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Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin, and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies. [ABSTRACT FROM AUTHOR]

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models' IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the "true" underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods. [ABSTRACT FROM AUTHOR]

The bias correction of climate model outputs is based on the main assumption of the time invariance of the bias, in which the statistical relationship between observations and climate model outputs in the historical period stays constant in the future period. The present study aims to assess statistical bias correction under nonstationary bias conditions and its implications on the simulated streamflow over two snowmelt-driven Canadian catchments. A pseudoreality approach is employed in order to derive a proxy of future observations. In this approach, CRCM-ECHAM5 ensemble simulations are used as pseudoreality observations to correct for bias in the CRCM-CGCM3 ensemble simulations in the reference (1971-2000) period. The climate model simulations are then bias corrected in the future (2041-70) period and compared with the future pseudoreality observations. This process demonstrates that biases (precipitation and temperature) remain after the bias correction. In a second step, the uncorrected and bias-corrected CRCM-CGCM3 simulations are used to drive the Soil and Water Assessment Tool (SWAT) hydrological model in both periods. The bias correction decreases the error on mean monthly streamflow over the reference period; such findings are more mixed over the future period. The results of various hydrological indicators show that the climate change signal on streamflow obtained with uncorrected and bias-corrected simulations is similar in both its magnitude and its direction for the mean monthly streamflow only. Regarding the indicators of extreme hydrological events, more mixed results are found with site dependence. All in all, bias correction under nonstationary bias is an additional source of uncertainty that cannot be neglected in hydrological climate change impact studies. [ABSTRACT FROM AUTHOR]

In this study, the authors contrast four century-long meteorological datasets comprising of two sets of observations [Climate Research Unit (CRU) and Parameter–Elevation Regressions on Independent Slopes Model (PRISM)] and two atmospheric reanalyses [Twentieth Century Reanalysis (20CR) and Florida Climate Institute–Florida State University Land–Atmosphere Regional Reanalysis version 1.0 (FLAReS1.0)] to diagnose the El Niño–Southern Oscillation (ENSO) forced variations on the streamflow in 28 watersheds spread across the southeastern United States (SEUS). The datasets are used to force three different lumped (calibrated) hydrological models with precipitation from these four sources of century-long datasets separately to obtain the median prediction from 1800 (=3 models × 600 simulations per model per watershed per season) multimodel estimates of seasonal mean streamflow across the 28 watersheds in the SEUS for each winter season from 1906 to 2005. The authors then compare and contrast themean streamflow and its variability estimates fromall three of the century-long climate forcings. The multimodel strategy of simulating the seasonal mean streamflow is to reduce the hydrological model uncertainty. The authors focus on the boreal winter season when ENSO influence on the SEUS climate variations is well known. The authors find that the atmospheric reanalysis over the SEUS is able to reasonably capture the ENSO teleconnections as depicted in the CRU and PRISM precipitation datasets. Even the observed decadal modulation of this teleconnection by Atlantic multidecadal oscillation (AMO) is broadly captured. The streamflow in the 28 watersheds also show similar consistency across the four datasets in that the positive correlations of the boreal winter Niño-3.4 SST anomalies with corresponding anomalies of streamflow, the associated shift in the probability density function of the streamflow with the change in phase of ENSO, and the decadal modulation of the ENSO teleconnection by the AMO are sustained in the streamflow simulations forced by all four climate datasets (CRU, PRISM, 20CR, and FLAReS1.0). However, the ENSO signal in the streamflow is consistently much stronger in the southern watersheds (over Florida) of the SEUS across all four climate datasets. During the negative phase of the AMO, however, there is a clear shift of the ENSO teleconnections with streamflow, with winter streamflows in northern watersheds (over the Carolinas) exhibiting much stronger correlations with the ENSO Niño-3.4 index relative to the southern watersheds of the SEUS. This study clearly indicates that the proposed methodology using FLAReS1.0 serves as a viable alternative to reconstruct twentieth-century SEUS seasonal winter hydrology that captures the interannual variations of ENSO and associated decadal variations forced by the AMO. However, it is found that the FLAReS1.0 forced streamflow is far from adequate in simulating the streamflow dynamics of the watershed over the SEUS at a daily time scale. [ABSTRACT FROM AUTHOR]