Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

Abstract

The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.

Exploring the link between drought indicators and impacts

“…A large body of literature exists on the vast amount of drought indicators (for recent reviews see Heim Jr., 2002;Keyantash and Dracup, 2002;Zargar et al, 2011) and many studies have assessed the linkage between different hydrometeorologic indicators (e.g., Anderson et al, 2011;Hao and AghaKouchak, 2014;Haslinger et al, 2014;Keyantash and Dracup, 2002;Steinemann, 2003;. While fewer studies explored the relationship between drought indicators and a quantitative impact variable, such as agricultural yield or a vegetation response proxy (e.g., Ceglar et al, 2012;Mavromatis, 2007;Potop, 2011;Quiring and Ganesh, 2010;Quiring and Papakryiakou, 2003;Rossi and Niemeyer, 2010;Sepulcre-Canto et al, 2012;Vicente-Serrano et al, 2012), only three studies have exploited text-based reports of drought impacts for evaluating the meaning of drought indicators or the statistical modeling of the likelihood of impact occurrence (Dieker et al, 2010;Blauhut et al, 2015;Stagge et al, 2015a).…”

Decadal predictability of soil water, vegetation, and wildfire frequency over North America

“…Although water storage and soil moisture are difficult to observe with sufficient spatial coverage, observational reconstructions of land hydrological variables and fire cycles (Maurer et al 2002;van den Dool et al 2003;Fan and van den Dool 2004;Littell et al 2009;Wada et al 2010) may still provide useful information to determine potential longer-term predictabilities. Recent advances in observing water storage and drought from satellite sensors (Anderson et al 2011;Famiglietti et al 2011) could further help to improve the initialization of statistical and numerical forecasts of soil moisture on interannual to decadal timescales. Unresolved issues that will be addressed in a series of forthcoming studies include the effects of ocean dynamics on the predictability of low-frequency atmosphere and land variability and the feedback of soil moisture variations on atmospheric temperatures and circulation (e.g., Rowntree and Bolton 1983;Atlas et al 1993;Koster et al 2000).…”

The GPCC Drought Index – a new, combined and gridded global drought index

“…But evapotranspiration also takes place at temperatures below 0 • C. Some work-arounds exist that solve this problem by assuming arbitrary values for the PET, such as 1 mm month −1 or even lower values. Although other parameterizations for the computation of the PET exist, such as the FAO Penman-Monteith equation (Allen et al, 1998), they need more input data than temperature and astronomical data. To our knowledge, no data sets exist that provide, for example, wind speed, radiation or humidity (dew point) with the same high timeliness, global coverage and spatial resolution as the applied temperature data set.…”

“…The differences between aridity and drought have to be kept in mind (see also Mishra and Singh (2010)). For example, droughts are categorized as meteorological, agricultural or hydrological droughts, with a variety of definitions (Anderson et al, 2010;Heim, 2002;McKee et al, 1993;Wilhite and Glantz, 1985) for each category. They can also differ with regard to the time span of the water deficit.…”