Network Analysis of Localized Spatial-Temporal Drought Patterns

Major drought events in the United States have heavily impacted the hydrologic system and its associated infrastructure; thus, negatively affecting energy and food production. Improved understanding of historical drought phenomena is critical for accurate forecasts of future droughts. Even though these forecasts are useful in assessing regional drought patterns, it is a challenge to evaluate local patterns mostly because Global Climate Models (GCM) data are not available at higher spatial scale. This research leverages downscaled ( 4km grid spacing) temperature and precipitation estimates from 10 GCM data under the business as usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) for the historical period (1965-2005) and near-term to future periods (2011-2050). The specific objectives are: 1) To reproduce historical (1965-2005) drought patterns and calculate near-term (2011-2017) to future (2018-2050) drought patterns over the conterminous United States. Here, proportions of land under drought and the spatial patterns is determined and compared for the 10 GCM data sets. 2) To uncover local variability in drought patterns using a network-based approach for the Tennessee and Lower Colorado river basins. Using two similarity measures, we evaluate the time series representation of PDSI between locations at 4km spatial resolution and define regions with similar characteristics. Persistent clusters within the 10 GCMs, or for both similarity measures, are isolated and described. Preliminary results based on 3 GCM data indicate distinct clusters within the Tennessee river basin. Our estimates of land proportions affected by drought agree with the known historical drought events of mid-1960s, late 1970s to early 1980s, early 2000s and between 2012 and 2015. The use of high-resolution data provides new insights into the sub-regional and localized drought phenomenon patterns, which could inform adaptation strategies.