NIVA
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Wave exposure (m2/s) was modelled, with a spatial resolution of 25 m, as an index using data on fetch (distance to nearest shore, island or coast), averaged wind speed and wind frequency (estimated as the amount of time that the wind came from one of 16 direction). Data on wind speed and direction were delivered by the Norwegian Meteorological Institute and averaged over a 10year period (i.e. 19952004). The model is run using the program WaveImpact based on the method “Simplified Wave Model” (SWM) developed and described by Isæus (2004). The method is a fetch model, where the fetch values are adjusted to simulate refraction and diffraction effects. The estimated fetch values for each of the 16 directions are multiplied with the average wind speed in the given direction. The model has been run by NIVA for the whole Norwegian coast, and has been used as part of the habitat modelling of the National program for mapping biodiversity – coast (Bekkby et al. 2013). The model has also been applied in several research projects in Norway (e.g. Bekkby et al. 2008, 2009, 2014, 2015, Bekkby & Moy 2011, Norderhaug et al. 2012, 2014, Pedersen et al. 2012, Rinde et al. 2014). The model has also been run for Sweden (e.g. Eriksson et al. 2004), Finland (Isæus & Rygg 2005), the Danish region of the Skagerrak coast and the Russian, Latvian, Estonian, Lithuanian and German territories of the Baltic Sea (Wijkmark & Isæus 2010). The wave exposure values range from Ultra sheltered to Extremely exposed (cf Wijkmark & Isæus 2010, similar to the EUNIS system of Davies & Moss 2004).

Region: the county Rogaland Number of field observations: 633 Field sampling year: 1992, 1993, 2012 Prevalence: 43 and 22 for 50% and 85% calcium carbonate content respectively Presence / absences: 275/358 and 142/491 for 50% and 85% calcium carbonate content respectively Method: BRT run with the Rpackage Dismo. The grid is made up by a combination of three BRT models; two simplified models including 12 and 13 predictors for carbonate sand with 50% calcium content) and one BRT model based on 10 predictor variables for carbonate sand with 85% calcium content; all run with tree complexity equal to 5. The grid constitutes the maximum predicted probability values among these three models, which all are “smoothed” by applying a neighborhood analysis (i.e. mean of 3x3 neighbor cells) in advance of combining the models). Number of predictor variables: 1013 Information about the predictor variables: depth, curvature at detailed, medium and coarse resolution (i.e. applying a 100, 500 and 1500 m moving calculating window respectively, based on the 25 m resolution DEM), wave exposure, slope of wave exposure, and optimal radiation index, all with 25 m resolution; minimum and average seafloor temperature, average seafloor salinity, minimum and average seafloor current speed, and slope of average seafloor current speed, the latter predictors from a hydrodynamic model with 800 m spatial resolution. AUC internal: 0.98

Region: the county Rogaland Method: BRT

• Model run in UTM zone 33N • The model was run in 2014 by NIVA • Data collected 20112013 by NGU, NIVA included absences from kelp surveys • The data were collected in Nordland county, but the models covers also a small part of Troms in the North and Trøndelag in the south. • 1078 datapoints, but only data down to 100 m depth and with more than 50 % carbonate content were included, resulting in 473 presences. • Modelled using Maxent on presence data, validated with 139 presences and 740 absences (the validation points are spatially biased towards areas shallower than 40 m depth) • Modelled the probability of finding carbonate sand with more that 50 % carbonate content (carbonate contact below 50 % is absence together with rocky seabed and other noncarbonate sand areas) • Input variables: depth, slope, curvature (with both 250, 500 and 1000 m calculation window), wave exposure, maximum current speed and mean current speed • Depth, slope, curvature and wave exposure had spatial resolution of 25 m, models developed by NIVA. The current speed had 200 m spatial resolution (developed from NorKyst800). • Cutoff value for identifying polygon presences: 0.31 • spatial (horizontal) distribution is……. • AUC=0.88 using independent validation presences and absences

Region: Nordland county Number of field observations: 4331 Field sampling years: 2011, 2012 Prevalence: 13% Presence / absences: 503 / 3828 Method: BRT run in R with the Rpackage Dismo. (GAMs were also tested and gave similar distribution.) Number of predictor variables: 9 Information about the predictor variables: curvature at coarse and medium detailed spatial resolution with a 1025 and 525 m moving calculating window respectively, based on a 25 m resolution DEM; DEM, slope, and wave exposure (all at 25 m resolution, the predictor variables are described in Rinde et al. 2006); mean salinity, mean current speed, maximum temperature and mean temperature, all with a 800 m resolution, but resampled to 25 m. AUC internal: 0.99.

Laminaria hyperborea kelp forest is modelled for the Troms coast of northern Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 431 data points and GAM analyses of presence and absence points (presence being kelp forest, absence being absence of kelp at all other densities). Data were collected 20082009 by Akvaplanniva, the model was run in 2011 by NIVA. The distribution model is based on depth, slope(log), wave exposure and median current speed, wave exposure being the most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator.

Laminaria hyperborea kelp forest is modelled for the Trondelag coast of Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 1170 data points and GAM analyses of presence and absence points (presence being kelp forest, absence being absence of kelp at all other densities). Data were collected 20072008 by the Norwegian Institute for Water Research (NIVA) and the Institute for Marine Research (IMR), the model was run in 2009 by NIVA. The distribution model is based on depth, slope, terrain curvature, wave exposure and median current speed, wave exposure being the far most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope, terrain curvature and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. Analyses showed that coverage (density of kelp defined as classes) increased with predicted probability. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator.

Carbonate sand deposits are modelled for the Trondelag coast of Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Carbonate sand deposits were defined as having at least 50 % carbonate content. The model was developed based on 1105 data points and GAM analyses of presence and absence points. Data were collected 20072008 by the Geological Survey of Norway (NGU), the model was run in 2009 by NIVA. The distribution model is based on depth, wave exposure and maximum current speed, depth being the most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator.

Region: The county Hordaland Number of field observations: 637 Field sampling year: 2004, 2005,2009, 2010 Prevalence: 34% Presence / absences: 215/422 Method: BRT run with the Rpackage Dismo. Number of predictor variables: 23 Information about the predictor variables: DEM (25 m resolution), slope, aspect, curvature at detailed, medium and coarse resolution (i.e. applying a 125, 525 and 1025 m moving calculating window respectively, based on the 25 m resolution DEM); wave exposure, latitude, longitude, and optimal radiation index, all with 25 m resolution; and maximum surface and seafloor current speed, slope of maximum surface and seafloor current speed, minimum surface and seafloor current speed, standard deviation of seafloor current speed, 10 and 90th percentile of surface and seafloor current speed, all current speed predictor variables with 200 m resolution, but resampled to 25 m. AUC independent data: 0.88

Region: The county Hordaland Number of field observations: 637 Field sampling year: 2007, 2009 Prevalence: 34% Presence / absences: 215/422 Method: BRT run with the Rpackage Dismo. Number of predictor variables: 10 Information about the predictor variables: DEM (25 m resolution), slope, aspect, curvature at detailed, medium and coarse resolution (i.e. applying a 125, 525 and 1025 m moving calculating window respectively, based on the 25 m resolution DEM); wave exposure, latitude, longitude, and optimal radiation index, all with 25 m spatial resolution. AUC independent data: 0.96