Thursday, 10 January 2019: 12:00 AM
North 126BC (Phoenix Convention Center - West and North Buildings)
The Palmer Drought Severity Index (PDSI) is a widely employed indicator of agricultural (soil moisture) drought variability that has recently come under intense scrutiny for its purported weaknesses related to its representation of land-surface processes (e.g., treatment of all precipitation as rain, lack of vegetation dynamics, etc). To the contrary, we demonstrate that PDSI still has significant utility in assessments of drought variability. As with physically-based land surface models, PDSI tracks the soil moisture balance as a function of inputs from precipitation and losses from actual evapotranspiration, avoiding the major issues that plague indicators that do not track a moisture balance and arbitrarily assign equal weight to potential evapotranspiration and precipitation (e.g., the Aridity Index, Standardized Precipitation Evapotranspiration Index). The calculation of PDSI itself only requires monthly climate inputs and has significantly lower computational requirements compared to more physically-based land-surface models, making it a valuable tool for assessing drought variability and performing high-volume sensitivity/attribution analyses as long as it can be shown to yield similar results as land-surface models. PDSI does indeed show remarkably similar year-to-year variability and long-term trends in soil moisture as those represented by more physically-based offline land-surface models (e.g., VIC) or from coupled climate model simulations and projections (e.g., CMIP5). PDSI does have difficulty resolving short-term or rapidly intensifying droughts (e.g., sub-seasonal scale) and in some cases may overestimate surface drying in response to warming, but these challenges are well documented, as are the circumstances under which the use of PDSI should be avoided. The demonstrated physical appropriateness of PDSI, in addition to its notable advantages for use in sensitivity analyses to account for effects of various climate variables or forcing datasets, thus underscore its continued utility as a drought index among the many legitimate tools used to investigate drought dynamics, particularly when the large uncertainties that still exist in more physically-based models are considered, especially their representation of vegetation processes.
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