or semiarid regions. The United Nations Environment Program reported that the European heatwave in 2003 was the world’s most costly weather disaster (Mazdiyasni & AghaKouchak, 2015). During 2003, multiple European countries faced an unprecedented heatwave that increased ozone concentrations and imposed substantial health‐related issues on the population (Poumadere et al., 2005).
In a recent study by Miralles et al. (2014), persistent atmospheric pressure patterns were found to have caused land–atmosphere feedbacks leading to extreme temperatures and megaheatwaves in the summers of both 2003 in France and 2010 in Russia (Figure 1.11). The process can be described in two parts: (a) during daytime where the heat is provided by a large‐scale horizontal advection that warms both the desiccated land surface and atmospheric boundary layer, and (b) during nighttime when the heat produced during the day is entrapped in the atmospheric layer high above waiting to reenter the atmospheric boundary layer following the next diurnal cycle. Given that the process could continue for several consecutive days, Miralles et al. (2014) suggested that this combination of multiday memory of land surface and atmospheric boundary layer could explain the occurrence of megaheatwaves. Hirschi et al. (2011) established a relationship between soil moisture deficit and hot summer extremes in southern Europe using quantile regression and found a higher correlation for the high end of the distribution of temperature extremes. The relationship between soil moisture deficit and hot summer extremes, therefore, can be used as an early warning tool for extreme heatwaves and associated drought. As soil moisture availability is lowered, sensible heat flux causes atmospheric heating while evaporative cooling is reduced. This affects the energy balance and can be used as an early warning for monitoring hot extremes and flash droughts caused by heatwaves. In a study by Mueller and Seneviratne (2012), it was found that hot extremes are often followed by a surface moisture deficit globally. Their results from quantile regressions indicated that both low and high number of hot days (NHD; number of days that the maximum temperature exceeds the 90th percentile) per month occur following a dry condition whereas, wet conditions occur prior to low NHD.
Figure 1.11 Temperature anomalies by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. (a) Compound heatwave and drought hazards in Russia during summer of 2010. (b) The unprecedented heatwave in Australia between 7 and 14 February 2017.
(Courtesy of NASA’s earth observatory: https://earthobservatory.nasa.gov/images)
Heatwaves, especially in Europe, are usually caused by two feedback mechanisms of high sensible heat emissions and upper‐air anticyclonic circulations, with the latter having more drastic effects (Cassou et al., 2005). Studies over Europe suggest that it is possible to have hot summers succeeding a normal or even wet winter and spring conditions, if the land surfaces are desiccated. The desiccated land surface in the Mediterranean region forms a dry air that diminishes clouds and reduces convection and the dry air is transported to the north by a southerly wind, where it dramatically increases temperature and ultimately evapotranspiration demand of vegetation. Rossby wave trains arising from sea surface temperature anomalies in the tropical Atlantic 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.
1.5. REMAINING CHALLENGES AND OPPORTUNITIES
The number of different satellite sensors observing our planet is ever increasing and as a result, better resolutions and different physical variables can be obtained. Some of these satellite missions appropriate for monitoring drought related variables that recently have been launched include ECOSTRESS, GPM, GOES R series, SMAP, Ice Cloud and Land Elevation Satellite 2 (ICESat‐2), and GRACE Follow‐On, and some are planned for launch in the near future such as Surface Water and Ocean Topography (SWOT), Landsat 9, Biomass, and FLuorescence EXplorer (FLEX). Initial challenges associated with the launch of new satellites, however, include inconsistencies in observations due to sensor changes, continuity of data, unforeseen uncertainties, data maintenance, and community acceptability. Among them, data continuity represents a great challenge both in terms of cost and time. Since satellite missions are often expected to have a life span of near a decade and an equal amount of time is required for the design of a new satellite, preparations must be made to ensure continuity of data through follow‐up missions. This is particularly essential for drought monitoring purposes, as over 30 years of data are required for drought monitoring and this often surpasses the ideal operational lifetime (e.g., a decade) of most satellites. Some of these follow‐up missions include the series of Landsat missions, ESA’s Sentinels, NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) and GPM, GRACE Follow‐On, and GOES‐R. The process of reconstructing the time series introduces another source of uncertainty to drought modeling. To provide an estimate of uncertainty associated with remote sensing products, a number of different statistical techniques can be used, including data assimilation (Massari et al., 2015), triple collocation analysis (TCA; W. B. Anderson et al., 2012), generalized triple collocation analysis (GCA; Dong & Crow, 2017), and spectrum analysis (Kumar et al., 2018). W. B. Anderson et al. (2012) used TCA to estimate the total observation error variance of the combined three different soil moisture products: thermal remote sensing by atmosphere‐land exchange inverse (ALEXI), microwave AMSR‐E, and simulations from physically based models. The TCA was validated for the 2010–2011 Horn of Africa drought and showed promising results.
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
