Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory

Abstract

[1] Although information about climate change and its implications is becoming increasingly available to water utility managers, additional tools are needed to translate this information into secondary products useful for local assessments. The anticipated intensification of the hydrologic cycle makes quantifying changes to hydrologic extremes, as well as associated water quality effects, of particular concern. To this end, this paper focuses on using extreme value statistics to describe maximum monthly flow distributions at a given site, where the nonstationarity is derived from concurrent climate information. From these statistics, flow quantiles are reconstructed over the historic record and then projected to 2100. This paper extends this analysis to an associated source water quality impact, whereby the corresponding risk of exceeding a water quality threshold is examined. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values correlated with high streamflow. Results demonstrate that based on climate change information from the most recent Intergovernmental Panel on Climate Change assessment report, the variability and magnitude of extreme streamflows substantially increase over the 21st century. Consequently, the likelihood of a turbidity exceedance increases, as do the associated relative costs. The framework is general and could be applied to estimate extreme streamflow under climate change at other locations, with straightforward extensions to other water quality variables that depend on extreme hydroclimate.

Revisiting the recent California drought as an extreme value

“…Adaptive approaches [Huerta and Sansó, 2007;Katz, 2010;Towler et al, 2010] should be useful in estimating time-varying parameters, but disentangling past relationships between precipitation, temperature, and drought variability in California also will require incorporating additional tree ring chronologies and spatial analyses that use higher resolution instrumental data rather than the coarser divisional data. Representations of PDSI that do not rely primarily on temperature-derived potential evapotranspiration also will be valuable in evaluating the causes of drought variability [Sheffield et al, 2012;Yuan and Quiring, 2014].…”

Extremes and Recurrence in Dynamical Systems

“…Note that, as a consequence of the previous theorem, under the assumptions D(u n ) and D (u n ) the statement of Theorem 3.1.6 remains valid. Therefore, in a variety of applications where one needs to study extremes of time series featuring a sufficiently fast decay of correlations, GEV-and GPD-based statistical inference methods are used almost interchangeably, under the overall consensus that following the POT approach is more efficient when the time series are not exceptionally long [3,5,43,85,4,44]. Differences between the two methods emerge when extremes come in clusters; this is discussed below in Sections 3.2.2-3.4.…”

Statistical modeling of daily and subdaily stream temperatures: Application to the Methow River Basin, Washington

“…[25] A flexible statistical framework has often been applied in the assessment and prediction of many water quality variables [Neumann et al, 2003[Neumann et al, , 2006Towler et al, 2010aTowler et al, , 2010bCaldwell and Rajagopalan. 2011].…”