for the period of 2002–2016 in the form of surface, underground, and ice and snow data collected by the Gravity Recovery and Climate Experiment (GRACE) mission."/>
Figure 1.9 Global map of annual water storage change for the period of 2002–2016 in the form of surface, underground, and ice and snow data collected by the Gravity Recovery and Climate Experiment (GRACE) mission.
(Courtesy of NASA’s earth observatory: https://earthobservatory.nasa.gov/images)
Nonetheless, a major limitation of GRACE when it comes to its application for drought monitoring is its monthly observations of TWS change. In addition, the derived GRACE‐TWS changes are limited to 17 years, which is insufficient for capturing climatological characteristics and drought analysis. Therefore, attempts have been made to reconstruct a longer time series of groundwater data utilizing both measurements in situ and statistical approaches such as artificial neural networks (Mohanty et al., 2015). To obtain higher spatial resolution data, GRACE observations can be assimilated into land surface models such as the CLSM (Koster et al., 2000) and the GRACE data assimilation system (GRACE‐DAS; Zaitchik et al., 2008).
1.3. MULTI‐INDICATOR DROUGHT MODELING
Although single drought indices can isolate a variable from a hydrological process and exploit its key characteristic (e.g., SPI enables detection of onset of drought), multiple and composite indices offer an opportunity to systematically capture critical hydrological variables in development of drought (Vicente‐Serrano et al., 2010). Indices such as the PDSI, and the Surface Water Supply Index (SWSI; Shafer & Dezman, 1982) were proposed to address drought in a context that incorporates meteorological, hydrological, and agricultural drought categories. Several bivariate indices have also been proposed that statistically describe the joint behavior of different categories of drought by means of Copula theory (e.g. Shojaeezadeh et al., 2018, 2019), such as the Joint Drought Index (JDI; Kao & Govindaraju, 2010) and Standardized Precipitation–Streamflow Index (SPSI; Modaresi Rad et al., 2017) that combine meteorological and hydrological droughts, or the Multivariate Standardized Drought Index (MSDI; Hao & AghaKouchak, 2013; Figure 1.10) that combines meteorological and agricultural droughts. The MSDI requires precipitation and soil moisture data and is used by the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) for monitoring agro‐meteorological drought (Hao et al., 2014). The Process‐based Accumulated Drought Index (PADI) was proposed in a multisensor integrated methodology called Evolution Process‐based Multi‐sensor Collaboration (EPMC) to quantify impacts of drought on crop production (Zhang et al., 2017). The advantage of the EPMC framework is that it is based on both crop phenology and drought development. The EPMC framework obtains moisture data from Global Land Data Assimilation System version 2 (GLDAS‐2.0), vegetation condition data from the AVHRR, and precipitation data from the Global Precipitation Climatology Center (GPCC). The PADI calculations can be provided on a weekly basis and it provides a new approach to monitor and assess agricultural drought.
Recent studies emphasize the importance of considering compounding effects of different extremes on the development of megahazards (Ashraf et al., 2018; Mazdiyasni & AghaKouchak, 2015; Moftakhari et al., 2017). Sadegh et al. (2018) proposed a framework for assessment of multiple designed scenarios of compound extreme events 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
