DATA

This incredible dataset of shoreline position at the mean sea level is provided by Vos, K., Harley, M., and Splinter K. (University of New South Wales) as part of their studies on shoreline change in the Pacific region (Vos et al., 2023). Details on CoastSat, the technique to extract the shoreline position, can be found in Vos et al. (2019). Use the data and let us know if anything does not work. 

Vos, K., Splinter, K.D., Harley, M.D.,  Simmons, J.A. and Turner, I.L., 2019. CoastSat: A Google Earth  Engine-enabled Python toolkit to extract shorelines from publicly  available satellite imagery. Environmental Modelling & Software, 122, p.104528.

Vos, K., Harley, M.D., Turner, I.L.  and Splinter, K.D., 2023. Pacific shoreline erosion and accretion  patterns controlled by El Niño/Southern Oscillation. Nature Geoscience, 16(2), pp.140-146.

Here we provide four time-slices of high resolution (9 km) wave climate data for the New Zealand waters. We developed a set of historical and projected (1993–2006, 2026–2046, 2080–2100) wave climatologies from 3 global climate models (ACCESS1-0, CNRM-CM5 and MIROC5) and two representative concentration pathways (RCP 4.5, RCP 8.5).

The database comprises a set of integrated and partitioned wave parameters downscaled from a global wave hindcast with SWAN. Three-hourly data at a 9Km resolution is available for visualisation and download throughout the whole New Zealand area.

A comprehensive description of the data, methods and validation, together with a wave climate analysis along New Zealand can be found in Seas and swells throughout New Zealand: A new partitioned hindcast

To download the dataset, please first go through the read me file (click here ).

The database comprises wave, beach and runup parameters measured on different beaches around the world.  Please make sure you cite the appropriate publication when using the data (not this page but the original authors). Collecting the data is hard work and needs to be acknowledged appropriately. 

Click to find a 0.25° spatial resolution database from 1870 to 2100 for different global climate models and scenarios.

Here you can find the data used in:  Montaño, J., Coco, G., Antolínez, J.A.A. et al. Blind testing of shoreline evolution models. Sci Rep 10, 2137 (2020). https://doi.org/10.1038/s41598-020-59018-y  

Translating coast and estuary research into Applications is the motivation behind CoastalSEA. The Apps are founded on 2 toolboxes, one to handle multi-dimensional data sets and the other to allow the rapid creation of graphical user interfaces with some default plotting and statistical tools and the ability to add bespoke tools. The Apps range from analysis of coastal data to modelling of estuary dynamics, considering a range of spatial and temporal scales. The suite includes:

dstoolbox: Tables   for multi-dimensional data sets with dimension and variable meta-data

muitoolbox: Build   bespoke model interfaces with default set of tools

Asmita: Morphological   model for inlets and estuaries

ChannelForm: Model   marine transgression of an estuary channel within a valley

CoastalTools: Analyse   coastal process data

CSTmodel: Hydraulic   model for convergent tidal-rivers

ModelSkill: Examine   model performance using a Taylor Plot and Skill Score

ModelUI: Set   of demonstration applications to illustrate use of muitoolbox

MRBreach: Examine   site hypsometry and design breaches for managed realignment sites

SedTools: Analyse   settling column data

WaveRayModel: Forward   and backward wave ray tracing model

Further background and access to the manuals is provided at www.coastalsea.ukand the Open Source code is available on GitHub at https://github.com/CoastalSEA.

https://plynett.github.io/tonys-java-apps/wavetheory.html

Here you can find the data  used in the manuscript:  " Rationalizing the differences amongst hydraulic relationships using a process-based model " (Xu et al., Water Resource Research, 2021). The excel file provides data and associated references.

Here you can find the data, codes and results used in the manuscript:  "The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions "