2 Sea-level rise (SLR) poses a range of threats to natural and built environments 1,2 , making assessments of SLR-induced hazards essential for informed decision-making 3 . We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30 m resolution predictions for more than 38,000 km 2 of diverse coastal landscape in the northeastern United States (U.S.). Probabilistic SLR projections, coastal elevation, and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response vs. inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge.This approach is well-suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints. As an alternative, we developed a data-driven coastal response (CR) model that considers both inundation and dynamic response using a range of SLR scenarios and datasets describing elevation and vertical land movement. We integrate these elements with land-cover information to assess CR likelihoods in the form of a dynamic probability, DP = 1-Prob. (inundate), using a Bayesian network Maine through Virginia, and includes a region with a wide range of coastal development, infrastructure, and environments found globally; including uplands, barrier beaches, spits, islands, mainland beaches, cliffs, rocky headlands, estuaries, and wetlands. The study area is defined by the -10 and +10 m elevation contours and mapped as a 30 m grid.To predict CR likelihoods (Figure 2), we first compute an adjusted land elevation with respect to projected sea levels:where AE represents the adjusted elevation with respect to a future sea level; E denotes the initial land elevation; SL is a projected sea level in the 2020s, 2030s, 2050s, or 2080s; and VLM gives the current rate of vertical land movement due to glacial isostatic adjustment, tectonics, and other non-climatic effects such as groundwater withdrawal and sediment compaction 15 . Sources of uncertainty in AE predictions include SLR projections, elevation data accuracy, vertical datum adjustments, and the interpolation of VLM rates from point data; these geospatially-explicit input uncertainties are propagated through the model to produce a probability mass function P(AE) for every grid cell (Figure 2c,d). Once generated, AEs are related through evaluation of their dynamic response potential with generalized landcover information and used to produce a CR likelihood (Figures 1, 2).Discretized AE predictions provide an estimated submergence level comparable to many existing inundation models 3, 16 (Figure 2). However, our predictions include several notable improvement...

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sealevel-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

Identification and mapping of hypervelocity impact crater (HICs) sites require significant effort on ground truthing data collection and local instrument-driven research. The recent advancements in Earth observation (EO) technology and geographical information systems (GIS) have increased our ability to study HICs. With EO imagery and relevant spatial data now readily available online at no cost, GIS and remote sensing provide a very attractive option in investigating the Earth's surface. In this framework, our study addresses the use of GIS and EO techniques by looking at a possible impact crater in upstate New York, United States. The Panther Mountain crater is thought to have been created by a meteor impact over 300,000 years ago during the Devonian or Mississippian geologic periods. Using freely available data from previous research, this study aimed at mapping land cover and geologic data and analyzing their correlation at Panther Mountain and it surrounding area. Findings of the study have showed encouraging results. A correlation between Panther Mountain's bedrock geology and vegetation was reported to be higher than the coefficient of the surrounding area. Similarly, the correlation between Panther Mountain's surficial geology type and vegetation was significantly lower than that of the other region. The significant difference in correlations between the two regions supports the Panther Mountain impact site. All in all, the present study also produced encouraging results as regards to the use of GIS in identifying potential hypervelocity crater sites.