Spatio-Temporal Analysis of Drought Severity Using Drought Indices and Deterministic and Geostatistical Methods (Case Study: Zayandehroud River Basin)

Authors

1 Desert Management Dept., International Desert Research Center (IDRC), University of Tehran

2 Faculty of Natural Resources, University of Tehran, Karaj, Iran

Abstract

     Drought monitoring is a fundamental component of drought risk management. It is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolation of spatially referenced data and the prediction of values for arbitrary points in the area of interest. In this research, several interpolation methods, including ordinary kriging (OK), indicator kriging (IK), residual kriging (RK), probability kriging (PK), simple kriging (SK), universal kriging (UK), and inverse distance weighted (IDW) techniques were assessed for the derivation of maps of drought indices at 19 climatic stations in Zayandehroud River Basin of Iran. Monthly rainfall data of period 1989 to 2013 were taken from 19 meteorological stations. The results showed that based on the used error criteria, kriging methods were chosen as the best method for spatial analysis of the drought indices and also, the lowest error (RMSE) and R2 is related to the kriging method. The results showed that SK and OK were more suitable for the spatial analysis of the Z-Score Index (ZSI) and the Standard Precipitation Index (SPI) index. The mean errors (RMSE) of kriging methods for ZSI and SPI indices were 0.40 and 0.19 respectively

Keywords


Agnew M.D., J.P. Palutikof, 2000. GIS-based construction of baseline climatologies for the Mediterranean using terrain variables. Climate Research, 14; 115–127.
Alijani, B., R. Yousefi Ramandi, 2015. Choosing the best geostatistics method for the zonation of drought and wet years (Case study: northwestern and central areas of Iran). Applied mathematics in Engineering, Management and Technology, 3(1); 120-130.
Alley, W.M., 1984. The Palmer Drought Severity Index: limitation and assumptions. Journal of Climate and Applied Meteorology, 23; 1100-1109.
Antoni`c O., J. Kriˇzan, A. Marki, D. Bukovec, 2001. Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. Ecological Modelling, 138; 255–263.
Apaydin, H., F. Kemal Sonmez, Y. Ersoy Yildirim, 2004. Spatial interpolation techniques for climate data in the GAP region in Turkey. Climate Research, 28; 31–40.
Asefjah B, F. Fanian, Z. Feizi, A. Abolhasani, H. Paktinat, M. Naghilou, A. Molaei Atani, M. Asadollahi, M. Babakhani, A. Kouroshnia, F. Salehi, 2014. Drought monitoring by using several meteorological drought indices (Case study: Salt Lake Basin of Iran). Desert, 19; 155-165.
Attorre, F.A., M.D.F. Sanctis, F. Brunoa, 2006. Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. International Journal of Climatology. Published online in Wiley Inter Science, www.interscience. Wiley.com) DOI: 10.1002/joc. 1495.
Azarakhshi, M., M. Mahdavi, H. Arzani, H. Ahmadi, 2011. Assessment of the Palmer drought severity index in arid and semi-arid rangeland: (Case study: Qom province, Iran). Desert, 16; 77-85.
Banejad, H., H. Zare Abyaneh, M.H., Nazarifar, A., Sabziparvar, 2006. Application of standard precipitation index (SPI) with Geostatistic Method for analyzing meteorological drought in Hamedan province. J. Agricultural Research Water, Soil & Plant in Agricultural, 6; 61-72.
Bazrafshan, J., A. Khalili, 2013. Spatial Analysis of Meteorological Drought in Iran from 1965 to 2003. Desert, 18; 63-71.
Bhuiyan, C., 2004. Various drought indices for monitoring drought condition in Aravalli Terrain of India. XXth ISPRS Congress, Istanbul, Turkey, 12-23.
Beran, M.A., J.A. Rodier, 1985. Hydrological aspects of drought. UNESCOWMO, Studies and Reports in Hydrology, No. 39, UNESCO, Paris, France.
Bernard Sharon M., 2007. Analysis of External Drift Kriging Algorithm with application to precipitation estimation in complex orography.
Collins, F., 2000. A comparison of Spatial Interpolation Techniques in Temperate Estimation. www.ncgia.ucsb.edu/conf/ SANTA_FE_CD_ROM/sf_papers/collins_fred/collins.html
Dastorani, M.T., H. Afkhami, 2011. Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert, 16; 39-48.
Deutsch, C.V., A.G. Journel, 1998. GSLIB: Geostatistical Software Library. Second Edition, New York, Oxford University Press.
Diodato, N., 2005. The influence of topographic co-variable on the spatial variability of precipitation over small regions of complex Terrain. J. climatology, 25; 351- 363.
Edossa, D.C., M.S. Babel, A.D. Gupta, 2010. Drought analysis in theAwash river basin, Ethiopia. Water Resour Manage, 24; 1440–1460.
Eivazi, M., A. Mosaedi, 2011. Monitoring and Spatial Analysis of Meteorological Drought in Golestan Province using Geostatistical Methods. Iranian Journal of Natural Resources, 64; 78-65.
Goovaerts, P., 2009. AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences. Computers & Geosciences, 35; 1255-1270.
Goovaerts, P., 1999. Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89; 1-46.
Jansen, M., V. Stuber, H. Wachter, R. Schulz, W. Schmidt, J. Saborowski, V. Mues, C. Eberl, B. Sloboda, 2002. Modeling of forest growth areas in Lower Saxony. In Spatial Modelling in Forest Ecology and Management a Case Study, Springer.
Jeyaseelan, A.T., 1999. Droughts & Floods Assessment and Monitoring Using Remote Sensing and GIS. Crop Inventory and Drought Assessment Division, National Remote Sensing Agency, Department of Space, Govt. of India, Hyderabad, pp. 291-313.
Khalili, D., T. Farnoud, H. Jamshidi, A.A. Kamgar- Haghighi, S. Zand-Parsa, 2011. Comparability Analyses of the SPI and ZSI Meteorological Drought Indices in Different Climatic Zones. Water Resour Manage, 25; 1737–1757.
Loukas, A., L. Vasiliades, 2004. Probabilistic analysis of drought spatiotemporal characteristics in Thessaly region, Greece. Natural Hazards and Earth System Science, 4; 719-731.
Livada, I., V.D. Assimakopoulos, 2007. Spatial and temporal analysis of drought in Greece using the StandaZSIzed Precipitation Index (SPI). Theoretical and Applied Climatology, 89(3-4); 143-153.
Matheron, G., 1971. The Theory of Regionalized Variables and Its Applications. Les Cahiers du Centre de Morphologie Mathematique. Ecole des Mines: Fountainbleau, France.
McKee T.B., N.J. Doeskin, J. Kleist, 1993. The relationship of drought frequency and duration to time scales. 8th Conference on Applied Climatology, Anaheim, CA. American Meteorological Society. Boston, MA, PP. 179-184.
Morid, S., V. Smakhtin, M. Moghaddasi, 2006. Comparison of seven meteorological indices for drought monitoring in Iran. International Journal of Climatology, 26; 971- 985.
Naserzadeh M.H, E. Ahmadi, 2012. Study of meteorological drought index in drought monitoring and mapping in Qazvin province. Geo. Sc. Ap. Res. J., 27; 141-162.
Ninyerola, M., X. Pons, J.M. Roure, 2000. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. International Journal of Climatology, 20; 1823–1841.
Nohegar, N., M. Heydarzadeh, A. Malekian, 2013. Assessment of Severity of Droughts Using Geostatistics Method, (Case Study: Southern Iran). Desert, 18; 79-87.
Pandey R.P., A. Pandey, R.V. Galkate, H.R.  Byun, B.C. Mal, 2010. Integrating hydro-meteorological and physiographic factors for assessment of  vulnerability to drought. Water Resour Manage, 24; 4199–4217.
Price, D.T., D.W. Mckenny, I.A. Nedler, M.F. Hutchinson, J.L. Kesteven, 2000. A comparison of two statistical methods for interpolation. Canadian monthly mean climate data. Agric. For. Metrol. 101; 81-94.
Rossi, G., T. Vega, B. Bonaccoso, 2007. Methods and tools for drought analysis and management. Sprin Pub., p. 47.
Rusoo, F., F. Napolitano, E. Gorgueei, 2005. Rainfall monitoring system over an urban area: The city of Rom. Hydrological processes, 19; 1007-1019.
Shahabfar A.R, J. Eitzinger, 2013. Spatio-Temporal Analysis of Droughts in Semi-Arid Regions by Using Meteorological Drought Indices. Atm. 4; 94-112.
Shepard, D., 1968. A two-dimensional interpolation functions for irregularly-spaced data. Proc. 23rd Association for Computing Machinery National Conf., Princeton, NJ, Association of Computing Machinery, 517–524.
Smakhtin, V.U., D.A. Hughes, 2007. Automated estimation and analyses of drought characteristics from monthly rainfall data. Environmental Modelling and Software, 22(6); 880-890.
Svoboda, M., 2004. Personal communication, National Drought Mitigation Center, USA.
Tsakiris, G., H. Vangelis, 2005. Establishing a drought index incorporating evapotranspiration. Eur Water, 9-10; 1-9.
Vasiliades, L., A. Loukas, N. Liberis, 2010. A water balanced derived drought index for Pinios River Basin, Greece. Water Resour Manage, Doi; 10.1007/s11269-010-9665-1.
Vangelis, H., M. Spiliotis, G. Tsakiris, 2010. Drought severity assessment based on bivariate probability analysis. Water Resour Manage, Doi; 10.1007/s11269-010-9704-y.
Ver Hoef, J.M., 1993. Universal kriging for ecological data. In Environmental Modelling with GIS, Goodchild MF, Parks BO, Steyaert LT (eds). Oxford University Press: New York, 447–453.
Wamwling, A., 2003. Accuracy of geostatistical prediction of yearly precipitation in Lower Saxony. Journal of Environmetrics, 14(7); 699-709.
Wilhite, D.A., 2000. Drought as a natural hazard: Concepts and definitions. Chapter 1, in D. A. Wilhite (ed.), Drought: A Global Assessment, Natural Hazards and Disasters Series, Routledge Publishers, U.K.
Wilhite, D.A., M.H. Glantz, 1985. Understanding the drought phenomenon: the role of definitions. Water International, 10; 111-120.
Zarei, R., M. Sarajian, S. Bazgeer, 2013. Monitoring Meteorological Drought in Iran Using Remote Sensing and Drought Indices. Desert, 18; 89-97.
Zheng, X., R. Basher, 1995. Thin-Plate Smoothing Spline Modeling of spatial climate data and its application to mapping South Pacific Rainfalls. Journal of Monthly Weather Review, 123; 3086-3102.