Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

The worldwide marine radiocarbon reservoir effect: definitions, mechanisms, and prospects

When a carbon reservoir has a lower radiocarbon content than the atmosphere, this is referred to as a reservoir effect. This is expressed as an offset between the radiocarbon ages of samples from the two reservoirs at a single point in time. The marine reservoir effect (MRE) has been a major concern in the radiocarbon community, as it introduces an additional source of error that is often difficult to accurately quantify. For this reason, researchers are often reluctant to date marine material where they have another option. The influence of this phenomenon makes the study of the MRE important for a broad range of applications. The advent of Accelerator Mass Spectrometry (AMS) has reduced sample size requirements and increased measurement precision, in turn increasing the number of studies seeking to measure marine samples. These studies rely on overcoming the influence of the MRE on marine radiocarbon dates through the worldwide quantification of the local parameter ΔR, that is, the local variation from the global average MRE. Furthermore, the strong dependence on ocean dynamics makes the MRE a useful indicator for changes in oceanic circulation, carbon exchange between reservoirs, and the fate of atmospheric CO2, all of which impact Earth's climate. This article explores data from the Marine Reservoir Database and reviews the place of natural radiocarbon in oceanic records, focusing on key questions (e.g., changes in ocean dynamics) that have been answered by MRE studies and on their application to different subjects

Steep declines in radioactive caesium after 30 years of monitoring alpine plants in mountain areas of central Norway

The Chernobyl accident exposed large areas of northern Europe to radiocaesium (137Cs). We investigated temporal and spatial variation in concentrations of radiocaesium among five functional groups of alpine plants at two mountain areas in central Norway over a 31-year period from 1991 to 2022. Average concentrations of radiocaesium were initially high in lichens and bryophytes at around 4600–6400 Bq/kg dry weight during 1991–1994 but then decreased dramatically over three decades to current concentrations of <200 Bq/kg for all plant groups in 2019–2022. The effective half-life of radiocaesium was estimated to be 4–6 years in lichens and mosses, 7–13 years in herbaceous plants, and 22–30 years in woody plants, which were less than the physical half-life of 30.2 years. Concentrations of radiocaesium were greater at the nutrient-poor site than at the nutrientrich site, probably due to greater deposition levels at higher elevations and the geographical pattern of the deposition. Functional groups of plants differed with higher concentrations among non-vascular than vascular plants. Common heather Calluna vulgaris was unusual among woody plants with high concentration of radiocaesium, especially in the new shoots. Our new estimates of concentrations and dynamics of radiocaesium for alpine plants in natural environments will be useful for modelling herbivore exposure and evaluating potential impacts on wildlife and human health. Chernobyl Plant uptake Lichens Mosses Radiocaesium Time series Vascular plantspublishedVersio

The role of GABA-B in sensorigating processing disorders in rat models, an autoradiographic study

INTRODUCTION: The process of sensorimotor gating is a neurological phenomenon referring to the brain’s ability to process and filter out stimuli in order to prevent an overflow of information. This phenomenon can be operationally measured by prepulse inhibition, which is the attenuation of a stimulus-induced startle response by introducing a milder preceding stimulus. Studies have shown that impairment of prepulse inhibition (PPI) has been correlated with diseases such as schizophrenia and autism spectrum disorder. Many brain areas, including the superior colliculus (SC), inferior colliculus (IC), mediodorsal thalamus (MD), basolateral amygdala (BLA), anterior cingulate cortex (ACC), and ventral hippocampus (VHPC), have been implicated in playing important roles in prepulse inhibition. While many studies have implicated GABA-A receptors in playing a role in PPI regulation, little work has been done on GABA-B receptors. An established rat model with induced prepulse inhibition impairment was used in this study. PPI impairment was induced via injection of the glutamate receptor antagonist dizocilpine. A subgroup of rats was also treated with the antihistamine pyrilamine to reverse the effects of dizocilpine. OBJECTIVES: The aims of this study are to: 1. Expand the understanding of prepulse inhibition in the context of neurological and developmental diseases such as autism spectrum disorder (ASD) and schizophrenia; 2. Identify potential significant differences within GABA-B receptor densities in the rat SC, IC, MD, BLA, ACC, or VHPC between treatment groups with and without dizocilpine and groups with and without pyrilamine. METHODS: Histological brain slides harvested from 36 Sprague-Dawley rats were provided by Dr. Edward Levin from Duke University’s Neurobehavioral Research Lab for this study. The brain slides were incubated in a radioligand solution specific for GABA-B receptors and exposed to autoradiograph film for approximately 12 weeks. The films were developed in a dark room and scanned electronically. GABA-B receptor densities were measured from the images and the data was analyzed using ANOVA and independent T tests. RESULTS: ANOVA testing revealed significant differences between treatment groups in the MD and VHPC. However, only the MD was found to have significant GABA-B receptor differences when comparing the dizocilpine and pyrilamine treatment groups to the control group. The VHPC was found to have significant differences in GABA-B receptor densities when directly comparing the dizocilpine group to the pyrilamine treatment group, rather than to the control group. There were no significant differences in GABA-B receptor densities as a result of either dizocilpine or pyrilamine treatment in the SC, IC, BLA, ACC, or VHPC. CONCLUSION: Changes in GABA-B receptor levels appear to play a role in both the impairment and rescue of PPI in the rat MD. It does not appear to play a role in the SC, IC, BLA, ACC, or VHPC for either the impairment or rescue of PPI function