A Vantage from Space Can Detect Earlier Drought Onset: An Approach Using Relative Humidity

Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems.

[1]  J. Dracup,et al.  On the definition of droughts , 1980 .

[2]  Steven M. Quiring,et al.  Monitoring Drought: An Evaluation of Meteorological Drought Indices , 2009 .

[3]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[4]  M. Palecki,et al.  THE DROUGHT MONITOR , 2002 .

[5]  Hirofumi Hashimoto,et al.  Monitoring and forecasting ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS) , 2009 .

[6]  M. Naresh Kumar,et al.  On the use of Standardized Precipitation Index(SPI) for drought intensity assessment , 2009 .

[7]  A. Aghakouchak A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought , 2015 .

[8]  John A. Dracup,et al.  The Quantification of Drought: An Evaluation of Drought Indices , 2002 .

[9]  K. Mo Drought onset and recovery over the United States , 2011 .

[10]  D. C. Edwards,et al.  Characteristics of 20th Century Drought in the United States at Multiple Time Scales. , 1997 .

[11]  J. Marengo,et al.  The drought of 2010 in the context of historical droughts in the Amazon region , 2011 .

[12]  磯貝 明,et al.  研究所紹介 米国農務省林産研究所,Atalla博士のグル-プ--Forest Products Laboratory(FPL),Forest Service(FS),United States Department of Agriculture(USDA) , 1997 .

[13]  A. Aghakouchak,et al.  A near real-time satellite-based global drought climate data record , 2012 .

[14]  Donald A. Wilhite,et al.  Drought : a global assessment , 2000 .

[15]  William L. Smith,et al.  AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems , 2003, IEEE Trans. Geosci. Remote. Sens..

[16]  M. Matsueda Predictability of Euro‐Russian blocking in summer of 2010 , 2011 .

[17]  David R. Easterling Global Data Sets for Analysis of Climate Extremes , 2013 .

[18]  Syukuro Manabe,et al.  Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity , 1967 .

[19]  Irving I. Gringorten,et al.  A plotting rule for extreme probability paper , 1963 .

[20]  Martha C. Anderson,et al.  Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States , 2011 .

[21]  Amir AghaKouchak,et al.  A generalized framework for deriving nonparametric standardized drought indicators , 2015 .

[22]  D. Wilhite Drought and Water Crises : Science, Technology, and Management Issues , 2005 .

[23]  N. McDowell,et al.  Numerical Terradynamic Simulation Group 1-2013 A Remotely Sensed Global Terrestrial Drought Severity Index , 2017 .

[24]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[25]  Yann Kerr,et al.  The hydrosphere State (hydros) Satellite mission: an Earth system pathfinder for global mapping of soil moisture and land freeze/thaw , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[26]  A. Aghakouchak,et al.  Global integrated drought monitoring and prediction system , 2014, Scientific Data.

[27]  Siegfried Schubert,et al.  NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA): Early Results and Future Directions , 2006 .

[28]  Anne Steinemann,et al.  Developing Multiple Indicators and Triggers for Drought Plans , 2006 .

[29]  Amir AghaKouchak,et al.  Extended contingency table: Performance metrics for satellite observations and climate model simulations , 2013 .

[30]  A. Aghakouchak,et al.  Multivariate Standardized Drought Index: A parametric multi-index model , 2013 .

[31]  Melissa Widhalm,et al.  The Lincoln Declaration on Drought Indices: Universal Meteorological Drought Index Recommended , 2011 .

[32]  Amir AghaKouchak,et al.  A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought , 2014 .

[33]  Amir AghaKouchak,et al.  A Nonparametric Multivariate Multi-Index Drought Monitoring Framework , 2014 .

[34]  Hui Wang,et al.  Baseline Probabilities for the Seasonal Prediction of Meteorological Drought , 2012 .

[35]  Rao S. Govindaraju,et al.  A copula-based joint deficit index for droughts. , 2010 .

[36]  Steven M. Quiring,et al.  Developing Objective Operational Definitions for Monitoring Drought , 2009 .

[37]  Lihang Zhou,et al.  AIRS near-real-time products and algorithms in support of operational numerical weather prediction , 2003, IEEE Trans. Geosci. Remote. Sens..