the dependable discharge of the Wonogiri watershed shows that there are two peak discharges, which happened on February II (the second half of February) and December II (the second half of December). However, the discharge is decreasing in July and reaching its lowest level in October II (the second half of October).

 

Doi: 10.28991/CEJ-2023-09-07-015

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Keywords


Rainfall; Run-Off; Model; W-Flow.

References


Qizi, M. D. A. (2021). Water Resources and Their Use in The National Economy. Builders of the Future, 1(2), 01–09. doi:10.37547/builders-10.

Loritz, R., Hrachowitz, M., Neuper, M., & Zehe, E. (2021). The role and value of distributed precipitation data in hydrological models. Hydrology and Earth System Sciences, 25(1), 147–167. doi:10.5194/hess-25-147-2021.

Loucks, D. P., & van Beek, E. (2017). Water Resources Planning and Management: An Overview. Water Resource Systems Planning and Management. Springer, Cham, Switzerland. doi:10.1007/978-3-319-44234-1_1.

Maity, S. K., & Maiti, R. (2017). Sedimentation under variable shear stress at lower reach of the Rupnarayan River, West Bengal, India. Water Science, 31(1), 67–92. doi:10.1016/j.wsj.2017.02.001.

Waskito, T. N., Bisri, M., Limantara, L. M., & Soetopo, W. (2022). Hydrological Prediction for Mapping the Potency of Break in the Saguling Dam, West Java Province, Indonesia. Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 57(4), 71–79. doi:10.35741/issn.0258-2724.57.4.6.

Arno, G., Muflihah, M., & Mujahidin, M. (2020). Uncertainty of Optimal Rain Gauge Coastal Region: Case Study Makassar. Journal of the Civil Engineering Forum, 1000(1000). doi:10.22146/jcef.58378.

Wu, H., Chen, Y., Chen, X., Liu, M., Gao, L., & Deng, H. (2020). New approach for optimizing rain gauge networks: A case study in the Jinjiang Basin. Water (Switzerland), 12(8), 2252. doi:10.3390/w12082252.

Tama, D. R., Limantara, L. M., Suhartanto, E., & Devia, Y. P. (2022). the Usage of Gpm Data in the Ungauged Wonogiri Catchment. Journal of Southwest Jiaotong University, 57(6), 1004–1010. doi:10.35741/issn.0258-2724.57.6.86.

Perera, H., Fernando, S., Gunathilake, M. B., Sirisena, T. A. J. G., & Rathnayake, U. (2022). Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka. Advances in Meteorology, 2022. doi:10.1155/2022/1926854.

Roca, R., Alexander, L. V., Potter, G., Bador, M., Jucá, R., Contractor, S., Bosilovich, M. G., & Cloché, S. (2019). FROGS: A daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products. Earth System Science Data, 11(3), 1017–1035. doi:10.5194/essd-11-1017-2019.

Gunathilake, M. B., Amaratunga, Y. V., Perera, A., Karunanayake, C., Gunathilake, A. S., & Rathnayake, U. (2020). Statistical evaluation and hydrologic simulation capacity of different satellite-based precipitation products (SbPPs) in the Upper Nan River Basin, Northern Thailand. Journal of Hydrology: Regional Studies, 32. doi:10.1016/j.ejrh.2020.100743.

Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., & Wagner, W. (2019). SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations. Earth System Science Data, 11(4), 1583–1601. doi:10.5194/essd-11-1583-2019.

Shi, J., Yuan, F., Shi, C., Zhao, C., Zhang, L., Ren, L., Zhu, Y., Jiang, S., & Liu, Y. (2020). Statistical evaluation of the latest GPM-Era IMERG and GSMaP satellite precipitation products in the Yellow River source region. Water (Switzerland), 12(4), 1006. doi:10.3390/W12041006.

Fatkhuroyan, F., Wati, T., Sukmana, A., & Kurniawan, R. (2018). Validation of Satellite Daily Rainfall Estimates Over Indonesia. Forum Geografi, 32(2), 170–180. doi:10.23917/forgeo.v32i2.6288.

Cong, W., Sun, X., Guo, H., & Shan, R. (2020). Comparison of the SWAT and InVEST models to determine hydrological ecosystem service spatial patterns, priorities and trade-offs in a complex basin. Ecological Indicators, 112, 106089. doi:10.1016/j.ecolind.2020.106089.

Gunathilake, M. B., Zamri, M. N. M., Alagiyawanna, T. P., Samarasinghe, J. T., Baddewela, P. K., Babel, M. S., Jha, M. K., & Rathnayake, U. S. (2021). Hydrologic Utility of Satellite-Based and Gauge-Based Gridded Precipitation Products in the Huai Bang Sai Watershed of Northeastern Thailand. Hydrology, 8(4), 165. doi:10.3390/hydrology8040165.

Jia, Y., & Cui, P. (2018). Contrastive analysis of temperature interpolation at different time scales in the alpine region by Anusplin. Plateau Meteorology, 37(3), 757-766.

Karunanayake, C., Gunathilake, M. B., & Rathnayake, U. (2020). Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks. Applied Computational Intelligence and Soft Computing, 2020, 1–11. doi:10.1155/2020/8821627.

Mulyono, B. H., Limantara, L. M., Sholichin, M., & Sisinggih, D. (2022). A Synthetic Precipitation Model to Determine the Usage of the Distribution of Rain as a Data Input in Calculating the Design Flood. Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 57(4), 416–427. doi:10.35741/issn.0258-2724.57.4.37.

Deltares. (2023).Wflow, HydroMT-Wflow, Delft, The Netherlands. Available online: https://www.deltares.nl/en/software-and-data/products/wflow (accessed on May 2023).

Li, F., Ma, G., Chen, S., & Huang, W. (2021). An ensemble modeling approach to forecast daily reservoir inflow using bidirectional long-and short-term memory (Bi-LSTM), variational mode decomposition (VMD), and energy entropy method. Water Resources Management, 35, 2941-2963. doi:10.1007/s11269-021-02879-3.

Asmelita, Limantara, L. M., Bisri, M., Soetopo, W., & Farni, I. (2022). Allocation of Existing Water Irrigation in Panti Rao. Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 57(3), 355–362. doi:10.35741/issn.0258-2724.57.3.29.


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DOI: 10.28991/CEJ-2023-09-07-015

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Copyright (c) 2023 Lily Montarcih Limantara, Danny Riyadi Tama, Ery Suhartanto, Yatnanta Padma Devia

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