A framework for investigating large-scale patterns as an alternative to precipitation for downscaling to local drought

Abstract

case study demonstrates: (1) where a large-scale predictor performs comparably (or better) than precipitation directly, then it is an appealing predictor choice to use with future projections, (2) when statistically downscaling to local scales, it is critical to consider internal variability, as it may be more important than predictor selection.

Remote effects spatial process models for modeling teleconnections

“…Pointwise regression models are commonly used to downscale climate data (e.g., Towler et al, ). The ENSO‐T model predicts precipitation P ( s , t 0 ) at a location s and new time point t 0 by applying a regression of training data P ( s , t ) onto local surface air temperature T ( s , t ) and the score a 1 ( t ) for the first SST EOF .…”

“…Statistical downscaling methods use large-scale variables to draw inference on regional variables. Similar to what is being proposed here, a type of statistical downscaling called perfect prognosis downscaling (Maraun et al, 2010) develops a statistical relationship between observed large-scale predictors and local-scale weather phenomena (e.g., Bruyere, Holland, & Towler, 2012;Towler, PaiMazumder, & Holland, 2016;Wilby et al, 1998). Common models used for perfect prognosis downscaling do not explicitly model spatial dependence.…”

“…While effective, the model ( 1) assumes relevant large-scale patterns are known a priori (e.g., ENSO). However, relevant teleconnection indices can depend on the study region and thus be unknown at the start of an analysis (Towler et al, 2016). The spatially varying coefficient model (1) will be inefficient if driven by poorly chosen teleconnection indices.…”

Characterizing the potential for drought action from combined hydrological and societal perspectives

“…Similarly, many different drought indices have been developed (Mishra and Singh, 2010). The main driver of drought in most definitions and indices of drought is natural climate variability (Van Loon, 2016a), which is where efforts to improve prediction and modeling have focused (see Mishra and Singh, 2011, and references therein). Even with advances in drought prediction, drought remains one of the most expensive hazards affecting the United States (NCDC, 2015), reinforcing the idea that social factors must also be considered for drought planning (Wilhite and Buchanan-Smith, 2005;Bachmair et al, 2016).…”

“…Recent work has highlighted how the natural and human causes of drought are intertwined and that researchers must consider both in any examination of drought (Van Loon, 2016a). This general notion has been echoed in the hydrologic science literature (Wagener et al, 2010), as well as the natural hazard (Jones and Preston, 2011) and climate change literature (Oppenheimer et al, 2014).…”