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Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi

Yıl 2022, , 33 - 46, 30.12.2022
https://doi.org/10.26650/JGEOG2022-1075304

Öz

Havzalarda aletli gözlemler havza süreçlerini anlamak için oldukça önemli bir konuma sahip olmasına rağmen tüm alanlarda aletli gözlem verilerini bulmak oldukça zordur. Bu çalışma ile akım gözlem istasyonu (AGİ) olmayan havzalarda düşük/yüksek akım karakteristiklerinin SWAT ile modellenmesi ve gözlemle arasındaki farklılıklarının karşılaştırılması amaçlanmıştır. Bu amaçla, Bartın Çayı havzası örnek alan olarak seçilmiş ve ALOS SYM temelinde 90 adet alt-havza çıkarılmıştır. Bu havzalarda arazi kullanımı, eğim ve toprak verisi çakıştırılarak Hidrolojik Tepki Birimleri/HRU elde edilmiştir. HRU ve havza içinde tüm hidrolojik süreçler su dengesi temelinde elde edilen meteorolojik verilerle simüle edilmiştir. Model sonuçları, E13A031 istasyonuna dayalı olarak SWAT-CUP vasıtasıyla kalibre edilmiştir. Modellenen sonuçların havza içi süreçleri modellemek için yeterli olduğu görülmüştür. Elde edilen sonuçlara göre hem düşük hem de yüksek akımlara ait farklı zaman serisi karakteristikleri (büyüklük, sıklık, süre, zamanlama) hesaplanmış ve gözlem verisiyle karşılaştırılmıştır. Modellenen düşük ve yüksek akım metrikleri genel olarak gözlem ile uyuşsa da, birçok belirsizlik kaynağından dolayı bazı akım metriklerini fazla veya düşük hesapladığını göstermiştir. Öte yandan, tüm alt-havzalara ait metrikler hesaplanmıştır. Sonuçlara göre, Kocanaz havzası diğer havzalara oranla düşük ve yüksek akım metriklerinde farklılık yansıtmıştır. Hidrolojik modellemeler bu bağlamda iklim değişikliği ve arazi kullanımı değişiminin anlaşılması açısından planlama ve havza yönetimi açısından fırsatlar sunmaktadır.

Destekleyen Kurum

Bursa Uludağ Üniversitesi, Bilimsel Araştırmalar Proje Birimi

Proje Numarası

OUAP(F)-2019/13

Teşekkür

Bu çalışma Bursa Uludağ Üniversitesi, Bilimsel Araştırmalar Proje Birimi tarafından (Proje No: OUAP(F)-2019/13) tarafından desteklenmiştir.

Kaynakça

  • Abbaspour, K. C. (2013). SWAT-CUP 2012. SWAT Calibration and Uncertainty Program-A User Manual. google scholar
  • Abbaspour, K. C., Rouholahnejad, E., Vaghefi,, Srinivasan, R., Yang, H., & Kl0ve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a highresolution large-scale SWAT model. Journal of Hydrology, 524, 733-752. google scholar
  • Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54(11), 8792-8812. google scholar
  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modelling and assessment- Part I: model development. Journal of American Water Resources Association 34(1), 73-89. google scholar
  • Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., ... & Kannan, N. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508. google scholar
  • Akbas, A., Freer, J., Ozdemir, H., Bates, P. D., & Turp, M. T. (2020). What about reservoirs? Questioning anthropogenic and climatic interferences on water availability. Hydrological Processes, 34(26), 5441-5455. google scholar
  • Amjad, M., Yilmaz, M. T., Yucel, I., & Yilmaz, K. K. (2020). Performance evaluation of satellite-and model-based precipitation products over varying climate and complex topography. Journal of Hydrology, 584, 124707. google scholar
  • Beven, K. J. (2011). Rainfall-runoff modelling: the primer. John Wiley & Sons. google scholar
  • Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrological Sciences Journal, 24(1), 43-69. google scholar
  • Beven, K., & Freer, J. (2001). Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of hydrology, 249(1-4), 11-29. google scholar
  • Beven, K., Smith, P. J., & Wood, A. (2011). On the colour and spin of epistemic error (and what we might do about it). Hydrology and Earth System Sciences, 15(10), 3123-3133. google scholar
  • Blöschl, G., & Sivapalan, M. (1995). Scale issues in hydrological modelling: a review. Hydrological processes, 9(3-4), 251-290. google scholar
  • Bond, N. (2021) Package “hydrostats”, The Comprehensive R Archive Network (CRAN), mevut olduğu yer: https://CRAN.R-project.org/ package=hydrostats, (Erişim tarihi, 12 Aralık 2021). google scholar
  • Box, G. E. (1979). Robustness in the strategy of scientific model building. In Robustness in statistics (pp. 201-236). Academic Press. google scholar
  • Brown, M. E., Escobar, V., Moran, S., Entekhabi, D., O’Neill, P. E., Njoku, E. G., ... & Entin, J. K. (2013). NASA’s soil moisture active passive (SMAP) mission and opportunities for applications users. Bulletin of the American Meteorological Society, 94(8), 1125-1128. google scholar
  • Bucak, T., Trolle, D., Andersen, H. E., Thodsen, H., Erdoğan, Ş., Levi, E. E., ... & Beklioğlu, M. (2017). Future water availability in the largest freshwater Mediterranean lake is at great risk as evidenced from simulations with the SWAT model. Science of the Total Environment, 581, 413-425. google scholar
  • Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J., Tang, G., ... & Papalexiou, S. M. (2021). The abuse of popular performance metrics in hydrologic modeling. Water Resources Research, 57(9), e2020WR029001. google scholar
  • Coxon, G., Freer, J., Westerberg, I. K., Wagener, T., Woods, R., & Smith, P. J. (2015). A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations. Water resources research, 51(7), 5531-5546. google scholar
  • Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., ... & Van Zyl, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704-716. google scholar
  • Entekhabi, D., Yueh, S., O’Neill, P. E., Kellogg, K. H., Allen, A., Bindlish, R., ... & West, R. (2014a). SMAP handbook-soil moisture active passive: Mapping soil moisture and freeze/thaw from space. google scholar
  • Entekhabi, D., Yueh, S., & De Lannoy, G. (2014b). SMAP handbook. google scholar
  • Ertürk, A., Ekdal, A., Gürel, M., Karakaya, N., Guzel, C., & Gönenç, E. (2014). Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed. Science of the Total Environment, 499, 437-447. google scholar
  • Fuka, D. R., C.A. MacAllister, A. T. Degaetano, and Z.M. Easton. (2013). Using the Climate Forecast System Reanalysis dataset to improve weather input data for watershed models. Hydrol. Proc. DOI: 10.1002/hyp.10073. google scholar
  • Görüm, T., & Fidan, S. (2021). Spatiotemporal variations of fatal landslides in Turkey. Landslides, 18(5), 1691-1705. google scholar
  • Grusson, Y., Anctil, F., Sauvage, S., & Sánchez Pérez, J. M. (2017). Testing the SWAT model with gridded weather data of different spatial resolutions. Water, 9(1), 54. google scholar
  • Gupta, H. V., Sorooshian, S., Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135143. google scholar
  • Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., ... & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. google scholar
  • Horton, P., Schaefli, B., & Kauzlaric, M. (2021). Why do we have so many different hydrological models? A review based on the case of Switzerland. google scholar
  • Hsu, K. L., Gao, X., Sorooshian, S., & Gupta, H. V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36(9), 1176-1190. google scholar
  • Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., ... & Cudennec, C. (2013). A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrological sciences journal, 58(6), 1198-1255. google scholar
  • Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., ... & Stocker, E. F. (2007). The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combinedsensor precipitation estimates at fine scales. Journal of hydrometeorology, 8(1), 38-55. google scholar
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  • Huffman, G. J., E. F. Stocker, D.T. Bolvin, E. J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Accessed 15 August 2021], 10.5067/ GPM/IMERGDF/DAY/06 google scholar
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All Models Are Wrong, But Some Are Useful: Determining the Low (Drought) and High (Flood) Flow Characteristics in Ungauged Basins

Yıl 2022, , 33 - 46, 30.12.2022
https://doi.org/10.26650/JGEOG2022-1075304

Öz

Although instrumental observations in the basin are essential for understanding the basin processes, it is challenging to have observational data in all locations. Therefore, this study aims to simulate the low/high flows and compare them with observation. With this aim, 90 sub-basins were generated, and Hydrological Response Units/HRU was obtained by overlaying data such as land use, slope, and soil. Hydrological processes were simulated based upon water balance using meteorological data within the basin and HRU. The model results were used for calibration via SWAT-CUP using the E13A031 station. The modeled results to simulate basin processes have been obtained as sufficient. The different time series characteristics (magnitude, frequency, duration, and timing) belonging to low and high flow characteristics have been estimated and compared with observed data. Even though there is good coherence between observed and modeled low/high flow metrics, there are many uncertainty sources caused to over and under estimation on some metrics. On the other hand, metrics to all sub-basins are calculated. According to the result, the Kocanaz basin reflects high differences in low/high flows metric compared to other basins. In this context, hydrological models offer opportunities for planning and watershed management to understand climate change and land-use change.

Proje Numarası

OUAP(F)-2019/13

Kaynakça

  • Abbaspour, K. C. (2013). SWAT-CUP 2012. SWAT Calibration and Uncertainty Program-A User Manual. google scholar
  • Abbaspour, K. C., Rouholahnejad, E., Vaghefi,, Srinivasan, R., Yang, H., & Kl0ve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a highresolution large-scale SWAT model. Journal of Hydrology, 524, 733-752. google scholar
  • Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54(11), 8792-8812. google scholar
  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modelling and assessment- Part I: model development. Journal of American Water Resources Association 34(1), 73-89. google scholar
  • Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., ... & Kannan, N. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508. google scholar
  • Akbas, A., Freer, J., Ozdemir, H., Bates, P. D., & Turp, M. T. (2020). What about reservoirs? Questioning anthropogenic and climatic interferences on water availability. Hydrological Processes, 34(26), 5441-5455. google scholar
  • Amjad, M., Yilmaz, M. T., Yucel, I., & Yilmaz, K. K. (2020). Performance evaluation of satellite-and model-based precipitation products over varying climate and complex topography. Journal of Hydrology, 584, 124707. google scholar
  • Beven, K. J. (2011). Rainfall-runoff modelling: the primer. John Wiley & Sons. google scholar
  • Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrological Sciences Journal, 24(1), 43-69. google scholar
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  • Beven, K., Smith, P. J., & Wood, A. (2011). On the colour and spin of epistemic error (and what we might do about it). Hydrology and Earth System Sciences, 15(10), 3123-3133. google scholar
  • Blöschl, G., & Sivapalan, M. (1995). Scale issues in hydrological modelling: a review. Hydrological processes, 9(3-4), 251-290. google scholar
  • Bond, N. (2021) Package “hydrostats”, The Comprehensive R Archive Network (CRAN), mevut olduğu yer: https://CRAN.R-project.org/ package=hydrostats, (Erişim tarihi, 12 Aralık 2021). google scholar
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  • Brown, M. E., Escobar, V., Moran, S., Entekhabi, D., O’Neill, P. E., Njoku, E. G., ... & Entin, J. K. (2013). NASA’s soil moisture active passive (SMAP) mission and opportunities for applications users. Bulletin of the American Meteorological Society, 94(8), 1125-1128. google scholar
  • Bucak, T., Trolle, D., Andersen, H. E., Thodsen, H., Erdoğan, Ş., Levi, E. E., ... & Beklioğlu, M. (2017). Future water availability in the largest freshwater Mediterranean lake is at great risk as evidenced from simulations with the SWAT model. Science of the Total Environment, 581, 413-425. google scholar
  • Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J., Tang, G., ... & Papalexiou, S. M. (2021). The abuse of popular performance metrics in hydrologic modeling. Water Resources Research, 57(9), e2020WR029001. google scholar
  • Coxon, G., Freer, J., Westerberg, I. K., Wagener, T., Woods, R., & Smith, P. J. (2015). A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations. Water resources research, 51(7), 5531-5546. google scholar
  • Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., ... & Van Zyl, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704-716. google scholar
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  • Entekhabi, D., Yueh, S., & De Lannoy, G. (2014b). SMAP handbook. google scholar
  • Ertürk, A., Ekdal, A., Gürel, M., Karakaya, N., Guzel, C., & Gönenç, E. (2014). Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed. Science of the Total Environment, 499, 437-447. google scholar
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  • Grusson, Y., Anctil, F., Sauvage, S., & Sánchez Pérez, J. M. (2017). Testing the SWAT model with gridded weather data of different spatial resolutions. Water, 9(1), 54. google scholar
  • Gupta, H. V., Sorooshian, S., Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135143. google scholar
  • Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., ... & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. google scholar
  • Horton, P., Schaefli, B., & Kauzlaric, M. (2021). Why do we have so many different hydrological models? A review based on the case of Switzerland. google scholar
  • Hsu, K. L., Gao, X., Sorooshian, S., & Gupta, H. V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36(9), 1176-1190. google scholar
  • Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., ... & Cudennec, C. (2013). A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrological sciences journal, 58(6), 1198-1255. google scholar
  • Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., ... & Stocker, E. F. (2007). The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combinedsensor precipitation estimates at fine scales. Journal of hydrometeorology, 8(1), 38-55. google scholar
  • Huffman, G. J., & Bolvin, D. T. (2018). TRMM and other data precipitation data set documentation. NASA, Greenbelt, USA, 28(2.3), 1. google scholar
  • Huffman, G. J., E. F. Stocker, D.T. Bolvin, E. J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Accessed 15 August 2021], 10.5067/ GPM/IMERGDF/DAY/06 google scholar
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Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Abdullah Akbaş 0000-0003-2024-0565

Hasan Özdemir 0000-0001-8885-9298

Proje Numarası OUAP(F)-2019/13
Yayımlanma Tarihi 30 Aralık 2022
Gönderilme Tarihi 17 Şubat 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Akbaş, A., & Özdemir, H. (2022). Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi. Journal of Geography(45), 33-46. https://doi.org/10.26650/JGEOG2022-1075304
AMA Akbaş A, Özdemir H. Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi. Journal of Geography. Aralık 2022;(45):33-46. doi:10.26650/JGEOG2022-1075304
Chicago Akbaş, Abdullah, ve Hasan Özdemir. “Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) Ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi”. Journal of Geography, sy. 45 (Aralık 2022): 33-46. https://doi.org/10.26650/JGEOG2022-1075304.
EndNote Akbaş A, Özdemir H (01 Aralık 2022) Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi. Journal of Geography 45 33–46.
IEEE A. Akbaş ve H. Özdemir, “Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi”, Journal of Geography, sy. 45, ss. 33–46, Aralık 2022, doi: 10.26650/JGEOG2022-1075304.
ISNAD Akbaş, Abdullah - Özdemir, Hasan. “Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) Ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi”. Journal of Geography 45 (Aralık 2022), 33-46. https://doi.org/10.26650/JGEOG2022-1075304.
JAMA Akbaş A, Özdemir H. Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi. Journal of Geography. 2022;:33–46.
MLA Akbaş, Abdullah ve Hasan Özdemir. “Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) Ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi”. Journal of Geography, sy. 45, 2022, ss. 33-46, doi:10.26650/JGEOG2022-1075304.
Vancouver Akbaş A, Özdemir H. Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi. Journal of Geography. 2022(45):33-46.