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Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data

Year 2019, Volume 30, Issue 6, 9597 - 9620, 01.11.2019
https://doi.org/10.18400/tekderg.421091

Abstract

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.

References

  • [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
  • [9] Panu U. S., Khalil M. & Elshorbagy A., Streamflow data infilling techniques based on concepts of groups and neural networks. In: Govindraju, R.S., Rao, A.R. (Eds.). Artificial Neural Networks in Hydrology. Kluwer Academic Publishers, Dordrecht, 235-258, 2000.
  • [10] Elshorbagy A., Simonovic S. P. & Panu U. S., Estimation of missing streamflow data using principles of chaos theory. Journal of Hydrology, 255, pp. 123-133, 2002. https://doi.org/10.1016/S0022-1694(01)00513-3
  • [11] Dastorani M.T., Moghadamnia A., Piri J., & Rico-Ramirez M., Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment 166: 421–434, 2010. https://doi.org/10.1007/s10661-009-1012-8
  • [12] Mohamoud Y. M., Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves. Hydrological Sciences, 53 (4), pp. 706-724, 2008. https://doi.org/10.1623/hysj.53.4.706
  • [13] Giustarini L., Parisot O., Ghoniem M., Hostache R., Trebs I., & Otjacques B., A user-driven case-based reasoning tool for infilling missing values in daily mean river flow records. Environmental Modelling & Software, 82, 308-320, 2016. https://doi.org/10.1016/j.envsoft.2016.04.013
  • [14] Gill M. K., Asefa T., Kaheil Y. & McKee M., Effect of missing data on performance of learning algorithms for hydrologic prediction: Implication to an imputation technique. Water Resour. Res., 43, W07416, 2007. https://doi.org/10.1029/2006WR005298
  • [15] Hirsch R. M., An evaluation of some record reconstruction techniques. Water Resour. Res., 15, 1781–1790, 1979. https://doi.org/10.1029/WR015i006p01781
  • [16] Wiche G. J., Benson R. D. & Emerson D. G., Streamflow at selected gaging stations on the James River in North Dakota and South Dakota, 1953–1982, with a section on climatology, Water Resources Investigations Report 89-4039, US Geological Survey, 99 pp, 1989.
  • [17] Emerson D. G., Vecchia A. V. & Dahl A. L., Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota. US Geological Survey Scientific Investigative Report 2005–5017, 13 pp, 2005.
  • [18] Asquith W. H., Roussel M. C. & Vrabel J., Statewide analysis of the drainage-area ratio method for 34 streamflow percentile ranges in Texas. US Geological Survey Scientific Investigative Report 2006–5286, 34 pp, 2006.
  • [19] Chen T., Ren L., Yuan F., Yang X., Jiang S., Tang T., Liu Y., Zhao C. & Zhang L., Comparison of spatial interpolation schemes for rainfall data and application in hydrological modeling. Water, 9, 342, 2017. https://doi.org/10.3390/w9050342
  • [20] Farmer W. H & Vogel R. M., Performance‐weighted methods for estimating monthly streamflow at ungauged sites. Journal of Hydrology 477: 240–250, 2013. https://doi.org/10.1016/j.jhydrol.2012.11.032
  • [21] Burgess T. M. & Webster R., Optimal interpolation and isarithmic mapping of soil properties: I. The semivariogram and punctual kriging. Journal of Soil Science, 31 pp. 315-331, 1980. https://doi.org/10.1111/j.1365-2389.1980.tb02084.x
  • [22] DSI, Management Plan for Porsuk Watershed, Final Report, State Water Works, Ankara, 2001.
  • [23] Hortness J. E., Estimating low flow frequency statistics for unregulated streams in Idaho. US Geol. Survey. Sci. Invest. Report 2006-5035, 2006.
  • [24] Sinnott R. W., Virtues of the Haversine. Sky and Telescope, vol. 68, no. 2, p. 159, 1984.
  • [25] Nash J. E. & Sutcliffe J. V., River flow forecasting through conceptual models. Part I—A discussion of principles. Journal of Hydrology, 10 (3), 282–290, 1970. https://doi.org/10.1016/0022-1694(70)90255-6

Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data

Year 2019, Volume 30, Issue 6, 9597 - 9620, 01.11.2019
https://doi.org/10.18400/tekderg.421091

Abstract

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.

References

  • [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
  • [9] Panu U. S., Khalil M. & Elshorbagy A., Streamflow data infilling techniques based on concepts of groups and neural networks. In: Govindraju, R.S., Rao, A.R. (Eds.). Artificial Neural Networks in Hydrology. Kluwer Academic Publishers, Dordrecht, 235-258, 2000.
  • [10] Elshorbagy A., Simonovic S. P. & Panu U. S., Estimation of missing streamflow data using principles of chaos theory. Journal of Hydrology, 255, pp. 123-133, 2002. https://doi.org/10.1016/S0022-1694(01)00513-3
  • [11] Dastorani M.T., Moghadamnia A., Piri J., & Rico-Ramirez M., Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment 166: 421–434, 2010. https://doi.org/10.1007/s10661-009-1012-8
  • [12] Mohamoud Y. M., Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves. Hydrological Sciences, 53 (4), pp. 706-724, 2008. https://doi.org/10.1623/hysj.53.4.706
  • [13] Giustarini L., Parisot O., Ghoniem M., Hostache R., Trebs I., & Otjacques B., A user-driven case-based reasoning tool for infilling missing values in daily mean river flow records. Environmental Modelling & Software, 82, 308-320, 2016. https://doi.org/10.1016/j.envsoft.2016.04.013
  • [14] Gill M. K., Asefa T., Kaheil Y. & McKee M., Effect of missing data on performance of learning algorithms for hydrologic prediction: Implication to an imputation technique. Water Resour. Res., 43, W07416, 2007. https://doi.org/10.1029/2006WR005298
  • [15] Hirsch R. M., An evaluation of some record reconstruction techniques. Water Resour. Res., 15, 1781–1790, 1979. https://doi.org/10.1029/WR015i006p01781
  • [16] Wiche G. J., Benson R. D. & Emerson D. G., Streamflow at selected gaging stations on the James River in North Dakota and South Dakota, 1953–1982, with a section on climatology, Water Resources Investigations Report 89-4039, US Geological Survey, 99 pp, 1989.
  • [17] Emerson D. G., Vecchia A. V. & Dahl A. L., Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota. US Geological Survey Scientific Investigative Report 2005–5017, 13 pp, 2005.
  • [18] Asquith W. H., Roussel M. C. & Vrabel J., Statewide analysis of the drainage-area ratio method for 34 streamflow percentile ranges in Texas. US Geological Survey Scientific Investigative Report 2006–5286, 34 pp, 2006.
  • [19] Chen T., Ren L., Yuan F., Yang X., Jiang S., Tang T., Liu Y., Zhao C. & Zhang L., Comparison of spatial interpolation schemes for rainfall data and application in hydrological modeling. Water, 9, 342, 2017. https://doi.org/10.3390/w9050342
  • [20] Farmer W. H & Vogel R. M., Performance‐weighted methods for estimating monthly streamflow at ungauged sites. Journal of Hydrology 477: 240–250, 2013. https://doi.org/10.1016/j.jhydrol.2012.11.032
  • [21] Burgess T. M. & Webster R., Optimal interpolation and isarithmic mapping of soil properties: I. The semivariogram and punctual kriging. Journal of Soil Science, 31 pp. 315-331, 1980. https://doi.org/10.1111/j.1365-2389.1980.tb02084.x
  • [22] DSI, Management Plan for Porsuk Watershed, Final Report, State Water Works, Ankara, 2001.
  • [23] Hortness J. E., Estimating low flow frequency statistics for unregulated streams in Idaho. US Geol. Survey. Sci. Invest. Report 2006-5035, 2006.
  • [24] Sinnott R. W., Virtues of the Haversine. Sky and Telescope, vol. 68, no. 2, p. 159, 1984.
  • [25] Nash J. E. & Sutcliffe J. V., River flow forecasting through conceptual models. Part I—A discussion of principles. Journal of Hydrology, 10 (3), 282–290, 1970. https://doi.org/10.1016/0022-1694(70)90255-6

Details

Primary Language English
Subjects Civil Engineering
Journal Section Articles
Authors

Mustafa Utku YILMAZ> (Primary Author)
KIRKLARELİ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0002-5662-9479
Türkiye


Bihrat ÖNÖZ>
İSTANBUL TEKNİK ÜNİVERSİTESİ, İNŞAAT FAKÜLTESİ
0000-0002-4531-2476
Türkiye

Publication Date November 1, 2019
Application Date May 4, 2018
Acceptance Date December 24, 2018
Published in Issue Year 2019, Volume 30, Issue 6

Cite

Bibtex @research article { tekderg421091, journal = {Teknik Dergi}, issn = {1300-3453}, address = {}, publisher = {UCTEA Turkish Chamber of Civil Engineering}, year = {2019}, volume = {30}, number = {6}, pages = {9597 - 9620}, doi = {10.18400/tekderg.421091}, title = {Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data}, key = {cite}, author = {Yılmaz, Mustafa Utku and Önöz, Bihrat} }
APA Yılmaz, M. U. & Önöz, B. (2019). Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data . Teknik Dergi , 30 (6) , 9597-9620 . DOI: 10.18400/tekderg.421091
MLA Yılmaz, M. U. , Önöz, B. "Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data" . Teknik Dergi 30 (2019 ): 9597-9620 <https://dergipark.org.tr/en/pub/tekderg/issue/42389/421091>
Chicago Yılmaz, M. U. , Önöz, B. "Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data". Teknik Dergi 30 (2019 ): 9597-9620
RIS TY - JOUR T1 - Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data AU - Mustafa UtkuYılmaz, BihratÖnöz Y1 - 2019 PY - 2019 N1 - doi: 10.18400/tekderg.421091 DO - 10.18400/tekderg.421091 T2 - Teknik Dergi JF - Journal JO - JOR SP - 9597 EP - 9620 VL - 30 IS - 6 SN - 1300-3453- M3 - doi: 10.18400/tekderg.421091 UR - https://doi.org/10.18400/tekderg.421091 Y2 - 2018 ER -
EndNote %0 Teknik Dergi Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data %A Mustafa Utku Yılmaz , Bihrat Önöz %T Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data %D 2019 %J Teknik Dergi %P 1300-3453- %V 30 %N 6 %R doi: 10.18400/tekderg.421091 %U 10.18400/tekderg.421091
ISNAD Yılmaz, Mustafa Utku , Önöz, Bihrat . "Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data". Teknik Dergi 30 / 6 (November 2019): 9597-9620 . https://doi.org/10.18400/tekderg.421091
AMA Yılmaz M. U. , Önöz B. Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data. Teknik Dergi. 2019; 30(6): 9597-9620.
Vancouver Yılmaz M. U. , Önöz B. Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data. Teknik Dergi. 2019; 30(6): 9597-9620.
IEEE M. U. Yılmaz and B. Önöz , "Evaluation of Statistical Methods for Estimating Missing Daily Streamflow Data", Teknik Dergi, vol. 30, no. 6, pp. 9597-9620, Nov. 2019, doi:10.18400/tekderg.421091