Search

In the eastern United States, the empirical probability distribution of extreme daily precipitation comprises heavy rainfall events stemming from North Atlantic tropical cyclones (TCs). At many locations, these events influence estimates of extreme value statistics (e.g., 100‐year event), thus have important bearing on the sizing of flood protection infrastructure and, in general, flood risk management and preparedness. Consequently, a characterization of location specific and regional patterns in precipitation extremes and changes therein has salience for both scientific and engineering concerns. To this end, analysis of seasonal and annual maximum daily precipitation at 667 long‐term stations across the eastern United States was pursued to ascertain recent changes in the extreme events over the 1950–2011 period. Three key results from this study illuminate less understood facets of recent changes in precipitation extremes: (a) an overall increase in the fraction of seasonal and annual maximum precipitation events linked to TCs, (b) a dramatic increase in the correlation between Accumulated Cyclone Energy Index and the leading principal component of extreme precipitation, and (c) changes in the spatial patterns of regions with highest TC‐related risk for heavy precipitation. [ABSTRACT FROM AUTHOR]

Changes in seasonality of extreme storms have important implications for public safety, storm water infrastructure, and, in general, adaptation strategies in a changing climate. While past research on this topic offers some approaches to characterize seasonality, the methods are somewhat limited in their ability to discern the diversity of distributional types for extreme precipitation dates. Herein, we present a comprehensive approach for assessment of temporal changes in the calendar dates for extreme precipitation within a circular statistics framework which entails: (a) three measures to summarize circular random variables (traditional approach), (b) four nonparametric statistical tests, and (c) a new nonparametric circular density method to provide a robust assessment of the nature of probability distribution and changes. Two 30 year blocks (1951-1980 and 1981-2010) of annual maximum daily precipitation from 10 stations across the state of Maine were used for our analysis. Assessment of seasonality based on nonparametric approach indicated nonstationarity; some stations exhibited shifts in significant mode toward Spring season for the recent time period while some other stations exhibited multimodal seasonal pattern for both the time periods. Nonparametric circular density method, used in this study, allows for an adaptive estimation of seasonal density. Despite the limitation of being sensitive to the smoothing parameter, this method can accurately characterize one or more modes of seasonal peaks, as well as pave the way toward assessment of changes in seasonality over time. [ABSTRACT FROM AUTHOR]

Warm season (June-September) hydroclimatology is a key determinant of freshwater supplies in the Korean Peninsula. In the Han River Basin, nearly three-fourths of annual discharge occurs during the warm season. Given the increased reliance on water supplies for numerous human and ecosystem functions, an integrated view of water supplies within the context of the regional hydroclimate will aid efforts to achieve water resources sustainability. To that end, this diagnostic study investigates several key aspects of the Han River hydrologic regime: (i) the warm season hydroclimatology; (ii) understanding the relative contribution of typhoon and non-typhoon precipitation events to warm season streamflow; and (iii) relationships with large-scale atmospheric teleconnections patterns and the implied seasonal predictability. Statistical analysis of modelled unimpaired streamflow indicates that the East Atlantic-Western Russia (EA-WR) teleconnection pattern modulates the Han River streamflow during the warm season. Furthermore, a linear modelling approach using the EA-WR and East Atlantic (EA) teleconnection indices offers a useful empirical framework for understanding the systematic shifts in streamflow probability distributions. Based on these results, potential use of this information for multi-objective water resources management, environmental flow prescriptions, proactive use of seasonal forecasts, and some open questions are briefly discussed. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]