Probability inundation map for a scenario combining storm surge and sea-level rise

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Tree heights and diameters of Switzerland's Sanasilva forests

Measurements of tree heights and crown sizes are essential in long-term monitoring of spatially distributed forests to assess the health of forests over time. In Switzerland, in 1994 and 1997, more than 4'500 trees have been recorded in a 8x8 km plot within the Sanasilva Inventory, which comprises the Swiss Level I sites of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests' (ICP Forests). Tree heights and crown sizes were measured for the dominant and co-dominant trees (n = 1,723), resulting in a data set from 171 plots in Switzerland, spreading over a broad range of climatic gradient and forest characteristics (species recorded = 20). Average tree height was 22.1 m, average DBH 34.6 cm and crown diameter 6.5 m. The data set presented here is open to use and shall foster research in allometric equation modelling.