Landslides are a serious threat to life and property throughout the world. The causes of landslides are various since multiple dynamic processes are involved in driving slope failures. One of these causes is prolonged rainfall, which affects slope stability in different ways. Water infiltrating in a hillslope may cause a rise of the piezometric surface, which, in turn, involves an increase of the pore water pressure and a decrease of the soil shear resistance. For this reason, knowledge of spatio-temporal dynamics of soil water content, infiltration processes and groundwater dynamics, is of considerable importance in the understanding and prediction of landslides dynamics. In this paper a spatially distributed and physically based approach is presented, which embeds a slope failure method in a hydrological model. The hydrological model here used is the tRIBS model (Triangulated Irregular Network Real-Time Integrated Basin Simulator) that allows simulation of most of spatial-temporal hydrologic processes (infiltration, evapotranspiration, groundwater dynamics and soil moisture conditions) that can influence landsliding. Slope stability is assessed by implementing the infinite slope model in tRIBS. The model, based on geotechnical and geomorphological characteristics, classifies each computational cell as unconditionally stable or conditionally stable. Soil moisture conditions resulting from precipitation can trigger landslides at conditionally stable locations. When a landslide occurs, the model also computes the amount of detached soil and its possible path downslope. Model performance has been initially tested on a small catchment with very steep slopes, located in the northern part of Sicily (Italy), after a sensitivity analysis concerning some model parameters.
We analyze how extreme rainfall intensities in the Eastern United States depend on temperature T, dew point temperature T d , and convective available potential energy CAPE, in addition to geographic sub-region, season, and averaging duration. When using data for the entire year, rainfall intensity has a quasi Clausius-Clapeyron (CC) dependence on T, with super-CC slope in a limited temperature range and a maximum around 25°C. While general, these features vary with averaging duration, season, the quantile of rainfall intensity, and to some extent geographic sub-region. By using T d and CAPE as regressors, we separate the effects of temperature on rainfall extremes via increased atmospheric water content and via enhanced atmospheric convection. The two contributions have comparable magnitudes, pointing at the need to consider both T d and atmospheric stability parameters when assessing the impact of climate change on rainfall extremes.
Tornadoes and severe thunderstorms kill people and damage property every year. Estimated U.S. insured losses due to severe thunderstorms in the first half of 2016 were $8.5 billion (US). The largest U.S. effects of tornadoes result from tornado outbreaks, which are sequences of tornadoes that occur in close succession. Here, using extreme value analysis, we find that the frequency of U.S. outbreaks with many tornadoes is increasing and that it is increasing faster for more extreme outbreaks. We model this behavior by extreme value distributions with parameters that are linear functions of time or of some indicators of multidecadal climatic variability. Extreme meteorological environments associated with severe thunderstorms show consistent upward trends, but the trends do not resemble those currently expected to result from global warming.
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