Joint Poster Session Drought Monitoring, Early Warning, and Projection in the 21st Century—Beyond PDSI (Posters)

Wednesday, 9 January 2019: 4:00 PM-6:00 PM
Hall 4 (Phoenix Convention Center - West and North Buildings)
Hosts: (Joint between the 33rd Conference on Hydrology; the 32nd Conference on Climate Variability and Change; and the 24th Conference on Applied Climatology )
Christa D. Peters-Lidard, NASA Goddard Space Flight Center, Earth Sciences Division, Greenbelt, MD; Pierre Gentine, Columbia Univ., Earth and Environmental Engineering, New York, NY; Michael Barlage, NCAR, RAL, Boulder, CO; Dennis P. Lettenmaier, Univ. of California, Geography, Los Angeles, CA and Daniel Barrie, NOAA, Climate Program Office/MAPP Program, Silver Spring, MD

As shown on the NIDIS drought portal ( current operational drought monitoring and early warning in the U.S. relies on a combination of the U.S. Drought Monitor (USDM; Svoboda et al., 2002) and the Weekly Palmer Drought Index (PDSI) (Palmer, 1965; Heim 2002; 2005).  In contrast to the USDM, which relies on a manual, convergence-of-evidence approach, the PDSI is an objective approach that has been attractive for monitoring, early warning, and climate projection (e.g., Abatzoglou et al., 2017; Dai, 2011; Cook et al., 2015)


While the water balance approach encapsulated in PDSI has the advantage that it may be estimated with limited input data, issues with the approach, particularly the calculation of potential evapotranspiration (PET) have been widely reported (e.g., Sheffield et al., 2012).    Moreover, the atmospheric-centric formulation of PDSI ignores the feedbacks from groundwater, soils, and vegetation on the drought state.  For monitoring and forecasting seasonal to interannual variability of drought, it is critical to capture the effects of soil moisture and vapor pressure deficit on increased surface resistances that reduce ET primarily though stomatal closure (Milly 2016; Novick et al. 2016).   For longer-term climate projections of drought, recent work further suggests that ignoring the stomatal response due to increasing CO2 in PDSI leads to an overestimation of future projected drought area, while other metrics that include actual ET (e.g., P-E) lead to dramatically reduced projections of future drought area (Swann et al., 2016).


There have been attempts to develop so-called “objective blends” that can mimic the USDM (e.g., Xia et al., 2014), but these efforts do not accurately reflect the response of vegetation to drought stress or the stomatal responses because the land surface models used in the North American Land Data Assimilation System (NLDAS) do not include prognostic vegetation states or represent VPD feedbacks and their controls on stomatal conductance.   Moreover, from a monitoring perspective, these models often do not reflect rapidly developing droughts like thermal remote sensing-based indices such as the Evaporative Stress Index (ESI; Otkin et al., 2013).  They also do not take advantage of other remotely sensed and in situ observations such as Soil Moisture from ground-based networks and SMAP and terrestrial water storage from GRACE.


With this context, the proposed theme for the Third NOAA MAPP Drought Task Force is Drought Monitoring, Early Warning, and Projection in the 21st Century—Beyond PDSI.   We seek an objective drought index that reflects the state of drought science and includes modern observational systems and models.  Ultimately, a well-informed and objective declaration of the state of drought must integrate various measures.  We invite submissions related to all aspects of drought monitoring, early warning and projection.

Root-Zone Soil Moisture Estimation Using In-Situ Measurements and Remote Sensing Data
Ning Zhang, Ohio State Univ., Columbus, OH; and S. M. Quiring

Impact of Climate Model “Downscaling” on Projected Water Resources in Arid Basins, Two Case Studies from Arizona
Eylon Shamir, Hydrologic Research Center, San Diego, CA; and E. Halper, T. Modrick, K. P. Georgakakos, H. I. Chang, T. Lahmers, and C. L. Castro

Using Remotely Sensed Precipitation Information and Vegetation Observations for Early Drought Detection and Near-Real Time Monitoring on a Global Scale
Olivier P. Prat, Cooperative Institute for Climate and Satellites/North Carolina State Univ., Asheville, NC; and R. D. Leeper, J. L. Matthews, B. R. Nelson, J. Adams, and S. Ansari

The 1983-2016 Climate Hazards Infrared, MERRA High Resolution Tmax with Stations (CHIRTSmax) Climate Data Record
Pete Peterson, Climate Hazards Group, Univ. of California, Santa Barbara, Santa Barbara, CA; and C. C. Funk, S. Peterson, S. Shukla, F. Davenport, J. Michaelsen, M. Landsfeld, G. Husak, L. S. Harrison, J. Rowland, M. Budde, K. R. Knapp, S. E. Nicholson, and T. Dinku

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