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Open AccessArticle

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

1
Water, Energy and Environmental Engineering Research Unit, University of Oulu, PO Box 4300, FIN-90014 Oulu, Finland
2
Department of Civil Engineering, Sharif University of Technology, Tehran 11155-9313, Iran
3
Department of Hydrology and Atmospheric Sciences, University of Arizona, PO Box 210011, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1624; https://doi.org/10.3390/w11081624
Received: 25 June 2019 / Revised: 30 July 2019 / Accepted: 2 August 2019 / Published: 6 August 2019
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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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. View Full-Text
Keywords: hydrological modeling; downscaling TRMM; direct runoff coefficient; water scarcity; lakes; Urmia; desiccation; land use; ungauged basin; SCS-CN; Kennessey hydrological modeling; downscaling TRMM; direct runoff coefficient; water scarcity; lakes; Urmia; desiccation; land use; ungauged basin; SCS-CN; Kennessey
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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.

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