Global Drought and Flood. Группа авторов

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Global Drought and Flood - Группа авторов


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With challenge comes opportunity. The focus of the scientific community should be on merging the information provided from different satellites and sensors, to underpin their uncertainties, and to offer long‐term and consistent data sets for drought analysis.

      Drought detection requires observation of a plethora of different climatic and biophysical variables. Observations in situ, however, do not provide a uniform spatial distribution and are limited to populated areas, hence satellite‐based observations provide a unique way to analyze and monitor drought at a global scale. Satellites offer observations for a wide range of climate variables such as precipitation, soil moisture, temperature, relative humidity, evapotranspiration, vegetation greenness, land‐cover condition, and water storage (Aghakouchak, Farahmand, et al., 2015; R. G. Allen et al., 2007; L. Wang & Qu, 2009; Whitcraft et al., 2015). Although remote sensing provides more opportunities for the scientific community to monitor Earth systems and offer better understanding of drought impact at regional to global scales, it is not without flaws or challenges. The main challenge is the insufficient length of the observed records provided for the variables of interest. Other challenges include data consistency, ease of access, quantifying uncertainty, and development of appropriate drought indices, which will be discussed throughout this chapter.

      This section presents the recent remote sensing techniques used for identification and quantification of drought as characterized by different climatic and biophysical variables.

      1.2.1. Precipitation

      A meteorological drought can be described as precipitation deficiency over a period of time (WMO, 1975), often represented in terms of an index of deviation from normal. Drought indices not only serve the scientific communities but they are also great tools for facilitating the decision‐making and policy‐making processes for stakeholders and managers when compared with the raw data. One of the most widely used and informative meteorological drought indices is the standardized precipitation index (SPI) developed by Mckee et al. (1993). Several other meteorological drought indices have also been proposed, including, but not limited to, precipitation effectiveness (Thornthwaite, 1931), antecedent precipitation (API; McQuigg, 1954), rainfall anomaly (RAI; Van Rooy, 1965), drought area (Bhalme & Mooley, 1980), effective precipitation (Byun & Wilhite, 1999), and rainfall variability indices (Oguntunde et al., 2011). The SPI is currently being used in many national operational and research centers and was recognized as a global measure to characterize meteorological drought by the World Meteorological Organization (WMO, 2009). Computation of SPI requires measured rainfall data and a normalization process of monthly data, either by utilizing an appropriate probability distribution function (PDF) to transform the rainfall PDF (e.g., gamma or Pearson type III probability distribution) into a standard normal distribution (Khalili et al., 2011), or by utilizing a nonparametric approach (Hao & AghaKouchak, 2014). Precipitation deficit can be specified for different timescales (e.g., from 1 to 24 months) when using SPI, where precipitation abnormalities in shorter timescales reflect soil moisture wet/dry conditions and longer timescales portray the wet/dry conditions of subsequent processes such as streamflow, reservoir levels, and ultimately groundwater.

An illustration of a rainfall map by NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite.

      (Courtesy: NASA’s Earth observatory: https://earthobservatory.nasa.gov/images)


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