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A GIS Tool to Estimate Flow at Ungaged Basins Using the Map Correlation Method

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10407))

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Abstract

Water resources management has been a critical component of sustainable resources planning. One of the most commonly used data in water resources management is streamflow measurements. Daily streamflow time series collected at a stream gage provide information on the temporal variation in water quantity where the gage is located. However, streamflow information is often needed at ungaged catchments especially when the stream gage network is not dense. One conventional approach to estimate streamflow at an ungaged catchment is to transfer streamflow measurements from the spatially closest stream gage, commonly referred to as the donor or reference gage using the drainage-area ratio method. Recently, the correlation between daily streamflow time series is proposed as an alternative to distance for reference stream gage selection. The Map Correlation Method (MCM) enables development of a map that demonstrates the spatial distribution of correlation coefficients between daily streamflow time series at a selected stream gage and all other locations within a selected study area. Although utility of the map correlation method has been demonstrated in various studies, due to its geostatistical analysis procedure it is time-consuming and hard to implement for practical purposes such as installed capacity selection of run-of-river hydropower plants during their feasibility studies. In this study, an easy-to-use GIS-based tool, called MCM_GIS is developed to apply the MCM in estimating daily time series of streamflow. MCM_GIS provides a user-friendly working environment and flexibility in choosing between two types of interpolation models, kriging and inverse distance weighting. The main motivation of this study is to increase practical application of the MCM by integrating it to the GIS environment. MCM_GIS can also carry out the leave-one-out cross-validation scheme to monitor the overall performance of the estimation. The tool is demonstrated on a case study carried out in Western Black Sea Region, Turkey. ESRI’s ArcGIS for Desktop product along with a Python script is utilized. The outcomes of inverse distance weighting and ordinary kriging are compared. Results of GIS-based MCM are in good agreement with the observed hydrographs.

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Authors and Affiliations

  1. Department of Geodetic and Geographic Information Technologies, Middle East Technical University, 06800, Ankara, Turkey

    Duygu Ocal

  2. Water Resources Laboratory, Department of Civil Engineering, Middle East Technical University, 06800, Ankara, Turkey

    Elcin Kentel

Authors
  1. Duygu Ocal
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  2. Elcin Kentel
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Correspondence to Duygu Ocal .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Ocal, D., Kentel, E. (2017). A GIS Tool to Estimate Flow at Ungaged Basins Using the Map Correlation Method. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-62401-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62400-6

  • Online ISBN: 978-3-319-62401-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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