Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data
Öz
In this study, it was aimed to investigate applicability of various statistical estimation methods for Porsuk River basin, which has sparse streamflow observations. Estimations were performed using regression, single and multiple donor stations based drainage area ratio, standardization with mean (SM), standardization with mean and standard deviation (SMS), inverse distance weighted methods. Two seperate studies were conducted for both partially missing data and completely missing data. In order to estimate streamflow statistics for use in SM and SMS methods, logarithmic regression equations were suggested. The promising results obtained from ensemble approaches will provide a significant hydrological contribution to streamflow estimations.
Anahtar Kelimeler
Kaynakça
- [1] Shu C. & Ouarda T. B. M. J., Improved methods for daily streamflow estimates at ungauged sites. Water Resour. Res., 48, p. W02523, 2012. https://doi.org/10.1029/2011WR011501
- [2] Razavi T. & Coulibaly P., An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds. Can. Water Resour. J., 42, pp. 2–20, 2017. https://doi.org/10.1080/07011784.2016.1184590
- [3] Kalteh A. M. & Hjorth P., Imputation of missing values in precipitation-runoff process database. Hydrology Research, 40 (4), pp. 420-432, 2009. https://doi.org/10.2166/nh.2009.001
- [4] Ergen K. & Kentel E., An integrated map correlation method and multiple‐source sites drainage‐area ratio method for estimating streamflows at ungauged catchments: A case study of the Western Black Sea Region, Turkey. Journal of Environmental Management, 166, 309–320, 2016. https://doi.org/10.1016/j.jenvman.2015.10.036
- [5] Hughes D. A. & Smakhtin V., Daily flow time series patching or extension: a spatial interpolation approach based on flow duration curves. Hydrological Sciences Journal, 41: 851–871, 1996. https://doi.org/10.1080/02626669609491555
- [6] Tencaliec P., Favre A. C., Prieur C. & Mathevet T., Reconstruction of missing daily streamflow data using dynamic regression models. Water Resour. Res., 51 (2015), pp. 9447-9463, 2015. https://doi.org/10.1002/2015WR017399
- [7] Patil S. & Stieglitz M., Controls on hydrologic similarity: role of nearby gauged catchments for prediction at an ungauged catchment. Hydrology and Earth System Sciences 16: 551–562, 2012. https://doi.org/10.5194/hess-16-551-2012
- [8] Elshorbagy A. A., Panu U. S. & Simonovic S. P., Group-based estimation of missing hydrological data: I. Approach and general methodology. Hydrological Sciences Journal, 45, 849–866, 2000. https://doi.org/10.1080/02626660009492388
Ayrıntılar
Birincil Dil
İngilizce
Konular
İnşaat Mühendisliği
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
1 Kasım 2019
Gönderilme Tarihi
4 Mayıs 2018
Kabul Tarihi
24 Aralık 2018
Yayımlandığı Sayı
Yıl 2019 Cilt: 30 Sayı: 6
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