Extreme Rainfall Events in Lund 1979–1980

Climate extremes are predicted to become more frequent and severe in the future, owing to rising greenhouse gas emissions in the atmosphere. Using appropriate trend indices, this study intends to analyze the effects of climate change on rainfall variability and extreme rainfall events in the millennium city of Mumbai. The analysis was carried out for the period 1985–2020 for 2 stations and 23 stations were selected (2006–2020) later from the dense rain gauge network of Municipal Corporation of Greater Mumbai (MCGM). To comprehend the trend and behaviour of rainfall and extreme events, three statistical models were used: Mann–Kendall test, Sen’s slope estimator, and simple linear regression; for abrupt change point detection, Pettitt’s test, standard normal homogeneity test, Buishand’s range test, and von Neumann ratio test were used. The results indicated that average annual rainfall in the study area is 2208.82 mm which is increasing at the rate of 5.18 mm/year (at 99% confidence level). The frequency of heavy (> 120 mm/day) and extreme heavy rainfall events (250 mm/day) increased over Santacruz after 1994 and in the case of Colaba, it increased after 2005. The examination of rainfall and temperature data series for abrupt change point identification suggests that several change points exist, with the highest change points occurring between 2001 and 2005. Extreme climate event scenarios shown here could have large-scale negative consequences for the ecosystem and ecological resources of the study area.

Extreme rainfall events, specifically in urban areas, have dramatic impacts on society and can lead to loss of life and property. Despite these hazards, little is known about the city-scale variability of heavy rainfall events. In the current study, gridded stage IV radar data from 2002 to 2015 are employed to investigate the clustering and the spatial variability of simultaneous rainfall exceedances in the greater New York area. Multivariate clustering based on partitioning around medoids is applied to the extreme rainfall events’ average intensity and areal extent for the 1- and 24-h accumulated rainfall during winter (December–February) and summer (June–August) seasons. The atmospheric teleconnections of the daily extreme event for winter and summer are investigated using compositing of ERA-Interim. For both 1- and 24-h durations, the winter season extreme rainfall events have larger areal extent than the summer season extreme rainfall events. Winter extreme events are associated with deep and organized circulation patterns that lead to more areal extent, and the summer events are associated with localized frontal systems that lead to smaller areal extents. The average intensities of the 1-h extreme rainfall events in summer are much higher than the average intensities of the 1-h extreme rainfall events in winter. A clear spatial demarcation exists within the five boroughs in New York City for winter extreme events. Resultant georeferenced cluster maps can be extremely useful in risk analysis and green infrastructures planning as well as sewer systems’ management at the city scale.

India is a predominantly agrarian country with most of the agriculture in India being rainfed. Summer monsoon means life-giving rains for the entire Indian subcontinent. In India, the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. Extreme wet and dry events play a vital role in the intra-seasonal variability of monsoon rainfall. Particularly, in recent times, the occurrences of wet spells over India have led to widespread floods in the country. With changing climatic conditions, the occurrence and prediction of these extreme events have become a major stabilizing factor for the millions inhabiting the country. In this study, using 117 years (1901-2017) of high resolution daily gridded rainfall data, trends in extreme high rainfall events over six regions of India have been examined. The temporal change in extreme rainfall events during the time period 1901-1950 and 1951-2017 in summer monsoon months (June, July, August and September) has also been examined, in order to understand the increasing effect of anthropogenic factors after 1950. Using standardized scores (z-scores), each grid cell is analyzed to identify the number of extreme wet spells experienced during the study period. The analysis reveals that the number of extreme wet events showed an increasing trend in the month of June, as against a decreasing trend in July, after 1950, in most of the regions. In the recent period, the number of wet extreme events also show a decreasing trend in the northeastern region, which is considered as one of the rainiest zones of the world. As the present study tries to evaluate the changing trend of extreme wet events, it will help in formulation of hazard management strategies and policies by government planners.

Previous examination of rain gauge observations over a five-year period at high elevations within a river basin of the southern Appalachian Mountains showed that half of the extreme (upper 2.5%) rainfall events were associated with an atmospheric river (AR). Of these extreme events having an AR association, over 73% were linked to a societal hazard at downstream locations in eastern Tennessee and western North Carolina. Our analysis in this study was expanded to investigate AR effects in the southern Appalachian Mountains on two river basins, located 60 km apart, and examine their influence on extreme rainfall, periods of elevated precipitation and landslide events over two time periods, the ‘recent’ and ‘distant’ past. Results showed that slightly more than half of the extreme rainfall events were directly attributable to an AR in both river basins. However, there was disagreement on individual ARs influencing extreme rainfall events in each basin, seemingly a reflection of its proximity to the Blue Ridge Escarpment and the localized terrain lining the river basin boundary. Days having at least one landslide occurring in western North Carolina were found to be correlated with long periods of elevated precipitation, which often also corresponded to the influence of ARs and extreme rainfall events.

The observed records of recent decades show increased economic damage associated with flash flooding in different regions of Saudi Arabia. An increase in extreme rainfall events may cause severe repercussions for the socio-economic sectors of the country. The present study investigated the observed rainfall trends and associated extremes over Saudi Arabia for the 42-year period of 1978–2019. It measured the contribution of extreme events to the total rainfall and calculated the changes to mean and extreme rainfall events over five different climate regions of Saudi Arabia. Rainfall indices were constructed by estimating the extreme characteristics associated with daily rainfall frequency and intensity. The analysis reveals that the annual rainfall is decreasing (5.89 mm decade−1, significant at the 90% level) over Saudi Arabia for the entire analysis period, while it increased in the most recent decade. On a monthly scale, the most significant increase (5.44 mm decade−1) is observed in November and the largest decrease (1.20 mm decade−1) in January. The frequency of intense rainfall events is increasing for the majority of stations over Saudi Arabia, while the frequency of weak events is decreasing. More extreme rainfall events are occurring in the northwest, east, and southwest regions of Saudi Arabia. A daily rainfall of ≥ 26 mm is identified as the threshold for an extreme event. It is found that the contribution of extreme events to the total rainfall amount varies from region to region and season to season. The most considerable contribution (up to 56%) is found in the southern region in June. Regionally, significant contribution comes from the coastal region, where extreme events contribute, on average, 47% of the total rainfall each month from October to February, with the largest (53%) in November. For the entire country, extreme rainfall contributes most (52%) in November and least (20%) in July, while contributions from different stations are in the 8–50% range of the total rainfall.

This work examines the period affected by extreme rainfall that triggers debris flow and the modification of the critical rainfall threshold for debris flows after extreme events in the Chenyulan watershed in central Taiwan. Three rainfall indices, Im (maximum hourly rainfall depth), Rd (maximum 24-h rainfall amount), and RI (= Im × Rd), were used to analyze the variations in rainfall thresholds for triggering debris flow. Five extreme rainfall events with RI > 365 cm2/h that triggered multiple debris flows are discussed. Critical rainfall (the rainfall threshold of Im, Rd, and RI for triggering debris flow) drops significantly at the initial stage after an extreme rainfall event; it then increases to revert to its original value after subsequent rainfall events. The lowered critical rainfall and the required recovery period were associated with the RI driven by an extreme rainfall event. Empirical relationships for modified critical rainfall and recovery period after an extreme rainfall event are also presented. The critical RI and Im (or Rd) could be reduced by 70% and 40%, respectively. The maximum recovery period was approximately three years after an extreme rainfall. The recovery period has an increasing trend with the increasing ratio of landslide area bought by an extreme rainfall.

Stable isotopic (δ18O and δ2H) and geospatial approach for evaluating extreme rainfall events

A statistically-based model is applied to forecast sea states for severe storms. The model is based on the application of a neural network and predicts extreme sea state parameters at specified locations. The results show that the neural network can be applied to forecast extreme sea state parameters. This requires a special treatment of the input data. The analysis shows that different types of input data and training data sets should be considered and the representativity of the training data set must be improved. Moreover, a sensitivity analysis should be conducted to remove excess information from the input data. The processing of data sets significantly reduces the number of parameters applied in the model and improves the prediction for most severe storms. The analysis indicates that this neural network model may be helpful in the selection of a measurement system for the forecasting of extreme sea state parameters. This is important because typical installations ofwave buoys along the coast have a limited forecasting applicability range.

To realize autonomous navigation of intelligent robots in a variety of settings, analysis and classification of places, and the ability to actively collect information are necessary. In this paper, visual data are organized into an orientation histogram to roughly express input images by extracting and cumulating straight lines according to direction angle. In addition, behavioral data are organized into a behavioral histogram by cumulating motions performed to avoid obstacles encountered while the robot is executing specified behavioral patterns. These visual and behavioral data are utilized as input data, and the probability that a place belongs to a specific class is calculated by designating the places already learnt by the robot as categories. The naïve Bayes classification method is employed, in which the probability is calculated that the input data belong to each specific category, and the category with the highest probability is then selected. The location of the robot is classified by merging the probabilities for visual and behavioral data. The experimental results are as follows. First, a comparison of behavioral patterns used by the robot to collect data about a place indicates that a rotational behavior pattern provides the best performance. Second, classification performance is more accurate with two types of input data than with a single type of data.

Thearticle deals with the possibilities and limitations of the use of artificial neural networks in forensicmedical practice. It is known that in the work of forensic medicine experts have to process and evaluatea large amount of information, which may belong to different types of data - the site inspection protocols,photographic materials, macroscopic data obtained during the autopsy of the corpse, the results of laboratorytests, medical records etc. All the received data should be studied, categorized and evaluated according tointernational standards. Modern computer technologies of artificial intelligence (artificial neural networks)can help in the handling of forensic medicine data, which, in turn, will reduce to a minimum the probabilityof mistakes in preparation of expert conclusions.The algorithms used in artificial neural networks, as a resultof processing different types of input data, can direct them to the resulting categorized outputs and structurethem.

Automated Crater detection from Co-registered optical images, elevation maps and slope maps using deep learning

Projected frequency of low to high-intensity rainfall events over India using bias-corrected CORDEX models

Rainfall erosivity is a critical parameter for soil degradation assessment. It is especially important in areas prone to soil erosion due to frequent or intense rainfall events. Rainfall erosivity is typically quantified through the EI30 index, which combines the rainfall's kinetic energy (E) with its maximum 30-minute intensity (I30). Rainfall data with high temporal resolution (e.g., 5-minute) are often limited in availability, particularly across large spatial scales. Gridded rainfall products usually provide half-hourly or hourly data and are frequently used despite the potential biases they introduce into erosivity calculations. In Austria, the introduction of INCA (Integrated Nowcasting through Comprehensive Analysis) has provided an opportunity to enhance rainfall erosivity studies. INCA offers rainfall data at a 15-minute temporal and 1-kilometer spatial resolution. In our study, INCA’s accuracy and limitations in capturing erosive rainfall events, especially extreme events, are carefully evaluated against high-resolution in-situ rainfall station data. Understanding the degree of underestimation or overestimation in EI30 calculations is crucial for applying INCA data in erosion simulations and according to soil conservation strategies. This study focuses on the Main Agricultural Production Zones (MAPZ) in Austria. The study evaluates the performance of INCA in calculating erosivity compared to a dense network of 5-minute resolution rainfall stations. It also investigates the occurrence probability of extreme erosive events using a probabilistic approach, providing insights into long-term erosion risks and regional differences in erosivity patterns. Eventually, the study examines the impact of temporal resolution on erosivity estimates, assessing biases introduced by coarser resolutions (15- and 30-minute) for both mean and extreme rainfall events. Results indicate that INCA overestimates 8.1% of the total event number but underestimates 3.1% of the total rainfall erosivity, with the largest under- and overestimation in east of Austria. On the other hand, the mean annual maximum EI30 was underestimated by 13.6%, and the south showed the most considerable underestimation. It was found that INCA can detect highly erosive events occurring in the in-situ datasets. The underestimation of EI30 sources from the temporally smoothened peak I30 rather than the E of the rainfall events. Long-term extreme EI30 were analyzed using the Generalized Extreme Value (GEV) distribution, revealing that EI30 values increase with longer return periods (e.g., 50 years) and that the southern region exhibits the largest EI30 values, indicating a greater risk of extreme erosive events. On the other hand,  INCA may emphasize more recent, potentially intense rainfall trends, leading to larger return levels. The impact assessment by coarser temporal resolutions on EI30 confirms that the underestimation substantially increases with lower temporal resolution, primarily due to I30 rather than E. Eventually, a 15-minute temporal resolution dataset may lead to acceptable underestimations across our investigated MAPZ; underestimations ranged from 2.8–8.5% in event numbers and 1.0–10.0% in total rainfall erosivity at 15-minute resolution and from 10.2–33.7% and 5.2–26.6%, respectively, at a 30-minute resolution. The results of this study highlight the potential value of INCA data as a practical source for rainfall erosivity assessments, particularly in regions with limited high-resolution rainfall measurements.

Mid‐tropospheric closed‐lows (cold‐core cut‐off lows and warm‐core tropical lows) are important rain producing weather systems for the southern Africa region. Over South Africa, most wide‐spread flood events are caused by these systems. It is therefore important to explore the potential impact of anthropogenic forcing on the occurrence of closed‐lows and extreme rainfall events over the region. Coupled global circulation models (CGCMs) can not be directly applied for this purpose because of their relatively low spatial resolution—some form of downscaling is required to adequately resolve these systems and the rainfall they cause. In this study, a variable‐resolution atmospheric global circulation model is applied as a regional climate model to simulate closed‐low characteristics over southern Africa under current and future forcings. The model is forced with greenhouse gas concentrations according to the A2 SRES scenario and with sea surface temperatures (SSTs) and sea‐ice as specified by the CSIRO Mk3 CGCM. The model projects a general decrease in closed‐low frequencies over the region, which occurs in association with a general strengthening of the subsiding branch of the Hadley cell. However, the climate‐change signal shows variation in time and space and certain sub‐regions are projected to experience an increase in closed‐low frequencies during certain seasons. A general increase in extreme rainfall events is projected over southern Africa despite the projected decrease in closed‐low frequencies. It is deduced that this increase in extreme rainfall events is driven by intense convective rainfall events occurring within more frequently forming tropical‐temperate cloud bands. Over Mozambique, extreme rainfall events are projected to increase in association with more frequently occurring closed‐lows. Copyright © 2012 Royal Meteorological Society

Mesoscale extreme rainfall events in West Africa: The cases of Niamey (Niger) and the Upper Ouémé Valley (Benin)