After a dental operation a former laboratory technician was referred to our clinic because of swelling of his tongue, lips, and gingival mucosa. Patch testing with the ICDRG standard test battery gave positive reactions to colophony, balsam of Peru, and turpentine peroxides. Further patch testing revealed hypersensitivity to peppermint oil (an ingredient of several dental preparations) due to the sensitizing properties of three ingredients: alpha-pinene, limonene, and phellandrene. These compounds also occur in turpentine oil, a substance used in the patient's laboratory.
Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.
Islands are particularly vulnerable to the effects of land cover change due to their limited size and remoteness. This study analyzes vegetation cover change in the agricultural area of Santa Cruz (Galapagos Archipelago) between 1961 and 2018. To reconstruct multitemporal land cover change from existing land cover products, a multisource data integration procedure was followed to reduce imprecision and inconsistencies that may result from the comparison of heterogeneous datasets. The conversion of native forests and grasslands into agricultural land was the principal land cover change in the non-protected area. In 1961, about 94% of the non-protected area was still covered by native vegetation, whereas this had decreased to only 7% in 2018. Most of the agricultural expansion took place in the 1960s and 1970s, and it created an anthropogenic landscape where 67% of the area is covered by agricultural land and 26% by invasive species. Early clearance of native vegetation took place in the more accessible—less rugged—areas with deeper-than-average and well-drained soils. The first wave of settlement consisted of large and isolated farmsteads, with 19% of the farms being larger than 100 ha and specializing in diary and meat production. Over the period of 1961–1987, the number of farms doubled from less than 100 to more than 200, while the average farm size decreased from 90 to 60 ha/farmstead. Due to labor constraints in the agricultural sector, these farms opted for less labor-intensive activities such as livestock farming. New farms (popping up in the 1990s and 2000s) are generally small in size, with <5 ha per farmstead, and settled in areas with less favorable biophysical conditions and lower accessibility to markets. From the 1990s onwards, the surge of alternative income opportunities in the tourism and travel-related sector reduced pressure on the natural resources in the non-protected area.
Abstract. Various regions in the world experience land cover and land use changes. One such a region is the Dry Chaco ecoregion in South America, characterized by deforestation and forest degradation since the 1980s. In this study, we simulated the water balance over the Dry Chaco and assessed the impact of land cover changes thereon, using three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS) with updated parameters. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based dynamic vegetation parameters instead of default climatological vegetation parameters, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and ‘efficiency space’ for various baseline and revised experiments showed that large regional and long-term differences relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, different LSM structures redistributed water differently in response to these parameter updates. A time series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature showed that no LSM setup significantly outperformed another for the entire region, and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the simulated surface soil moisture bias relative to pixel-scale in situ observations, and the simulated Tb bias relative to regional Soil Moisture Ocean Salinity (SMOS) observations.
<p>The South-American Dry Chaco is a unique ecoregion as it is one of the largest sedimentary plains in the world hosting the planet&#8217;s largest dry forest. The 787.000 km&#178; region covers parts of Argentina, Paraguay, and Bolivia and is characterized by a negative climatic water balance as a consequence of limited rainfall inputs (800 mm/year) and high temperatures (21&#176;C). In combination with the region&#8217;s extreme flat topography (slopes < 0.1%) and shallow groundwater tables, saline soils are expected in substantial parts of the region. In addition, it is expected that large-scale deforestation processes disrupt the hydrological cycle resulting in rising groundwater tables and further increase the risk for soil salinization.</p><p>In this study, we identified the regional-scale patterns of subsurface soil salinity in the Dry Chaco. &#160;Field data were obtained during a two-month field campaign in the dry season of 2019. A total of 492 surface- and 142 subsurface-samples were collected along East-West transects to determine soil electric conductivity, pH, bulk density and humidity. Spatial regression techniques were used to reveal the topographic and ecohydrological variables that are associated with subsurface soil salinity over the Dry Chaco. The hydrological information was obtained from a state-of-the-art land surface model with an improved set of satellite-derived vegetation and land cover parameters.</p><p>In the presentation, we will present a subsurface soil salinity map for a part of the Argentinean Dry Chaco and provide relevant insights into the driving mechanisms behind it.</p>
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