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Estimation of peak discharge and flood volume in ungauged basins using HydroCAD software

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accurately estimating peak discharge and flood volume in small and data-deficient basins within larger watersheds is crucial for effective flood planning and management. This study focuses on the Kamalvand Central Site and the Faculty of Agriculture and Natural Resources (FANR) at Lorestan University to determine maximum flow and flood volume. This estimation is achieved by applying the SCS-CN method, utilizing rainfall patterns generated by the fractal method within the HydroCAD software environment. Flow measurements were taken during precipitation events on December 18, 2021 (39 mm of precipitation) and January 03, 2022 (29 mm of precipitation) for the FANR basin and Sub-basin 2 of the Kamalvand site to validate the rainfall-runoff model. The results from the SCS-CN model were compared with empirical methods, such as Fuller and Creager, to assess its performance. The results showed that the fractal method could accurately estimate precipitation duration and return periods. In addition, the IDF of this method, compared with Ghahraman, showed low RMSE in the period of low returns (from 2 to 100 years). The relationship between the scale exponent and weighted moment order for all stations was linear and statistically significant at the 1% level. The highest peak flow and runoff volume in the FANR Basin belong to Sub-basin 1, measuring 22.35 m3/s and 1,32,000 m3 for a 25-year return period, respectively. Furthermore, the highest values for these variables in the Kamalvand basin are associated with Sub-basin 2, which covers an area of 237 hectares and has a curve number of 80, with a concentration-time of 18.4 min. In this context, peak flow and flood volume for the 25-year return period were estimated at 23.49 m3/s and 54,540 m3, respectively. Flow measurement results from both basins indicated the accuracy of the SCS-CN model in simulating and estimating flow, with correlation coefficients of 0.88 and 0.94 and Nash-Sutcliffe coefficients of 0.74 and 0.52 for the Kamalvand and FANR basins, respectively. Moreover, the results demonstrated the high performance of the SCS-CN model in estimating peak flow based on designed rainfall patterns using the fractal method and utilizing the curve number obtained from Sentinel 2 imagery within the HydroCAD software environment, compared to empirical methods like Fuller and Creager, for return periods of 25, 50, and 100 years. Therefore, this rapid and practical approach is recommended for other ungauged basins.
Słowa kluczowe
Czasopismo
Rocznik
Strony
635--659
Opis fizyczny
Bibliogr. 36 poz.
Twórcy
  • Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran
  • Soil Conservation and Watershed Management Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran
Bibliografia
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  • 2. Al-Ghobari H, Dewidar A, Alataway A (2020) Estimation of surface water runoff for a semi-arid area using RS and GIS-based SCS- CN method. Water 12:1924. https://doi.org/10.3390/w12071924
  • 3. Alizadeh A (2006) Principles of Applied Hydrology, 26th edn. Imam Reza Publication, Mashhad
  • 4. Al-Juaidi AEM (2018) A simplified GIS-based SCS-CN method for the assessment of land-use change on runoff. Arab J Geosci 11:269. https://doi.org/10.1007/s12517-018-3621-4
  • 5. Amatya DM, Walega A, Callahan TJ, Morrison A, Vulava V, Hitchcock DR, Epps T (2022) Storm event analysis of four forested catchments on the Atlantic coastal plain using a modified SCS-CN rainfall-runoff model. J Hydrol 608:127772
  • 6. Amutha R, Porchelvan P (2009) Estimation of surface runoff in Malat tar sub-watershed using SCS-CN method. J Indian Soc Remote Sens 37:291
  • 7. Azhdary Moghaddam M, Heravi Z (2018) Evaluation of IDF curve production methods by relationship based on nature of combination of fractal of precipitation. J Water Soil Conserv 24(6):271-282. https://doi.org/10.22069/JWSC.2018.11418.2582
  • 8. Babaali H, Ramak Z, Sepahvand R (2019) Estimation of design flood using fractal theory and HEC-HMS model (case study: Khorramabad River Basin). Water Soil 32(6):1097-1107 ((In Persian))
  • 9. Bahrami S, Imeni S (2019a) Evaluation of several empirical models in estimating annual runoff (case study: Hesarak Catchment in Northwest of Tehran). J Geogr Environ Plan 30(2):55-74
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  • 16. Gavhane KP, Mishra AK, Sarangi A et al (2023) Estimation of surface runoff potential of an ungauged watershed in semi-arid region using geospatial techniques. Arab J Geosci 16:402. https://doi.org/10.1007/s12517-023-11497-9
  • 17. Gupta L, Dixit J (2022) Estimation of rainfall-induced surface run- off for the Assam region, India, Using the GIS-Based NRCS-CN Method. J Maps 18(2):428-440. https://doi.org/10.1080/17445647.2022.2076624
  • 18. Hejazi A, Mezbani M (2017) The estimation of runoff volume and maximum discharge by using curve number (CN) Method (case study in Darrehshahr Drainage Basin). Hydrogeomorphology 2(5):63-81 ((in Persian))
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  • 21. Khzr BO, Ibrahim GRF, Hamid AA et al (2021) Runoff estimation using SCS-CN and GIS techniques in the Sulaymaniyah sub-basin of the Kurdistan region of Iraq. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01549-z
  • 22. Kumar A, Kanga S, Taloor AK, Singh SK, Durin B (2021) Surface runoff estimation of Sind River basin using integrated SCS-CN and GIS techniques. HydroRes 4:61-74. https://doi.org/10.1016/j.hydres.2021.08.001
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  • 25. McCuen, Richard H (1982) A Guide to Hydrologic Analysis Using SCS Methods, Prentice Hall
  • 26. Mishra U, Kumar GK, Gupta SK, Sarah Kiron P, Vinay N, Ara L (2021) Runoff volume estimation by SCS-CN method through Arc-GIS approach. In: Gupta LM, Ray MR, Labhasetwar PK (eds) Advances in civil engineering and infrastructural development. Lecture Notes in Civil Engineering, vol 87. Springer, Singapore. https://doi.org/10.1007/978-981-15-6463-5_32
  • 27. Noorigheidari MH (2012) Determine of design maximum intensity of precipitation by combined fractal theory and generalized extreme value distribution. Irrig Sci Eng 35(2):83-90.
  • 28. Safavi HR, Dadjou S, Naeimi G (2019) Extraction of intensity-duration-frequency (IDF) curves under climate change, case study: Isfahan synoptic station. Iran-Water Resour Res 15(2):217-227
  • 29. Satheeshkumar S, Venkateswaran S, Kannan R (2017) Rainfall-runoff estimation using SCS-CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India. Model Earth Syst Environ 3:24. https://doi.org/10.1007/s40808-017-0301-4
  • 30. Sharma SB, Singh AK (2014) Assessment of the flood potential on a lower Tapi Basin tributary using SCS-CN method integrated with remote sensing GIS data. J Geogr Nat Disast 4(2):1-7
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  • 32. Taher TM (2015) Integration of GIS database and SCS-CN method to estimate runoff volume of Wadis of intermittent flow. Arab J Sci Eng 40:685-692
  • 33. Tirkey AS, Pandey AC, Nathawat MS (2014) Use of highresolution satellite data, GIS and NRCS-CN technique for the estimation of rainfall-induced run-off in small catchment of Jharkhand India. Geocarto Int 29(7):778-791. https://doi.org/10.1080/10106049.2013.841773
  • 34. Zeng Z, Tang G, Hong Y, Zeng C, Yang Y (2017) Development of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications. Remote Sens Lett 8(6):528-536
  • 35. Zhang WY (2019) Application of NRCS-CN method for estimation of watershed runoff and disaster risk. Geomat Nat Haz Risk 10(1):2220-2238
  • 36. Zolfaghari H, Tahmasebipour N, Baharvandi N (2014) Simulation of Kashkan Basin rainfall-runoff relations using SCS method. Geogr Environ Sustain 4(1):1-12
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2ef88683-6900-4510-bd34-da60678e154a
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