Drought and Global Climate Change

Drought and Global Climate Change

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Drought is a multi-causal and complex environmental issue, and can have serious socioeconomic consequences. Recently, IPCC (the Intergovernmental Panel on Climate Change) in Fourth Assessment Report (AR4) concluded that South Asia and the Middle East would experience sever, prolonged droughts as a result of global climate changes, explicitly the increase in greenhouse gases in the atmosphere (IPCC, 2007). Drought is a weather-related natural disaster whose effect is aggravated by human activities. Sometime drought affects large regions and even several countries for a long period of time. Drought has a serious impact on food productivity of a land, and even on the life expectancy of inhabitants. The aftermath of drought involves in socioeconomic, ecological issues (WGA, 1996) (Jeyaseelan, 2005; Pongracza et al., 1996). Iran encompassing drylands has been periodically jeopardized by drought events, which have devastatingly affected society and environment (Shamsipour et al., 2008). Therefore, the study of drought needs several sources of datasets. In other words, the design of a planning project for a region for sustainable development, the acquisition of updated data is critical, particularly for countries with arid to semi-arid climates.
The recent innovations in remote sensing methods have brought new solutions to study of environmental problems in geosciences. In the assessment of natural hazards like drought, remote sensing provides rapid, instant spatial data about the natural phenomena; they are useful in decision-makings as well as weather forecasts (Sunyurp et al., 2004). The monitoring of drought via remote sensing depends on the factors that cause drought (Jeyaseelan, 2005). Drought indicators and variables, obtained through remotely sensed data, can carry some uncertainties, which is induces by the sensitivity of factors, or their dependency to weather and environment conditions. Additionally, some non-standard algorithms might lead to wrong estimation of drought intensity.
More effective methods for increase accuracy of assessment and analysis of remotely sensed data are applying models which can combine in data layers. Geographic Information Systems (GIS) are used to combine the layers of data in the modeling of drought. Recently, space technologies, such RS and GIS, and the numerical modeling techniques have been developed as powerful tools for the ecological assessment of environment (Krivtsov, 2004; MacMillan et al., 2004; Store and Jokimäki, 2003). Utilizing these technologies not only supply a platform to support multi-level and hierarchically integrated analysis on resource and environment, but also integrate the obtained information in a comparative theoretical ecosystem analysis. Meanwhile, Plummer (2000) argued that perspectives of combining ecological models and remotely sensed data would focus on the estimation of accuracy, the issues of spatial and temporal scale, and long-term comprehensive datasets.

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(Ainong et al., 2006).
The main purpose of this study is to reduce errors and to improve efficiency and accuracy in the estimation of drought by remote sensing indices and the application of multivariate models. Therefore, this study introduces a new methodology for incorporating satellite data into an integrated fuzzy and AHP approach in order to model the accurate predicting of drought.
Fuzzy modeling
Fuzzy model is a logic system based on fuzzy set theory to approximation and reasoning of a system (Zadeh 1965, 1975). In other words, a fuzzy model provides a basis for reasoning, controlling and decision making of systems, even in uncertain conditions (Mahonen and Frantti, 2000). Fuzzy set theory deals with linguistic variables, which are converted from quantitative and qualitative variables. A fuzzy set includes the sets of adjectives of linguistic variables. Each fuzzy set is defined through a membership function (MF), which maps the input variable into fuzzy output. As a result, the fuzzy value indicates a degree of trueness, taking on from complete exclusion (0) to absolute inclusion (1). In other words, the membership value represents a certain degree of belonging of an object to a fuzzy set.
In recent years, there are a lot of successful fuzzy models such as hydrology model Chen (1997), multi-objective fuzzy optimum selection (Chen, 1994), fuzzy pattern recognition and fuzzy decision making.

The main reason of application of fuzzy model is the pre-existing and inherent uncertainty in data. In drought studies, the paucity of data and the unqualified data are restrictions. The acquisition of NOAA –AVHRR images faces with many limiting factors, such as uncertainty in the estimated spectral radiance and corruption in the sensors. Moreover, the spatial resolution of satellite images produces some certainties, namely, the effect of mixed pixels (Shalan et al, 2003).

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