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An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling

Year 2024, , 773 - 782, 01.06.2024
https://doi.org/10.21597/jist.1406563

Abstract

The accurate modelling of streamflow is highly significant for hydrological monitoring, water resource management, and climate change studies. Streamflow simulation with lumped hydrological models has been widely performed by researchers. However, the parameter calibration process is a major obstacle in these models. In the present study, a conceptual rainfall-runoff model (TUW model) was used to simulate streamflow in the sub-basin of the Upper Euphrates Basin during the time period 1991-2009. The Differential Evolution Optimization (DEoptim) algorithm were tested for the automatic parameter calibration of the lumped version of TUW model, in the study area. The model is calibrated using two objective function named and Nash–Sutcliffe efficiency (NSE) and Kling-Gupta Efficiency (KGE). Additionally, percent bias (PBias) was used to evaluate the performance of the model. For the objective function NSE, calibration and validation results indicated good agreement between observed and simulated streamflow data with NSE, 0.76 and 0.76 and KGE, 0.73 and 0.75 and PBias (%), -0.8 and -7.5, respectively. Similarly for KGE objective function, the calibration results produced a NSE of 0.71, KGE of 0.85, and PBias (%) of -0.9, while validation results revealed a NSE of 0.72, KGE of 0.84, and PBias (%) of -7.2. It can be concluded that the applicability of the DEoptim algorithm for the estimation of the parameters of the TUW model is confirmed by the case study. The findings of the study can serve as a guide for researchers and be useful in achieving watershed management goals.

References

  • Adnan, M., Nabi, G., Poomee, M. S. & Ashraf, A. (2017). Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: acase of Gilgit River Basin. Geoscence. Frontiers, 8(5), 941–949. https://doi.org/10.1016/j.gsf.2016.08.008.
  • Alizadeh, Z. & Yazdi, J. (2023). Calibration of hydrological models for ungauged catchments by automatic clustering using a differential evolution algorithm: The Gorganrood river basin case study. Journal of Hydroinformatics , 25 (3), 645–662.
  • Ardia, D., Boudt, K., Carl, P., Mullen, M. K. & Peterson, G. B. (2011). Differential evolution with DEoptim. The R Journal, 3(27).
  • Atanaw, S. B., Zimale, F.A., Ayenew, T. & Ayele, G. T. (2023). Modeling future hydrological responses through parameter optimization and climate change scenarios in Dirima Watershed, Ethiopia. Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-023-01817-z
  • Behrouz, M.S., Zhu, Z., Matott, L.S. & Rabideau, A.J. (2020). A new tool for automatic calibration of the storm water management model (SWMM). Journal of Hydrology, 581, 124436.
  • Brziak, A., Kubáň, M., Kohnová, S. & Szolgay, J. (2020). Comparison of the variances of a lumped and semi-distributed model parameters. Acta Hydrologica Slovaca, 21(2), 172-177.
  • Cao, R., Vilar, J. M. & Devia, A. (2009). Modelling consumer credit risk via survival analysis. Sort-Statistics and Operations Research Transactions, 33(1), 3-30.
  • Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A. & Wagener, T. (2015). Virtual laboratories: new opportunities for collaborative water science. Hydrology and Earth System Sciences, 19, 2101– 2117.
  • Durgut, P.G. & Ayvaz, M.T. (2023). A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model. Journal of Hydrology, 617, 128976.
  • Farkas, C., Kværnø, S. H., Engebretsen, A., Barneveld, R. & Deelstra, J. (2016). Applying profile and catchment-based mathematical models for evaluating the run-off from a Nordic catchment. Journal of Hydrology and Hydromechanics, 64(3), 218–225. https://doi.org/10.1515/johh-2016-0022.
  • Garna, R. K., Fuka, D. R., Faulkner, J. W., Collick, A. S. & Easton, Z. M. (2023). Watershed model parameter estimation in low data environments. Journal of Hydrology Regional Studies, 45, 101306. https://doi.org/10.1016/j.ejrh.2022.101306
  • Gupta, H. V., Sorooshian, S. & Yapo, P. O. (1998). Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resources Research, 34, 751–763. https://doi.org/10.1029/97WR03495.
  • Hafizi, H. & Sorman, A. A. (2022). Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin. Atmosphere, 13, 143. https://doi.org/10.3390/ atmos13010143.
  • Hopur, B. (2017). A new basin management concept for Turkey: National basin management strategy. Biyolojik Çeşitlilik Ve Koruma, 10(2), 20-25.
  • Kling, H., Fuchs, M. & Paulin, M. (2012). Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, 424 (425), 264–277. https://doi.org/10.1016/j.jhydrol.2012.01.011.
  • Legates, D R. & McCabe, G. J. Jr. (1999) Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35:233–241.
  • Mancipe-Munoz, N.A., Buchberger, S.G., Suidan, M.T. & Lu, T. (2014). Calibration of rainfall- runoff model in urban watersheds for stormwater management assessment. Journal of Water Resources Planning Management, 140 (6).
  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900.
  • Mullen, K. M., Ardia, D., Gil, D. L., Windover, D. & Cline, J. (2011). DEoptim: an R package for global optimization by differential evolution. Journal of Statistical Software, 40(6):1–26. https:// doi. org/ 10. 18637/ JSS. V040. I06
  • Nash, J. E & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/https://doi.org/10.1016/0022-1694(70)90255-6
  • Neri, M., Parajka, J. & Toth, E. (2020). Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: The role of nested catchments and gauging station density. Hydrology and Earth System Sciences, 24, 5149–5171.
  • Nhemachena, C., Nhamo, L., Matchaya, G., Nhemachena, C. R., Muchara, B., Karuaihe, S. T. & Mpandeli, S. (2020). Climate Change Impacts on Water and Agriculture Sectors in Southern Africa: Threats and Opportunities for Sustainable Development. Water , 12, 2673. https://doi.org/10.3390/w12102673
  • Parajka, J., Merz, R. & Blöschl, G. (2005). A comparison of regionalisation methods for catchment model parameters. Hydrology and Earth System Sciences, 9(3), 157–171. https://doi.org/ 10.5194/hess-9-157-2005.
  • Parajka, J., Merz, R. & Blöschl, G. (2007). Uncertainty and multiple objective calibration in regional water balance modelling: Case study in 320 Austrian catchments. Hydrological Processes, 21, 435–446.
  • Piniewski, M., Szcześniak, M., Kardel, I., Berezowski, T., Okruszko, T., Srinivasan, R., Schuler, D. V. & Kundzewicz, Z. V. (2017). Hydrological modelling of the Vistula and Odra river basins using SWAT. Hydrological Sciences Journal, 62 (8), 1266–1289. doi:10.1080/02626667.2017.1321842
  • Rozos, E. (2023). Assessing Hydrological Simulations with Machine Learning and Statistical Models. Hydrology, 10, 49. https://doi.org/10.3390/ hydrology10020049
  • Shamsi, U.S. & Koran, J. (2017). Continuous calibration. Journal of Water Management Modeling, 25, 1–9.
  • Sirisena, T. A. J. G., Maskey, S. & Ranasinghe, R. (2020). Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin. Remote Sensing, 12, 22: 3768. https://doi.org/10.3390/rs12223768.
  • Sleziak, P., Holko, L., Danko, M. & Parajka, J. (2020). Uncertainty in the Number of Calibration Repetitions of a Hydrologic Model in Varying Climatic Conditions. Water, 12, 2362. https://doi.org/10.3390/w12092362
  • Sleziak, P., Szolgay, J., Hlavčová, K. & Parajka, J. (2016). The impact of the variability of precipitation and temperatures on the efficiency of a conceptual rainfall-runoff model. Slovak Journal of Civil Engineering, 24 (4), 1–7. https://doi.org/10.1515/sjce-2016-0016.
  • Sleziak, P., Výleta, R., Hlavˇcová, K., Danáˇcová, M., Aleksi´c, M., Szolgay, J. & Kohnová, S. A. (2021) Hydrological Modeling Approach for Assessing the Impacts of Climate Change on Runoff Regimes in Slovakia. Water, 13, 3358. https://doi.org/10.3390/w13233358.
  • Storn, R. & Price, K. (1997). Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.
  • Thiemig, V., Rojas, R., Zambrano-Bigiarini, M. & De Roo, A. (2013). Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499, 324–338.
  • Tiwari, D., Trudel, M., & Leconte, R. (2024). On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow. Hydrology and Earth System Sciences, 28, 1127–1146.
  • Viglione, A. & Parajka, J. (2014). TUWmodel: Lumped hydrological model for educational purposes. Version 0.1-4. https://cran.rproject. org/web/packages/TUWmodel/index.html.
  • Ye, Y., Song, X., Zhang, J., Kong, F. & Ma, G. (2014) Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach. Frontiers of Earth Science, 8, 264–281. https://doi.org/10.1007/s11707-014-0424-0
  • Yenigün, K., Bilgehan, M., Gerger, R. & Mutlu, M. (2010). A comparative study on prediction of sediment yield in the Euphrates basin. International Journal of the Physical Sciences, 5(5), 518-534. doi: 2B15E0C25933
  • Yilmaz, M., Tosunoglu, F. & Demirel, M. C. (2021). Comparison of conventional and differential evolution-based parameter estimation methods on the flood frequency analysis. Acta Geophys, 69, 1887–1900. https://doi.org/10.1007/s11600-021-00645-y
  • Zhong, D., Dong, Z., Fu, G., Bian, J., Kong, F., Wang, W & Zhao, Y. (2021). Trend and change points of streamflow in the Yellow River and their attributions. Journal of Water and Climate Change , 12 (1), 136–151. doi: https://doi.org/10.2166/wcc.2020.144
Year 2024, , 773 - 782, 01.06.2024
https://doi.org/10.21597/jist.1406563

Abstract

References

  • Adnan, M., Nabi, G., Poomee, M. S. & Ashraf, A. (2017). Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: acase of Gilgit River Basin. Geoscence. Frontiers, 8(5), 941–949. https://doi.org/10.1016/j.gsf.2016.08.008.
  • Alizadeh, Z. & Yazdi, J. (2023). Calibration of hydrological models for ungauged catchments by automatic clustering using a differential evolution algorithm: The Gorganrood river basin case study. Journal of Hydroinformatics , 25 (3), 645–662.
  • Ardia, D., Boudt, K., Carl, P., Mullen, M. K. & Peterson, G. B. (2011). Differential evolution with DEoptim. The R Journal, 3(27).
  • Atanaw, S. B., Zimale, F.A., Ayenew, T. & Ayele, G. T. (2023). Modeling future hydrological responses through parameter optimization and climate change scenarios in Dirima Watershed, Ethiopia. Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-023-01817-z
  • Behrouz, M.S., Zhu, Z., Matott, L.S. & Rabideau, A.J. (2020). A new tool for automatic calibration of the storm water management model (SWMM). Journal of Hydrology, 581, 124436.
  • Brziak, A., Kubáň, M., Kohnová, S. & Szolgay, J. (2020). Comparison of the variances of a lumped and semi-distributed model parameters. Acta Hydrologica Slovaca, 21(2), 172-177.
  • Cao, R., Vilar, J. M. & Devia, A. (2009). Modelling consumer credit risk via survival analysis. Sort-Statistics and Operations Research Transactions, 33(1), 3-30.
  • Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A. & Wagener, T. (2015). Virtual laboratories: new opportunities for collaborative water science. Hydrology and Earth System Sciences, 19, 2101– 2117.
  • Durgut, P.G. & Ayvaz, M.T. (2023). A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model. Journal of Hydrology, 617, 128976.
  • Farkas, C., Kværnø, S. H., Engebretsen, A., Barneveld, R. & Deelstra, J. (2016). Applying profile and catchment-based mathematical models for evaluating the run-off from a Nordic catchment. Journal of Hydrology and Hydromechanics, 64(3), 218–225. https://doi.org/10.1515/johh-2016-0022.
  • Garna, R. K., Fuka, D. R., Faulkner, J. W., Collick, A. S. & Easton, Z. M. (2023). Watershed model parameter estimation in low data environments. Journal of Hydrology Regional Studies, 45, 101306. https://doi.org/10.1016/j.ejrh.2022.101306
  • Gupta, H. V., Sorooshian, S. & Yapo, P. O. (1998). Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resources Research, 34, 751–763. https://doi.org/10.1029/97WR03495.
  • Hafizi, H. & Sorman, A. A. (2022). Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin. Atmosphere, 13, 143. https://doi.org/10.3390/ atmos13010143.
  • Hopur, B. (2017). A new basin management concept for Turkey: National basin management strategy. Biyolojik Çeşitlilik Ve Koruma, 10(2), 20-25.
  • Kling, H., Fuchs, M. & Paulin, M. (2012). Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, 424 (425), 264–277. https://doi.org/10.1016/j.jhydrol.2012.01.011.
  • Legates, D R. & McCabe, G. J. Jr. (1999) Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35:233–241.
  • Mancipe-Munoz, N.A., Buchberger, S.G., Suidan, M.T. & Lu, T. (2014). Calibration of rainfall- runoff model in urban watersheds for stormwater management assessment. Journal of Water Resources Planning Management, 140 (6).
  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900.
  • Mullen, K. M., Ardia, D., Gil, D. L., Windover, D. & Cline, J. (2011). DEoptim: an R package for global optimization by differential evolution. Journal of Statistical Software, 40(6):1–26. https:// doi. org/ 10. 18637/ JSS. V040. I06
  • Nash, J. E & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/https://doi.org/10.1016/0022-1694(70)90255-6
  • Neri, M., Parajka, J. & Toth, E. (2020). Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: The role of nested catchments and gauging station density. Hydrology and Earth System Sciences, 24, 5149–5171.
  • Nhemachena, C., Nhamo, L., Matchaya, G., Nhemachena, C. R., Muchara, B., Karuaihe, S. T. & Mpandeli, S. (2020). Climate Change Impacts on Water and Agriculture Sectors in Southern Africa: Threats and Opportunities for Sustainable Development. Water , 12, 2673. https://doi.org/10.3390/w12102673
  • Parajka, J., Merz, R. & Blöschl, G. (2005). A comparison of regionalisation methods for catchment model parameters. Hydrology and Earth System Sciences, 9(3), 157–171. https://doi.org/ 10.5194/hess-9-157-2005.
  • Parajka, J., Merz, R. & Blöschl, G. (2007). Uncertainty and multiple objective calibration in regional water balance modelling: Case study in 320 Austrian catchments. Hydrological Processes, 21, 435–446.
  • Piniewski, M., Szcześniak, M., Kardel, I., Berezowski, T., Okruszko, T., Srinivasan, R., Schuler, D. V. & Kundzewicz, Z. V. (2017). Hydrological modelling of the Vistula and Odra river basins using SWAT. Hydrological Sciences Journal, 62 (8), 1266–1289. doi:10.1080/02626667.2017.1321842
  • Rozos, E. (2023). Assessing Hydrological Simulations with Machine Learning and Statistical Models. Hydrology, 10, 49. https://doi.org/10.3390/ hydrology10020049
  • Shamsi, U.S. & Koran, J. (2017). Continuous calibration. Journal of Water Management Modeling, 25, 1–9.
  • Sirisena, T. A. J. G., Maskey, S. & Ranasinghe, R. (2020). Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin. Remote Sensing, 12, 22: 3768. https://doi.org/10.3390/rs12223768.
  • Sleziak, P., Holko, L., Danko, M. & Parajka, J. (2020). Uncertainty in the Number of Calibration Repetitions of a Hydrologic Model in Varying Climatic Conditions. Water, 12, 2362. https://doi.org/10.3390/w12092362
  • Sleziak, P., Szolgay, J., Hlavčová, K. & Parajka, J. (2016). The impact of the variability of precipitation and temperatures on the efficiency of a conceptual rainfall-runoff model. Slovak Journal of Civil Engineering, 24 (4), 1–7. https://doi.org/10.1515/sjce-2016-0016.
  • Sleziak, P., Výleta, R., Hlavˇcová, K., Danáˇcová, M., Aleksi´c, M., Szolgay, J. & Kohnová, S. A. (2021) Hydrological Modeling Approach for Assessing the Impacts of Climate Change on Runoff Regimes in Slovakia. Water, 13, 3358. https://doi.org/10.3390/w13233358.
  • Storn, R. & Price, K. (1997). Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.
  • Thiemig, V., Rojas, R., Zambrano-Bigiarini, M. & De Roo, A. (2013). Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499, 324–338.
  • Tiwari, D., Trudel, M., & Leconte, R. (2024). On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow. Hydrology and Earth System Sciences, 28, 1127–1146.
  • Viglione, A. & Parajka, J. (2014). TUWmodel: Lumped hydrological model for educational purposes. Version 0.1-4. https://cran.rproject. org/web/packages/TUWmodel/index.html.
  • Ye, Y., Song, X., Zhang, J., Kong, F. & Ma, G. (2014) Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach. Frontiers of Earth Science, 8, 264–281. https://doi.org/10.1007/s11707-014-0424-0
  • Yenigün, K., Bilgehan, M., Gerger, R. & Mutlu, M. (2010). A comparative study on prediction of sediment yield in the Euphrates basin. International Journal of the Physical Sciences, 5(5), 518-534. doi: 2B15E0C25933
  • Yilmaz, M., Tosunoglu, F. & Demirel, M. C. (2021). Comparison of conventional and differential evolution-based parameter estimation methods on the flood frequency analysis. Acta Geophys, 69, 1887–1900. https://doi.org/10.1007/s11600-021-00645-y
  • Zhong, D., Dong, Z., Fu, G., Bian, J., Kong, F., Wang, W & Zhao, Y. (2021). Trend and change points of streamflow in the Yellow River and their attributions. Journal of Water and Climate Change , 12 (1), 136–151. doi: https://doi.org/10.2166/wcc.2020.144
There are 39 citations in total.

Details

Primary Language English
Subjects Water Resources Engineering
Journal Section İnşaat Mühendisliği / Civil Engineering
Authors

Muhammet Yılmaz 0000-0002-9844-6654

Early Pub Date May 28, 2024
Publication Date June 1, 2024
Submission Date December 18, 2023
Acceptance Date March 26, 2024
Published in Issue Year 2024

Cite

APA Yılmaz, M. (2024). An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Journal of the Institute of Science and Technology, 14(2), 773-782. https://doi.org/10.21597/jist.1406563
AMA Yılmaz M. An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Iğdır Üniv. Fen Bil Enst. Der. June 2024;14(2):773-782. doi:10.21597/jist.1406563
Chicago Yılmaz, Muhammet. “An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling”. Journal of the Institute of Science and Technology 14, no. 2 (June 2024): 773-82. https://doi.org/10.21597/jist.1406563.
EndNote Yılmaz M (June 1, 2024) An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Journal of the Institute of Science and Technology 14 2 773–782.
IEEE M. Yılmaz, “An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling”, Iğdır Üniv. Fen Bil Enst. Der., vol. 14, no. 2, pp. 773–782, 2024, doi: 10.21597/jist.1406563.
ISNAD Yılmaz, Muhammet. “An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling”. Journal of the Institute of Science and Technology 14/2 (June 2024), 773-782. https://doi.org/10.21597/jist.1406563.
JAMA Yılmaz M. An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Iğdır Üniv. Fen Bil Enst. Der. 2024;14:773–782.
MLA Yılmaz, Muhammet. “An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling”. Journal of the Institute of Science and Technology, vol. 14, no. 2, 2024, pp. 773-82, doi:10.21597/jist.1406563.
Vancouver Yılmaz M. An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Iğdır Üniv. Fen Bil Enst. Der. 2024;14(2):773-82.