Araştırma Makalesi


DOI :10.26650/JGEOG2022-1075304   IUP :10.26650/JGEOG2022-1075304    Tam Metin (PDF)

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

Abdullah AkbaşHasan Özdemir

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.

DOI :10.26650/JGEOG2022-1075304   IUP :10.26650/JGEOG2022-1075304    Tam Metin (PDF)

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

Abdullah AkbaşHasan Özdemir

Although instrumental observations in basins are essential for understanding basin processes, acquiring observational data from all locations is challenging. Therefore, this study aims to simulate low and high flows and compare them with observations. With this aim, 90 sub-basins were generated, and hydrological response units (HRUs) were obtained by overlaying data such as land use, slope, and soil. Hydrological processes were simulated based on water balance using meteorological data within the basin and the HRUs. The model results were used for calibration by means of the SWAT-CUP using data from station E13A031. The modeled results that were obtained for simulating basin processes are considered sufficient. The different time series characteristics (i.e., magnitude, frequency, duration, and timing) belonging to low and high flow characteristics have been estimated and compared with the observed data. Even though good coherence was present between the observed and modeled low/high flow metrics, many sources of uncertainty exist that caused over- and under-estimations regarding some metrics. Furthermore, the metrics for all sub-basins have been calculated. According to the results, the Kocanaz basin reflects high differences in the metrics for low and high flows compared to the other basins. In this context, hydrological models offer opportunities for planning and watershed management in order for understanding climate and land-use changes.


GENİŞLETİLMİŞ ÖZET


Instrumental observations in basins have an important place in understanding the spatial and temporal variations of river systems through processes such as sediment transportation, floods, and droughts. Furthermore, understanding the impact of climatic and anthropogenic changes in basins requires comprehensive and reliable data, because the processes for distinguishing whether something is climatic or anthropogenic in basins are so complex. However, observing reliable data for each basin is no easy task. Even if the positive trend for observational instruments such as gauges increases around the globe, obtaining data in some areas such as mountainous or densely forested areas remain quite difficult. For this reason, hydrological models that simulate all water balance parameters (e.g., runoff, evapotranspiration) are useful tools for modeling ungauged areas. However, these models still contain various types of uncertainties due to the many potential sources that result from things such as the internal structure of the models, observations, and scale factors. Although some observations made through remote sensing retrieve data from water balance parameters around the globe, even they need to be corrected by models. Therefore, hydrological models can help extract reliable information about earth system processes.

This study models the Bartın basin, which has been exposed to many flood events at higher frequencies, in order to obtain low (drought) and high (flood) characteristics for ungauged basins. Based on the Advance Land Observation System 30-meter Digital Elevation Model (ALOS World 3D-30m DEM), 90 sub-basins have extracted for the whole area of the Bartın river system. Furthermore, hydrological response unit (HRU) was generated by overlaying many different datasets such as land-use, soil, and slope using the full multiple HRU definition, with 1901 HRUs being obtained within the Bartın Basin. Simulations were carried out based on the water balance at a daily scale using many meteorological data from the Turkish State Meteorological Service (MGM). The model parameters that are sensitive to high and low flows were calibrated using the SWAT-CUP calibration program. The calibration used the 1970-1986 period based on Station E13A031 data and used the 1987-2002 period for validation. The model results have been deemed sufficient for simulating the basin water balance based upon the evaluation criteria. After modeling the ungauged basin, many characteristics such as magnitude, frequency, duration, and timing with regard to low and high flows were calculated using the threshold values of the percentiles.

A comparison of the flow metrics illustrates good coherence to be present between the modeled and observed data regarding the high flow and low flow characteristics in terms of magnitude, frequency, duration, and timing. However, over- and under-estimations also occurred for some metrics such as timing, duration, and magnitude of the low and high flows, in particular for high flows. This situation may have emerged due to the many different sources of uncertainty, such as using scales from other data sources in the model, the number of the gauges/meteorological stations, and the number of calibration simulations. Moreover, the study has mapped different high and low flow metrics to distinguish spatial gradients in ungauged basins. The maps demonstrate the Kocanaz basin to vary in terms of timing, magnitude, duration, and frequency of the high and low flow characteristics compared to other sub-basins. In particular, the timing of the low and high flows in this basin explains the impact of topography (snowmelt) and different weather systems that can influence metrics.

Using these kinds of hydrological models can also help understand future perspectives on the dynamic earth. For example, land-use change has immense impacts on rainfall-runoff relations in terms of hydrological modeling. Furthermore, climate change will also affect many statistical characteristics of climatic sub-systems. Adding parameters to hydrological models will make comprehending the changing world more sensible. In this context, hydrological models offer opportunities for planning and watershed management in terms of understanding climate change and land-use change. 


PDF Görünüm

Referanslar

  • 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

Atıflar

Biçimlendirilmiş bir atıfı kopyalayıp yapıştırın veya seçtiğiniz biçimde dışa aktarmak için seçeneklerden birini kullanın


DIŞA AKTAR



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. Coğrafya Dergisi, 0(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. Coğrafya Dergisi. 2022;0(45):33-46. https://doi.org/10.26650/JGEOG2022-1075304


ABNT

Akbaş, A.; Özdemir, H. Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi. Coğrafya Dergisi, [Publisher Location], v. 0, n. 45, p. 33-46, 2022.


Chicago: Author-Date Style

Akbaş, Abdullah, and Hasan Özdemir. 2022. “Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi.” Coğrafya Dergisi 0, no. 45: 33-46. https://doi.org/10.26650/JGEOG2022-1075304


Chicago: Humanities Style

Akbaş, Abdullah, and Hasan Özdemir. Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi.” Coğrafya Dergisi 0, no. 45 (Jun. 2023): 33-46. https://doi.org/10.26650/JGEOG2022-1075304


Harvard: Australian Style

Akbaş, A & Özdemir, H 2022, 'Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi', Coğrafya Dergisi, vol. 0, no. 45, pp. 33-46, viewed 9 Jun. 2023, https://doi.org/10.26650/JGEOG2022-1075304


Harvard: Author-Date Style

Akbaş, A. and Özdemir, H. (2022) ‘Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi’, Coğrafya Dergisi, 0(45), pp. 33-46. https://doi.org/10.26650/JGEOG2022-1075304 (9 Jun. 2023).


MLA

Akbaş, Abdullah, and Hasan Özdemir. Tüm Modeller Yanlıştır, Ancak Bazıları Faydalıdır: Akım Gözlem İstasyonu Bulunmayan Havzalarda Düşük (Kurak) ve Yüksek (Taşkın) Akım Davranışlarının Belirlenmesi.” Coğrafya Dergisi, vol. 0, no. 45, 2022, pp. 33-46. [Database Container], https://doi.org/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. Coğrafya Dergisi [Internet]. 9 Jun. 2023 [cited 9 Jun. 2023];0(45):33-46. Available from: https://doi.org/10.26650/JGEOG2022-1075304 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”. Coğrafya Dergisi 0/45 (Jun. 2023): 33-46. https://doi.org/10.26650/JGEOG2022-1075304



ZAMAN ÇİZELGESİ


Gönderim17.02.2022
Kabul07.07.2022
Çevrimiçi Yayınlanma30.12.2022

LİSANS


Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.


PAYLAŞ




İstanbul Üniversitesi Yayınları, uluslararası yayıncılık standartları ve etiğine uygun olarak, yüksek kalitede bilimsel dergi ve kitapların yayınlanmasıyla giderek artan bilimsel bilginin yayılmasına katkıda bulunmayı amaçlamaktadır. İstanbul Üniversitesi Yayınları açık erişimli, ticari olmayan, bilimsel yayıncılığı takip etmektedir.