نوع مقاله : مقاله پژوهشی
نویسندگان
1 Associate Professor, Department of Water Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 M. Engineering., Department of Water Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Different interpolation methods, each with their own strengths and weaknesses, are used to temporal and spatial estimation of precipitation. The accuracy of this estimate depends on the density of point data measured at rain gauge stations. In this study, the effects of the rain gauge network density on the accuracy of different interpolation methods were investigated in producing a monthly precipitation zoning map for the Samian catchment in Ardabil, Iran. The effectiveness of Bayesian Kriging, Completely Regularized Spline, Local Polynomial, Inverse Distance Weight, Ordinary Kriging, Ordinary Co-Kriging, Simple Kriging, Simple Co-Kriging, Spline with Tension, Thin Plate Spline and Universal Kriging interpolation methods was evaluated in two gauges density scenarios using evaluation criteria over 2011-2013. It was found that Inverse distance weight and Bayesian kriging methods had the best performance for the first scenario with high gauge density (16 Stations) and the second scenario with low gauge density (11 Stations), respectively. Moreover, analysis of variations in indicators across scenarios revealed that reducing the rain gauge network density by 30% improved evaluation metrics in only 40% of cases. Bayesian Kriging and Completely Regularized Splines were introduced as the preferred methods for improving evaluation indices change, and Local polynomial and simple co-Kriging methods were selected as the superior methods for improving evaluation indices quantity.
کلیدواژهها [English]
2- Agnew, M.D. and Palutikof, J.P., 2000. GIS-based construction of baseline climatologies for the Mediterranean using terrain variables. Climate research, 14(2), pp.115-127.
3- Buytaert, W., Celleri, R., Willems, P., De Bievre, B. and Wyseure, G., 2006. Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes. Journal of Hydrology, 329(3-4), pp.413-421. doi.org/10.1016/j.jhydrol.2006.02.031.
4- Caloiero, T., Pellicone, G., Modica, G. and Guagliardi, I., 2021. Comparative analysis of different spatial interpolation methods applied to monthly rainfall as support for landscape management. Applied Sciences, 11(20), p.9566.. https://doi.org/10.3390/app11209566.
5- da Silva, A.S.A., Stosic, B., Menezes, R.S.C. and Singh, V.P., 2019. Comparison of interpolation methods for spatial distribution of monthly precipitation in the state of Pernambuco, Brazil. Journal of Hydrologic Engineering, 24(3), p.04018068. doi.org/10.1061/(ASCE)HE.1943-5584.000174.
6- Dehghani, M., Salehi, S., Mosavi, A., Nabipour, N., Shamshirband, S. and Ghamisi, P., 2020. Spatial analysis of seasonal precipitation over Iran: Co-variation with climate indices. ISPRS International Journal of Geo-Information, 9(2), p.73. doi.org/10.3390/ijgi9020073.
7- Dirks, K.N., Hay, J.E., Stow, C.D. and Harris, D., 1998. High-resolution studies of rainfall on Norfolk Island: Part II: Interpolation of rainfall data. Journal of Hydrology, 208(3-4), pp.187-193. doi.org/10.1016/S0022-1694(98)00155-3.
8- Ghorbani, M.A., Mahmoud Alilou, S., Javidan, S. and Naganna, S.R., 2021. Assessment of spatio-temporal variability of rainfall and mean air temperature over Ardabil province, Iran. SN Applied Sciences, 3, pp.1-10.
9- Gilewski, P., 2021. Impact of the grid resolution and deterministic interpolation of precipitation on rainfall-runoff modeling in a sparsely gauged mountainous catchment. Water, 13(2), p.230. doi.org/10.3390/w13020230.
10- Gribov, A. and Krivoruchko, K., 2011. Local polynomials for data detrending and interpolation in the presence of barriers. Stochastic environmental research and risk assessment, 25, pp.1057-1063.
11- Hadi, S.J. and Tombul, M., 2018. Comparison of spatial interpolation methods of precipitation and temperature using multiple integration periods. Journal of the Indian Society of Remote Sensing, 46, pp.1187-1199.
12- Hernandez-Stefanoni, J.L. and Ponce-Hernandez, R., 2006. Mapping the spatial variability of plant diversity in a tropical forest: comparison of spatial interpolation methods. Environmental monitoring and assessment, 117, pp.307-334.
13- Hohmann, C., Kirchengast, G., Sungmin, O., Rieger, W., and Foelsche, U. 2021. Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network. Water, 13(10), 1381. doi.org/10.3390/w13101381
14- Hoogenboom, G., 2000. Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and forest meteorology, 103(1-2), pp.137-157. doi.org/10.1016/S0168-1923(00)00108-8.
15- Hutchinson, M. 1993. On thin plate splines and kriging. Computing science and statistics, 55-55.
16- Isaaks, E. H. & SRIVASTAVA, R. 1989. An introduction to applied geostatistics. Oxford University Press, New York.
17- Jalili Pirani, F. and Modarres, R., 2020. Geostatistical and deterministic methods for rainfall interpolation in the Zayandeh Rud basin, Iran. Hydrological Sciences Journal, 65(16), pp.2678-2692. https://doi.org/10.1080/02626667.2020.1833014.
18- Javari, M. 2017. Spatial variability of rainfall trends in Iran. Arabian Journal of Geosciences, 10, 78.
19- Khalili, K., Tahoudi, M.N., Mirabbasi, R. and Ahmadi, F., 2016. Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic Environmental Research and Risk Assessment, 30, pp.1205-1221.
20- Krause, P., Boyle, D.P. and Bäse, F., 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in geosciences, 5, pp.89-97. doi.org/10.5194/adgeo-5-89-2005.
21- Kumari, M., Singh, C.K. and Basistha, A., 2017. Clustering data and incorporating topographical variables for improving spatial interpolation of rainfall in mountainous region. Water Resources Management, 31, pp.425-442.
22- Lam, N.S.N., 1983. Spatial interpolation methods: a review. The American Cartographer, 10(2), pp.129-150. doi.org/10.1559/152304083783914958.
23- Legates, D.R. and McCabe Jr, G.J., 1999. Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water resources research, 35(1), pp.233-241. doi.org/10.1029/1998WR900018.
24- Li, J. and Heap, A.D., 2008. A review of spatial interpolation methods for environmental scientists.
25- Ly, S., Charles, C. and Degre, A., 2011. Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15(7), pp.2259-2274. doi.org/10.5194/hess-15-2259-2011.
26- Matheron, G. 1969. Le krigeage universel (Universal kriging) Vol. 1. Cahiers du Centre de Morphologie Mathematique, Ecole des Mines de Paris, Fontainebleau, 83 p. https://doi.org/10.1016/j.geoderma.2003.08.018
27- Mirzaei, R. and Sakizadeh, M., 2016. Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environmental Science and Pollution Research, 23, pp.2758-2769.
28- Mitáš, L. and Mitášová, H., 1988. General variational approach to the interpolation problem. Computers & Mathematics with Applications, 16(12), pp.983-992. doi.org/10.1016/0898-1221(88)90255-6.
29- Otieno, H., Yang, J., Liu, W. and Han, D., 2014. Influence of rain gauge density on interpolation method selection. Journal of Hydrologic Engineering, 19(11), p.04014024. doi.org/10.1061/(ASCE)HE.1943-5584.0000964.
30- Plouffe, C.C., Robertson, C. and Chandrapala, L., 2015. Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka. Environmental Modelling & Software, 67, pp.57-71. doi.org/10.1016/j.envsoft.2015.01.011.
31- Rousta, I., Doostkamian, M., Haghighi, E., Ghafarian Malamiri, H.R. and Yarahmadi, P., 2017. Analysis of spatial autocorrelation patterns of heavy and super-heavy rainfall in Iran. Advances in Atmospheric Sciences, 34, pp.1069-1081.
32- Saatsaz, M., 2020. A historical investigation on water resources management in Iran. Environment, Development and Sustainability, 22(3), pp.1749-1785.
33- Schaback, R., 2005. Multivariate interpolation by polynomials and radial basis functions. Constructive Approximation, 21(3), pp.293-317.
34- Shepard, D., 1968, January. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM national conference (pp. 517-524). doi.org/10.1145/800186.810616.
35- Swan, A., 1998. GOOVAERTS, P. 1997. Geostatistics for Natural Resources Evaluation. Applied Geostatistics Series. xiv+ 483 pp. New York, Oxford: Oxford University Press. Price£ 46.95 (hard covers). ISBN 0 19 511538 4. Geological Magazine, 135(6), pp.819-842.
36- Vicente-Serrano, S.M., Saz-Sánchez, M.A. and Cuadrat, J.M., 2003. Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Climate research, 24(2), pp.161-180.
39- Yang, X., Xie, X., Liu, D.L., Ji, F. and Wang, L., 2015. Spatial interpolation of daily rainfall data for local climate impact assessment over greater Sydney region. Advances in Meteorology, 2015(1), p.563629. doi.org/10.1155/2015/563629.
40- Zimmerman, D., Pavlik, C., Ruggles, A. and Armstrong, M.P., 1999. An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology, 31, pp.375-390.
)