Progress in Advancing Drought Monitoring and Prediction

Description:

Building on the previous special collection entitled “Advancing Drought Monitoring and Prediction,” this special collection focuses on scientific research to advance the U.S.'s capability to monitor and predict drought, including the development of new data and methodologies. The results presented in this collection represent the outcomes of research in large part funded by NOAA’s National Integrated Drought Information System (NIDIS) through NOAA’s Modeling, Analysis, Predictions and Projections (MAPP) program, also leveraging other U.S. agencies’ investments, and coordinated within the framework of the Third MAPP Drought Task Force (DTF3). The collection is divided broadly into papers addressing monitoring and those addressing the prediction problem, but also includes an important focus on improving our understanding of past droughts. The papers provide a state-of-the-practice / state-of-the-science assessment of the modern drought challenge and efforts to understand and manage it. The collection includes an introductory paper, 10.1175/BAMS-D-20-0087.1, that motivates the research and highlights some recent advances in drought monitoring and prediction systems.

Collection organizers:

Christa D. Peters-Lidard, NASA Goddard Space Flight Center
Pierre Gentine, Columbia University
Michael Barlage, NOAA/Environmental Modeling Center
Dennis Lettenmaier, University of California, Los Angeles
David M. Mocko, SAIC at NASA Goddard Space Flight Center
Daniel Barrie, NOAA Climate Program Office

Progress in Advancing Drought Monitoring and Prediction

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Christa D. Peters-Lidard, David M. Mocko, Lu Su, Dennis P. Lettenmaier, Pierre Gentine, and Michael Barlage

Abstract

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

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