moisture wet/dry conditions and longer timescales portray the wet/dry conditions of subsequent processes such as streamflow, reservoir levels, and ultimately groundwater.
Since the root cause of droughts is deficit in precipitation, meteorological drought indices, and in particular SPI, are suitable indices for revealing the onset of drought (Hao & Aghakouchak, 2013). Indeed, precipitation is regarded as a key component in drought analysis. Clustering approaches have been used as a common practice to identify spatially homogeneous drought areas by utilizing meteorological drought indices such as SPI (Santos et al., 2010). Assessment of temporal variability of metrological drought utilizing SPI, however, has shown formation of noncoherent clusters in spatiotemporal clustering (Modaresi Rad & Khalili, 2015). This is due to precipitation’s large spatial variability, which creates diverse spatial patterns even at small scales. Considering spatial variability of precipitation is crucial, since a dense and evenly distributed network of gauging stations is required for describing spatiotemporal characteristics of drought. Similarly, ground‐based weather radars also suffer from spatial discontinuity and are error prone due to contamination by surface backscatter, uncertainty of approximation of relation between reflectivity and rain rate, and bright band effects, making them unfeasible for global applications (Kidd et al., 2012; Wolff & Fisher, 2008). As a result, a more robust approach would be to use satellite observations that would produce gridded data as an input not only for drought models, but also for meteorological and hydrological models such as weather research and forecasting (WRF) and variable infiltration capacity (VIC).
Figure 1.1 Rainfall map by NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite. (a) Average rate of rainfall per day for the period of 1998‐2011. (b) A tropical storm in southeast Texas causing record‐breaking floods, produced using the IMERG precipitation product.
(Courtesy: NASA’s Earth observatory: https://earthobservatory.nasa.gov/images)
Visible (VIS) satellite images provide information about cloud thickness and infrared (IR) images provide information on cloud top temperature and cloud height that are used to estimate precipitation rate via different retrieval algorithms (Joyce & Arkin, 1997; Sapiano & Arkin, 2009; Turk et al., 1999). Geostationary (GEO) VIS/IR satellites offer approximately a 15–30 min frequency of observations, but their accuracies are disputed. On the other hand, passive microwave (MW) sensors capture data of hydrometeor signals and scattering signals of raindrops, snow, and ice contents in the lower atmosphere and sense the bulk emission from liquid water, and therefore provide a more accurate estimation of precipitation rate (Behrangi et al., 2014). The MW sensors, however, often face difficulties distinguishing between light rain and clouds and have less frequent overpass (almost two observations per a day). Therefore, it is suggested that a combination of both MW and VIS/IR satellite observations can result in more accurate estimations (Joyce et al., 2004). Currently, a variety of precipitation satellite data sets or products exist, amongst which that of the Tropical Rainfall Measuring Mission (TRMM) has found notable success towards improving the forecast of extreme events (Figure 1.1a). This data set is a joint mission between the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) that advances the understanding of tropical rainfalls over the ocean by providing three‐dimensional images. The mission was launched in 1997 and terminated in 2015, and the project was continued in 2014 by NASA's Goddard Space Flight Center and JAXA as Global Precipitation Measurement (GPM), with a new calibration standard for the rest of the satellite constellation and a core observatory that possessed a Dual‐frequency Precipitation Radar (DPR) and a GPM Microwave Imager (GMI) (Hou et al., 2014). Other satellite precipitation data sets include the Climate Predicting Center (CPC) Morphing Technique (CMORPH; Joyce et al., 2004), CPC Merged Analysis of Precipitation (CMAP; Xie & Arkin, 1997), TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al., 2007), Special Sensor Microwave Imager (SSM/I; Ferraro, 1997), Global Precipitation Climatology Project (GPCP; Adler et al., 2003), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN; Figure 1.2; Ashouri et al., 2015; Hsu et al., 1997; S. Sorooshian et al., 2000), and the new GPM mission known as the Integrated MultisatellitE Retrievals for GPM (IMERG; Figure 1.1b; Huffman et al., 2015).
Figure 1.2 Near real‐time 0.04° precipitation information provided by the Global Water and Development Information (G‐WADI) map server of University California at Irvine using the PERSIANN‐Cloud Classification System (PERSIANN‐CCS).
One of the major challenges associated with satellite precipitation data is measurement or inference uncertainty due to the presence of uncorrected biases (A. Sorooshian et al., 2008). Studies have shown that although TMPA can be used to produce reliable results when driving hydrological models for monthly streamflow simulation, it does not perform well at the daily timescale (Meng et al., 2014). Since precipitation is a key variable in hydrology, the problem with uncertainty is further aggravated if it is left untreated in drought monitoring and hydrological modeling. As a result, several post‐processing techniques have been developed for bias correction (Khajehei et al., 2018; Madadgar & Moradkhani, 2014). For further information regarding the validation process against ground‐based measurements, interested reader is referred to AghaKouchak et al. (2012), Lu et al. (2018), Mateus et al. (2016), Nasrollahi et al. (2013), Y. Tian et al. (2009), and Xu et al. (2017). Another limitation of satellite precipitation data is associated with their short length of record. Drought analysis requires at least a minimum of 30 years of data (Mckee et al., 1993). Therefore, the near‐real‐time satellite precipitation products such as GPCP with nearly 19 years of recorded data cannot single‐handedly be used to develop drought‐monitoring systems. To remedy this shortcoming, near‐real‐time satellite data are combined with the long‐term GPCP to produce the required timespan for drought calculation (AghaKouchak & Nakhjiri, 2012). In their study, AghaKouchak and Nakhjiri (2012) used a merged product of GPCP (1979–2009) and PERSIANN (2010 to the present) in a Bayesian data‐merging framework to produce a near‐real‐time meteorological drought monitoring system using SPI.
1.2.2. Soil Moisture
Agricultural drought is a result of precipitation deficit plus accumulated evapotranspiration over a prolonged period of time that eventually leads to extended periods of low soil moisture that affect crop yields and livestock production (Cunha et al., 2015). Agricultural drought disrupts the chain of supply and demand of agricultural products and contributes to socioeconomic drought (Wilhite & Glantz, 1985). Soil moisture is a key component of agricultural drought and defines the readily available water that plants can access from the soil through their root system. Soil moisture regulates the water and energy exchange between the land surface and the atmosphere. It also influences the partitioning of nonintercepted precipitation into surface runoff and infiltrations and influences the partitioning of net radiation into sensible, latent, and ground heat fluxes that are essential climate variables (WMO, 2006). Soil moisture condition directly reflects ecosystem functionality and agricultural productivity, therefore an agricultural drought influences the economy at local to global scales (IPCC, 2007; Ryu et al., 2014).
Warm surface temperature and rapidly
