Drought events causing over one billion dollars in damage occur, on average, nearly every year in the contiguous United States (CONUS). Event-specific costs often reach annual levels of $4–7 billion (e.g., the 2014–15 western drought) and sometimes as high as $30 billion (e.g., calendar year 2012 losses from the drought in the central and western United States). The magnitude, breadth, and profound lasting effect of these events on social and economic systems necessitates attention from local, state, regional, and national entities that have responsibility for providing, maintaining, and planning water resources and supplying relevant information.
Monitoring the state of drought depends on integrating and discerning between myriad indicators of the water cycle (Keyantash and Dracup 2002). Dozens of indicators are in common use (e.g., Heim 2002; Svoboda and Fuchs 2017), and each indicator captures particular aspects of hydrologic variability and various types and phases of drought, such as meteorological, agricultural, and hydrological; however, no single indicator is comprehensive. These indicators rely on a diverse array of data sources, including remote sensing, in situ measurements, and human observation, for directly measured quantities such as precipitation, temperature, soil moisture, and streamflow and indirectly estimated quantities such as vegetation biomass and evapotranspiration. Drought indicators form the foundation for drought monitoring (Svoboda 2000) and prediction (Sheffield et al. 2014; Dai 2013), and recent advances in understanding have underscored the need to consider more complex and realistic physical processes when developing these indicators. The goal of this paper is to discuss the technical state of drought monitoring and prediction; to evaluate legacy indicators as well as recent advances in monitoring products, in a case study context; and to discuss cross-cutting issues with indicator construction, performance, and choice.
This work represents an ongoing interest of NOAA’s Drought Task Force (DTF; Wood et al. 2015; Hoerling et al. 2014; Schubert et al. 2016), which has extensively discussed and evaluated the merits of drought indicators, especially in the context of compound drought events with strong temperature and precipitation contributions and under emergent conditions such as climate change and increased human management of the water cycle (Zhou et al. 2019a,b). These discussions build on years of DTF science supporting advances in the indicator suite and drought monitoring capabilities and products. The case study framing of this article was proposed by the first DTF (Wood et al. 2015), and has continued to serve as a framework for the second and third DTFs. Case studies provide an opportunity to benchmark the performance of capabilities in a real-world context, testing the ability of different methods and products to function properly in discerning the causes of and conditions in actual drought events, which are highly integrative of multiple forcing factors and stresses in the water cycle.