Homogeneous Spatial Units (HSU) - a Pan-European geographical basis for environmental and socio-economic modelling

The spatial data set delineates areas with similar environmental properties regarding soil, terrain morphology, climate and affiliation to the same administrative unit (NUTS3 or comparable units in size) at a minimum pixel size of 1km2. The scope of developing this data set is to provide a link between spatial environmental information (e.g. soil properties) and statistical data (e.g. crop distribution) available at administrative level. Impact assessment of agricultural management on emissions of pollutants or radiative active gases, or analysis regarding the influence of agricultural management on the supply of ecosystem services, require the proper spatial coincidence of the driving factors. The HSU data set provides e.g. the link between the agro-economic model CAPRI and biophysical assessment of environmental impacts (updating previously spatial units, Leip et al. 2008), for the analysis of policy scenarios. Recently, a statistical model to disaggregate crop information available from regional statistics to the HSU has been developed (Lamboni et al. 2016). The HSU data set consists of the spatial layers provided in vector and raster format as well as attribute tables with information on the properties of the HSU. All input data for the delineation the HSU is publicly available. For some parameters the attribute tables provide the link between the HSU data set and e.g. the soil map(s) rather than the data itself. The HSU data set is closely linked the USCIE data set.

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.

Late Miocene cyclicity and intercore correlations for ODP Sites 127-794 and 127-797

During Leg 127, the formation microscanner (FMS) logging tool was used as part of an Ocean Drilling Program (ODP) logging program for only the second time in the history of the program. Resistivity images, also known as FMS logs, were obtained at Sites 794 and 797 that covered nearly the complete Yamato Basin sedimentary sequence to a depth below 500 mbsf. The FMS images from these two sites at the northeastern and southwestern corners of the Yamato Basin thus were amenable to comparison. A strong visual correlation was noticed between the FMS logs taken in Holes 794B and 797C in an upper Miocene interval (350-384 mbsf), although the two sites are approximately 360 km apart. In this interval, the FMS logs showed a series of more resistive thin beds (10-200 cm) alternating with relatively lower resistivity layers: a pattern that was manifested by alternating dark (low resistivity) and light (high resistivity) banding in the FMS images. We attribute this layering to interbedding of chert and porcellanite layers, a common lithologic sequence throughout Japan (Tada and Iijima, 1983, doi:10.1306/212F82E7-2B24-11D7-8648000102C1865D). Spatial frequency analysis of this interval of dominant dark-light banding showed spatial cycles of period of 1.1 to 1.3 and 0.6 m. This pronounced layering and the correlation between the two sites terminate at 384 mbsf, coincident with the opal-CT to quartz transition at Site 794. We think the correlation in the FMS logs might well extend earlier in the middle Miocene, but the opal-CT to quartz transition obscures this layering below 384 mbsf. Although 34 m is only a small part of the core recovered at these two sites, it is significant because it represents an area of extremely poor core recovery and an interval for which a near-depositional hiatus was postulated for Site 797, but not for Site 794.