Current research on flooding risk often focuses on understanding hazards, de-emphasizing the complex pathways of exposure and vulnerability. We investigated the use of both hydrologic and social demographic data for flood exposure mapping with Random Forest (RF) regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US. Topographic characteristics best described flood claim frequency, but RF prediction skill was improved at both spatial scales when socioeconomic data was incorporated. Substantial improvements occurred at the tract-level when the percentage of minority residents, housing stock value and age, and the political dissimilarity index of voting precincts were used to predict insurance claims. Census tracts with higher numbers of claims and greater densities of low-lying tax parcels tended to have low proportions of minority residents, newer houses, and less political similarity to state level government. We compared this data-driven approach and a physically-based pluvial flood routing model for prediction of the spatial extents of flooding claims in two nearby catchments of differing land use. The floodplain we defined with physically based modeling agreed well with existing federal flood insurance rate maps, but underestimated the spatial extents of historical claim generating areas. In contrast, RF classification incorporating hydrologic and socioeconomic demographic data likely overestimated the flood-exposed areas. Our research indicates that quantitative incorporation of social data can improve flooding exposure estimates.

The hypothesis of ecohydrological separation (ES) proposes that the water contained in surface soils is not uniformly extracted by root water uptake nor uniformly displaced by infiltration. Rather vegetation selectively removes water held under tension, and water infiltrating wet soil will bypass much of the water‐filled pore space. Methodological differences across previous studies have contributed to disagreement concerning the prevalence of ES. We measured stable isotopes of O and H in precipitation, snowpack, canopy throughfall, and stream water over a period of 18 months in a temperate catchment. At six locations across a wetness gradient, we sampled bulk soil water isotopes weekly and xylem water of Eastern hemlock and American beech stems seasonally. We used these observations in a soil column model including StorAge Selection functions to estimate the isotopic composition and ages of groundwater recharge and ET. Our findings suggest ES may exist with spatial and temporal heterogeneity. Root water uptake ages possibly vary between Eastern hemlock and American beech, suggesting functional strategies for water uptake may control the presence of ES. Newly infiltrated water bypassing the shallow soil was the most likely explanation for bulk soil isotopic measurements made at upslope locations during the winter and summer seasons, whereas rapid displacement of stored soil water by infiltrated waters was the most likely during the spring and fall seasons. Future research incorporating high temporal frequency soil and plant xylem water isotopic measurements applied to StorAge Selection functions may provide a useful framework for understanding rooting zone isotope dynamics.

Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991) utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland). The sensitive model parameters (of 11 total considered) include wind drag, the depth-induced breaking γ_B, and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters, and depth-induced breaking α_B) and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large number of interactions between parameters and a nonlinear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.