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ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY

Year 2021, Volume 9, Issue 1, 1 - 14, 22.02.2021
https://doi.org/10.20290/estubtdb.726491

Abstract

Porsuk River Basin, located in the Central Anatolia, Turkey, has a drainage area of 10818.41 km2 and a total length of 460 km, which makes it a significant region for a variety of hydrological and hydropower studies. Therefore, it is necessary to determine the rainfall distribution over the basin so that related projects and processes, such as dam planning works, can be properly performed. To fulfil this aim, the meteorological data gauged between 1927-2015 by 15 different stations within and around the study area were used to perform interpolation functions with three widely known spatial distribution methods, namely Thiessen Polygons (TP), Spline (SP) and Inverse Distance Weighting (IDW), for the determination of the rainfall distribution. Moreover, the reliability of these methods was also evaluated and compared in terms of their Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Correlation Coefficient (R2) values. The results revealed that IDW method, in general, was the most appropriate option for Porsuk River Basin in comparison with SP and TP methods, as MAE, MSE, RMSE and R2 values of this method was found 33.359, 1710.385, 41.357 and 0.7118 respectively. However, TP displayed smoother results at the points where the rain gauges were closer to each other or dense.


References

  • [1] Daly C, Neilson RP, Phillips DL. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 1994; 33(2):140–158.
  • [2] Foufoula-Georgiou E, Georgakakos KP. Hydrologic advances in space-time precipitation modeling and forecasting. In: Bowles DS, O'Connell PE, editors. Recent advances in the modeling of hydrologic systems. Netherlands: Springer, 1991. pp 47–65.
  • [3] Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane JE. Precipitation: Measurement, remote sensing, climatology and modeling. Atmos Res 2009; 94(4):512–533.
  • [4] Basistha A, Arya DS, Goel NK. Spatial distribution of rainfall in Indian Himalayas–a case study of Uttarakhand region. Water Resour Manag 2008; 22(10):1325–1346.
  • [5] Cooley D, Nychka D, Naveau P. Bayesian spatial modeling of extreme precipitation return levels. J Am Stat Assoc 2007; 102 (479):824–840.
  • [6] Valent P, Výleta R. Calculating areal rainfall using a more efficient IDW interpolation algorithm. Int J Eng Res Sci 2015; 1(7):9–17.
  • [7] Guenni L, Hutchinson MF. Spatial interpolation of the parameters of a rainfall model from ground-based data. J Hydrol 1998; 212:335–347.
  • [8] Barros AP, Lettenmaier DP. Dynamic modeling of orographically induced precipitation. Rev Geophys 1994; 32(2):265–284.
  • [9] Nearing MA, Jetten V, Baffaut C, Cerdan O, Couturier A, Hernandez M, Le Bissonnais Y, Nicols MH, Nunes JP, Renschler CS. Modeling response of soil erosion and runoff to changes in precipitation and cover. Catena 2005; 61(2-3):131–154.
  • [10] Ajayi IR, Afolabi MO, Ogunbodede EF, Sunday AG. Modeling rainfall as a constraining factor for Cocoa yield in Ondo State. Am J Sci Ind Res 2010; 1(2):127–134.
  • [11] Chen FW, Liu CW. Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 2012; 10(3):209–222.
  • [12] Duong VN, Philippe G. Rainfall uncertainty in distributed hydrological modelling in large catchments: an operational approach applied to the Vu Gia-Thu Bon catchment-Viet Nam. In: 3rd IAHR Europe Congress; 14-16 April 2014; Porto, Portugal.
  • [13] Earls J, Dixon B. Spatial interpolation of rainfall data using ArcGIS: A comparative study. In: 27th Annual ESRI International User Conference; 18-22 June 2007; San Diego,USA.
  • [14] Mair A, Fares A. Comparison of rainfall interpolation methods in a mountainous region of a tropical island. J Hydrol Eng 2010; 16(4):371–383.
  • [15] Mamassis N, Koutsoyiannis D. Influence of atmospheric circulation types on space‐time distribution of intense rainfall. J Geophys Res Atmos 1996; 101(D21):26267–26276.
  • [16] Noori MJ, Hassan HH, Mustafa YT. Spatial estimation of rainfall distribution and its classification in Duhok governorate using GIS. J Water Resour Prot 2014; 6(2):75–82.
  • [17] Shi Y, Li L, Zhang L. Application and comparing of IDW and Kriging interpolation in spatial rainfall information. In: Chen J, Pu Y, editors. Geoinformatics 2007: Geospatial Information Science. Nanjing, China: SPIE Publications, 2007. 67531I.
  • [18] Tao T, Chocat B, Suiqing L, Kunlun X. Uncertainty analysis of interpolation methods in rainfall spatial distribution–a case of small catchment in Lyon. J Water Resour Prot 2009; 1(2):136–144.
  • [19] Tomczak M. Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. J Geogr In Decis. Anal 1998; 2(2):18–30.
  • [20] Wagner PD, Fiener P, Wilken F, Kumar S, Schneider K. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J Hydrol 2012; 464:388–400.
  • [21] Zhang , Lu X, Wang X. Comparison of Spatial Interpolation Methods Based on Rain Gauges for Annual Precipitation on the Tibetan Plateau. Polish J Environ Stud 2016; 25(3):1339–1345.
  • [22] Li J, Heap AD. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecol Inform 2011; 6(3-4):228–241.
  • [23] Bakış R, Altan M, Gümüşlüoğlu E, Tuncan A, Ayday C, Önsoy H, Olgun K. Porsuk Havzası Su Potansiyelinin Hidroelektrik Enerji Üretimi Yönünden İncelenmesi (Investigation the Water Potential of Porsuk River Basin In Terms of Hydropower Generation). Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimar Fakültesi Derg (J. Eskişehir Osmangazi Univ. Eng. Archit. Fac.) 2008; 21(2):125–162.
  • [24] Bakış R, Bayazıt Y, Ahmady DM. Analysis and Comparison of Spatial Rainfall Distribution Applying Non-Geostatistical/Deterministic Interpolation Methods: The Case of Porsuk River Basin, Turkey. In: 2nd IWA Regional Symposium on Water, Wastewater Environment: The Past, Present and Future of the World’s Water Resources. 22-23 March 2017; İzmir, Turkey.
  • [25] Köse E, Çiçek A, Uysal K, Tokatl C., Arslan N, Emiroğlu Ö. Evaluation of surface water quality in Porsuk Stream. Anadolu Univ Sci Technol Life Sci Biotechnol 2016; 4(2):81–93.
  • [26] Schwanghart W, Kuhn NJ. TopoToolbox: A set of Matlab functions for topographic analysis. Environ Model Softw 2010; 25(6):770–781.
  • [27] Ly S, Charles C, Degré A. Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review. Biotechnol Agron Société Environ 2013; 17(2):392–406.
  • [28] Lebel T, Basti G., Obled C, Creuti J.D. On the accuracy of areal rainfall estimation: a case study. Water Resour Res 1987; 23:2123–2134.
  • [29] Carver SJ, Corneli SC, Heywood DI. An Introduction to Geographical Information Systems. London: UK: Pearson Education Limited, 2006.
  • [30] Chow VT. Handbook of applied hydrology: a compendium of water-resources technology. New York, USA: McGraw-Hill Companies, 1964.
  • [31] Goovaerts P. Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 1999; 34(3-4):227–242.
  • [32] Barbalh F.D, Silv GNF, Formiga KTM. Average rainfall estimation: methods performance comparison in the Brazilian semi-arid. J Water Resour Prot 2014; 6:97–103.
  • [33] Wahba G. Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data. In: Cheney W, editor. Approximation Theory III. New York, USA: Academic Press, 1980. pp. 905-912.
  • [34] Hutchinson MF. The application of thin plate smoothing splines to continent-wide data assimilation. BMRC Research Report 27. Melbourne. pp. 104–113, 1991.
  • [35] Tatalovich Z, Wilson JP, Cockburn M. A comparison of thiessen polygon, kriging, and spline models of potential UV exposure. Cartogr Geogr Inf Sci 2006; 33(3):217–231.
  • [36] Childs C. Interpolating surfaces in ArcGIS spatial analyst. ArcUser 2004; July-September 3235:32–35.
  • [37] Zheng X, Basher R. Thin-plate smoothing spline modeling of spatial climate data and its application to mapping South Pacific rainfalls. Mon Weather Rev 1995; 123(10):3086–3102.
  • [38] Merwade VM, Maidmen DR, Goff JA. Anisotropic considerations while interpolating river channel bathymetry. J Hydrol 2006; 331(3-4):731–741.
  • [39] Ruelland D, Ardoin-Bardin S, Billen G, Servat E. Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa. J Hydrol 2008; 361(1-2):96–117.
  • [40] Ahmady DM. Coğrafi bilgi sistemleri kullanılarak Porsuk havzasinda baraj planlama çalışmalarının araştırılması (Investigastion of dam planning studies in Porsuk basin using geographical information systems). MSc, Anadolu University, Eskişehir, Turkey, 2017.

ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY

Year 2021, Volume 9, Issue 1, 1 - 14, 22.02.2021
https://doi.org/10.20290/estubtdb.726491

Abstract

Porsuk River Basin, located in the central Anatolia, Turkey, has a drainage area of 10818.41 km2 and a total length of 460 km, which makes it a significant region for a variety of hydrological and hydropower studies. Therefore, it is necessary to determine the rainfall distribution over the basin so that related projects and processes, such as dam planning works, can be properly performed. To fulfill this aim, the meteorological data gauged between 1927-2015 by 15 different stations within and around the study area were used to perform interpolation functions with three widely known spatial distribution methods, namely Thiessen Polygons (TP), Spline (SP) and Inverse Distance Weighting (IDW), to estimate the rainfall distribution. Moreover, the reliability of these methods were also evaluated and compared in terms of their Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Correlation Coefficient (R2) values. The results revealed that IDW method, in general, was the most appropriate option for Porsuk River Basin comparison with methods of SP and TP, as it generated the MAE, MSE, RMSE and R2 values of 33.359, 1710.385, 41.357 and 0.7118 respectively. However, TP displayed a smoother result at the point, where the rain gauges are closer to each other or highly densed.

References

  • [1] Daly C, Neilson RP, Phillips DL. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 1994; 33(2):140–158.
  • [2] Foufoula-Georgiou E, Georgakakos KP. Hydrologic advances in space-time precipitation modeling and forecasting. In: Bowles DS, O'Connell PE, editors. Recent advances in the modeling of hydrologic systems. Netherlands: Springer, 1991. pp 47–65.
  • [3] Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane JE. Precipitation: Measurement, remote sensing, climatology and modeling. Atmos Res 2009; 94(4):512–533.
  • [4] Basistha A, Arya DS, Goel NK. Spatial distribution of rainfall in Indian Himalayas–a case study of Uttarakhand region. Water Resour Manag 2008; 22(10):1325–1346.
  • [5] Cooley D, Nychka D, Naveau P. Bayesian spatial modeling of extreme precipitation return levels. J Am Stat Assoc 2007; 102 (479):824–840.
  • [6] Valent P, Výleta R. Calculating areal rainfall using a more efficient IDW interpolation algorithm. Int J Eng Res Sci 2015; 1(7):9–17.
  • [7] Guenni L, Hutchinson MF. Spatial interpolation of the parameters of a rainfall model from ground-based data. J Hydrol 1998; 212:335–347.
  • [8] Barros AP, Lettenmaier DP. Dynamic modeling of orographically induced precipitation. Rev Geophys 1994; 32(2):265–284.
  • [9] Nearing MA, Jetten V, Baffaut C, Cerdan O, Couturier A, Hernandez M, Le Bissonnais Y, Nicols MH, Nunes JP, Renschler CS. Modeling response of soil erosion and runoff to changes in precipitation and cover. Catena 2005; 61(2-3):131–154.
  • [10] Ajayi IR, Afolabi MO, Ogunbodede EF, Sunday AG. Modeling rainfall as a constraining factor for Cocoa yield in Ondo State. Am J Sci Ind Res 2010; 1(2):127–134.
  • [11] Chen FW, Liu CW. Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 2012; 10(3):209–222.
  • [12] Duong VN, Philippe G. Rainfall uncertainty in distributed hydrological modelling in large catchments: an operational approach applied to the Vu Gia-Thu Bon catchment-Viet Nam. In: 3rd IAHR Europe Congress; 14-16 April 2014; Porto, Portugal.
  • [13] Earls J, Dixon B. Spatial interpolation of rainfall data using ArcGIS: A comparative study. In: 27th Annual ESRI International User Conference; 18-22 June 2007; San Diego,USA.
  • [14] Mair A, Fares A. Comparison of rainfall interpolation methods in a mountainous region of a tropical island. J Hydrol Eng 2010; 16(4):371–383.
  • [15] Mamassis N, Koutsoyiannis D. Influence of atmospheric circulation types on space‐time distribution of intense rainfall. J Geophys Res Atmos 1996; 101(D21):26267–26276.
  • [16] Noori MJ, Hassan HH, Mustafa YT. Spatial estimation of rainfall distribution and its classification in Duhok governorate using GIS. J Water Resour Prot 2014; 6(2):75–82.
  • [17] Shi Y, Li L, Zhang L. Application and comparing of IDW and Kriging interpolation in spatial rainfall information. In: Chen J, Pu Y, editors. Geoinformatics 2007: Geospatial Information Science. Nanjing, China: SPIE Publications, 2007. 67531I.
  • [18] Tao T, Chocat B, Suiqing L, Kunlun X. Uncertainty analysis of interpolation methods in rainfall spatial distribution–a case of small catchment in Lyon. J Water Resour Prot 2009; 1(2):136–144.
  • [19] Tomczak M. Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. J Geogr In Decis. Anal 1998; 2(2):18–30.
  • [20] Wagner PD, Fiener P, Wilken F, Kumar S, Schneider K. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J Hydrol 2012; 464:388–400.
  • [21] Zhang , Lu X, Wang X. Comparison of Spatial Interpolation Methods Based on Rain Gauges for Annual Precipitation on the Tibetan Plateau. Polish J Environ Stud 2016; 25(3):1339–1345.
  • [22] Li J, Heap AD. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecol Inform 2011; 6(3-4):228–241.
  • [23] Bakış R, Altan M, Gümüşlüoğlu E, Tuncan A, Ayday C, Önsoy H, Olgun K. Porsuk Havzası Su Potansiyelinin Hidroelektrik Enerji Üretimi Yönünden İncelenmesi (Investigation the Water Potential of Porsuk River Basin In Terms of Hydropower Generation). Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimar Fakültesi Derg (J. Eskişehir Osmangazi Univ. Eng. Archit. Fac.) 2008; 21(2):125–162.
  • [24] Bakış R, Bayazıt Y, Ahmady DM. Analysis and Comparison of Spatial Rainfall Distribution Applying Non-Geostatistical/Deterministic Interpolation Methods: The Case of Porsuk River Basin, Turkey. In: 2nd IWA Regional Symposium on Water, Wastewater Environment: The Past, Present and Future of the World’s Water Resources. 22-23 March 2017; İzmir, Turkey.
  • [25] Köse E, Çiçek A, Uysal K, Tokatl C., Arslan N, Emiroğlu Ö. Evaluation of surface water quality in Porsuk Stream. Anadolu Univ Sci Technol Life Sci Biotechnol 2016; 4(2):81–93.
  • [26] Schwanghart W, Kuhn NJ. TopoToolbox: A set of Matlab functions for topographic analysis. Environ Model Softw 2010; 25(6):770–781.
  • [27] Ly S, Charles C, Degré A. Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review. Biotechnol Agron Société Environ 2013; 17(2):392–406.
  • [28] Lebel T, Basti G., Obled C, Creuti J.D. On the accuracy of areal rainfall estimation: a case study. Water Resour Res 1987; 23:2123–2134.
  • [29] Carver SJ, Corneli SC, Heywood DI. An Introduction to Geographical Information Systems. London: UK: Pearson Education Limited, 2006.
  • [30] Chow VT. Handbook of applied hydrology: a compendium of water-resources technology. New York, USA: McGraw-Hill Companies, 1964.
  • [31] Goovaerts P. Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 1999; 34(3-4):227–242.
  • [32] Barbalh F.D, Silv GNF, Formiga KTM. Average rainfall estimation: methods performance comparison in the Brazilian semi-arid. J Water Resour Prot 2014; 6:97–103.
  • [33] Wahba G. Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data. In: Cheney W, editor. Approximation Theory III. New York, USA: Academic Press, 1980. pp. 905-912.
  • [34] Hutchinson MF. The application of thin plate smoothing splines to continent-wide data assimilation. BMRC Research Report 27. Melbourne. pp. 104–113, 1991.
  • [35] Tatalovich Z, Wilson JP, Cockburn M. A comparison of thiessen polygon, kriging, and spline models of potential UV exposure. Cartogr Geogr Inf Sci 2006; 33(3):217–231.
  • [36] Childs C. Interpolating surfaces in ArcGIS spatial analyst. ArcUser 2004; July-September 3235:32–35.
  • [37] Zheng X, Basher R. Thin-plate smoothing spline modeling of spatial climate data and its application to mapping South Pacific rainfalls. Mon Weather Rev 1995; 123(10):3086–3102.
  • [38] Merwade VM, Maidmen DR, Goff JA. Anisotropic considerations while interpolating river channel bathymetry. J Hydrol 2006; 331(3-4):731–741.
  • [39] Ruelland D, Ardoin-Bardin S, Billen G, Servat E. Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa. J Hydrol 2008; 361(1-2):96–117.
  • [40] Ahmady DM. Coğrafi bilgi sistemleri kullanılarak Porsuk havzasinda baraj planlama çalışmalarının araştırılması (Investigastion of dam planning studies in Porsuk basin using geographical information systems). MSc, Anadolu University, Eskişehir, Turkey, 2017.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Recep BAKIŞ>
Eskişehir Teknik Üniversitesi
0000-0002-1371-1085
Türkiye


Yıldırım BAYAZIT>
BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ
0000-0002-8699-4741
Türkiye


Dost Mohammed AHMADY>
Eskişehir Teknik Üniversitesi
0000-0002-8623-9009
Türkiye


Saye Nihan ÇABUK> (Primary Author)
Eskişehir Teknik Üniversitesi
0000-0003-4859-2271
Türkiye

Supporting Institution Anadolu Üniversitesi
Project Number 1506F500
Publication Date February 22, 2021
Published in Issue Year 2021, Volume 9, Issue 1

Cite

Bibtex @research article { estubtdb726491, journal = {Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler}, issn = {2667-419X}, address = {btdb@anadolu.edu.tr}, publisher = {Eskisehir Technical University}, year = {2021}, volume = {9}, number = {1}, pages = {1 - 14}, doi = {10.20290/estubtdb.726491}, title = {ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY}, key = {cite}, author = {Bakış, Recep and Bayazıt, Yıldırım and Ahmady, Dost Mohammed and Çabuk, Saye Nihan} }
APA Bakış, R. , Bayazıt, Y. , Ahmady, D. M. & Çabuk, S. N. (2021). ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY . Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler , 9 (1) , 1-14 . DOI: 10.20290/estubtdb.726491
MLA Bakış, R. , Bayazıt, Y. , Ahmady, D. M. , Çabuk, S. N. "ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY" . Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 9 (2021 ): 1-14 <https://dergipark.org.tr/en/pub/estubtdb/issue/60418/726491>
Chicago Bakış, R. , Bayazıt, Y. , Ahmady, D. M. , Çabuk, S. N. "ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY". Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 9 (2021 ): 1-14
RIS TY - JOUR T1 - ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY AU - RecepBakış, YıldırımBayazıt, Dost MohammedAhmady, Saye NihanÇabuk Y1 - 2021 PY - 2021 N1 - doi: 10.20290/estubtdb.726491 DO - 10.20290/estubtdb.726491 T2 - Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler JF - Journal JO - JOR SP - 1 EP - 14 VL - 9 IS - 1 SN - 2667-419X- M3 - doi: 10.20290/estubtdb.726491 UR - https://doi.org/10.20290/estubtdb.726491 Y2 - 2020 ER -
EndNote %0 Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY %A Recep Bakış , Yıldırım Bayazıt , Dost Mohammed Ahmady , Saye Nihan Çabuk %T ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY %D 2021 %J Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler %P 2667-419X- %V 9 %N 1 %R doi: 10.20290/estubtdb.726491 %U 10.20290/estubtdb.726491
ISNAD Bakış, Recep , Bayazıt, Yıldırım , Ahmady, Dost Mohammed , Çabuk, Saye Nihan . "ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY". Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 9 / 1 (February 2021): 1-14 . https://doi.org/10.20290/estubtdb.726491
AMA Bakış R. , Bayazıt Y. , Ahmady D. M. , Çabuk S. N. ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler. 2021; 9(1): 1-14.
Vancouver Bakış R. , Bayazıt Y. , Ahmady D. M. , Çabuk S. N. ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler. 2021; 9(1): 1-14.
IEEE R. Bakış , Y. Bayazıt , D. M. Ahmady and S. N. Çabuk , "ANALYSIS AND COMPARISON OF SPATIAL RAINFALL DISTRIBUTION APPLYING DIFFERENT INTERPOLATION METHODS IN PORSUK RIVER BASIN, TURKEY", Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler, vol. 9, no. 1, pp. 1-14, Feb. 2021, doi:10.20290/estubtdb.726491