Global Drought and Flood. Группа авторов

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Global Drought and Flood - Группа авторов


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are an example of anticyclonic circulations that result in heatwaves and droughts (Cassou et al., 2005). Ferranti and Viterbo (2006) argued that the formation of desiccated soil reduces energy evaporated as latent heat while increasing sensible heat, which in turn enhances the ratio of sensible over latent heat fluxes. Accordingly, the dry soil increases the thickness of the lower layer of the troposphere that favors the development of anticyclonic circulation anomalies. Warmer sea surface temperatures in the Mediterranean Sea also contributes to development of anticyclonic circulations; nevertheless, soil moisture content at the beginning of summer is the major determining factor for development of concurrent summer heatwaves and flash droughts (Feudale & Shukla, 2007; Zampieri et al., 2009). The concurrence of heatwaves and droughts has yet to be fully explored and further global scale studies are required for developing appropriate strategies to mitigate drought‐related losses.

      When it comes to data processing and analysis of satellite imageries, different algorithms can help in distinguishing pixels and identifying objects, such as deep learning methods. There are some atmospheric features, however, that act as a barrier for certain optical and infrared satellite instruments and result in data inconsistencies. Optical‐based vegetation indicators are error prone when the area studied has atmospheric effects, cloud cover, aerosols, and water vapor (Andela et al., 2013). Moreover, optical satellite observation only reflects information from the top of the canopy. These problems can be resolved using microwave sensors that provide the opportunity to monitor carbon cycling during drought episodes over the long term. A unique approach would be to combine the vegetation optical depth (VOD; Owe et al., 2001) with optical based methods (i.e., NDVI) for a complementary analysis that considers both canopy top greenness and biomass. Combination of microwave, optical, and lidar observations provides an opportunity to monitor ecosystem response to drought that often continues even after drought recovery (C. D. Allen et al., 2010). Recent studies indicate that some variables such as snow and relative humidity can be integrated into drought monitoring models for improving estimations of drought recovery and detection of its onset, respectively (AghaKouchak et al., 2014; Rott et al., 2010).

      Another challenging issue with remote sensing observations is the process of preserving large historical records, as it requires large and costly infrastructure and help of professional to store these data. Climatic data records can be merged together to produce longer records that would be appropriate for assessment of drought and monitoring (AghaKouchak & Nakhjiri, 2012). For example, several attempts have been made to generate NDVI from observations of multiple satellite missions including AVHRR and MODIS (Beck et al., 2011; Pinzon & Tucker, 2014; Tucker et al., 2005).

      A change in satellite sensors, such as a follow‐up mission, is introduction of a great deal of uncertainty in modeling drought, and these uncertainties are often unquantified (Mehran et al., 2014). Therefore, an ideal way to tackle the problem is to provide uncertainty bounds along with raw observations. This uncertainty and the structural and parameter uncertainty resulting from model‐based simulations can be merged together to help decision making in operational applications (Sadegh, Ragno, et al., 2017). Such models and indicators are now being used more frequently and they quantify the uncertainty associated with satellite observations (AghaKouchak & Mehran, 2013; Entekhabi et al., 2010; Gebremichael, 2010). Therefore, the more remote sensing data are tailored for drought assessment, the more decision makers and drought experts can be engaged with remote sensing data.

      1 Adler, R.F., Huffman, G.J., Chang, A., Ferraro, R., Xie, P.P., Janowiak, J., et al. (2003). The version‐2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4(6), 1147–1167.

      2 AghaKouchak, A., Cheng, L., Mazdiyasni, O., & Farahmand, A. (2014). Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysical Research Letters, 41(24), 8847–8852. https://doi.org/10.1002/2014GL062308

      3 Aghakouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D., & Hain, C.R. (2015). Remote sensing of drought: Progress, challenges, and opportunities.


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