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    Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

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    water-11-01624-v2.pdf
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    Author
    Akbari, Mahdi
    Haghighi, Ali Torabi
    Aghayi, Mohammad Mahdi
    Javadian, Mostafa
    Tajrishy, Masoud
    Klove, Bjorn
    Affiliation
    Univ Arizona, Dept Hydrol & Atmospher Sci
    Issue Date
    2019-08
    Keywords
    hydrological modeling
    downscaling TRMM
    direct runoff coefficient
    water scarcity
    lakes
    Urmia
    desiccation
    land use
    ungauged basin
    SCS-CN
    Kennessey
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    Publisher
    MDPI
    Citation
    Akbari, M.; Torabi Haghighi, A.; Aghayi, M.M.; Javadian, M.; Tajrishy, M.; Kløve, B. Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran. Water 2019, 11, 1624.
    Journal
    WATER
    Rights
    © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4-9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling.
    Note
    Open access journal
    ISSN
    2073-4441
    DOI
    10.3390/w11081624
    Version
    Final published version
    Sponsors
    Urmia Lake Restoration Committee; University of Oulu
    ae974a485f413a2113503eed53cd6c53
    10.3390/w11081624
    Scopus Count
    Collections
    UA Faculty Publications

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