on 29 June 2018 to the International Space Station (ISS) that provides ET estimates. This product has a spatial resolution of ~70 m and temporal resolution of approximately 3 days and is variable depending on ISS. Different ECOSTRESS data products are available for download through the United States Geological Survey (USGS) satellite image query tool (https://earthexplorer.usgs.gov/).
Figure 1.6 Three‐month Standardized Precipitation Evapotranspiration Index (SPEI) with 1° spatial resolution. (a) Global view of SPEI for November 2018. (b) Time series of SPEI for 9.25° E and 31.25° S in Africa.
(Source: The Standardized Precipitation Evapotranspiration Index (SPEI), http://spei.csic.es/map/maps.html)
1.2.5. Snow
Snow and ice are key components of the hydrological cycle. The lack of snow and ice storage in the snow‐dominated regions significantly impacts the availability of water throughout the dry seasons, and influences reservoir operation, flood risk management, recreation, tourism, energy production, navigation, and river ecology (Staudinger et al., 2014). Therefore, snow shortage in snow‐dominated mountain watersheds drives a range of adverse economic and social outcomes. Studies suggest that the occurrence of earlier peak discharge in western United States due to a warmer climate results in increased periods of summer water stress, which can in turn change forest structure (Harpold et al., 2014; Harpold, 2016). The continuous changes in the climate of snow‐dominated watersheds (i.e., less snow, more rain, and earlier snowmelt) motived researchers to introduce the concept of snow drought (Hatchett & McEvoy, 2018). Only a little research has been undertaken, however, with respect to developing a snow‐based indicator of drought. Currently, there is no generally accepted classification scheme for snow droughts. Three key metrics, the peak snow water equivalent (SWE), the date of peak SWE (DPS), and the snow disappearance date (SDD), however, have been used to characterize snow drought in the mountainous watersheds. The concept of snow drought can be defined as below‐average SWE at approximately when the maximum SWE typically occurs (Hatchett & McEvoy, 2018). Different definitions of snow drought have been proposed throughout the literature, including Van Loon and Van Lanen (2012) and Harpold et al. (2017).
Figure 1.7 Evaporative Stress Index (ESI) derived from observations of land surface temperatures and leaf area index from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar‐orbiting Partnership (NPP). (a) The drought in New England that put crops and businesses under stress. (b) The drought that reduced food production and increased famine in the Greater Horn of Africa.
(Courtesy: NASA’s earth observatory: https://earthobservatory.nasa.gov/images)
Van Loon and Van Lanen (2012) described different scenarios of snow related drought according to their development: (a) rain‐to‐snow season drought, (b) cold snow season drought, and (c) warm snow season drought. Rain‐to‐snow season drought is developed due to a shortage of rainfall in the rain season (spring, summer and/or autumn) and ends in snow season (winter) with precipitation being in the form of snow. Consequently, soil moisture, streamflow, and groundwater remain relatively low until the upcoming melt season. Cold snow season drought is a result of abnormally low temperature in the snow season and a possible coincidence with below‐average precipitation that can be categorized into three subtypes of A, B, and C. Subtype A describes climates with continuous snow cover during winter and below zero temperature. Early beginning of the snow season is the main driver of this drought type. Subtype B has the same climate as A, however, delay in snowmelt due to low temperature at the end of winter drives this type of snow drought. Subtype C is climate with a temperature around zero and limited snow accumulation in winter. Snowmelt often provides recharge to groundwater and streamflow during snow season. An abnormal temperature drop in winter results in an intermediate shortage of water for a few weeks to months duration.
Harpold et al. (2017) divided snow drought into two categories: (a) warm snow drought, where accumulated precipitation during October–March is larger than the long‐term average and SWE on 1st April is less than the long‐term average; (b) dry snow drought, where accumulated precipitation for the same period is less than the long‐term average and SWE on 1st April is less than the long‐term average SWE.
In addition to the conceptual definition of snow drought, operational systems require relevant indicators to monitor snow drought. These indicators should provide insight on frequency, severity, and duration of snow drought and help to develop prevention and mitigation strategies (Paneque, 2015). Indices such as SPI or PDSI are widely used to characterize hydrological droughts, however, these indices do not explicitly account for the effects of snow on water availability in snow‐dominated watersheds (Mote et al., 2016). Staudinger et al. (2014) proposed a drought index that would account for snow. This new index, termed Standardized Snow Melt and Rain Index (SMRI), is calculated similar to the SPI but uses summation of snowmelt and rain as input. The authors proposed an algorithm based on temperature threshold that does not require snow data and utilizes temperature and precipitation to model snow. It should be noted that the output of any snow model could be used to calculate the index. Knowles et al. (2017) developed a snow aridity index (SAI) to assess ecosystem disturbance based on a long history of snow remote sensing. The SAI is defined as a ratio of the sum of 1st April to 31st August PET to maximum SWE (PET/SWE; Figure 1.8). It can be argued that SAI is a suitable index for characterizing snow drought since it uses both potential evapotranspiration and SWE (Knowles et al., 2017, 2018).
Snowpack is often characterized in terms of snow albedo (SA), snow depth (SD), SWE, DPS, snow covered area (SCA), and fractional snow‐covered area (fSCA) (Kongoli et al., 2012). Remote sensing can effectively describe the relationship between snowpack dynamics and climate variability (Guan et al., 2012). Using remote sensing techniques and retrieval algorithms to measure snow‐related variables may provide insight for real‐time snow drought monitoring. The following provides a very short review of different remote sensing data and products that can be used to characterize snowpack.
Snow possesses a strong spectral gradient that ranges from high albedo in visible wavelengths to low reflectance in middle infrared wavelengths. Therefore, a commonly used method such as the band ratios can be utilized to map and monitor snow cover (Lettenmaier et al., 2015). The Normalized Difference Snow Index (NDSI) is one that shows the presence of snow on the ground. The NDSI algorithm distinguishes between snow and most cloud types, therefore, it better characterizes the snow cover areas than fSCA. The NDSI utilizes the reflectance ratios to detect snow and is described as the normalized difference between green and SWIR reflectance (R Green− R SWIR2)/(R Green + R SWIR2) (Hall et al., 2002). Hatchett and McEvoy (2018) suggested using NDSI in conjunction with data from ground‐based observation networks to monitor snow drought. In forested regions, however, the NDSI has shown poor snow identification accuracy and the recently developed Normalized Difference Forest Snow Index (NDFSI) can
