استادیار اقلیمشناسی، گروه جغرافیا، دانشگاه گلستان، گرگان، ایران
عنوان مقاله [English]
Since the emergence of human civilization, drought has had extreme and sometimes catastrophic effects on human livelihoods. Although drought itself is not a disaster, its impact on people and the environment may sometimes yield disastrous consequences; so a primary requirement is to better understand the natural and social dimensions associated with drought (Wilhite 2000). Given that drought is a gradually developing natural phenomena, problems regularly arise when establishing drought start and end dates, as also the spatial extent of drought owing to the complex nature of drought and also the difficulty of separating ‘dry periods’ with ‘drought periods’. Given the importance of drought forecasting and classification to reduce associated risks, many efforts have been undertaken over the years to calculate and understand all aspects of drought. For example Palmer (1965) was first to initiate statistical methods (in 1946) for establishing drought occurrence using rainfall, temperature and soil moisture parameters or recently, the Standardized Precipitation Index (SPI) has become a popular and widely used indicator of drought owing to its easy computation and flexibility across spatial and temporal scales .Droughts are an annual concern to Iran, seriously affecting agriculture, water resources and ecosystems in one or more region(s). Iran is exceptionally water scarce; for instance, in 2002 approximately eight million hectares of agricultural land suffered from drought, causing revenue loss amounting to millions of US Dollars (Darvishi et al. 2008). Thus, considerable scientific efforts have been made to categorize and monitor drought in the region.
The TOPSIS index has previously been used to assess drought/wetness conditions in Iran, but only using a few parameters (mean annual wind speed [km/h], total annual precipitation [mm], mean annual temperature [˚C], and number of rain days) (Koshakhlagh et al. 2008, Roshan et al. 2012). In other cases, parameters used to calculate a drought index have not been validated for their accuracy (e.g. Kazemi Rad et al. 2012). To this end, and for the first time, we use a combination of climate/environmental parameters which are entered into the TOPSIS Algorithm; years are then ranked statistically for Golestan Province Weather Stations (Iran) based on dry/wet conditions. The focus of this paper is to: 1) present the TOPSIS computational method, 2) demonstrate the step-wise sequence for calculating and ranking the drought index using the method of similarity to ideal solution (TOPSIS), and 3) validate the TOPSIS method through calculating drought/wetness values for four stations in Golestan Province weather station using more conventional methods (i.e. PNPI, SPI, BMDI and RِِDI) for calculating drought intensity and finally zoning Golestan Province on base of TOPSIS index.
TOPSIS, which is one of the multi-criteria decision-making methods, was first presented by Hwang and Yoon (1981) and soon received global interest for numerous scientific applications as wide ranging as the aeronautical industry (Wang and Chang 2007), engineering risk assessment (Wang and Elhag 2006) and decision making in management (Antuchevičiené et al. 2010).TOPSIS is a multi-criteria method to identify solutions from a finite set of alternatives. The basic principle is that the chosen alternative should have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution (Chen et al. 2011).
Using this method, we use seven single and combined climatological parameters which are applied to the years 1971 to 2011, with data obtained from the Golestan province weather stations. The parameters include average rainfall, number of days with precipitation, effective precipitation based on the method of land reclamation of America (U.S Bureau of Reclamation Method [USBR]), the ratio of highest daily precipitation to total monthly precipitation, evapotranspiration (Torrent White Method), and maximum/minimum temperature. The index has no temporal-scale limitations and may thus be applicable to scales ranging from days to seasons.
To validate the outputs, values for four stations were compared to four customary drought indices (PNPI, RDI, BMDI, SPI), and correlated well with these overall (r = 0.9), thus confirming the high reliability of the TOPSIS algorithm. However, the TOPSIS method has a distinct advantage over other methods as it considers important variables influencing wetness that the other methods have not incorporated into their models, hence also some of the differences in output results between TOPSIS and the other methods. A further advantage of TOPSIS is that the climatic variables required are available for most stations, or alternatively, variables such as evapotranspiration or effective rainfall can easily be calculated using simple experimental calculations. In contrast, other reliable methods frequently used, such as the Palmer method, are spatially limited in their application as these rely on less readily available data, such as for instance soil moisture. The results obtained from the TOPSIS algorithm are thus relatively consistent with those from other methods, yet TOPSIS offers some distinct advantages and should thus be considered as a reliable future application tool for establishing dry/wet conditions and trends.