Abstract
Many studies have projected that as the climate changes, the magnitudes of extreme precipitation events in the Northeastern United States are likely to continue increasing, regardless of the emission scenario. To examine this issue, we analyzed observed and modeled daily precipitation frequency (PF) estimates in the Northeastern US on the rain gauge station scale based on both annual maximum series (AMS) and partial duration series (PDS) methods. We employed four Coupled Model Intercomparison Project Phase 5 (CMIP5) downscaled data sets, including a probabilistic statistically downscaled data set developed specifically for this study. The ability of these four data sets to reproduce the observed features of historical point PF estimates was compared, and the two with the best historical accuracy, including the newly developed probabilistic data set, were selected to produce projected PF estimates under two CMIP5-based emission scenarios, namely Representative Concentration Pathway 4.5 (RCP4.5) and Representative Concentration Pathway 8.5 (RCP8.5). These projections indeed demonstrate a likely increase in PF estimates in the Northeastern US with noted differences in magnitudes and spatial distributions between the two data sets and between the two scenarios. We also quantified how the exceedance probabilities of the historical PF estimate values are likely to increase under each scenario using the two best performing data sets. Notably, an event with a current exceedance probability of 0.01 (a 100-year event) may have an exceedance probability for the second half of the 21st century of ≈0.04 (a 27-year event) under the RCP4.5 scenario and ≈0.05 (a 19-year event) under RCP8.5. Knowledge about the projected changes to the magnitude and frequency of heavy precipitation in this region will be relevant for the socio-economic and environmental evaluation of future infrastructure projects and will allow for better management and planning decisions.
Highlights
The time series of extreme precipitation events in the United States are not stationary [1], as the statistics describing them have been observed to change over the past several decades [2]
Many metrics could be used to describe the features of these extreme precipitation events, among which precipitation frequency (PF) analysis was selected for this study, as it is important to hydrology design [8]
For every partial duration series (PDS) threshold selection, generally have less than a 10% error compared with the observational results and no significant biases the UWPD data generally have less than a 10% error compared with the observational results and no (Figure 3f–h), while the Localized Constructed Analogs (LOCAs) data generally have more than a 20% error and a systematic bias toward significant biases (Figure 3f–h), while the LOCA data generally have more than a 20% error and a drier values, especially PDS qt98 (Figure 3j–l)
Summary
The time series of extreme precipitation events in the United States are not stationary [1], as the statistics describing them have been observed to change over the past several decades [2] They will likely continue to change in the future as the climate changes (e.g., [3]). Informed by these results, we select the best data sets and use them to present possible changes to the PF estimates in the future under two different Representative. For the full model names, we refer to the CMIP5 website: https://cmip.llnl.gov/cmip5/docs/CMIP5_modeling_
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