LITMIR.BIZ

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.