University of Oulu

Akbari, M..; Torabi Haghighi, A.; Aghayi, M.M; Javadian, M.; Tajrishy, M.; Kløve, B. An Assimilation of satellite-based data for hydrological mapping of precipitation and direct runoff coefficient for the Lake Urmia basin in Iran. Water 2019, 8(11), 1624. doi: http://dx.doi.org/10.3390/w11081624

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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Author: Akbari, Mahdi1; Torabi Haghighi, Ali1; Aghayi, Mohammad Mahdi2;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, PO Box 4300,FIN-90014 Oulu, Finland
2Department of Civil Engineering, Sharif University of Technology, Tehran 11155-9313, Iran
3Department of Hydrology and Atmospheric Sciences, University of Arizona, PO Box 210011,Tucson, AZ 85721, USA
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019092329266
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2019
Publish Date: 2019-09-23
Description:

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.

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Series: Water
ISSN: 2073-4441
ISSN-E: 2073-4441
ISSN-L: 2073-4441
Volume: 11
Issue: 8
Article number: 1624
DOI: 10.3390/w11081624
OADOI: https://oadoi.org/10.3390/w11081624
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Subjects:
Funding: This research was funded by Urmia Lake Restoration Committee and University of Oulu.
Copyright information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/