Philip J. Ward Vrije Universiteit Amsterdam, The Netherlands
1 Progress, Challenges, and Opportunities in Remote Sensing of Drought
Arash Modarresi Rad1, Amir AghaKouchak2, Mahdi Navari3, and Mojtaba Sadegh4
1 Department of Computing, Boise State University, Boise, Idaho, USA
2 Department of Civil and Environmental Engineering, and Department of Earth System Science, University of California Irvine, Irvine, California, USA
3 NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
4 Department of Civil Engineering, Boise State University, Boise, Idaho, USA
ABSTRACT
Drought, one of the most daunting natural hazards, is linked to other hazards such as heatwaves and wildfires, and is related to global and regional food security. Given the severe environmental and socioeconomic ramifications of droughts, comprehensive and timely analysis of droughts’ onset, development, and recovery at proper spatial and temporal scales is of paramount importance. Droughts are categorized by different variables, such as precipitation, soil moisture, and streamflow, depending on the target of the analysis. The root cause of droughts, however, is sustained below‐average precipitation. Large‐scale oceanic and atmospheric circulations drive precipitation variability, and hence droughts should be analyzed from a continental to global perspective. Given the spatial scale of interest, as well as the poor spatial resolution and temporal inconsistency of ground observations, multisensor remotely sensed climatological, hydrological, and biophysical variables offer a unique opportunity to model droughts from different perspectives (meteorological, agricultural, hydrological, and socioeconomic) and at the global scale. It is also often required to model droughts using multiple indices and analyze feedbacks between droughts and other hazards, such as heatwaves. Multiple satellites, missions, and sensors offer invaluable information for multi‐indicator modeling of droughts and their feedbacks with other natural hazards in an era of big data. Remote sensing satellite data, however, are associated with major challenges including temporal limitations, consistency within and between multiple sensors and data sets, reliability, lack of uncertainty assessment, managing data volumes, and paucity of research on translating remote sensing of drought into actionable science. With challenge comes opportunity. The focus of the scientific community should be on merging the information provided from different satellites and sensors, to underpin their uncertainties, and to offer long‐term and consistent data sets for drought analysis.
1.1. INTRODUCTION
Drought is a recurring natural feature of climate and is defined as below‐normal precipitation, usually over an extended period of time (Wilhite & Buchanan‐Smith, 2005). Droughts cause billions of dollars of damage to multiple sectors globally, specifically to agriculture. Droughts may also cause, or co‐occur with, other hazards such as heatwaves, which collectively escalate the ramifications of this natural hazard (Raei et al. 2018). Indeed, the concurrence of climatic extremes, in particular droughts and heat waves, can result in forest fires (Goulden, 2018; Silva et al., 2018; Taufik et al., 2017), land degradation and desertification (Hutchinson & Herrmann, 2016; Olagunju, 2015; Vicente‐Serrano et al., 2015), water shortage for agriculture and urban water supply (AghaKouchak, Farahmand, et al., 2015; Gober et al., 2016; Khorshidi et al., 2019; Van Loon et al., 2016), and economic impacts, and may prompt water bankruptcy (Howitt et al., 2014; Madani et al., 2016). Therefore, the impacts of drought are complex and can propagate to regions outside the area of its occurrence. Drought is often categorized in four groups: meteorological, agricultural, hydrological, and socioeconomic (Dracup et al., 1980). Meteorological drought is defined as precipitation deficiency over a long period, and it best represents the onset of drought (Utah Division of Water Resources, 2007). An extended period of meteorological drought results in soil moisture deficit as evapotranspiration continues despite the lack of precipitation, which leads to agricultural drought (Cunha et al., 2015). Persistence of metrological drought ultimately reduces overall water supply and drought is manifested in a hydrological form (Modaresi Rad et al., 2016). Socioeconomic drought then occurs as supply and demand of some economic goods are impacted by meteorological, agricultural, and hydrological droughts (Shiferaw et al., 2014). The observed changes in temporal patterns of precipitation associated with unsustainable water withdrawal may escalate the drought severity around the globe (Mallakpour et al., 2018; U.S. Global Change Research Program, 2018); and large‐scale changes in weather patterns are likely to affect water storage around the globe and threaten water supply particularly in arid and semi‐arid regions (Ault et al., 2014).
Drought detection requires observation of a plethora of different climatic and biophysical variables. Observations in situ, however, do not provide a uniform spatial distribution and are limited to populated areas, hence satellite‐based observations provide a unique way to analyze and monitor drought at a global scale. Satellites offer observations for a wide range of climate variables such as precipitation, soil moisture, temperature, relative humidity, evapotranspiration, vegetation greenness, land‐cover condition, and water storage (Aghakouchak, Farahmand, et al., 2015; R. G. Allen et al., 2007; L. Wang & Qu, 2009; Whitcraft et al., 2015). Although remote sensing provides more opportunities for the scientific community to monitor Earth systems and offer better understanding of drought impact at regional to global scales, it is not without flaws or challenges. The main challenge is the insufficient length of the observed records provided for the variables of interest. Other challenges include data consistency, ease of access, quantifying uncertainty, and development of appropriate drought indices, which will be discussed throughout this chapter.
1.2. PROGRESS IN REMOTE SENSING OF DRIVERS OF DROUGHT
This section presents the recent remote sensing techniques used for identification and quantification of drought as characterized by different climatic and biophysical variables.
1.2.1. Precipitation
A meteorological drought can be described as precipitation deficiency over a period of time (WMO, 1975), often represented in terms of an index of deviation from normal. Drought indices not only serve the scientific communities but they are also great tools for facilitating the decision‐making and policy‐making processes for stakeholders and managers when compared with the raw data. One of the most widely used and informative meteorological drought indices is the standardized precipitation index (SPI) developed by Mckee et al. (1993). Several other meteorological drought indices have also been proposed, including, but not limited to, precipitation effectiveness (Thornthwaite, 1931), antecedent precipitation (API; McQuigg, 1954), rainfall anomaly (RAI; Van Rooy, 1965), drought area (Bhalme & Mooley, 1980), effective precipitation (Byun & Wilhite, 1999), and rainfall variability indices (Oguntunde et al., 2011). The SPI is currently being used in many national operational and research centers and was recognized as a global measure to characterize meteorological drought by the World Meteorological Organization (WMO, 2009). Computation of SPI requires measured rainfall data and a normalization process of monthly data, either by utilizing an appropriate probability distribution function (PDF) to transform the rainfall PDF (e.g., gamma or Pearson type III probability distribution) into a standard normal distribution (Khalili et al., 2011), or by utilizing a nonparametric approach (Hao & AghaKouchak, 2014). Precipitation deficit can be specified for different timescales (e.g., from 1 to 24 months) when using SPI, where precipitation
