Araştırma Makalesi

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

Cilt: 14 Sayı: 2 1 Haziran 2024
PDF İndir
EN

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

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. 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.
  2. 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.
  3. Ardia, D., Boudt, K., Carl, P., Mullen, M. K. & Peterson, G. B. (2011). Differential evolution with DEoptim. The R Journal, 3(27).
  4. 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
  5. 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.
  6. 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.
  7. 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.
  8. 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.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Su Kaynakları Mühendisliği

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

28 Mayıs 2024

Yayımlanma Tarihi

1 Haziran 2024

Gönderilme Tarihi

18 Aralık 2023

Kabul Tarihi

26 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 14 Sayı: 2

Kaynak Göster

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
1.Yılmaz M. An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Iğdır Üniv. Fen Bil Enst. Der. 2024;14(2):773-782. doi:10.21597/jist.1406563
Chicago
Yılmaz, Muhammet. 2024. “An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling”. Journal of the Institute of Science and Technology 14 (2): 773-82. https://doi.org/10.21597/jist.1406563.
EndNote
Yılmaz M (01 Haziran 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
[1]M. Yılmaz, “An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling”, Iğdır Üniv. Fen Bil Enst. Der., c. 14, sy 2, ss. 773–782, Haz. 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 (01 Haziran 2024): 773-782. https://doi.org/10.21597/jist.1406563.
JAMA
1.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, c. 14, sy 2, Haziran 2024, ss. 773-82, doi:10.21597/jist.1406563.
Vancouver
1.Muhammet Yılmaz. An Automatic Parameter Calibration Method for the TUW Model in Streamflow Modeling. Iğdır Üniv. Fen Bil Enst. Der. 01 Haziran 2024;14(2):773-82. doi:10.21597/jist.1406563