of peak SWE, however, many studies have shown that the peak SWE happens at different times. Margulis et al. (2016) showed that the assumption of 1st April peak SWE can lead to a significant underestimation of peak SWE. They also highlighted the role of elevation and interannual variability of peak SWE in the Sierra Nevada (California). Snow models and observations in situ are complementary tools that can be used in conjunction with remote sensing to accurately estimate the peak SWE and the date of peak SWE.
Although application of snow‐based drought indices for drought monitoring by remote sensing has been increased recently (Knowles et al., 2017; Sadegh, Love, et al., 2017; Staudinger et al., 2014), the majority of research incorporates satellite observations of snow data into land‐surface and climate models (He et al., 2011; Kumar et al. 2014, Margulis et al., 2006, 2016). Global drought models based on snow are primarily challenged by the time lag between occurrences of precipitation as snow and changes in ground and surface waters that could vary between weeks to months depending on catchment characteristics and climate (Aghakouchak, Farahmand, et al., 2015; Van Loon & Van Lanen, 2012). As a final note, interested readers are encouraged to explore the different snow drought tools available online at (https://www.drought.gov/drought/data‐maps‐tools/snow‐drought).
1.2.6. Groundwater
Prolonged meteorological droughts can severely affect groundwater levels and the problem is further exacerbated if it is followed by an anthropogenic drought (AghaKouchak, Feldman, et al., 2015; Alborzi et al. 2018). A decrease in groundwater recharge results in lower groundwater discharge and storage, a condition that is defined as a groundwater drought (Mishra & Singh, 2010). The lack of any imposed restriction for groundwater abstraction enhances hydrological drought, which is often overlooked due to poor understanding of hydrological cycle relations (Van Loon et al., 2016). The overuse of groundwater due to anthropogenic influences not only magnifies the drought condition, but also can cause permanent damage such as decreases in groundwater storage capacity and subsequent land subsidence (Famiglietti et al., 2011; Faunt et al., 2015; Taravatrooy et al. 2018). The lack of continuous spatiotemporal measurements of groundwater levels at a groundwater monitoring station (well) makes it difficult to characterize groundwater drought; however, with the launch of the GRACE satellites it has become possible to study the dynamics of water storages at a global scale (Wahr et al., 2006). The GRACE (2002–2017) and GRACE Follow‐On (2018 to present) satellites monitor changes in water storage compring groundwater, surface water reservoir, soil moisture, and snow water storage components.
The GRACE missions provide global changes in total water storage by converting gravity anomalies into changes of water equivalent height (Rodell & Famiglietti, 2002; Figure 1.9). The observed terrestrial water storage (TWS) from GRACE has spatial resolution of 150,000 km2 per grid that cannot be used for regional assessments; however, downscaling techniques are alternatives for obtaining data with finer resolution (Zaitchik et al., 2008). Recent studies are more focused on developing the Mass Concentration blocks (mascons) approach that fits intersatellite ranging observations from GRACE, unlike the previously applied standard spherical harmonic approach (Watkins et al., 2015). The mascons approach smoothes the process of implementing geophysical constraints that help filter out the noise. Studies by University of Texas Center for Space Research (Save et al., 2012), Goddard Space Flight Center (GSFC; Luthcke et al., 2013), and JPL (Watkins et al., 2015) have shown that mascons can present higher spatial resolution with lower uncertainties. Several studies evaluated the applicability of GRACE‐TWS changes for analyzing and monitoring drought (Famiglietti et al., 2011; Scanlon et al., 2012; Thomas et al., 2017). An example is the Standardized Groundwater Index (SGI), a quantile‐based index with values bounded between 0 and 1 and values above and below 0.5 indicate wet and dry conditions, respectively (Bloomfield & Marchant, 2013). A threshold of 0.2 identifies the onset of drought and a sustained drought of greater severity would be indicated by SGI values below that threshold. Li and Rodell (2015) introduced the Groundwater Drought Index (GWI), which is able to detect and monitor groundwater deficits by means of outputs from a Catchment Land Surface Model (CLSM). The GRACE Groundwater Drought Index (GGDI) was developed to evaluate California’s Central Valley groundwater drought using GRACE‐TWS observations (Thomas et al., 2017). It was found that GGDI is highly correlated to GWI, which uses measurements of groundwater level in situ as an input, suggesting that the groundwater storage anomalies obtained from GRACE can be used as valid input for groundwater drought indices.
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