IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v22y2008i9p1221-1239.html
   My bibliography  Save this article

Development of a GIS Interface for Estimation of Runoff from Watersheds

Author

Listed:
  • J. Patil
  • A. Sarangi
  • O. Singh
  • A. Singh
  • T. Ahmad

Abstract

Development of accurate surface runoff estimation techniques from ungauged watersheds is relevant in Indian condition due to the non-availability of hydrologic gauging stations in majority of watersheds. Besides this, the high budgetary requirements for installation of gauging stations are another limiting factor in India, which leads to the use of surface runoff estimation techniques for ungauged watersheds. Natural Resources Conservation Services Curve Number (NRCS-CN) method is one of the most widely used methods for quick and accurate estimation of surface runoff from ungauged watershed. Also, the coupling of NRCS-CN techniques with the advanced Geographic Information System (GIS) capabilities automates the process of runoff prediction in timely and efficient manner. Keeping view of this, a GIS interface was developed using the in-built macro programming language, Visual Basic for Applications (VBA) of ArcGIS® tool to estimate the surface runoff by adopting NRCS-CN technique and its three modifications. The developed interface named as Interface for Surface Runoff Estimation using Curve Number techniques (ISRE-CN), was validated using the recorded data for the periods from 1993 to 2001 of a gauged watershed, Banha in the Upper Damodar Valley in Jharkhand, India. The observed runoff depths for different rainfall events in this study watershed was compared with the predicted values of NRCS-CN methods and its three modifications using statistical significance tests. It was revealed that using all the rainfall data for different AMC conditions, the modified CN I performed the best [R 2 (coefficient of determination) = 0.92; E (model efficiency) = 0.89) followed by modified CN III method (R 2 = 0.88; E = 0.87), while the modified CN II (R 2 = 0.42; E = 0.36) failed to predict accurately the surface runoff from Banha watershed. Moreover, under AMC based estimations, the modified CN I method also performed best ( R 2 = 0.95; E = 0.95) for AMC II condition, while the modified CN II performed the worst in all the AMC conditions. However, the developed Interface in ArcGIS® needs to be tested in other watershed systems for wider applicability of the modified CN methods. Copyright Springer Science+Business Media B.V. 2008

Suggested Citation

  • J. Patil & A. Sarangi & O. Singh & A. Singh & T. Ahmad, 2008. "Development of a GIS Interface for Estimation of Runoff from Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(9), pages 1221-1239, September.
    • Handle: RePEc:spr:waterr:v:22:y:2008:i:9:p:1221-1239
    DOI: 10.1007/s11269-007-9222-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-007-9222-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-007-9222-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. S. Mishra & Vijay Singh & J. Sansalone & V. Aravamuthan, 2003. "A Modified SCS-CN Method: Characterization and Testing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(1), pages 37-68, February.
    2. Sarangi, A. & Bhattacharya, A.K., 2005. "Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India," Agricultural Water Management, Elsevier, vol. 78(3), pages 195-208, December.
    3. S. Mishra & M. Jain & P. Bhunya & V. Singh, 2005. "Field Applicability of the SCS-CN-Based Mishra–Singh General Model and its Variants," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(1), pages 37-62, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. B. Yan & X. Su & Y. Chen, 2009. "Functional Structure and Data Management of Urban Water Supply Network Based on GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2633-2653, October.
    2. Ahmet Altin & Fatma Bakir & İsmail Özölçer, 2010. "The Evaluation of Kurtboğazı Dam (Ankara, Turkey) from Hydro-Geochemical and Environmental Aspects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(4), pages 747-759, March.
    3. Chang-Shian Chen & Frederick Chou & Boris Chen, 2010. "Spatial Information-Based Back-Propagation Neural Network Modeling for Outflow Estimation of Ungauged Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4175-4197, November.
    4. R. Bhalla & Neil Pelkey & K. Devi Prasad, 2011. "Application of GIS for Evaluation and Design of Watershed Guidelines," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 113-140, January.
    5. Chunlin Li & Miao Liu & Yuanman Hu & Tuo Shi & Min Zong & M. Todd Walter, 2018. "Assessing the Impact of Urbanization on Direct Runoff Using Improved Composite CN Method in a Large Urban Area," IJERPH, MDPI, vol. 15(4), pages 1-14, April.
    6. Vojtek Matej & Vojteková Jana, 2016. "GIS-based Approach to Estimate Surface Runoff in Small Catchments: A Case Study," Quaestiones Geographicae, Sciendo, vol. 35(3), pages 97-116, September.
    7. Muhammad Ajmal & Jae-Hyun Ahn & Tae-Woong Kim, 2016. "Excess Stormwater Quantification in Ungauged Watersheds Using an Event-Based Modified NRCS Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1433-1448, March.
    8. Rejani Raghavan & Kondru Venkateswara Rao & Maheshwar Shivashankar Shirahatti & Duvvala Kalyana Srinivas & Kotha Sammi Reddy & Gajjala Ravindra Chary & Kodigal A. Gopinath & Mohammed Osman & Mathyam P, 2022. "Assessment of Spatial and Temporal Variations in Runoff Potential under Changing Climatic Scenarios in Northern Part of Karnataka in India Using Geospatial Techniques," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    9. Rajat Agarwal & P. Garg & R. Garg, 2013. "Remote Sensing and GIS Based Approach for Identification of Artificial Recharge Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2671-2689, May.
    10. Muhammad Ajmal & Jae-Hyun Ahn & Tae-Woong Kim, 2016. "Excess Stormwater Quantification in Ungauged Watersheds Using an Event-Based Modified NRCS Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1433-1448, March.
    11. Shirisha Pulukuri & Venkata Reddy Keesara & Pratap Deva, 2018. "Flow Forecasting in a Watershed using Autoregressive Updating Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2701-2716, June.
    12. Ismail Chenini & Abdallah Mammou & Moufida El May, 2010. "Groundwater Recharge Zone Mapping Using GIS-Based Multi-criteria Analysis: A Case Study in Central Tunisia (Maknassy Basin)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 921-939, March.
    13. Bahram Choubin & Farzaneh Sajedi Hosseini & Omid Rahmati & Mansor Mehdizadeh Youshanloei, 2023. "A step toward considering the return period in flood spatial modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 431-460, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
    2. Nayara P. V. Andrade & Marcelo R. Viola & Samuel Beskow & Tamara L. Caldeira & Li Guo & Carlos R. Mello, 2020. "Assessment of Spatial and Temporal Soil Water Storage Using a Distributed Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 5031-5046, December.
    3. Zandra A. Cunha & Carlos R. Mello & Samuel Beskow & Marcelle M. Vargas & Jorge A. Guzman & Maíra M. Moura, 2023. "A Modeling Approach for Analyzing the Hydrological Impacts of the Agribusiness Land-Use Scenarios in an Amazon Basin," Land, MDPI, vol. 12(7), pages 1-20, July.
    4. Rejani Raghavan & Kondru Venkateswara Rao & Maheshwar Shivashankar Shirahatti & Duvvala Kalyana Srinivas & Kotha Sammi Reddy & Gajjala Ravindra Chary & Kodigal A. Gopinath & Mohammed Osman & Mathyam P, 2022. "Assessment of Spatial and Temporal Variations in Runoff Potential under Changing Climatic Scenarios in Northern Part of Karnataka in India Using Geospatial Techniques," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    5. Sushindra Kumar Gupta & Jaivir Tyagi & Gunwant Sharma & A. S. Jethoo & P. K. Singh, 2019. "An Event-Based Sediment Yield and Runoff Modeling Using Soil Moisture Balance/Budgeting (SMB) Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3721-3741, September.
    6. Jung, Jae-Woon & Yoon, Kwang-Sik & Choi, Dong-Ho & Lim, Sang-Sun & Choi, Woo-Jung & Choi, Soo-Myung & Lim, Byung-Jin, 2012. "Water management practices and SCS curve numbers of paddy fields equipped with surface drainage pipes," Agricultural Water Management, Elsevier, vol. 110(C), pages 78-83.
    7. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.
    8. Sarangi, A. & Singh, Man & Bhattacharya, A.K. & Singh, A.K., 2006. "Subsurface drainage performance study using SALTMOD and ANN models," Agricultural Water Management, Elsevier, vol. 84(3), pages 240-248, August.
    9. Liu, Xiaozhi & Kang, Shaozhong & Li, Fusheng, 2009. "Simulation of artificial neural network model for trunk sap flow of Pyrus pyrifolia and its comparison with multiple-linear regression," Agricultural Water Management, Elsevier, vol. 96(6), pages 939-945, June.
    10. Agnieszka Petryk & Edyta Kruk & Marek Ryczek & Lenka Lackóová, 2023. "Comparison of Pedotransfer Functions for Determination of Saturated Hydraulic Conductivity for Highly Eroded Loess Soil," Land, MDPI, vol. 12(3), pages 1-13, March.
    11. Mohamed Elhakeem & Athanasios Papanicolaou, 2009. "Estimation of the Runoff Curve Number via Direct Rainfall Simulator Measurements in the State of Iowa, USA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2455-2473, September.
    12. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    13. Anctil, François & Filion, Mélanie & Tournebize, Julien, 2009. "A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment," Ecological Modelling, Elsevier, vol. 220(6), pages 879-887.
    14. Cyr, Jean-François & Landry, Mathieu & Gagnon, Yves, 2011. "Methodology for the large-scale assessment of small hydroelectric potential: Application to the Province of New Brunswick (Canada)," Renewable Energy, Elsevier, vol. 36(11), pages 2940-2950.
    15. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:22:y:2008:i:9:p:1221-1239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.