where weighted average of these possible events was used to derive threshold quantiles. The development of multi‐index drought monitoring indices has also enhanced the prediction of drought onset, development, and termination. The Vegetation Drought Response Index (VegDRI; Brown et al., 2008; Tadesse et al., 2005) uses the satellite‐based observations of vegetation conditions, climate‐based drought indices, and other biophysical information to represent drought effects on vegetation health. Note that vegetation stress derived from the Normalized Difference Vegetation Index (NDVI; Rouse et al., 1974) could be associated with other natural causes such as flooding, pest infestation, fire, etc. The United States Drought Monitor (USDM; Svoboda et al., 2002) uses measurements in situ, satellite‐based indices such as the Vegetation Health Index (VHI), ESI, VegDRI and, GRACE TWS along with expert opinion to produce maps of drought conditions on a weekly basis. The recently developed Composite Drought Index (CDI) can represent unique characteristics of drought in three categories: meteorological, hydrological, and agricultural (Waseem et al., 2015). The CDI utilizes measurements of precipitation and streamflow made in situ, along with land surface temperature and a NDVI derived from MODIS.
Figure 1.10 Near real‐time drought monitoring and prediction system by the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) using the Multivariate Standardized Drought Index (MSDI) for February 2016 based on the Modern‐Era Retrospective analysis for Research and Applications (MERRA) data set. D0 indicates abnormally dry; D1 moderate drought; D2 severe drought; D3 extreme drought; D4 exceptional drought; and the same applies to the wetness (W) scale.
Other examples of composite drought indices based on retrieved satellite observations include SDCI (Rhee et al., 2010) and MIDI (Zhang & Jia, 2013). The SDCI merges TRMM‐based precipitation data with land surface temperature (LST) and NVDI, and was proposed for agricultural drought monitoring purposes. In this approach, the value of each component is scaled between 0 and 1 and different weights are assigned to each of the components (SDCI = αLST + βTRMM + γNDVI, α + β + γ = 1). Rhee et al. (2010) demonstrated that over both arid and humid/subhumid regions, SDCI is a more accurate tool compared to NDVI and VHI (Kogan, 1995) for agricultural drought monitoring. Likewise, the MIDI was proposed for monitoring short‐term meteorological droughts (Zhang & Jia, 2013). The MIDI combines TRMM‐based precipitation data (in the form of the Precipitation Condition Index; PCI) with LST (in the form of the Temperature Condition Index; TCI) and soil moisture (in the form of the Soil Moisture Condition Index; SMCI) obtained from AMSR‐E (MIDI = αPCI + βSMCI + (1 − α − β)TCI). These composite drought indices unlike the Copula‐based methods are suitable for combining drought indicators that are not highly correlated with each other.
1.4. DROUGHT AND HEATWAVES FEEDBACKS
The occurrence of flash droughts that are caused by heatwaves, unlike those due to the lack of precipitation, often cannot be monitored properly, and hence early warning systems fail to prevent losses. High temperatures associated with heatwaves reduce soil moisture and increase ET, thereby having a direct impact on the agricultural sector (Mo & Lettenmaier, 2015). The Risk Management Agency of the United States Department of Agriculture (USDA; https://www.rma.usda.gov/data/sob.html) reports that livestock stress due to withering of crops sustains economic losses that are billions of dollars. Heatwaves have also contributed to a decrease in efficiency of power plants (Zamuda et al., 2013), an increase in air pollution and therefore proliferating mortality, respiratory and cardiovascular morbidity (Poumadere et al., 2005), an increase in intensity, duration, and size of wildfires that takes a toll on the economy in several ways (Zamuda et al., 2013). A sequence of multiple extreme climate events can cause catastrophic disasters and are recognized by the Intergovernmental Panel on Climate Change (IPCC) as compound events (Leonard et al., 2014). Chiang et al. (2018), utilizing historical observations from Climate Research Unit (CRU), detected that in southern and northeastern United States, warming rates associated with droughts have been rising faster than average climate. They found, however, that the accelerated warming associated with droughts does not hold for arid 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