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pp. 1037-1054 | Article Number: ijese.2017.070
Published Online: June 18, 2017
Abstract
In this study, represents a new climatic modeling of monthly rainfall for Iran (1975–2014), presented with the spatially variability, patterning monthly rainfalls series available in the 140 stations and rainfall points. Eight special interpolation methods were estimated and considered: the inverse distance weighting (IDW), the ordinary kriging (OK), the simple kriging (SK), the universal kriging (UK), the indicator kriging (IK), the probability kriging (PK), the disjunctive kriging (DK) and the empirical Bayesian kriging (EBK). The results of the several methods were studied and assessed by the validation indicators, evaluating the outcomes from the methods with the actual rainfalls series and predicting various residuals amounts. The eight methods presented suitable for IDW, OK, UK and EBK than for other methods with the least RMSE (IDW=0.497, OK=0.37, UK=0.398 and EBK=0.189), and for the spatial variability, rather than another patterns, as well at 31200 rainfall points in January than 37261 points for another series. The suitable and best outcomes were realized with EBK and OK utilized for the actual rainfalls series in the Iran. The EBK and OK perfected the precision of the rainfall spatial variability analysis with respect to IDW and UK. We present a method of rainfall monthly patterns, using the EBK and OK to predict any spatial variations in the monthly rainfall for the period of 2014–2064 over Iran.
Keywords: interpolation methods, spatial variations, precipitation patterns, Modeling
References
Ahmadi, S., & Sedghamiz, A. (2008). Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environmental Monitoring and Assessment, 138(1-3), 357-368. doi: 10.1007/s10661-007-9803-2
Arslan, H. (2012). Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: The case of Bafra Plain, Turkey. Agricultural Water Management, 113(0), 57-63. doi: http://dx.doi.org/10.1016/j.agwat.2012.06.015
Babak, O. (2014). Inverse distance interpolation for facies modeling. Stochastic Environmental Research and Risk Assessment, 28(6), 1373-1382. doi: 10.1007/s00477-013-0833-8
Baker, B. H., Kröger, R., Brooks, J. P., Smith, R. K., & Czarnecki, J. M. P. (2015). Investigation of denitrifying microbial communities within an agricultural drainage system fitted with low-grade weirs. Water research, 87, 193-201.
Barbulescu, A. (2015). A New Method for Estimation the Regional Precipitation. Water Resources Management, 1-10.
Benavides, R., Montes, F., Rubio, A., & Osoro, K. (2007). Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agricultural and Forest Meteorology, 146(3–4), 173-188. doi: http://dx.doi.org/10.1016/j.agrformet.2007.05.014
Bohling, G. (2005). INTRODUCTION TO GEOSTATISTICS
And VARIOGRAM ANALYSIS: C&PE 940, 17 October 2005.http://people.ku.edu/~gbohling/geostats.
Çelik, R. (2015). Temporal changes in the groundwater level in the Upper Tigris Basin, Turkey, determined by a GIS technique. Journal of African Earth Sciences, 107(0), 134-143. doi: http://dx.doi.org/10.1016/j.jafrearsci.2015.03.004
Cheng, S.-J., Hsieh, H.-H., & Wang, Y.-M. (2007). Geostatistical interpolation of space–time rainfall on Tamshui River basin, Taiwan. Hydrological Processes, 21(23), 3136-3145. doi: 10.1002/hyp.6535
de Amorim Borges, P., Franke, J., da Anunciação, Y. M. T., Weiss, H., & Bernhofer, C. (2015). Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil. Theoretical and Applied Climatology, 1-14.
Dokou, Z., Kourgialas, N. N., & Karatzas, G. P. (2015). Assessing groundwater quality in Greece based on spatial and temporal analysis. Environmental monitoring and assessment, 187(12), 1-18.
Dumitrescu, A., Birsan, M. V., & Manea, A. (2015). Spatio‐temporal interpolation of sub‐daily (6 h) precipitation over Romania for the period 1975–2010. International Journal of Climatology.
Dummer, T. J. B., Yu, Z. M., Nauta, L., Murimboh, J. D., & Parker, L. (2015). Geostatistical modelling of arsenic in drinking water wells and related toenail arsenic concentrations across Nova Scotia, Canada. Science of The Total Environment, 505(0), 1248-1258. doi: http://dx.doi.org/10.1016/j.scitotenv.2014.02.055
Elbeih, S. F. (2015). An overview of integrated remote sensing and GIS for groundwater mapping in Egypt. Ain Shams Engineering Journal, 6(1), 1-15. doi: http://dx.doi.org/10.1016/j.asej.2014.08.008
ESRI. (2014a). Spatial Statistics Tools, ArcMap 10.3. ESRI, Redlands, California. ESRI, ArcMap 10.3. ESRI, Redlands, California. doi: 10.1002/jbio.201400127
ESRI. (2014b). Using ArcGIS geostatistical analyst: Esri Redlands.
Fand, B. B., Tonnang, H. E. Z., Kumar, M., Bal, S. K., Singh, N. P., Rao, D. V. K. N., . . . Minhas, P. S. (2014). Predicting the impact of climate change on regional and seasonal abundance of the mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) using temperature-driven phenology model linked to GIS. Ecological Modelling, 288(0), 62-78. doi: http://dx.doi.org/10.1016/j.ecolmodel.2014.05.018
Fares, A., Awal, R., Michaud, J., Chu, P.-S., Fares, S., Kodama, K., & Rosener, M. (2014). Rainfall-runoff modeling in a flashy tropical watershed using the distributed HL-RDHM model. Journal of Hydrology, 519, Part D(0), 3436-3447. doi: http://dx.doi.org/10.1016/j.jhydrol.2014.09.042
Feizizadeh, B., Shadman Roodposhti, M., Jankowski, P., & Blaschke, T. (2014). A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Computers & Geosciences, 73(0), 208-221. doi: http://dx.doi.org/10.1016/j.cageo.2014.08.001
Ford, T. W., & Quiring, S. M. (2014). Comparison and application of multiple methods for temporal interpolation of daily soil moisture. International Journal of Climatology, 34(8), 2604-2621. doi: 10.1002/joc.3862
Goovaerts, P. (2005). Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. International Journal of Health Geographics, 4(1), 31.
Gundogdu, I. (2015). Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps. Theoretical and Applied Climatology, 1-6. doi: 10.1007/s00704-015-1619-3
Gundogdu, I. B. (2015). Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps. Theoretical and Applied Climatology, 1-6.
Haberlandt, U. (2007). Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. Journal of Hydrology, 332(1–2), 144-157. doi: http://dx.doi.org/10.1016/j.jhydrol.2006.06.028
Hayakawa, Y. S., Oguchi, T., & Lin, Z. (2008). Comparison of new and existing global digital elevation models: ASTER G-DEM and SRTM-3. Geophysical Research Letters, 35, (L17404), 404. doi: 10.1029/2008gl035036
Hengl, T. (2009). A Practical Guide to Geostatistical Mapping: Office for Official Publications of the European Communities, Luxembourg.
Henley, S. (2012). Nonparametric geostatistics: Springer Science & Business Media.
Htwe, T. N., Brinkmann, K., & Buerkert, A. (2015). Spatio-temporal assessment of soil erosion risk in different agricultural zones of the Inle Lake region, southern Shan State, Myanmar. Environmental monitoring and assessment, 187(10), 1-14.
Huang, J., Huang, Y., Pontius Jr, R. G., & Zhang, Z. (2015). Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed. Ocean & Coastal Management, 103(0), 14-24. doi: http://dx.doi.org/10.1016/j.ocecoaman.2014.10.007
Huang, J., Shi, Z., & Biswas, A. (2015). Characterizing anisotropic scale-specific variations in soil salinity from a reclaimed marshland in China. Catena, 131, 64-73.
Javari, M. (2015). A Study of Impacts of Temperature Components on Precipitation in Iran Using SEM-PLS-GIS. Earth Science & Climatic Change, s3.004(2328), 1-14.
Johnston, K., Ver Hoef, J. M., Krivoruchko, K., & Lucas, N. (2001). Using ArcGIS geostatistical analyst (Vol. 380): Esri Redlands.
Karacan, C. Ö., Olea, R. A., & Goodman, G. (2012). Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation. International Journal of Coal Geology, 90–91(0), 50-71. doi: http://dx.doi.org/10.1016/j.coal.2011.10.010
Karagiannis-Voules, D.-A., Odermatt, P., Biedermann, P., Khieu, V., Schär, F., Muth, S., . . . Vounatsou, P. (2015). Geostatistical modelling of soil-transmitted helminth infection in Cambodia: Do socioeconomic factors improve predictions? Acta Tropica, 141, Part B(0), 204-212. doi: http://dx.doi.org/10.1016/j.actatropica.2014.09.001
Keshavarzi, B., Ebrahimi, P., & Moore, F. (2015). A GIS-based approach for detecting pollution sources and bioavailability of metals in coastal and marine sediments of Chabahar Bay, SE Iran. Chemie der Erde - Geochemistry, 75(2), 185-195. doi: http://dx.doi.org/10.1016/j.chemer.2014.11.003
Kisaka, M. O., Mucheru-Muna, M., Ngetich, F., Mugwe, J., Mugendi, D., Mairura, F., . . . Makokha, G. (2015). Potential of deterministic and geostatistical rainfall interpolation under high rainfall variability and dry spells: case of Kenya’s Central Highlands. Theoretical and Applied Climatology, 1-16.
Machiwal, D., & Jha, M. K. (2014). Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques. Journal of Hydrology: Regional Studies(0). doi: http://dx.doi.org/10.1016/j.ejrh.2014.11.005
Martínez-Cob, A. (1996). Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. Journal of Hydrology, 174(1–2), 19-35. doi: http://dx.doi.org/10.1016/0022-1694(95)02755-6
Masoud, A. A. (2014). Groundwater quality assessment of the shallow aquifers west of the Nile Delta (Egypt) using multivariate statistical and geostatistical techniques. Journal of African Earth Sciences, 95(0), 123-137. doi: http://dx.doi.org/10.1016/j.jafrearsci.2014.03.006
Mentis, D., Hermann, S., Howells, M., Welsch, M., & Siyal, S. H. (2015). Assessing the technical wind energy potential in Africa a GIS-based approach. Renewable Energy, 83(0), 110-125. doi: http://dx.doi.org/10.1016/j.renene.2015.03.072
Mirzaei, R., & Sakizadeh, M. (2015). Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environmental Science and Pollution Research, 1-12.
Odeh, I. O. A., Crawford, M., & McBratney, A. B. (2006). Chapter 32 Digital Mapping of Soil Attributes for Regional and Catchment Modelling, using Ancillary Covariates, Statistical and Geostatistical Techniques. In A. B. M. P. Lagacherie & M. Voltz (Eds.), Developments in Soil Science (Vol. Volume 31, pp. 437-622): Elsevier.
Oliver, M. A., & Webster, R. (2015). Basic Steps in Geostatistics: The Variogram and Kriging: Springer.
Paparrizos, S., Maris, F., & Matzarakis, A. (2016). Integrated analysis of present and future responses of precipitation over selected Greek areas with different climate conditions. Atmospheric Research, 169, 199-208.
Peña-Angulo, D., Brunetti, M., Cortesi, N., & Gonzalez-Hidalgo, J. C. (2016). A new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods. International Journal of Geographical Information Science, 30(11), 2109-2132. doi: 10.1080/13658816.2016.1155712
Pereira, P., Oliva, M., & Misiune, I. (2015). Spatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methods. Theoretical and Applied Climatology, 1-16.
Plouffe, C. C. F., Robertson, C., & 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, 57-71. doi: http://dx.doi.org/10.1016/j.envsoft.2015.01.011
Pohlmann, H. (1993). Geostatistical modelling of environmental data. Catena, 20(1–2), 191-198. doi: http://dx.doi.org/10.1016/0341-8162(93)90038-Q
Polemio, M., & Lonigro, T. (2015). Trends in climate, short-duration rainfall, and damaging hydrogeological events (Apulia, Southern Italy). Natural Hazards, 75(1), 515-540.
Scheuerer, M., & Hamill, T. M. (2015). Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities*. Monthly Weather Review, 143(4), 1321-1334.
Shahbazi, F., Aliasgharzad, N., Ebrahimzad, S. A., & Najafi, N. (2013). Geostatistical analysis for predicting soil biological maps under different scenarios of land use. European Journal of Soil Biology, 55(0), 20-27. doi: http://dx.doi.org/10.1016/j.ejsobi.2012.10.009
Suparta, W., & Rahman, R. (2016). Spatial interpolation of GPS PWV and meteorological variables over the west coast of Peninsular Malaysia during 2013 Klang Valley Flash Flood. Atmospheric Research, 168, 205-219.
Verdin, A., Rajagopalan, B., Kleiber, W., & Funk, C. (2015). A Bayesian kriging approach for blending satellite and ground precipitation observations. Water Resources Research, 51(2), 908-921. doi: 10.1002/2014WR015963
Wang, S., Huang, G. H., Lin, Q. G., Li, Z., Zhang, H., & Fan, Y. R. (2014). Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. International Journal of Climatology, 34(14), 3745-3751. doi: 10.1002/joc.3941
Wu, T., & Li, Y. (2013). Spatial interpolation of temperature in the United States using residual kriging. Applied Geography, 44(0), 112-120. doi: http://dx.doi.org/10.1016/j.apgeog.2013.07.012
Xu, W., Zou, Y., Zhang, G., & Linderman, M. (2014). A comparison among spatial interpolation techniques for daily rainfall data in Sichuan Province, China. International Journal of Climatology, n/a-n/a. doi: 10.1002/joc.4180
Yang, X., Xie, X., Liu, D. L., Ji, F., & Wang, L. (2015). Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region. Advances in Meteorology, 2015.
Zhang, Y., Vaze, J., Chiew, F. H., & Li, M. (2015). Comparing flow duration curve and rainfall–runoff modelling for predicting daily runoff in ungauged catchments. Journal of Hydrology, 525, 72-86.