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

Yıl 2022, Sayı: 45, 33 - 46, 30.12.2022

Abdullah Akbaş Hasan Özdemir

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.

Anahtar Kelimeler

Hidrolojik Modelleme, Taşkın/Kuraklık, SWAT, Bartın Çayı Havzası

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.

All Models Are Wrong, But Some Are Useful: Determining the Low (Drought) and High (Flood) Flow Characteristics in Ungauged Basins

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.

Anahtar Kelimeler

Hydrological Modelling, Flood/Drought, SWAT, Bartın Stream Basin

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
• 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
• 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
• Karaca, M., Deniz, A., & Tayanç, M. (2000). Cyclone track variability over Turkey in association with regional climate. International Journal of Climatology: A Journal of the Royal Meteorological Society, 20(10), 1225-1236. google scholar
• Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., & Kirschbaum, D. B. (2017). So, how much of the Earth’s surface is covered by rain gauges?. Bulletin of the American Meteorological Society, 98(1), 69-78. google scholar
• Krause, P., Boyle, D. P., Base, F (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in geosciences, 5, 89-97. google scholar
• Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., ... & Bettadpur, S. (2019). Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges. Water Resources Research, 55(9), 7564-7586. google scholar
• Liu, Y., & Gupta, H. V. (2007). Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water resources research, 43(7). google scholar
• Martens, B., Miralles, D. G., Lievens, H., Van Der Schalie, R., De Jeu, R. A., Fernandez-Prieto, D., ... & Verhoest, N. E. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5), 1903-1925. google scholar
• Melsen, L. A., Teuling, A. J., Torfs, P. J., Zappa, M., Mizukami, N., Mendoza, P. A., ... & Uijlenhoet, R. (2019). Subjective modeling decisions can significantly impact the simulation of flood and drought events. Journal of Hydrology, 568, 1093-1104. google scholar
• Monteith, J. L. (1965). Evaporation and environment. In Symposia of the society for experimental biology (Vol. 19, pp. 205-234). Cambridge: Cambridge University Press (CUP). google scholar
• 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. google scholar
• Nash, J.E. Sutcliffe, J.V. (1970). River flow forecasting through conceptual models. Part I. A discussion of principles. Journal of Hydrology, 10, 282-290. doi:10.1016/0022-1694(70)90255-6 google scholar
• Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute. google scholar
• Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., ... & Sorooshian, S. (2019). The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Scientific data, 6(1), 1-10. google scholar
• Özdemir, H. (2007). SCS CN Yağış-akış modelinin CBS ve uzaktan algılama yöntemleriyle uygulanması: Havran Çayı Havzası örneği (Balıkesir). Coğrafi Bilimler Dergisi, 5(2), 1-12. google scholar
• Özdemir, H. (2011). Havza morfometrisi ve taşkınlar. Fiziki Coğrafya Araştırmaları, 457-474. google scholar
• Ozdemir, H., Sampson, C. C., de Almeida, G. A., & Bates, P. D. (2013). Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrology and Earth System Sciences, 17(10), 4015-4030. google scholar
• Ozdemir, H., & Elbaşı, E. (2015). Benchmarking land use change impacts on direct runoff in ungauged urban watersheds. Physics and Chemistry of the Earth, Parts A/B/C, 79, 100-107. google scholar
• Peel, M. C., & McMahon, T. A. (2020). Historical development of rainfallrunoff modeling. Wiley Interdisciplinary Reviews: Water, 7(5), e1471. google scholar
• R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, 635 Austria. Retrieved from http://www.r-project.org/index.html google scholar
• Sidle, R. C. (2021). Strategies for smarter catchment hydrology models: incorporating scaling and better process representation. Geoscience Letters, 8(1), 1-14. google scholar
• SCS, 1956, 1964, 1971, 1985, 1993. Hydrology, National Engineering Handbook, Supplement A, Section 4, Chapter 10. Soil Conservation Service, USDA, Washington, DC. google scholar
• Sk0ien, J. O., Bloschl, G., & Western, A. W. (2003). Characteristic space scales and timescales in hydrology. Water Resources Research, 39(10). google scholar
• Srinivasulu, S., & Jain, A. (2006). A comparative analysis of training methods for artificial neural network rainfall-runoff models. Applied Soft Computing, 6(3), 295-306. google scholar
• Strauch, M., Bernhofer, C., Koide, S., Volk, M., Lorz, C., & Makeschin, F. (2012). Using precipitation data ensemble for uncertainty analysis in SWAT streamflow simulation. Journal of Hydrology, 414, 413424. google scholar
• Sunde, M. G., He, H. S., Hubbart, J. A., & Urban, M. A. (2017). Integrating downscaled CMIP5 data with a physically based hydrologic model to estimate potential climate change impacts on streamflow processes in a mixed-use watershed. Hydrological Processes, 31(9), 1790-1803. google scholar
• Tatli, H., Nüzhet Dalfes, H., & Sibel Menteş, Ş. (2004). A statistical downscaling method for monthly total precipitation over Turkey. International Journal of Climatology: A Journal of the Royal Meteorological Society, 24(2), 161-180. google scholar
• Türkeş, M. (1996). Spatial and temporal analysis of annual rainfall variations in Turkey. International Journal of Climatology: A Journal of the Royal Meteorological Society, 16(9), 1057-1076. google scholar
• Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., & Watkins, M. M. (2004). GRACE measurements of mass variability in the Earth system. Science, 305(5683), 503-505. google scholar
• Turoglu, H., Ozdemir, H. (2005). Bartın’da Sel ve Taşkınlar: Sebepler, Etkiler, Önleme ve Zarar Azaltma Önerileri. Çantay Kitabevi. google scholar
• Turoğlu, H. (2007). Flood and flash floods analysis for Bartın River Basin. In International River Basin Management Congress, Proceeding (pp. 0-14). google scholar