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Article

Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand

1
Center of Excellence in Sustainable Disaster Management (CESDM), Walailak University, Nakhon Si Thammarat 80160, Thailand
2
School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand
3
Haramaya Institute of Technology, School of Water Resources and Environmental Engineering, Haramaya University, Dire Dawa P.O. Box 138, Ethiopia
4
Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Wencheng Guo
Water 2021, 13(21), 3113; https://doi.org/10.3390/w13213113
Received: 28 August 2021 / Revised: 27 October 2021 / Accepted: 1 November 2021 / Published: 4 November 2021
(This article belongs to the Special Issue Inevitable Connection of River Flow Modeling, GIS, and Hydrogeology)
This research aims to estimate baseflow (BF) and baseflow index (BFI) in ungauged basins in the southern part of Thailand. Three spatial interpolation methods (namely, inverse distance weighting (IDW), kriging, and spline) were utilized and compared in regard to their performance. Two baseflow separation methods, i.e., the local minimum method (LM) and the Eckhardt filter method (EF), were investigated. Runoff data were collected from 65 runoff stations. These runoff stations were randomly selected and divided into two parts: 75% and 25% for the calibration and validation stages, respectively, with a total of 36 study cases. Four statistical indices including mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and combined accuracy (CA), were applied for the performance evaluation. The findings revealed that monthly and annual BF and BFI calculated by EF were mostly lower than those calculated by LM. Furthermore, IDW gave the best performance among the three spatial interpolation techniques by providing the highest r-value and the lowest MAE, RMSE, and CA values for both the calibration and validation stages, followed by kriging and spline, respectively. We also provided monthly and annual BF and BFI maps to benefit water resource management. View Full-Text
Keywords: Eckhardt filter method; inverse distance weighting; kriging; local minimum method; spline; ungauged basin Eckhardt filter method; inverse distance weighting; kriging; local minimum method; spline; ungauged basin
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MDPI and ACS Style

Ditthakit, P.; Nakrod, S.; Viriyanantavong, N.; Tolche, A.D.; Pham, Q.B. Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand. Water 2021, 13, 3113. https://doi.org/10.3390/w13213113

AMA Style

Ditthakit P, Nakrod S, Viriyanantavong N, Tolche AD, Pham QB. Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand. Water. 2021; 13(21):3113. https://doi.org/10.3390/w13213113

Chicago/Turabian Style

Ditthakit, Pakorn, Sarayod Nakrod, Naunwan Viriyanantavong, Abebe Debele Tolche, and Quoc Bao Pham. 2021. "Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand" Water 13, no. 21: 3113. https://doi.org/10.3390/w13213113

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