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An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications

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  • 1 * Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland
  • | 2 Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
  • | 3 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin
  • | 4 Center for Satellite Applications and Research, NOAA/NESDIS, College Park, Maryland
  • | 5 NOAA/CPC, College Park, Maryland
  • | 6 ** National Drought Mitigation Center, University of Nebraska, Lincoln, Nebraska
  • | 7 Pontificia Universidad Católica de Chile, Santiago, Chile
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Abstract

Comparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought, particularly during periods of rapid onset. This paper compares the evaporative stress index (ESI)—a diagnostic fast-response indicator describing evapotranspiration (ET) deficits derived within a thermal remote sensing energy balance framework—with prognostic estimates of soil moisture (SM), ET, and runoff anomalies generated with the North American Land Data Assimilation System (NLDAS). Widely used empirical indices based on thermal remote sensing [vegetation health index (VHI)] and precipitation percentiles [standardized precipitation index (SPI)] were also included to assess relative performance. Spatial and temporal correlations computed between indices over the contiguous United States were compared with historical drought classifications recorded in the U.S. Drought Monitor (USDM). Based on correlation results, improved forms for the ESI were identified, incorporating a Penman–Monteith reference ET scaling flux and implementing a temporal smoothing algorithm at the pixel level. Of all indices evaluated, anomalies in the NLDAS ensemble-averaged SM provided the highest correlations with USDM drought classes, while the ESI yielded the best performance of the remote sensing indices. The VHI provided reasonable correlations, except under conditions of energy-limited vegetation growth during the cold season and at high latitudes. Change indices computed from ESI and SM time series agree well, and in combination offer a good indicator of change in drought severity class in the USDM, often preceding USDM class deterioration by several weeks. Results suggest that a merged ESI–SM change indicator may provide valuable early warning of rapidly evolving “flash drought” conditions.

Corresponding author address: M. C. Anderson, Agricultural Research Service, USDA, 10300 Baltimore Ave., Beltsville, MD 20705. E-mail: martha.anderson@ars.usda.gov

This article is included in the Advancing Drought Monitoring and Prediction Special Collection.

Abstract

Comparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought, particularly during periods of rapid onset. This paper compares the evaporative stress index (ESI)—a diagnostic fast-response indicator describing evapotranspiration (ET) deficits derived within a thermal remote sensing energy balance framework—with prognostic estimates of soil moisture (SM), ET, and runoff anomalies generated with the North American Land Data Assimilation System (NLDAS). Widely used empirical indices based on thermal remote sensing [vegetation health index (VHI)] and precipitation percentiles [standardized precipitation index (SPI)] were also included to assess relative performance. Spatial and temporal correlations computed between indices over the contiguous United States were compared with historical drought classifications recorded in the U.S. Drought Monitor (USDM). Based on correlation results, improved forms for the ESI were identified, incorporating a Penman–Monteith reference ET scaling flux and implementing a temporal smoothing algorithm at the pixel level. Of all indices evaluated, anomalies in the NLDAS ensemble-averaged SM provided the highest correlations with USDM drought classes, while the ESI yielded the best performance of the remote sensing indices. The VHI provided reasonable correlations, except under conditions of energy-limited vegetation growth during the cold season and at high latitudes. Change indices computed from ESI and SM time series agree well, and in combination offer a good indicator of change in drought severity class in the USDM, often preceding USDM class deterioration by several weeks. Results suggest that a merged ESI–SM change indicator may provide valuable early warning of rapidly evolving “flash drought” conditions.

Corresponding author address: M. C. Anderson, Agricultural Research Service, USDA, 10300 Baltimore Ave., Beltsville, MD 20705. E-mail: martha.anderson@ars.usda.gov

This article is included in the Advancing Drought Monitoring and Prediction Special Collection.

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