Evaluation of the Impact of Rain Gauge Network Density on Precipitation Distribution Modelling in Türkiye: A Case Study of Antalya Basin
DOI:
https://doi.org/10.24925/turjaf.v12i5.814-820.6316Keywords:
Antalya Basin, rain gauge, geostatistics, precipitation, GISAbstract
Accurate precipitation patterns and potential data are fundamental for water resources management, planning, and developing studies. Precipitation has the most spatial and temporal variance than other climate elements. Therefore, a denser observation network than the other climate elements is needed for rainfall. This study aims to determine the effect of the rain gauge network density and location on precipitation pattern, amount, and volume in the Antalya Basin. To this end, two different data sets were used in the study. In Data Set-1, precipitation data from State Meteorological Service (MGM) stations were used. In Data Set-2, precipitation data from MGM and State Hydraulic Works (DSI) stations were combined and used. The most widely used geostatistical Ordinary Kriging (OK) method was utilized for spatial interpolation of precipitation data. The accuracy of the data sets was tested using the cross-validation technic and the results were compared using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency (NSE). With the Data Set-1, NSE: 0,64, R2: 0,64, MAE: 123,75, and RMSE were calculated as 145,83. With the Data Set-2, NSE: 0,77, R2: 0,77, MAE: 111,55, and RMSE were calculated as 135,22. Compared to Data Set-1, Data Set-2 has lower error rates and higher accuracy. Combining the data from MGM and DSI provided rain gauge density and homogeneity in the study area. At the same time, this application also increased the success of the interpolation method. The areal precipitation depth of the Basin was calculated to be 763 mm with the MGM stations, it was 887.1 mm with the Data Set-2. The use of DSI rain gauges has modified the precipitation pattern and potential of the Antalya Basin.
References
Akgül, M. A., & Aksu H. (2021). Areal Precipitation Estimation Using Satellite Derived Rainfall Data over an Irrigation Area. Turkish Journal of Agriculture - Food Science and Technology, 9(2), 386-394.
Aksu, H. H., & Güngör, A. (2020). Burdur ili yağış potansiyeli analizi ve değerlendirilmesi. NÖHÜ Müh. Bilim. Derg. 9(1), 308-322.
Aksu, H. H. (2021). Basin-based precipitation potential of Turkey. Arab. J. Geosci. 14, 2470.
Aksu, H. H. (2023a). Estimation and analysis of seasonal rainfall distribution and potential of Türkiye and Its 25 main watersheds. Atmosphere 14, 800. https://doi.org/10.3390/ atmos14050800.
Aksu, H. H. (2023b). Analysis of Monthly Precipitation at the Basin Scale in Türkiye. Türk Tarım - Gıda Bilim ve Teknoloji Dergisi, 11(10), 1973–1985.
Atalay, İ. (2010). Uygulamalı Klimatoloji. Meta Yayınevi, İzmir.
Berndt, C., & Haberlandt, U. (2018). Spatial interpolation of climate variables in Northern Germany: Influence of temporal resolution and network density. J. Hydrol. Reg. Stud. 15, 184–202.
Bostan, P. A., & Akyürek, S. Z. (2007). Spatial modelling of the mean annual precıpıtatıon of Turkey by using secondary variables. TMMOB Harita ve Kadastro Mühendisleri Odası, Ulusal Coğrafi Bilgi Sistemleri Kongresi. Trabzon, Türkiye.
Çetin, M., & Tülücü, K. (1998). Doğu akdeniz bölgesinde aylık yağışların yersel değişimlerinin jeoistatistik yöntemle incelenmesi. Turkish Journal of Engineering and Environmental Science. 22, 279-288.
DSİ, (2016). Antalya Havzası Master Plan Ara Raporu, Ankara.
Du, M., Huang, S., Leng, G., Huang, Q., Guo, Y., & Jiang, J. (2023). Multi-timescale-based precipitation concentration dynamics and their asymmetric impacts on dry and wet conditions in a changing environment. Atmospheric Research. 291, 106821. https://doi.org/10.1016/ j.atmosres.2023.106821.
Girons Lopez, M., Wennerström, H., Nordén, L., & Seibert, J. (2015) Location and density of rain gauges for the estimation of spatial varying precipitation. Geografiska Annaler: Series A, Physical Geography, 97(1), 167-179. https://doi. org/10.1111/geoa.12094
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, USA.
Goovaerts, P. (2008). Kriging and semivariogram deconvolution in the presence of irregular geographical units. Math Geosci. 40, 101–128.
Isaaks, E. H., & Srivastava, R. M. (1989). Applied Geostatistics. Oxford University Press, New York.
Li, H., Wang, D., Singh, V. P., Wang, Y., & Wu, J. (2021). Developing an entropy and copula-based approach for precipitation monitoring network expansion. J. Hydrol. 598, 126366.
Li, J., & Heap, A. D. (2011). Spatial interpolation methods applied in the environmental sciences: A review. Environ. Model. Softw. 53, 173–189.
Li, L., & Revesz, P. (2004). Interpolation methods for spatio-temporal geographic data. Comput. Environ. Urban Syst. 28, 201–227.
MGM, (2024). Erişim tarihi: (18 Şubat 2024). https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=A&m=ANTALYA.
Raja, N. B., Aydin, O., Türkoğlu, N., & Çiçek, İ. (2017). Space time kriging of precipitation variability in Turkey for the period 1976–2010. Theoret Appl Climatol. 129 (1–2), 293–304. https://doi.org/10. 1007/s00704-016-1788-8
Sattari, M. T., Rezazadeh-Joudi, A., & Kusiak, A. (2017). Assessment of different methods for estimation of missing data in precipitation studies. Hydrol. Res. 48 (4), 1032–1044.
Selek, B., & Aksu, H. (2020). Water resources potential of Turkey, In: Harmancioglu N, Altinbilek D (eds) Water Resources of Turkey, Vol 2, Springer, Cham.
Simoyama, F. O., Croope, S., Neto, L., & Santos, L. (2023). Optimization of rain gauge networks-A systematic literature review. Socio-Economic Planning Sciences, 86, 101469. https://doi.org/10.1016/j.seps.2022.101469.
St-Hilaire, A., Ouarda, T. B., Lachance, M., Bobée, B., Gaudet, J., & Gignac, C. (2003). Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study. Hydrol. Process, 17 (18), 3561–3580.
SYGM, (2016). Antalya Havzası Taşkın Yönetim Planı, Ankara.
SYGM, (2018). Antalya Havzası Kuraklık Yönetim Planı, Ankara.
Şensoy, S., Demircan, M., Ulupınar, U., & Balta, İ. (2008). Türkiye İklimi. Erişim tarihi: (16 Nisan 2023). https://www.mgm.gov.tr/FILES/genel/makale/31_climateofturkey.pdf
Tabatabaei, S. M., Dastourani, M., Eslamian, S., Nazeri Tahroudi, M. (2022). Ranking and optimizing the rain-gauge networks using the entropy–copula approach (Case study of the Siminehrood Basin, Iran). Appl Water Sci. 12(9),1–13.
Türkeş, M. (2010). Klimatoloji ve Meteoroloji. Kriter Yayınevi, İstanbul.
Türkoğlu, N., Aydin, O., Duman, N., & Çiçek, İ. (2016). Türkiye’de yağışın farklı mekânsal enterpolasyon yöntemleriyle karşılaştırılması. Journal of Human Sciences. 13 (3), 5636
Usul, N. (2017). Mühendislik Hidrolojisi. ODTÜ Yayıncılık, Ankara.
Webster, R., & Oliver, M. A. (2007). Geostatistics for Environmental Scientists. John Wiley & Sons, Chichester. http://dx.doi.org/10.1002/9780470517277.
WMO, (2008). World Meteorological Organization, Guide to Hydrological Practices, No:168
WWDR, (2020). The United Nations World Water Development Report 2020: Water and Climate Change. UNESCO, Paris. Erişim tarihi: (18 Şubat 2024). https://www.unwater.org/publications/un - world-water-development-report-2020.pdf
Zeng, Q., Chen, H., Xu, C-Y., Jie, M-X., Chen, J., Guo, S-L., & Liu, J. (2018). The effect of rain gauge density and distribution on runoff simulation using a lumped hydrological modelling approach. J Hydrol, 563, 106–22.
Zhou, L., Koike, T., Takeuchi, K., Rasmy, M., Onuma, K., Ito. H., Selvarajah, H., Liu, L., Li, X., & Ao, T. (2022). A study on availability of ground observations and its impacts on bias correction of satellite precipitation products and hydrologic simulation efficiency. J. Hydrol. 610 (24), 127595 https://doi.org/10.1016/j.jhydrol.2022.127595.
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