Storm surges that occur along low-lying, densely populated coastlines can leave devastating societal, economical, and ecological impacts. To protect coastal communities from flooding, return periods of storm tides, defined as the combination of the surge and tide, must be accurately evaluated. Here we present storm tide return periods using a novel integration of two modelling techniques. For surges induced by extratropical cyclones, we use a 38-year time series based on the ERA5 climate reanalysis. For surges induced by tropical cyclones, we use synthetic tropical cyclones from the STORM dataset representing 10,000 years under current climate conditions. Tropical and extratropical cyclone surge levels are probabilistically combined with tidal levels, and return periods are computed empirically. The COAST-RP dataset contains storm tide levels representing the 1, 2, 5, 10, 25, 50, 100, 250, 500, and 1000-year return period.

Coastal flooding is driven by both high tides and/or storm surge, the latter being caused by strong winds and low pressure in tropical and extratropical. The combination of storm surge and the astronomical tide is defined as the storm tide. To gain understanding into the threat imposed by coastal flooding and to identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation and assess the coastal flood hazard. Most models capable of simulating coastal inundation at the global scale follow a simple planar approach, often referred to as bathtub models. The main limitations of this type of model are that they implicitly assume an infinite flood duration and do not capture relevant physical processes. In this study we develop a method to generate hydrographs called HGRAPHER, and provide a global dataset of storm tide hydrographs. These hydrographs represent the typical shape of an extreme storm tide at a certain location along the global coastline. We test the sensitivity of the HGRAPHER method with respect to two main assumptions that determine the shape of the hydrograph, namely the surge event sampling threshold and coincidence in time of the surge and tide maxima. These hydrographs can be used to move away from planar to more advanced dynamic inundation modelling techniques at large scales. The dataset consists of storm tide hydrographs corresponding to the 1-in-100 year RP. The storm tide levels correspond to the COAST-RP dataset. There are two hydrographs available per output location, one that represents average tide conditions and one for spring tide conditions.

Observed temperatures measured in weather stations that were used in the bias removing analysis. Data from São luís, Natal, São Mateus, Iguape, and Rio Grande (from 2007 to 2019). The observed dataset was measured in automatic weather stations from the Brazilian National Institute of Meteorology (INMET; https://portal.inmet.gov.br/)

Part of the image service RP APPEALS Coastal. Coastal Zones is a single feature with Commercial, Marina, Marsden, Mooring, Whangarei Marine and General Marine Zones. The Marsden Point Port Boundary zone was changed slightly along its eastern boundary in July 2022 and added to the APPEALS viewer June 2023

test datasets for hydroflows

for individual data licenses see the data_catalog.yml file

v0.1.10 Update sfincs-model files to be compatible with hydromt_fiat v0.5.3 and fiat-toolbox v0.1.16

v0.1.9 Update CMIP6 derived data

v0.1.8 Add Aggregation areas in Delft-FIAT model

v0.1.7 Add CMIP6 derived stats

v0.1.6 Add CMIP6 data

v0.1.5 Add Coast-RP data

v0.1.4 Update wflow test data

v0.1.3: Added Merit-Basins and updated GPEX

v0.1.2: Added model simulations (missing global-data.tar.gz)

v0.1.1: Added GPEX & GTSM data

This dataset maps the spatial and temporal variability of the physical sensitivity of Canada’s marine coasts in a changing climate for two time periods (early and late 21st century), and also provides the change in sensitivity between the two time periods. It employs decadal mean wave height, change in relative sea level, ground ice, coastal materials, backshore slope, and tide range, as described above, to derive a sensitivity index. Sensitivity in the early 21st century is termed CSI_2000s (Fig. 10), sensitivity in the late 21st century is CSI_2090s (Fig. 11), while the difference between the early and late century sensitivity fields is the predicted change in sensitivity over the century (Fig. 12). Coastal sensitivity indices were calculated using the μ-statistics method, a nonparametric approach of combines indicators into a summary index (Wittkowski 2004). The calculation is based on coastal types, defined as a unique sequence of indicators which are the ranked score from 1-5 for each of the six variables defined above. For mapping purposes, the results are classified into five groups indicating areas of very low sensitivity (< -500), low sensitivity (-500 to -150), medium sensitivity (-151 to 150), high sensitivity (151 to 500), and very high sensitivity (> 500).https://dfo-mpo.gc.ca/science/rp-pr/accasp-psaccma/projects-projets/004-eng.htmlhttps://open.canada.ca/data/en/dataset/725913a9-f3ad-169f-d75a-1d80af639756

This data release provides flooding extent polygons (flood masks) and depth values (flood points) based on wave-driven total water levels for 22 locations within the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands. For each of the 22 locations there are eight associated flood mask polygons and flood depth point files: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. These flood masks can be combined with economic, ecological, and engineering tools to provide a rigorous financial valuation of the coastal protection benefits of coral reefs of the United States, Territories, and Affiliated Islands. The degradation of coastal habitats, particularly coral reefs, raises risks by exposing communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous, economic terms as artificial defenses such as seawalls, and therefore often not considered in decision-making. Engineering, ecologic, social, and economic tools were combined to provide a quantitative valuation of the coastal protection benefits of the coral reefs of the United States. The goal of this effort was to identify how, where, and when coral reefs provide the most significant coastal flood reduction benefits socially and economically under current and future climate change scenarios. A risk-based valuation framework to estimate the risk reduction benefits from coral reefs and provide annual expected benefits in social and economic terms was followed. The methods follow a sequence of steps integrating physics-based hydrodynamic modeling, quantitative geospatial modeling, and economic and social analyses to quantify the hazard, the role of coral reefs in reducing the hazard, and the resulting consequences (described in Storlazzi and others, 2019).