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Auswahl der wissenschaftlichen Literatur zum Thema „Rainfall Intensity Modeling“
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Zeitschriftenartikel zum Thema "Rainfall Intensity Modeling"
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Sadeghi, Hamed, Farshad Yazdani Bene Kohal, Mostafa Gholami, Pouya Alipanahi und Dongri Song. „Hydro-mechanical modeling of a vegetated slope subjected to rainfall“. E3S Web of Conferences 382 (2023): 13004. http://dx.doi.org/10.1051/e3sconf/202338213004.
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Shallow landslides triggered by heavy rainfalls have caused casualties and economic losses to domestic infrastructures and industries worldwide. Rainfall mainly reduces the soil matric suction and the shear resistance, resulting in shallow landslides. Vegetation is an eco-friendly and cost-effective method for stabilizing slopes prone to shallow landslides. This research aims to investigate the hydrological and mechanical effects of vegetation on slope stability through a numerical study approach. Vegetated and bare slopes were subjected to a recorded climate condition and two rainfall scenarios of high intensity (HI) and low intensity (LI). Matric suction and factor of safety of vegetated and bare slopes subjected to rainfall were investigated. The matric suction of the vegetated slope at the surface was approximately four times greater than the bare slope after the HI scenario. However, the matric suction is about three times greater in the LI scenario. The results indicate that planting on slopes would reduce the vulnerability of bare slopes to the HI rainfall due to the higher matric suction and additional cohesion induced by the root system. These findings suggest that using vegetation in Rasht, Iran, where the climate data were collected, has considerable potential for stabilizing slopes.
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Widowati, Adi Putri Anisa. „Hydraulic and Hydrologic Modeling of Steep Channel of Putih River, Magelang District, Central Java Province, Indonesia“. Journal of the Civil Engineering Forum 3, Nr. 3 (18.09.2017): 125. http://dx.doi.org/10.22146/jcef.26507.
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Hydrologic and hydraulic modeling are important to be conducted to examine the watershed response based on a rainfall input, especially over disaster-prone watershed such as Putih River watershed in Magelang, Central Java Province. A GIS-based grid-based distributed rainfall-runoff model was used to simulate the rainfall-runoff transformation. A two-dimensional hydrodynamic flow modeling was then carried out to simulate the flood processes on the stream and floodplain area. A sensitivity analysis was conducted on infiltration rate, Manning’s n value, and rainfall intensity. Infiltration rate, Manning’s n value, and rainfall intensity give considerable effects to the resulted flow hydrographs. The modeling results show that the results of hydrologic-hydraulic modeling is in good agreement with the observed results.
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Sumargo, Bagus, Dian Handayani, Alvi Pauziah Lubis, Irman Firmasyah und Ika Yuni Wulansari. „Detection of Factors Affecting Rainfall Intensity in Jakarta“. Jurnal Ilmu Lingkungan 23, Nr. 1 (08.01.2024): 133–40. https://doi.org/10.14710/jil.23.1.133-140.
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The increased intensity of rainfall is becoming one of the most pressing climate-related issues in many parts of the world. Detecting the factors that affect rainfall intensity requires a combination of modern technologies, such as weather satellites, radar systems, and advanced atmospheric models. Extreme conditions (outliers) often occur. This study aims to model data that is not symmetric or contains outliers. This study examines and models quantile regression on daily rainfall intensity in Jakarta which has extreme rainfall events. The results of the study found that the extreme values in the daily rainfall intensity data in Jakarta are outliers and the assumptions on modeling using linear regression are not satisfied so that the characteristics of the parameter estimator based on OLS do not have BLUE characteristic. In modeling with quantile regression using six quantiles 0.25, 0.50, 0.75, 0.95, 0.99, and 0.9999 with consideration of these quantile values representing all parts of the data distribution including extreme values, it was found that the factors affecting rainfall intensity in Jakarta are different in each rainfall intensity condition. The best model is shown by quantile 0.999 with a coefficient of determination of 58.21%. Based on the best model, it is known that the factors affecting extreme rainfall are maximum temperature, dew point temperature, air humidity, wind speed, air pressure, and length of irradiation. This study indicates that quantile regression can provide a more detailed insight into how these variables affect rainfall intensity in various rainfall conditions ranging from low rainfall to extreme rainfall.
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Négyesi, Klaudia, und Eszter Dóra Nagy. „The connection between time of concentration and rainfall intensity based on rainfall-runoff modeling“. Időjárás 128, Nr. 4 (2024): 439–50. https://doi.org/10.28974/idojaras.2024.4.3.
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The study aims to examine the relation between rainfall intensities and times of concentration based on rainfall-runoff modeling using the recently developed features of the Hydrologic Engeneering Center – Hydrologic Modeling System (HEC-HMS) modeling software. The time of concentration is generally considered a constant characteristic of a catchment. However, various publications have shown that response time is a dynamic property and a function of rainfall intensity. Model simulations were performed to gain more insight into the relationship mentioned. The applicability of the dynamic time of concentration was examined with the help of a recent version of the HECHMS software that can interpret the dynamic relationship between time of concentration and rainfall intensity. The models were built for characteristic and dynamic cases. In the characteristic case, the time of concentration values of the catchments were calculated using the commonly applied Wisnovszky empirical equation, while in the dynamic case, the applicability of the rainfall intensity, i.e., the time of concentration function, was examined. The applicability of the new HEC-HMS feature was reviewed, and the relationship between the time of concentration and rainfall intensity was confirmed. The dynamic approach improved the models’ performance, especially where the Wisnovszky equation yields an inadequate estimation of the time of concentration based on the results.
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Hermawan, Koko, Khori Sugianti, Antonina Martireni, Nugroho Aji Satrio und Yunarto. „Spatial and Temporal Analysis Prediction of Landslide Susceptibility Using Rainfall Infiltration and Grid-based Slope Stability Methods in West Bandung area of West Java-Indonesia“. IOP Conference Series: Earth and Environmental Science 1173, Nr. 1 (01.05.2023): 012031. http://dx.doi.org/10.1088/1755-1315/1173/1/012031.
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Abstract West Bandung, West Java, is an area with a high level of landslide susceptibility. Landslides in West Bandung occurred 142 times during rainfall in the last ten years. This paper presents the results of landside susceptibility modeling in the West Bandung area of West Java Province, Indonesia, considering the spatial characteristics of the rainfall data, slope and soil properties using the TRIGRS model. This research is based on conditions in the field in the form of landslide locations, soil engineering properties, soil thickness, Digital Elevation Model, and rainfall data. The effect of one-day antecedent rainfall intensity was considered in this study, i.e., 12 hours of antecedent rainfall. The results of the TRIGRS modelling showed that the intensity of rainfall antecedent of rainfall influenced the slope stability in the study area. The TRIGRS model results indicate that the predicted landslide susceptibility distribution agrees with the historical landslide events.
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Dikko, H. G. „Modeling the Distribution of Rainfall Intensity using Quarterly Data“. IOSR Journal of Mathematics 9, Nr. 1 (2013): 11–16. http://dx.doi.org/10.9790/5728-0911116.
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Dan'azumi. „Modeling the Distribution of Rainfall Intensity using Hourly Data“. American Journal of Environmental Sciences 6, Nr. 3 (01.03.2010): 238–43. http://dx.doi.org/10.3844/ajessp.2010.238.243.
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Kumar, Pappu, Madhusudan Narayan und Mani Bhushan. „Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar“. BOHR International Journal of Civil Engineering and Environmental Science 2, Nr. 1 (2023): 65–73. http://dx.doi.org/10.54646/bicees.008.
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Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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Kumar, Pappu, Madhusudan Narayan und Mani Bhushan. „Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar“. BOHR International Journal of Civil Engineering and Environmental Science 2, Nr. 1 (2023): 65–73. http://dx.doi.org/10.54646/bijcees.008.
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Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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Kumar, Pappu, Madhusudan Narayan und Mani Bhushan. „Rainfall intensity duration frequency curve statistical analysis and modeling for Patna, Bihar“. BOHR International Journal of Civil Engineering and Environmental Science 1, Nr. 2 (2023): 66–75. http://dx.doi.org/10.54646/bijcees.2023.08.
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Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on aweekly, seasonal, and annual basis (19812020). First, utilizing the intensity-duration-frequency (IDF) curve and therelationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period(19812020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhousegas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. Onestrategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal,normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and returntimes of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall andrecurrence interval. Findings:Based on findings, the Gumbel approach produced the highest intensity values, whereas the otherapproaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell duringthe monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall,92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that theyearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examinerainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence intervalmathematical correlations were also developed. Further regression analysis revealed that short wave irrigation,wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and value:The results of the rainfall IDF curves can provide useful information to policymakers inmaking appropriate decisions in managing and minimizing floods in the study area
Dissertationen zum Thema "Rainfall Intensity Modeling"
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Mayeux, Brian Clifford, und Brian Clifford Mayeux. „The relative importance of rainfall intensity versus saturated hydraulic conductivity for runoff modeling of semi-arid watersheds“. Thesis, The University of Arizona, 1995. http://hdl.handle.net/10150/626771.
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When using distributed rainfall runoff models in order to simulate the runoff
volume and time distribution, one faces the problem of how to represent the spatial
distribution of rainfall intensity and soil characteristics when the actual continuous
distributions are unknown.
There are two objectives for this thesis. The first is to investigate, for semi-arid
regions, how the scale of rainfall intensity and soil features affects the simulation of rainfall
excess and which is of more importance. The second is to utilize probability distribution
theory to develop a scheme which represents both the spatial distribution of rainfall intensi
ty and the saturated hydraulic conductivity (Ks) in order to accurately simulate the true
runoff for semi-arid regions when knowing limited statistical information (mean and
variance) on each feature (rainfall intensity and Ks).
All conclusions made are assuming that they hold for semi-arid regions only and
the· assumed true watershed output is the model simulation which uses the finest resolution
for soil features, vegetation, and rainfall intensity. Furthermore, the model used for this
study provided very accurate simulations of the actual streamflow for the watershed used
(Walnut Gulch).
It was found that, when using real data for rainfall and soils, the spatial distribution
for rainfall intensity is more important to represent than that of soil features with respect to
accurately reproducing the assumed true streamflow. However, when using synthetic data generated from probability distributions, it was found that, for semi-arid regions, the
spatial distribution of Ks was of more importance. Hence, certain conclusions concerning
which is more important to spatially characterize with respect to accurately simulating
streamflow can be different, depending upon if one uses synthetic data versus real data.
The lognormal distribution was found to produce an excellent fit to the Ks data and
the exponential distribution was found to produce an excellent fit to the spatial rainfall
intensity distribution. However, this goodness-of-fit (for rainfall) can be dependent upon
time and/or the amount of localization which the storm possesses. The rainfall parameters
for the probability distributions of rainfall intensity were assumed to change with time but
were not related at all to location within the watershed.
When using the probability distributions to characterize the spatial rainfall intensity
and Ks distributions, it was found that characterizing the Ks distribution provided more
accurate simulations than characterizing the rainfall intensity distribution. It was also
found that spatially characterizing both rainfall intensity and Ks simultaneously provided
more accurate simulations than just representing one and not the other.
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Martini, Tommaso. „statistical and probabilistic approaches to hydrological data analysis : rainfall patterns, copula-like models and first passage timeapproximations“. Electronic Thesis or Diss., Pau, 2024. http://www.theses.fr/2024PAUU3051.
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L'analyse des données de précipitations et la modélisation des nombreuses variables associées sont essentielles dans des domaines tels que l'agriculture, l'écologie et l'ingénierie, et reposent sur des archives historiques en raison de la complexité des systèmes hydrologiques. Les séries de précipitations quotidiennes obtenues à partir de réseaux de pluviomètres sont parmi les plus utilisées. Un modèle fiable et flexible pour un site unique est crucial pour développer des modèles multi-sites plus complexes tenant compte des corrélations spatiales observées dans un réseau dense de stations. Compte tenu de l'intérêt croissant pour l'étude des cycles de pluie et de sécheresse, les modèles discrets en deux parties, qui séparent l'occurrence des précipitations de leur quantification, sont des outils utiles pour décrire les précipitations quotidiennes à une station. Dans ce contexte, nous examinons d'abord la modélisation des temps d'inter-arrivée des précipitations à l'aide de la famille Hurwitz-Lerch-Zeta et de deux autres distributions associées, jamais utilisées dans ce cadre. Basée sur les relations entre les temps d'inter-arrivée et d'autres variables temporelles, une méthodologie pour leur modélisation et analyse empirique est détaillée. Cette procédure, ainsi que la performance d'ajustement des distributions susmentionnées, est démontrée sur un ensemble de données couvrant divers régimes de précipitations.La modélisation fiable des variables de précipitations est également cruciale pour aborder le changement climatique. Un point de départ pour détecter ces changements est la modélisation multivariée des variables de précipitations, car des changements dans leurs inter-relations peuvent refléter des altérations climatiques régionales. Les copules sont bien connues et appréciées pour leur flexibilité, mais elles perdent leur efficacité avec les vecteurs aléatoires discrets, compromettant leur unicité et entraînant des incohérences qui minent les procédures inférentielles typiques des cas continus. Récemment, Gery Geenens a proposé une nouvelle approche fondée sur des idées historiques liées à l'analyse des tableaux de contingence. Ses idées théoriques, combinées à la procédure d'ajustement proportionnel itératif, ouvrent la voie à de nouveaux modèles (semi-paramétriques ou paramétriques) pour des vecteurs aléatoires discrets bivariés à support fini. Pour cela, nous démontrons une décomposition de type Sklar d'une fonction de masse de probabilité discrète bivariée entre ses marges et une fonction de masse de probabilité copule, sur laquelle reposent les modèles mentionnés. Les procédures d'inférence et d'ajustement sont étudiées théoriquement et empiriquement.L'impact des précipitations sur les masses d'eau et les surfaces terrestres est aussi important que leur modélisation. Par exemple, déterminer le temps nécessaire pour que les précipitations fassent dépasser les niveaux des rivières au-delà d'un seuil de crue est essentiel pour la prévision et la gestion des inondations. Plus généralement, il est souvent crucial de déterminer le moment où certains seuils hydrologiques sont franchis par une quantité hydrologique donnée. Lorsque cette valeur est modélisée par un processus stochastique, le problème se reformule en termes de temps de passage initial. Dans ce contexte, le calcul pratique de la densité de probabilité et de la fonction de distribution du temps de passage initial est délicat. Nous proposons une méthode d'approximation basée sur une expansion en série. Les résultats théoriques sont accompagnés de discussions sur les aspects computationnels. Des expériences numériques étendues sont menées pour le mouvement brownien géométrique et le processus de Cox-Ingersoll-RossAnalysis of rainfall data and subsequent modeling of the many variables concerning rainfall is fundamental to many areas such as agricultural, ecological and engineering disciplines and, due to the complexity of the underlying hydrological system, it relies heavily on historical records. Daily rainfall series obtained from rain gauge networks are arguably the most used. A reliable and flexible single site model is the fundamental starting point of any more complex multi-site model taking into account the spatial correlations arising when observing a dense network of stations. Given the ever-growing interest in analysing the alternance between period of continuous rainfall and periods of drought, two-part discrete time models accounting separately for rainfall occurrence and rainfall amount processes are an useful tool to describe the behaviour of daily rainfall at a single station. In this context, we initially investigate the modeling of daily rainfall interarrival times through a family of discrete probability distributions known as the Hurwitz-Lerch-Zeta family, along with two other distributions which are deeply related to the latter and have never been considered with this aim. Building up on the relationships between the interarrival times and certain other temporal variables, a methodology for their modeling and empirical analysis is detailed. The latter procedure and the fitting performance of the aforementioned distributions is shown on a dataset composed of a variety of rainfall regimes.Moreover, being able to provide reliable modeling of rainfall related variables is essential in the well known issue of climate change. A starting point in detecting change is the multivariate modeling of rainfall variables, as a perceivable shift in the inter-relationships between these could reflect climate changes in a region. In this context, copulas are well known and valued for their flexibility. However, they lose their charm when dealing with discrete random vectors. In this case, the uniqueness of the copula is compromised, leading to inconsistencies which basically break down the theoretical underpinnings of the inferential procedures commonly used in the continuous case. Recently, Gery Geenens made a compelling case for a new approach, grounding its beliefs in historical ideas regarding the statistical analysis of contingency tables. The theoretical insights he gives, coupled with a computational tool known as iterative proportional fitting procedure, open up the path to our development of novel (semi-parametric or parametric) models for finitely supported bivariate discrete random vectors. With this aim, we prove a sklar-like decomposition of a bivariate discrete probability mass function between its margins and a copula probability mass function, on which the previously mentioned models hinge upon. Related inferential and goodness of fit procedures are investigated, both theoretically and empirically.Of the same significance as modeling the behavior of rainfall is its impact on water bodies and land surfaces. For istance, understanding the time it takes for rainfall to cause river levels to exceed a flood stage is of paramount importance for flood prediction and management. More in general, it is often crucial to determine the time at which certain hydrological thresholds are crossed by some hydrological quantity. When the latter's value in time is modelled by a stochastic process, the problem mentioned above can be restated in terms of the well known first passage time problem. In this context, a practical closed form computation of the first passage time probability density and distribution function is a delicate issue. Regarding this, we propose an approximation method based on a series expansion. Theoretical results are accompanied by discussions on the computational aspects. Extensive numerical experiments are carried out for the geometric Brownian motion and the Cox-Ingersoll-Ross process
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DI, NAPOLI MARIANO. „Spatial prediction of landslide susceptibility/intensity through advanced statistical approaches implementation: applications to the Cinque Terre (Eastern Liguria, Italy)“. Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1076506.
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Landslides are frequently responsible for considerable huge economic losses and casualties in mountainous regions especially nowadays as development expands into unstable hillslope areas under the pressures of increasing population size and urbanization (Di Martire et al. 2012). People are not the only vulnerable targets of landslides. Indeed, mass movements can easily lay waste to everything in their path, threatening human properties, infrastructures and natural environments.
Italy is severely affected by landslide phenomena and it is one of the most European countries affected by this kind of phenomena. In this framework, Italy is particularly concerned with forecasting landslide effects (Calcaterra et al. 2003b), in compliance with the National Law n. 267/98, enforced after the devastating landslide event of Sarno (Campania, Southern Italy). According to the latest Superior Institute for the Environmental Protection and Research (ISPRA, 2018) report on "hydrogeological instability" of 2018, it emerges that the population exposed to landslides risk is more than 5 million and in particular almost half-million falls into very high hazard zones. The slope stability can be compromised by both natural and human-caused changes in the environment. The main reasons can be summarised into heavy rainfalls, earthquakes, rapid snow-melts, slope cut due to erosions, and variation in groundwater levels for the natural cases whilst slopes steepening through construction, quarrying, building of houses, and farming along the foot of mountainous zone correspond to the human component.
This Ph.D. thesis was carried out in the Liguria region, inside the Cinque Terre National Park. This area was chosen due to its abundance of different types of landslides and its geological, geomorphological and urban characteristics. The Cinque Terre area can be considered as one of the most representative examples of human-modified landscape. Starting from the early centuries of the Middle Ages, local farmers have almost completely modified the original slope topography through the construction of dry-stone walls, creating an outstanding terraced coastal landscape (Terranova 1984, 1989; Terranova et al. 2006; Brandolini 2017). This territory is extremely dynamic since it is characterized by a complex geological and geomorphological setting, where many surficial geomorphic processes coexist, along with peculiar weather conditions (Cevasco et al. 2015). For this reason, part of this research focused on analyzing the disaster that hit the Cinque Terre on October, 25th, 2011. Multiple landslides took place in this occasion, triggering almost simultaneously hundreds of shallow landslides in the time-lapse of 5-6 hours, causing 13 victims, and severe structural and economic damage (Cevasco et al. 2012; D’Amato Avanzi et al. 2013). Moreover, this artificial landscape experienced important land-use changes over the last century (Cevasco et al. 2014; Brandolini 2017), mostly related to the abandonment of agricultural activity. It is known that terraced landscapes, when no longer properly maintained, become more prone to erosion processes and mass movements (Lesschen et al. 2008; Brandolini et al. 2018a; Moreno-de-las-Heras et al. 2019; Seeger et al. 2019).
Within the context of slope instability, the international community has been focusing for the last decade on recognising the landslide susceptibility/hazard of a given area of interest. Landslide susceptibility predicts "where" landslides are likely to occur, whereas, landslide hazard evaluates future spatial and temporal mass movement occurrence (Guzzetti et al., 1999). Although both definitions are incorrectly used as interchangeable. Such a recognition phase becomes crucial for land use planning activities aimed at the protection of people and infrastructures. In fact, only with proper risk assessment governments, regional institutions, and municipalities can prepare the appropriate countermeasures at different scales. Thus, landslide susceptibility is the keystone of a long chain of procedures that are actively implemented to manage landslide risk at all levels, especially in vulnerable areas such as Liguria. The methods implemented in this dissertation have the overall objective of evaluating advanced algorithms for modeling landslide susceptibility.
The thesis has been structured in six chapters. The first chapter introduces and motivates the work conducted in the three years of the project by including information about the research objectives. The second chapter gives the basic concepts related to landslides, definition, classification and causes, landslide inventory, along with the derived products: susceptibility, hazard and risk zoning, with particular attention to the evaluation of landslide susceptibility. The objective of the third chapter is to define the different methodologies, algorithms and procedures applied during the research activity. The fourth chapter deals with the geographical, geological and geomorphological features of the study area. The fifth chapter provides information about the results of the applied methodologies to the study area: Machine Learning algorithms, runout method and Bayesian approach. Furthermore, critical discussions on the outcomes obtained are also described. The sixth chapter deals with the discussions and the conclusions of this research, critically analysing the role of such work in the general panorama of the scientific community and illustrating the possible future perspectives.
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Masingi, Vusi Ntiyiso. „Modeling long-term monthly rainfall variability in selected provinces of South Africa using extreme value distributions“. Thesis, 2021. http://hdl.handle.net/10386/3457.
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Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2020Several studies indicated a growing trend in terms of frequency and severity of extreme events. Extreme rainfall could cause disasters that lead to loss of property and life. The aim of the study was to model the monthly rainfall variability in selected provinces of South Africa using extreme value distributions. This study investigated the best-fit probability distributions in the five provinces of South Africa. Five probability distributions: gamma, Gumbel, log-normal, Pareto and Weibull, were fitted and the best was selected from the five distributions for each province. Parameters of these distributions were estimated by the method of maximum likelihood estimators. Based on the Akaike information criteria (AIC) and Bayesian information criteria (BIC), the Weibull distribution was found to be the best-fit probability distribution for Eastern Cape, KwaZulu-Natal, Limpopo and Mpumalanga, while in Gauteng the best-fit probability distribution was found to be the gamma distribution. Monthly rainfall trends detected using the Mann–Kendall test revealed significant monotonic decreasing long-term trend for Eastern Cape, Gauteng and KwaZulu-Natal, and insignificant monotonic decreasing longterm trends for Limpopo and Mpumalanga. Non-stationary generalised extreme value distribution (GEVD) and non-stationary generalized Pareto distribution (GPD) were applied to model monthly rainfall data. The deviance statistic and likelihood ratio test (LRT) were used to select the most appropriate model. Model fitting supported stationary GEVD model for Eastern Cape, Gauteng and KwaZulu-Natal. On the other hand, model fitting supported non-stationary GEVD models for maximum monthly rainfall with nonlinear quadratic trend in the location parameter and a linear trend in the scale parameter for Limpopo, while in Mpumalanga the non-stationary GEVD model, which has a nonlinear quadratic trend in the scale parameter and no variation in the location parameter fitted well to the maximum monthly rainfall data. Results from the non-stationary GPD models showed that inclusion of the time covariate in our models was not significant for Eastern Cape, hence the bestfit model was the stationary GPD model. Furthermore, the non-stationary GPD model with a linear trend in the scale parameter provided the best-fit for KwaZulu-Natal and Mpumalanga, while in Gauteng and Limpopo the nonstationary GPD model with a nonlinear quadratic trend in the scale parameter fitted well to the monthly rainfall data. Lastly, GPD with time-varying thresholds was applied to model monthly rainfall excesses, where a penalised regression cubic smoothing spline was used as a time-varying threshold and the GPD model was fitted to cluster maxima. The estimate of the shape parameter showed that the Weibull family of distributions is appropriate in modelling the upper tail of the distribution for Limpopo and Mpumalanga, while for Eastern Cape, Gauteng and KwaZulu-Natal, the exponential family of distributions was found to be appropriate in modelling the upper tail of the distribution. The dissertation contributes positively to the body of knowledge in extreme value theory application to rainfall data and makes recommendations to the government agencies on the long-term rainfall variability and their negative impact on the economy.
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Mashishi, Daniel. „Modeling average monthly rainfall for South Africa using extreme value theory“. Thesis, 2020. http://hdl.handle.net/10386/3399.
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Thesis (M. Sc. (Statistics)) -- University of Limpopo, 2020The main purpose of modelling rare events such as heavy rainfall, heat waves, wind speed, interest rate and many other rare events is to try and mitigate the risk that might arise from these events. Heavy rainfall and floods are still troubling many countries. Almost every incident of heavy rainfall or floods might result in loss of lives, damages to infrastructure and roads, and also financial losses. In this dissertation, the interest was in modelling average monthly rainfall for South Africa using extreme value theory (EVT). EVT is made up mainly of two approaches: the block maxima and peaks-over thresh old (POT). This leads to the generalised extreme value and the generalised Pareto distributions, respectively. The unknown parameters of these distri butions were estimated using the method of maximum likelihood estimators in this dissertation. According to goodness-of-fit test, the distribution in the Weibull domain of attraction, Gumbel domain and generalised Pareto distri butions were appropriate distributions to model the average monthly rainfall for South Africa. When modelling using the POT approach, the point process model suggested that some areas within South Africa might experience high rainfall in the coming years, whereas the GPD model suggested otherwise. The block maxima approach using the GEVD and GEVD for r-largest order statistics also revealed similar findings to that of the GPD. The study recommend that for future research on average monthly rainfall for South Africa the findings might be improved if we can invite the Bayesian approach and multivariate extremes. Furthermore, on the POT approach, time-varying covariates and thresholds are also recommended.
National Research Foundation (NRF) and South African Weather Service (SAWS)
Buchteile zum Thema "Rainfall Intensity Modeling"
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Hashino, Michio. „Stochastic Formulation of Storm Pattern and Rainfall Intensity-Duration Curve for Design Flood“. In Hydrologic Frequency Modeling, 303–14. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3953-0_21.
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Bandara, H. A. A. I. S., und Ryo Onishi. „High Resolution Numerical Weather Simulation for Orographic Precipitation as an Accurate Early Warning Tool for Landslide Vulnerable Terrains“. In Progress in Landslide Research and Technology, 239–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44296-4_11.
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AbstractAccurate early warning for rain-induced landslides is still challenging due to regional and local variations of rainfall prediction due to low accuracy, and resolution. The “Multi-Scale Simulator for the Geoenvironment (MSSG)” system, developed by the Tokyo Institute of Technology, Japan Agency for Marine-Earth Science and Technology and Waseda University allows for high-resolution simulations and seamless modeling of weather and climate interactions, and employs advanced meteorological aspects.MSSG simulations compared with rainfall data recorded in the Aranayaka automated rain gauge for past events, including the devastating landslide in 2016. The simulations achieved satisfactory results in reproducing rainfall events. Higher-resolution simulations exhibited higher maximum rainfall intensity and cumulative rainfall accumulation. This study emphasizes the importance of considering finer scales in meteorological simulations to effectively capture the intricate variations associated with extreme rainfall events. This study places significant emphasis on the importance of considering finer scales in meteorological simulations in order to confirm the necessity of high resolutions to capture the temporal and spatial variations of orographic rainfall.
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Erzagian, Egy, Wahyu Wilopo und Teuku Faisal Fathani. „Landslide Susceptibility Zonation Using GIS-Based Frequency Ratio Approach in the Kulon Progo Mountains Area, Indonesia“. In Progress in Landslide Research and Technology, 115–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44296-4_3.
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AbstractLandslides cause many casualties, environmental damage, property losses, and psychological impacts. Landslides frequently occur in the Kulon Progo Mountains area of Indonesia and are especially triggered by high-intensity rainfall between November and March. Research on landslide susceptibility in the Kulon Progo Mountains area can be a relevant tool to prevent or reduce the risk of landslide potential. Therefore, this research aims to develop a landslide susceptibility map using the frequency ratio (FR) method. Controlling factors, namely, elevation, slope, aspect, lithology, lineament density, distance from streams, distance from roads, land use, and rainfall, were combined with landslide data to develop a landslide susceptibility map by GIS software. Seven hundred and forty-four landslide data points were acquired from field surveys, Google Earth image interpretation, and the Regional Disaster Management Agency (BPBD) of the Kulon Progo, Purworejo, and Magelang regencies. Landslide data were randomly selected for map modeling (80%) and validation (20%). The FR analysis shows that the research area can be classified into four landslide susceptibility zones based on the LSI value: low (LSI 4.05–7.85), moderate (LSI 7.86–9.85), high (LSI 9.86–12.81), and very high (LSI 12.82–19.46). The landslide susceptibility map was validated using the area under the curve (AUC) method, resulting in a success rate of 80.1% and a predictive rate of 81.3%.
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Osorio, Andrés F., Rubén Montoya, Franklin F. Ayala und Juan D. Osorio-Cano. „Reconstructing the Eta and Iota Events for San Andrés and Providencia: A Focus on Urban and Coastal Flooding“. In Disaster Risk Reduction, 39–67. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6663-5_3.
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AbstractHurricanes Eta and Iota were the most intense events during the 2020 Atlantic hurricane season, and their passage caused serious infrastructure affectations and even human losses in the Archipelago of San Andrés, Providencia, and Santa Catalina due to the extreme winds, storm surge flooding, and rainfall flooding. Numerical modeling and field measurements were used to reconstruct the effects of these events on the archipelago. The simulations were conducted with WAVEWATCHIII, SWAN, XBeach, Storm Water Management Model (SWMM), and a parametric model for hurricane winds. A differentiated contribution of each hazard on physical infrastructure, coastal ecosystems, and population is represented through: winds up to 50 m/s, significant wave heights (Hs) between 1 and 6 m in intermediate waters (around 10 m deep) associated with flood levels in the order of 2 m on the coast, and flood distances varying between 12 and 904 m. A spatial distribution of Hs and the contribution of wave run-up and storm surge in some areas of the archipelago showed the importance of mangrove and coral reef ecosystems to mitigate the intensity of Eta and Iota on the coast. This study encourages science-based decision-making and provides information for policymakers to consolidate risk assessments in vulnerable zones like the archipelago.
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Shiba, S., R. Ito und T. Sueishi. „Effect of Rainfall Intensity on Acid Rain Formation by Absorption of Sulfur Dioxide“. In Water Pollution: Modelling, Measuring and Prediction, 735–48. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3694-5_51.
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Kalsnes, Bjørn, und Vittoria Capobianco. „Use of Vegetation for Landslide Risk Mitigation“. In Springer Climate, 77–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86211-4_10.
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AbstractLandslide risk management involves several activities, modelling being a required premise for most of them. Modelling of climate-induced landslides include both the analysis of the triggering process, i.e. static slope stability analysis and dynamic propagation (run-out) analysis. These analyses are vital for mapping purposes, as well as for selection of effective means to reduce the landslide risk when this exceeds a certain value of tolerance. With the prospect of increasing rainfall duration and intensity in parts of Europe, the need for further development of modelling tools is evident. In recent years, the use of Nature-Based Solutions (NBS) for mitigation of natural hazards has further demonstrated the need for developing the modelling tools. The use of vegetation as NBS is increasingly being used for erosion protection and shallow landslide mitigation. For slope stability analyses, the use of vegetation makes the modelling more complex for a number of reasons, mostly linked to the influence of vegetation on both the soil–atmosphere interaction (i.e. rainfall interception, evapotranspiration) and the soil hydro-mechanical properties. All effects that are difficult to model due to lack of knowledge and to large variations in time and space. Even though there is an increasing activity in the geotechnical environment to incorporate the effects of vegetation in the modelling for quantifying the change in slope stability (i.e. calculate slope safety factor), the status is far from being at the level of traditional landslide modelling tools. More efforts are therefore needed in the years to come to demonstrate that the use of vegetation as a viable and effective measure in landslide risk mitigation management can be verified in a more quantifiable manner.
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Koutsoyiannis, Demetris, und Theano Iliopoulou. „Ombrian curves advanced to stochastic modeling of rainfall intensity“. In Rainfall, 261–84. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-12-822544-8.00003-2.
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Lazzari, Maurizio, Marco Piccarreta, Ram L. Ray und Salvatore Manfreda. „Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence“. In Landslides - Investigation and Monitoring. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92730.
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Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.
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Panda, Sudhanshu S., Debasmita Misra, Devendra M. Amatya, Johnny M. Grace III und Anita Thompson. „Advances in modeling soil erosion risk“. In Burleigh Dodds Series in Agricultural Science, 127–49. Burleigh Dodds Science Publishing, 2024. http://dx.doi.org/10.19103/as.2023.0131.09.
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Soil erosion is particularly affected by climate change, especially increases in the amount and intensity of rainfall. The Revised Universal Soil Loss Equation (RUSLE2) developed by USDA-ARS has been developed to quantify soil erosion. However, it does not consider current climate and topographic conditions for estimating those factors, including the rainfall erosivity factor (R-factor) which is particularly significant given more frequent extreme storm events resulting from climate change. This chapter reviews developing a modified RUSLE model using ArcGIS ModelBuilder automation to develop processes for improving USLE factors so that soil erosion quantification can be estimated more precisely in both temporal and spatial dimensions.
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Degefe Merga, Damtew. „Perspective chapter: Responses of the water balance components under land use/land cover and climate change using Geospatial and hydrologic modeling in the Dhidhessa Sub-Basin, Ethiopia“. In Global Warming - A Concerning Component of Climate Change [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1001907.
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The multiple key causes of the hydrological cycle are LULC variability and climate change. The issues of LULC effect on water balances like soil water, evapotranspiration, percolation, base flow, discharge, and water yield through changing land environmental factors and altering soil as well as atmospheric limitations. Climate change, and the other, can direct effect rainfall as well as temperature, causing shifts in watersheds and water resource distribution. Changes of intensity, amplitude, and duration of rainfall influence the amount and variation of river flow, which often exacerbates floods and droughts while also having a negative impact on local and regional water resources. As a result, evaluating the effects of water balance may be vital for water policy and administration. The scientific community and policymakers have paid close attention to LULC research and evaluating the climate impacts
Konferenzberichte zum Thema "Rainfall Intensity Modeling"
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Thomas, M., T. G. Schmitt, U. Leinweber und H. Gysi. „Usage of Radar Measured Rainfall Intensity Distributions in Urban Runoff Modelling“. In Specialty Symposium on Urban Drainage Modeling at the World Water and Environmental Resources Congress 2001. Reston, VA: American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40583(275)37.
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Konuk, I., U. O. Akpan und D. P. Brennan. „Random Field Modeling of Rainfall-Induced Soil Movement“. In 2002 4th International Pipeline Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/ipc2002-27165.
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Natural oil and gas transmission pipeline networks often traverse regions where potential slow ground movements may affect pipeline structural integrity. One of the primary causes of slow ground movement in any region involves the duration, amount, and intensity of rainfall. The phenomenon of rainfall-induced slow ground movement is characterized by both spatial and temporal variability, and involves uncertainties that are best modeled using a probabilistic methodology. A random field modeling strategy is formulated in this study, in which spatial and temporal correlations between rainfall and ground movement are accounted for. The random field formulation advanced in the current study has a number of significant features and capabilities, including modeling the spatial and temporal relationship between rainfall and slope movement for specified pipeline routes, predicting the likelihood of exceeding slope movement thresholds for various precipitation levels and intensities, and providing maps of risk for slope movement, which can be used as a guide in pipeline route planning, selection, and adaptation strategies for the design and maintenance of oil and gas infrastructure. These capabilities have been implemented and encapsulated into the software tool VSLOPE, which has been tested using monthly rainfall and field data for various locations.
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Heshani, P. H. T. D., H. G. L. N. Gunawardhana und J. Sirisena. „Incorporating rainfall projections into hydrological modeling for enhanced design hydrograph estimation“. In Civil Engineering Research Symposium 2024, 51–52. Department of Civil Engineering, University of Moratuwa, 2024. http://dx.doi.org/10.31705/cers.2024.26.
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In the context of changing climate conditions, the design of hydrographs faces increasing uncertainties due to shifts in precipitation patterns, hydrological regimes, and a rise in extreme weather events. This study assesses potential uncertainties in design hydrographs linked to future climate change in the Kalu River Basin, Sri Lanka, focusing on the Ellagawa sub-basin. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was selected based on a comprehensive literature review to account for anticipated changes in rainfall patterns and their impact on streamflow. Seven precipitation gauging stations (Alupolla, Balangoda, Galatura, Halwathura, Pussella, Ratnapura, and Wellandura) were chosen following World Meteorological Organization (WMO) guidelines, based on data availability and percentages of missing data. Streamflow data for Ellagawa and Ratnapura stations were obtained from the Sri Lankan Irrigation Department, with daily precipitation and streamflow data from 1980 to 2017 used for analysis. The model was calibrated and validated using data from four extreme events identified through frequency analysis, each associated with daily precipitation levels corresponding to a 100-year return period. Future changes in precipitation extremes were evaluated using outputs from three General Circulation Models (GCMs): CNRM-CM6-1, HadGEM3-GC31-LL, and MRI-ESM2-0, under two Shared Socioeconomic Pathways (SSP2 and SSP5) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), downscaled to local scale, focusing on the period from 2081-2100. The annual maximum daily precipitation for both observed and projected scenarios was analyzed using Generalized Extreme Value (GEV), Weibull, and Gamma distribution functions. The Nash-Sutcliffe efficiency (NSE) coefficients, ranging from 0.79 to 0.85 during calibration and validation, indicated a close match between simulated and observed river flows. Different GCMs and SSPs predicted varying changes in rainfall regimes and design hydrographs. Specifically, factors such as the frequency and intensity of extreme precipitation events, changes in the seasonal distribution of rainfall, and prolonged dry spells were identified as critical drivers affecting peak flow in the future. Compared to the baseline period (1980-2017), annual total rainfall is projected to increase by -8% to 40% under SSP2-4.5 and -10% to 36% under SSP5-8.5. The maximum daily precipitation is expected to rise from 79 mm to 139 mm under SSP2-4.5 and from 82 mm to 138 mm under SSP5-8.5. Consequently, the peak flow of the design hydrograph may increase by 3% to 106%. These findings underscore the importance of considering climate change uncertainties in hydrological and hydraulic design. By integrating future climate projections into design processes, engineers and policymakers can better adapt infrastructure and planning to evolve conditions, enhancing resilience and sustainability in water management systems.
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Hassanpour, Pezhman. „Model of a Fluid-Level System for the Design and Analysis of Detention Basins Considering Recent Weather Extreme Events and Historic Precipitation Data“. In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-116564.
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Abstract Engineering systems such as flood control dams and storm drain systems are designed to adhere to specific requirements outlined in engineering design codes. These codes heavily rely on local historic climate and precipitation data, which are updated every few years. However, the revision cycles of engineering codes and climate data are not necessarily synchronized. Consequently, it can take a decade or more for a shift in historic climate data to be reflected in the engineering codes. Meanwhile, climate change has increased the frequency and severity of extreme weather conditions. To address the impact of climate change until federal and state governments and regulatory agencies adopt new approaches, design engineers and firms may need to employ mathematical models to assess the resilience of their designs against recent changes in the local climate. The purpose of these models is not to replace the code requirements but to provide an additional layer of confidence that infrastructure can withstand future extreme weather conditions based on the most up-to-date data available. This paper utilizes a model of a fluid-level system comprising three interconnected tanks to simulate a runoff rainwater detention basin in Northern California. Each tank represents a pond within the detention basin and is modeled as a truncated cone, exhibiting nonlinear capacitance. The pipes connecting the tanks are modeled as resistances. Additionally, a bi-linear model is employed to represent a two-level pumping station that discharges water from the last pond into a man-made flood control channel. Sub-hourly rainfall intensity records from the National Weather Service (NWS) were obtained for the few days leading to the most recent historic highs. By integrating these data numerically, the inlet flow rate to each tank was determined as a function of time, enabling the calculation of rainfall water levels. The primary objective of this modeling exercise is to answer the question of whether the detention basin would overflow under specific rainfall intensity conditions. The significance of this question lies in the fact that overflow from one or more ponds can result in flooding events affecting adjacent buildings and infrastructure. Our model demonstrates that, despite meeting the construction code, the detention basin may experience overflows and cause damage to the serviced area under certain conditions. Consequently, future resilience may require a redesign of the basin.
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Niranjana, J. S., Feba Paul, Hridya D. Nambiar, Ashly Joy und Neethu Roy. „Flood Risk Assessment of Thiruvananthapuram City, Kerala“. In International Web Conference in Civil Engineering for a Sustainable Planet. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.112.21.
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Flood is one of the most dangerous and deadliest natural hazards in the world which devastates both life and economy to a very large extent. In Kerala, climate change induced floods are becoming an annual problem. In the midyear of 2018 and 2019, Thiruvananthapuram, the capital city of Kerala, witnessed heavy rainfall and strong winds which resulted in widespread damage in various parts of the City. Flood risk assessment study provides a comprehensive detail of geographic areas and elements that are vulnerable to the particular hazard. As far as Thiruvananthapuram is considered, most of the flood risk assessment studies available were found to be based only on a specific catchment or stream. This paper discusses the need of flood risk assessment study of Thiruvananthapuram City and also focuses on estimating the intensity of storm causing flood. In this work, the major natural drains and the places prone to drainage concentration are delineated from Digital Elevation Model of the study area. The drainage map and land use map are prepared using ArcGIS and ERDAS software respectively. The hydraulic modeling is done using HEC-RAS software and simulations for different rainfall intensities are carried out to estimate the magnitude of flood and to identify the major flood prone areas in the City. This study presents a systematic methodology that can be adopted for flood risk assessment of urban cities, especially when there is less available data.
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BEILICCI, Erika Beata Maria, und Robert BEILICCI. „Influence of Rainfall Characteristics on Runoff in a Small Watershed“. In Air and Water – Components of the Environment 2021 Conference Proceedings. Casa Cărţii de Ştiinţă, 2021. http://dx.doi.org/10.24193/awc2021_13.
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Due to climate change, extreme rainfall is more frequent, and the phenomenon of drought and desertification in some parts of the world is accentuated. Scientists forecast that these trends to continue as the planet continue to warm. An increasingly common phenomenon is the occurrence of flash floods in areas where human intervention on natural conditions has been significant. Over this intervention is superimposed the modification of the characteristics of extreme rainfalls (duration, intensity, height), resulting a series of negative consequences on the ecosystems of the watersheds. For their protection, a more accurate forecast of the size and times of occurrence of the maximum water flows and levels in different sections are needed. This forecast must be made with appropriate methods, such as the use of advanced hydroinformatic tools. This paper analyses the influence of rainfall characteristics on runoff in a small watershed, using rainfall-runoff phenomenon modelling. The modelling is realized using advanced hydroinformatic tool MIKE11, developed by Danish Hydraulic Institute (DHI).
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Molikevych, Roman S. „CURRENT FLOODING CONDITIONS OF SETTLEMENTS IN THE KHERSON REGION (UKRAINE)“. In 22nd International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022v/3.2/s12.05.
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Kherson region has the largest share of irrigated land among all regions of Ukraine. At the same time, it is the largest arid region with a significant area of drainageless massifs. All this contributes to the occurrence of flooding of territories. The main task of the research is to identify modern factors of flooding of settlements in the Kherson region (Ukraine). The goals of the study were to analyze the hydrogeological conditions in the places of flooding, to identify modern changes in the rainfall regime and their impact on the frequency and intensity of flooding in the region, to determine the role of fluctuations in the water level in the Kakhovsky Reservoir and subsequent fluctuations in the groundwater level in regions with a high risk of flooding. The impact of irrigation and water losses from main irrigation canals on the intensification of flooding processes is also considered. Comparative, cartographic methods and GIS modeling were used in the research. It was established that the main causes of flooding are intense torrential rains in the summer-autumn period, in combination with a rise in the groundwater level due to excessive irrigation and fluctuations in the water level in the adjacent reservoir. The results of the research can be used to prevent flooding both in these territories and in others with a similar regime of hydrogeological conditions and economic use of territories.
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„Improved rainfall frequency analysis through separation of storm intensity and storm arrival frequency“. In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.oshea.
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Razali, Irfan Haziq, Aizat Mohd Taib, Wan Hanna Melini Wan Mohtar, Norinah Abd Rahman und Siti Amirah Aziz. „Numerical modelling on the effect of rainfall intensity on slope stability“. In ADVANCES IN FRACTURE AND DAMAGE MECHANICS XX. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0133892.
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„Changes in intensity-frequency-duration relationship of heavy rainfalls at a station in Melbourne“. In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l12.yilmaz.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Rainfall Intensity Modeling"
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Wagner, Anna, Christopher Hiemstra, Glen Liston, Katrina Bennett, Dan Cooley und Arthur Gelvin. Changes in climate and its effect on timing of snowmelt and intensity-duration-frequency curves. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41402.
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Snow is a critical water resource for much of the U.S. and failure to account for changes in climate could deleteriously impact military assets. In this study, we produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S. For selected rivers, we performed seasonal trend analysis of discharge extremes. We calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, we generated intensity-duration-frequency curves (IDF) to find rainfall intensities at several return levels. Generally, our results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase. This increase in rainfall intensity could result in major flood events, demonstrating the importance of accounting for climate changes in infrastructure planning.
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Matus, Sean, und Daniel Gambill. Automation of gridded HEC-HMS model development using Python : initial condition testing and calibration applications. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/46126.
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The US Army Corps of Engineers’s (USACE) Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) rainfall-runoff model is widely used within the research community to develop both event-based and continuous rainfall-runoff models. The soil moisture accounting (SMA) algorithm is commonly used for long-term simulations. Depending on the final model setup, 12 to 18 parameters are needed to characterize the modeled watershed’s canopy, surface, soil, and routing processes, all of which are potential calibration parameters. HEC-HMS includes optimization tools to facilitate model calibration, but only initial conditions (ICs) can be calibrated when using the gridded SMA algorithm. Calibrating a continuous SMA HEC-HMS model is an iterative process that can require hundreds of simulations, a time intensive process requiring automation. HEC-HMS is written in Java and is predominantly run through a graphical user interface (GUI). As such, conducting a long-term gridded SMA calibration is infeasible using the GUI. USACE Construction Engineering Research Laboratory (CERL) has written a workflow that utilizes the existing Jython application programming interface (API) to batch run HEC-HMS simulations with Python. The workflow allows for gridded SMA HEC-HMS model sensitivity and calibration analyses to be conducted in a timely manner.