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Drainage basins of the Northern Apennines, particularly in the clayey settings, bear among the highest rates of landsliding worldwide. A history of major land cover changes has left a landscape characterized by sparse, coppice-managed forest, transitional shrubs, and actively eroding badlands. Historical trends of landslide occurrence are examined in the Sillaro River basin (139 km2) in relation to land cover and climatic changes. To this purpose we have compiled a multi-temporal (1954–2018) landslide inventory (n = 1164) across twelve sequential photo sets that bears decadal (7- to 15-yr) and finer (2- to 6-yr) temporal resolution respectively before and after 1996. To account for changes in meteorological forcing, we examine: (i) the total annual precipitation (PRCPTOT); (ii) the annual maximum daily precipitation (RX1day); and (iii) the precipitation fraction (R99pTOT) due to extremely wet days. We find that landslide activity is strongly controlled by lithology, with landslide densities in claystones 3-to-4 times higher than in marl-sandstone alternations. This difference is chiefly associated with badlands, which are the most active land cover type and where new scars at a site could recur up to nine times. To evaluate the influence of varying temporal resolution on inventory completeness, hence on inference about land cover and climatic effects, we constrain the time scales of landslide visibility and assess the relative rates of undersampling. We find that visibility functions decline non-linearly with time, and that an inventory compiled at 5-year resolution would be missing up to 20 % of the landslide scars, with the size of an additional 27 % that would be underestimated due to revegetation. Overall, detection of entire landslide scars, which varies with land cover, becomes rare after 13 years in transitional shrubs, and after 17 years in badlands and managed forest. The historical analysis shows that landslide count: (i) increases in 1955–1976, a period o

In the past 50 years, a large variety of statisti- cally based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, which are applicable to a large range of spa- tial scales, use a large variety of input thematic data, differ- ent model combinations, and several approaches to evaluate the models’ performance. Despite the numerous applications available in the literature, a standard approach for suscepti- bility modeling and zonation is still missing. The literature search revealed that several software pro- gram and tools are available to evaluate regional slope stability using physically based analysis, but only a few use statistically based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possi- bility to perform and combine different statistical suscepti- bility models and to evaluate their performances and asso- ciated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for sta- tistically based landslide susceptibility modeling which in- tegrates LAND-SE. LAND-SUITE completes and extends LAND-SE, adding functionalities to (i) facilitate input data preparation, (ii) perform preliminary and exploratory analy- sis of the available data, and (iii) test different combinations of variables and select the optimal thematic/explanatory set. LAND-SUITE provides a tool to assist the user during the data preparatory phase and to perform diversified statistically based landslide susceptibility applications.

The Science for Humanitarian Emergencies and Resilience (SHEAR) programme supports world-leading research to enhance the quality, availability and use of risk and forecast information. Forecasting rainfall-induced landslides is a difficult yet important task that can provide time to take action to save lives, reduce economic losses and help to mitigate the impacts of landslides. The type, quality and accessibility of data directly constrain the choice of approach used for the landslide forecasting and its skill. However, in many landslide-prone countries, limited resources, and lack of investment lead to limited data availability and/or insufficient quality data for informed forecasts. This paper collates understanding from SHEAR consortium members on key considerations for developing territorial (‘regional-scale’) landslide forecasts, particularly in developing country contexts.

Subsidence, the lowering of Earth's land surface, is a potentially destructive hazard that can be caused by a wide range of natural or anthropogenic triggers but mainly results from solid or fluid mobilization underground. Subsidence due to groundwater depletion (1) is a slow and gradual process that develops on large time scales (months to years), producing progressive loss of land elevation (centimeters to decimeters per year) typically over very large areas (tens to thousands of square kilometers) and variably affects urban and agricultural areas worldwide. Subsidence permanently reduces aquifer-system storage capacity, causes earth fissures, damages buildings and civil infrastructure, and increases flood susceptibility and risk. During the next decades, global population and economic growth will continue to increase groundwater demand and accompanying groundwater depletion (2) and, when exacerbated by droughts (3), will probably increase land subsidence occurrence and related damages or impacts. To raise awareness and inform decision-making, we evaluate potential global subsidence due to groundwater depletion, a key first step toward formulating effective land-subsidence policies that are lacking in most countries worldwide.

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.

[EN] Modelling rockfall phenomena is complex and requires various inputs, including an accurate location of the source areas. Source areas are controlled by geomorphological, geological, or other geo-environmental factors and may largely influence the results of the modelling. In the Canary Islands, rockfalls are extremely common and pose a major threat to society, costing lives, disrupting infrastructure, and destroying livelihoods. In 2011, the volcanic event on the island of El Hierro triggered numerous rockfalls that affected strategic infrastructures, with a substantial impact on the local population. During the emergency, the efforts performed to map the source areas and to model the rockfalls in the considerably steep landscape characterising the island were not trivial. To better identify the rockfall source areas, we propose a probabilistic modelling framework that applies a combination of multiple statistical models using the source area locations mapped in the field as the dependent variable and a set of thematic data as independent variables. The models use as input morphometric parameters derived from the Digital Elevation Model and lithological data as an expression of the mechanical behaviour of the rocks. The analysis of different training and validation scenarios allowed us to test the model sensitivity to the input data, select the optimal model training configuration, and evaluate the model applicability outside the training areas. The final map obtained from the model for the entire island of El Hierro provides the probability of a given location being a potential source area and can be used as the input for rockfall runout simulation modelling.

Landslide susceptibility modelling — a crucial step towards the assessment of landslide hazard and risk — has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term "landslide path dependency', for the firsttime. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in additionto a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide pathdependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventionalplus landslide path dependency variables — while avoiding the need for

Landslides are a major category of natural disasters, causing loss of lives, livelihoods and property. The critical roles played by triggering (such as extreme rainfall and earthquakes), and intrinsic factors (such as slope steepness, soil properties and lithology) have previously successfully been recognized and quantified using a variety of qualitative, quantitative and hybrid methods in a wide range of study sites. However, available data typically do not allow to investigate the effect that earlier landslides have on intrinsic factors and hence on follow-up landslides. Therefore, existing methods cannot account for the potentially complex susceptibility changes caused by landslide events. In this study, we used a substantially different alternative approach to shed light on the potential effect of earlier landslides using a multi-temporal dataset of landslide occurrence containing 17 time slices. Spatial overlap and the time interval between landslides play key roles in our work. We quantified the degree to which landslides preferentially occur in locations where landslides occurred previously, how long such an effect is noticeable, and how landslides are spatially associated over time. We also investigated whether overlap with previous landslides causes differences in landslide geometric properties. We found that overlap among landslides demonstrates a clear legacy effect (path dependency) that has influence on the landslide affected area. Landslides appear to cause greater susceptibility for follow-up landslides over a period of about 10 years. Follow-up landslides are on average larger and rounder than landslides that do not follow earlier slides. The effect of earlier landslides on follow-up landslides has implications for understanding of the landslides evolution and the assessment of landslide susceptibility.

An analysis of drought conditions on the Lake Trasimeno area (Umbria, Central Italy) and of their influence on the lake levels is presented. Lake Trasimeno is one of the largest Italian lakes, and its economic and environmental importance is very high. The analysis of temperature data (1963-2014) shows that annual temperature is increasing – in accordance with what is known for Central Italy and the Mediterranean area – with a significant gradient of about 0.023°C/ year. No significant annual and seasonal rainfall trends were observed over the Lake Trasimeno catchment. The power spectrum analysis of rainfall and lake level fluctuations shows that both periodograms have high statistical confidence levels (>99%) for annual and semi-annual cycles. The annual cycles of the periodogram of lake level fluctuations show a higher statistical confidence level than semi-annual cycles. Some other cycles such as the El-Niño Southern oscillation, North Atlantic oscillation, and solar activity are highlighted, with significance levels lower than that of annual and semi-annual cycles. The standardized precipitation (SPI) and standardized reconnaissance drought indices, at different time scales, show that frequency and duration of extreme and severe droughts have increased in the last 25 years. A significant relationship between 12-month SPI and 12-month standardized lake levels fluctuations was obtained for the 1989-2014 period, indicating that SPI12 can be a useful indicator to represent drought severity for systems such as the Lake Trasimeno by considering lake level fluctuations rather than lake levels.

We give an overview of QPACE 2, which is a custom-designed supercomputer based on Intel Xeon Phi processors, developed in a collaboration of Regensburg University and Eurotech. We give some general recommendations for how to write high-performance code for the Xeon Phi and then discuss our implementation of a domain-decomposition-based solver and present a number of benchmarks., Comment: plenary talk at Lattice 2014, to appear in the conference proceedings PoS(LATTICE2014), 15 pages, 9 figures

Udgivelsesdato: 2007-Sep, A single-nucleotide variant, C/T(-13910), located 14 kb upstream of the lactase gene (LCT), has been shown to be completely correlated with lactase persistence (LP) in northern Europeans. Here, we analyzed the background of the alleles carrying the critical variant in 1,611 DNA samples from 37 populations. Our data show that the T(-13910) variant is found on two different, highly divergent haplotype backgrounds in the global populations. The first is the most common LP haplotype (LP H98) present in all populations analyzed, whereas the others (LP H8-H12), which originate from the same ancestral allelic haplotype, are found in geographically restricted populations living west of the Urals and north of the Caucasus. The global distribution pattern of LP T(-13910) H98 supports the Caucasian origin of this allele. Age estimates based on different mathematical models show that the common LP T(-13910) H98 allele (approximately 5,000-12,000 years old) is relatively older than the other geographically restricted LP alleles (approximately 1,400-3,000 years old). Our data about global allelic haplotypes of the lactose-tolerance variant imply that the T(-13910) allele has been independently introduced more than once and that there is a still-ongoing process of convergent evolution of the LP alleles in humans.