Consequences of More Extreme Precipitation Regimes for Terrestrial Ecosystems
Amplification of the hydrological cycle as a consequence of global warming is forecast to lead to more extreme intra-annual precipitation regimes characterized by larger rainfall events and longer intervals between events. We present a conceptual framework, based on past investigations and ecological theory, for predicting the consequences of this underappreciated aspect of climate change. We consider a broad range of terrestrial ecosystems that vary in their overall water balance. More extreme rainfall regimes are expected to increase the duration and severity of soil water stress in mesic ecosystems as intervals between rainfall events increase. In contrast, xeric ecosystems may exhibit the opposite response to extreme events. Larger but less frequent rainfall events may result in proportional reductions in evaporative losses in xeric systems, and thus may lead to greater soil water availability. Hydric (wetland) ecosystems are predicted to experience reduced periods of anoxia in response to prolonged intervals between rainfall events. Understanding these contingent effects of ecosystem water balance is necessary for predicting how more extreme precipitation regimes will modify ecosystem processes and alter interactions with related global change drivers.
Long-Term Trends in Extreme Precipitation Events over the Conterminous United States and Canada
This paper describes the results of an analysis of trends in short duration (1‐7 days) extreme precipitation events that have a recurrence interval of 1 yr or longer for stations in the United States and Canada. This definition of extreme precipitation was chosen because such events are highly correlated with hydrologic flooding in some U.S. regions. The dominant temporal characteristic of a national event composite index is significant low-frequency variability. There were lengthy periods of a below-average number of events in the 1930s and 1950s and an above-average number of events in the early 1940s, early 1980s, and 1990s. Regional variations often differ substantially from the national composite. A simple linear analysis indicates that the overall trend covering the period 1931‐96 has been upward at a highly statistically significant rate over the southwest United States and in a broad region from the central Great Plains across the middle Mississippi River and southern Great Lakes basins. The national trend for the United States is upward at a rate of 3% decade 21 for the period 1931‐96. While the annual trend for Canada is upward for the period 1951‐93, it is not statistically significant. Although the high statistical significance of the results is partially a consequence of the low frequency during the 1930s and 1950s located in the first half of the record, the latter half of the record exhibits an upward trend nearly identical to the entire record. However, an analysis of a 101-yr record of midwestern stations shows that heavy precipitation event frequencies around the turn of the twentieth century (1896‐1906) were higher than for other periods of comparable length, except for 1986‐96. Although data were not available in digital form to extend the analysis back to 1896 for the entire United States, the midwestern analysis shows that interpretation of the recent upward trends must account for the possibility of significant natural forcing of variability on century timescales.
Bayesian Spatial Modeling of Extreme Precipitation Return Levels
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply it to a region in Colorado. Separate hierarchical models are constructed for the intensity and the frequency of extreme precipitation events. For intensity, we model daily precipitation above a high threshold at 56 weather stations with the generalized Pareto distribution. For frequency, we model the number of exceedances at the stations as binomial random variables. Both models assume that the regional extreme precipitation is driven by a latent spatial process characterized by geographical and climatological covariates. Effects not fully described by the covariates are captured by spatial structure in the hierarchies. Spatial methods were improved by working in a space with climatological coordinates. Inference is provided by a Markov chain Monte Carlo algorithm and spatial interpolation method, which provide a natural method for estimating uncertainty.
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