NDFSI utilizes near‐infrared in place of the green band, which has a higher reflectance that is useful when there is snow in a forested area (X. Y. Wang et al., 2015).
Several satellites are capable of detecting fSCA, such as the AVHRR, MODIS, and Landsat. However, a common issue with AVHRR is the inadequacy of its 1 km spatial resolution for snow mapping on small catchments (Simpson et al., 1998; H. Xu et al., 1993). Rott et al. (2010) suggested that the Cold Regions Hydrology High‐Resolution Observatory (CoReH2O) from the ESA would deliver accurate and spatially detailed observations of snow mass. MODIS and Landsat Thematic Mapper (TM) alleviate this shortcoming to some extent by offering observations with a spatial resolution of 250 m and 30 m, respectively (Hall et al., 2002). A major concern regarding the optical‐based satellites, however, is the discontinuity of observations due to the presence of clouds. Clouds hinder the spatiotemporal consistency of snow cover due to having similar reflectance properties to snow in a wide range of the electromagnetic spectrum (Aghakouchak, Farahmand, et al., 2015). On the other hand, microwave measurements can be used to estimate fSCA and SD even in the presence of clouds, since they do not depend on sunlight reflection. Similar to optical‐based satellites, the microwave observations become flawed once SD exceeds 30 cm and in melting conditions (Foster et al., 1997; Walker & Goodison, 1993). Therefore, more accurate and consistent measurements of SD retrievals can be achieved through an integrated framework by combining observations from both types of satellites (Durand et al., 2008; Foster et al., 2011). In an effort to simulate the spatiotemporal distribution of SWE in mountainous regions, the NASA Jet Propulsion Laboratory (JPL) Airborne Snow Observatory (ASO) provides near‐weekly lidar surveys. The derived SDs obtained from lidar scanners are then assimilated into hydrological models to produce higher temporal resolution of SWE distribution and volume. Recently, Hedrick et al. (2018) combined the iSnobal physically based distributed snowmelt model with ASO and produced daily SWE images with spatial resolution of 50 m.
Figure 1.8 A below‐normal snowpack observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. (a) Percent of fractional snow cover on 25 January 2016. (b) Below normal conditions in 29 January 2018.
(Courtesy of NASA’s earth observatory: https://earthobservatory.nasa.gov/images)
Snow water equivalent is a critical parameter for hydrological applications and the characterization of snowpacks, and is commonly estimated using passive microwave signals utilizing empirical relationships or radiative transfer models. Well‐known limitations of spaceborne passive microwave data, such as coarse spatial resolution, saturation in deep snowpack, and signal loss in wet snow, however, present major drawbacks for passive microwave retrieval algorithms. Brodzik et al. (2016) developed high‐resolution passive microwave brightness temperature data that can be used to improve the SWE estimate in mountainous regions with complex physiography.
Peak SWE is an important variable in snow hydrology, traditionally, 1st April has been set as the date 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.
