Choose by clicking on the map or by selecting from the Location menu.
The table below shows:
Date or Return Period | Height* |
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The table below shows specific amounts of local sea-level rise and specific probabilities that global sea-level rise will meet or exceed the ITF scenarios (adapted from Table 2.4 in the ITF Report).
Scenario | 2050 | 2100 | 3ºC GSW | 5ºC GSW | VHE/LCP |
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The graphs above show how the frequency of flooding will evolve during the 21st century for the selected location, threshold, and scenario. The first graph shows number of flooding days per year, while the second graph shows how the flooding days are distributed within a future year. The likely and very likely ranges provide a range of possibilities accounting for unpredictable natural fluctuations in sea level and storminess.
This tool provides projections and analysis of high-tide flooding days at the locations of tide gauges. If a tide gauge does not exist at the desired location, analysis from the closest tide gauge can provide useful information. However, it is important to consider the potential impact of local factors that can differ even over short distances such as land subsidence.
You can select a location in two ways:
Not every "flooding" threshold corresponds to tangible impacts. Choosing a relevant flooding threshold is essential for understanding and determining the impacts of current and future high-tide flooding. One way to determine a relevant threshold is to look at tide-gauge observations on the Observed Flooding tab in this tool and determine whether past high sea-level events above various thresholds correspond to the timing and/or frequency of known impacts.
You can select a threshold in two ways:
*Predetermined threshold heights were obtained from the CO-OPS API For Data Retrieval and were last updated in this tool on December 31, 1999.
The Flooding Days projections provided in this tool are based on the most recent sea-level rise scenarios from the 2022 U.S. Interagency Task Force (ITF) report and can be viewed on the Sea-Level Rise tab in this tool. These scenarios use information from the IPCC 6th Assessment Report to produce individual scenarios corresponding to various levels of risk tolerance.
You can select one of five ITF scenarios from the dropdown menu.
To fully explore the ITF scenarios, see the Interagency Sea Level Rise Scenario Tool. and these frequently asked questions.
From the selected combination of location, threshold, and scenario, the Projected Flooding tab shows the expected number of flooding days per year during the 21st century. The projections are based on the approach of Thompson et al. (2021) but updated to incorporate the 2022 ITF scenarios. See the Methodology link on this page for more details.
High-tide flooding is—as the name suggests—flooding that occurs at high tide, but it is not necessarily due to the tidal forces of the moon and sun alone. There are a variety of factors that contribute to any given high sea-level event. Some of them have to do with the astronomical forces that generate tides while others do not.
Most people are aware that some high tides are higher than others. The spring-neap cycle, for example, is related to the alignment of the earth, moon, and sun, and causes the height of high tides to get higher and lower roughly twice per month. Most people are also aware that sea level rise will cause the highest tides to get even higher and cause flooding thresholds to be exceeded more often (Figure 1).
However, there are myriad factors across time and space scales that affect how often the ocean height will exceed a given threshold. For example:
Tidal amplitude does not just vary on a quasi-monthly basis due to the spring-neap cycle—it also varies from season to season and year to year. More specifically, there are substantial 4.4- and 18.6-year cycles in the tides with important implications for the frequency of coastal flooding.
Changes in ocean circulation and year-to-year variations in climate cause average sea level to rise and fall over periods of months or years, with phenomena such as El Niño and changes in the strength of the Gulf Stream) being two leading factors along the Pacific and Atlantic coastlines of the U.S., respectively.
Changes in storminess or short-term chaotic ocean variability (i.e., ocean "weather" like eddies) can lead to differences in flooding frequency from one month or year to the next.
It is possible for multiple factors such as those listed above to coincide and produce a "cluster" of flooding events. For example, there was a period of state-wide, repeated coastal flooding in Hawai`i during summer 2017, which was related to the combination of seasonally high tides, eddies, El Niño, and and weak trade winds. Similar situations can occur elsewhere, which explains why locations affected by high-tide flooding tend to experience occasional, severe years with many events while other years experience few or none at all.
The methodology used to produce the high-tide flooding projections in this tool takes into account the tendency of flooding events to "cluster" together in occasional—yet inevitable—severe months or years. As a result, the projections provide a range of possibilities for any given year, acknowledging that some years will be worse than others, and it is not possible to know in advance which years those will be. The projections leverage the predictability inherent in certain contributions (e.g., tidal amplitude and climate-change-induced sea level rise) and use statistical methods to account for everything else.
The methodology used to produce the high-tide flooding projections in this tool follows the process developed by Thompson et al. (2021). The method has been updated to incorporate the most recent sea-level rise scenarios from the 2022 Interagency Task Force report and applied to any location within the U.S. and its territories for which there is sufficient tide gauge data.
The machinery of the projection algorithm is based around the idea that the probability mass distribution governing the number of threshold exceedances in a given year can be parameterized as a function of the height difference between the threshold of interest and the height of the highest tides of the year. For example, if the threshold is far above the height of the highest tides, then the probability of exceedance is low, and one would expect the probability of zero events to be high with small probabilities of multiple events (Figure 2a). Alternatively, if the threshold is relatively close to the height of the highest tides, one would expect high probabilities of multiple events with lower probabilities of zero and many events (Figure 2b). In practice, the method employs the flexible beta-binomial probability mass distribution to represent the varying shapes of the distribution as the height of the highest tides varies relative to the height of the threshold. The parameters of the beta-binomial distribution are estimated as functions of the difference between threshold and highest tides on a location- and month-specific basis via an analysis of available tide gauge data.
Once the parameters of the distribution are established, projections of flooding days requires projections of the highest tides of the month. We define the highest tides of the month as the annual 99th percentile of astronomical tidal variability PLUS monthly mean sea level. The latter acts to change the baseline of the tidal variability similar to Figure 1. In order to project this quantity, we use three ingredients:
Ensemble projection of astronomical tidal variability. The ensemble tidal projections employed here are based on Gaussian process representations of periodic and stochastic variations in the amplitude and phase of major tidal constituents. These projections account for co-variability between certain constituents and mean sea level, as well as trends in tidal amplitude related to non-climatic factors.
Projections of local mean sea level trends and acceleration. It is essential to use local mean sea level projections that incorporate estimates of local and regional vertical land motion, as well as spatial differences in the response of ocean surface height to climate change (e.g., ice melt fingerprints). The high-tide flooding projections in this tool are based on sea-level rise scenarios from the 2022 Interagency Task Force report
Ensemble projections of stochastic variability in monthly mean sea level. These projections are based on Gaussian process representations of unpredictable variations in local monthly mean sea level primarily related to atmosphere-ocean dynamics.
The schematic in Figure 3 illustrates how these ingredients are combined into an ensemble prediction of the 99th percentile of tidal height for each month during the 21st century. When further combined with a user defined threshold and parameterization of the beta-binomial probability mass distribution tuned using tide gauge data, these components produce a probabilistic estimate for the number of flooding days above the threshold in each month and year.