Winter storms cause severe damage in German forests. Different modelling approaches have already been used to try and map endangered areas to minimize the risk of wind damage by stand adaption. Prevalent models for Germany include empirical-statistical and hybrid-mechanistic models, such as ForestGALES (FG). As of yet, FG is not extensively used in Germany as its parametrization requires extensive experimental efforts to derive regionally sensitive species-specific parameters. Here, we implement a statistical calibration approach for German forest conditions with observed damage from single tree data, soil types, topography (topex) and gust speed data. We use simulated annealing to generate new species-specific values for the tree species, Norway spruce, European beech, and Douglas fir from within the range of all coniferous (deciduous) species for Norway spruce and Douglas fir (European beech) and an additional 10 % buffer around the default species-specific values for each species. We compare two optimization approaches: First, we aim to maximize the Matthew's correlation coefficient (MCC), which is calculated from the confusion matrix, applying a fixed classification threshold of 0.5. In comparison to the optimization at a fixed threshold, we optimized the species-specific parameters by maximizing the area-under-curve (AUC) value directly generated from the receiver-operator characteristic (ROC) analysis. We compare our statistical parametrizations for the considered species to those currently implemented in FG and validate the resulting damage probabilities based on confusion matrices and related performance measures. We created separate parametrizations for a single-tree and stand-wide analysis of storm damage risk, which we validated with gust speed data for Germany. Our results show, that for the single-tree method, MCC improved for all species: By 0.26 (0.22) for the calibration (validation) subset for Douglas fir, by 0.22 (0.18) for Norway spruce and by 0.08 (0.05) for European beech. The optimization for the stand-method shows an increase in MCC as well, with results not being considered due to low numbers of observation data. We show that for German forests, FG's predictive capability can be improved by statistical optimization when no tree-pulling data is available, which could be valuable for creating further regionalizations of FG.

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below -65 degrees C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day-1 and 48 mm h-1, combined with the region's soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters.

Rwanda, in eastern tropical Africa, is a small, densely populated country where climatic disasters are often the cause of considerable damage and deaths. Landslides are among the most frequent hazards, linked to the country's peculiar configuration including high relief with steep slopes, humid tropical climate with heavy rainfall, intense deforestation over the past 60 years, and extensive use of the soil for agriculture. The Karongi region, in the west-central part of the country, was affected by an exceptional cluster of more than 700 landslides during a single night (6-7 May 2018) over an area of 100 km2. We analyse the causes of this spectacular event based on field geological and geomorphology investigation and CHIRPS and ERA5-Land climate data. We demonstrate that (1) the notably steep slopes favoured soil instability; (2) the layered soil and especially the gravelly, porous C horizon allowed water storage and served as a detachment level for the landslides; (3) relatively low intensity, almost continuous rainfall over the previous two months lead to soil water-logging; and (4) acoustic waves from thunder or mechanical shaking by strong wind destabilized the water-logged soil through thixotropy triggering the landslides. This analysis should serve as a guide for forecasting landslide-triggering conditions in Rwanda.

Northern China was hit by 13 unprecedented mega dust events in spring 2023. However, a comprehensive understanding of the relative contributions of potential dust sources to dust concentrations in China remains elusive, threatening air quality, damaging ecosystems, and further complicating dust forecasting and warning efforts. The impact of five major Asian dust sources on China and its downstream regions has been accurately quantified using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). Notably, dust particles originating from Mongolia play a crucial role in downstream air pollution. Approximately 56% (82.7 mu g m-3) of the dust in North China originated from Mongolia, while Mongolia contributed nearly 51% (15.9 mu g m-3) of the dust in the Korean Peninsula and surroundings. In southwest China, the prevalence of dust was predominantly attributable to sources within Inner Mongolia, China (46%). Due to geographical constraints, dust in the Tibetan Plateau mainly originated from dust sources in Xinjiang, China. Topographic blocking by the Tibetan Plateau and limitations on local dust emissions are further unfavorable to the long-distance transport of dust from South Asia to downstream regions. We also highlight the importance of variation in surface soil parameters in driving frequent dust events in spring 2023. Our findings emphasize the urgent need for collaborative research and policymaking to effectively address international dust disaster mitigation.

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.

Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined. In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast models, could predict where tree fall occurred during a damaging windstorm. We used information on tree characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a function of its size and species, and the underlying soil. The railway network was then divided into 500 m sections and the statistics of the CWS, tree height, and species composition (broadleaf/conifer mix) within each were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on 18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of 74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most probable sections of the railway network to experience tree fall during an approaching damaging storm. Such information could be of value in firstly planning remedial work along railway lines, and secondly preparing the railway network ahead of a major storm.

Atmospheric aerosols are important aspects of climate research due to their impact on radiative forcing. In the present study, the aerosol optical depth (ADD), the Angstrom exponent (alpha) and the single scattering albedo (SSA) over the urban region of Hyderabad, India, were examined using Sun/Sky radiometer measurements during January-December, 2008. AOD showed higher values on certain Julian days coinciding with the occurrence of wintertime dust storm events in the Gulf Region and biomass burning due to forest fires over Indian Region. The AOD values during wintertime dust event are about similar to 55% higher than those on normal days. The SSAs show positive and negative trends in alpha (R-2 = 0.71) and black carbon (BC) aerosols (R-2 = 0.44), respectively. The aerosol size distribution shows a bimodal pattern with fine (similar to 0.15 mu m) and coarse (similar to>7 mu m) mode during January-December, 2008. The MODIS AOD showed positive correlation with sky radiometer-derived AOD values (R-2 = 0.68). (C) 2011 Elsevier B.V. All rights reserved.