A hierarchical Bayesian regional model for nonstationary precipitation extremes in Northern California conditioned on tropical moisture exports. Issue 3 (12th March 2015)

• Record Type: Journal Article
• Authors: Steinschneider, Scott; Lall, Upmanu
• Is Part Of: Water resources research. Volume 51:Issue 3(2015:Mar.)
• Page Start: 1472
• Page End: 1492
• Publication Date: 2015-03-12
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2014WR016664
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Warm, moist, and longitudinally confined tropical air masses are being linked to some of the most extreme precipitation and flooding events in the midlatitudes. The interannual frequency and intensity of such atmospheric rivers (ARs), or tropical moisture exports (TMEs), are connected to the risk of extreme precipitation events in areas where moisture convergence occurs. This study presents a nonstationary, regional frequency analysis of precipitation extremes in Northern California that is conditioned on the interannual variability of TMEs entering the region. Parameters of a multisite peaks‐over‐threshold model are allowed to vary conditional on the integrated moisture delivery from TMEs over the area. Parameters are also related to time‐invariant, local characteristics to facilitate regionalization to ungaged sites. The model is developed and calibrated in a hierarchical Bayesian framework to support partial pooling and enhance regionalization skill. The model is cross validated along with two alternative, increasingly parsimonious formulations to assess the additional skill provided by the covariates. Climate diagnostics are also used to better understand the instances where TMEs fail to explain variations in rainfall extremes to provide a path forward for further model improvement. The modeling structure is designed to link seasonal forecasting and long‐term projections of TMEs directly to regional models of extremes used for risk estimation. Results suggest that the inclusion of TME‐based information greatly improves the characterization of extremes, particularly for their frequency of occurrence. Diagnostics indicate that the model could be further improved by considering an index for frontal systems as an additional covariate.</p> </abstract>