Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring

Abstract

Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed data, namely SMOPS, and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm, was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the Southeastern United States. The model-based estimates are then compared against the corresponding US Drought Monitoring System archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents, however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM-DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition.

Drought Early Warning in Agri-Food Systems

“…The remote sensing literature offers numerous examples proposing earth observation techniques to support assessment of drought conditions [23]. Increasing access to open data, high-resolution remote sensors, and enhanced computing facilities have led to a new set of sophisticated techniques for DEWS in agriculture experiencing drought stress conditions [24], which monitor evapotranspiration [25], soil moisture [26], ground water fluxes [26,27], and precipitation.…”

Unraveling the Relationship Between Tropical Storms and Agricultural Drought

“…Droughts are extreme events associated with water deficiency for an extended period that causes economic and environmental losses and can be classified into four types: meteorological drought (precipitation deficit), agricultural drought (soil moisture deficit), hydrological drought (streamflow deficit), and socioeconomic drought (social responses in terms of water supply and demands) (Wilhite & Glantz, 1985). Drought studies are steadily conducted to understand the causes, to plan for future drought, and to characterize different types of droughts (Folger et al., 2013; Gavahi et al., 2020; Kuwayama et al., 2019; Madadgar & Moradkhani, 2013; Staudinger et al., 2014). The southeast US is gaining more attention for drought risk due to more frequent and severe droughts (Engström et al., 2020; Martin et al., 2020).…”

“…Soil moisture is known as a key variable for agricultural drought monitoring in a multitude of studies (Berg & Sheffield, 2018; Gavahi et al., 2020; Hao & AghaKouchak, 2013; Martínez‐Fernández et al., 2016; L. Xu et al., 2020, 2019; Y. Xu et al., 2018; Yin et al., 2020). Moreover, soil moisture is an important component in the hydrological cycle, which is estimated through in situ stations, satellite remote sensing, or hydrologic modeling.…”

“…Different downscaling approaches are developed based on the need of high‐resolution soil moisture data (Guevara & Vargas, 2019; Montzka et al., 2018; Yin et al., 2020). Moreover, recent studies have utilized 1 km scale soil moisture data for drought monitoring and revealed finer resolution data provide more detailed information in heterogeneous areas (Abbaszadeh et al., 2021, 2019; Fang et al., 2021; Gavahi et al., 2020; Yin et al., 2020). Literature indicates the inefficacy of previous studies in identifying the TS‐drought relationship at regional or local level as they did not use the soil moisture data at kilometer or sub‐kilometer scales which are crucial for effective agricultural practices and farm‐level studies (Brun & Barros, 2014; Kam et al., 2013; Maxwell et al., 2013).…”

Decreasing water resources in Southeastern U.S. as observed by the GRACE satellites

“…Although Southeastern U.S. (hereafter 'the Southeast') may seem to have abundant water resources from a climatological perspective, enjoying a humid subtropical climate with precipitation originating from local convection and tropical storms in the summer, and frontal action in the winter, it has experienced multiple severe droughts during the last few decades as well as intermittent fast-onset flash droughts (Kunkel et al, 2013;Praskievicz, 2019;Gavahi et al, 2020). The flash droughts, typically occurring during the warm season as a result of high temperatures in combination with low precipitation amounts (Otkin et al, 2018), are particularly concerning for a region like the Southeast where agriculture and forestry constitute significant parts of the regional economy, and where riparian water-rights laws limit opportunities to use available surface water for irrigation, adding to the region's vulnerability (Engström et al, 2020).…”