Bibliografie tematiche / Rainfall probabilities

Letteratura scientifica selezionata sul tema "Rainfall probabilities"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 26 luglio 2024

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Articoli di riviste sul tema "Rainfall probabilities"

1

Zohrab A. Samani e George H. Hargreaves. "Estimating Rainfall Probabilities from Average Values". Applied Engineering in Agriculture 2, n. 2 (1986): 141–43. http://dx.doi.org/10.13031/2013.26729.

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2

Sarkar, Raju, e Kelzang Dorji. "Determination of the Probabilities of Landslide Events—A Case Study of Bhutan". Hydrology 6, n. 2 (16 giugno 2019): 52. http://dx.doi.org/10.3390/hydrology6020052.

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Abstract (sommario):
Landslides have been and are prominent and devastating natural disasters in Bhutan due to its orography and intense monsoonal rainfall. The damage caused by landslides is huge, causing significant loss of lives, damage to infrastructure and loss of agricultural land. Several methods have been developed to understand the relationship between rainfall and landslide incidences. The most common method to understand the relationship is by defining thresholds using empirical methods which are expressed in either intensity-duration or event rainfall-duration terms. However, such thresholds determine the results in a binary form which may not be useful for landslide cases. Apart from defining thresholds, it is significant to validate the results. The article attempts to address both these issues by adopting a probabilistic approach and validating the results. The region of interest is the Chukha region located along the Phuentsholing-Thimphu Highway, which is a significant trade route between neighbouring countries and the national capital Thimphu. In the present study, probabilities are determined by Bayes’ theorem considering rainfall and landslide data from 2004 to 2014. Singular (rainfall intensity, rainfall duration and event rainfall) along with a combination (rainfall intensity and rainfall duration) of precipitation parameters were considered to determine the probabilities for landslide events. A sensitivity analysis was performed to verify the determined probabilities. The results depict that a combination of rainfall parameters is a better indicator to forecast landslides as compared to single rainfall parameter. Finally, the probabilities are validated using landslide records for 2015 using a threat score. The validation signifies that the probabilities can be used as the first line of action for an operational landslide warning system.
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V.D, SONDGE, SONTAKKE J.S e SHELGE B.S. "Aberrations in monsoons in assured rainfall area of Parabhani - Meteorologic characterization". Madras Agricultural Journal 87, september (2000): 384–88. http://dx.doi.org/10.29321/maj.10.a00478.

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Rainfall records of 52 years (1944-95) of Parbhani station located in the assured rainfall zone of Maharashtra state were critically examined for establishing the long term averages of monthly rainfall and its temporal variability by deploying appropriate statistical techniques. The deviations in normal time(s) of onset and withdrawal of monsoon, depths of monthly rainfall and their distribution were defined as aberrations. The results revealed that the average monsoon rainfall (monthly total) of 849.96 mm was distributed in the proportion of 18.13, 26.94, 25.28, 21.61 and 7.93 per cent during June to October, respectively. The variabilities in normal rainfall during crucial months of August (67.69 %) and October (119.48 %) were relatively higher than remaining monsoon months. The probabilities of normal onset (25th MW) and withdrawal (39th MW) were 44.23 and 50 per cent, respectively. The corresponding probabilities of aberrations were 55.77 and 50 per cent. The per cent probabilities of aberrations in seasonal (June to October) amount of rainfall was 56.54 per cent, with higher proportion of below normal (39.23) than its above normal (27.31) rainfall during June to October. The per cent probabilities of recupation of preceeding deficiency in succeeding months decreased with the advancement of time of occurrence of deficiency.
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KRIPALANI, RH, e SV SINGH. "Rainfall probabilities and amounts associated with monsoon depressions over India". MAUSAM 37, n. 1 (1 gennaio 1986): 111–16. http://dx.doi.org/10.54302/mausam.v37i1.2189.

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Composite charts of distribution of probabilities of 24-hr rainfall amounts >=2.5 mm and >= 65 mm and the average 24-hr rainfall amounts over India are determined for various geographical locations of the depressions by using, daily rainfall data of 220 stations for a 16-year period. The composite charts of rainfall probabilities are used to issue forecasts of probability of rain at 12 stations during five independent years. These forecasts when evaluated by a proper quadratic score showed skill score of 10%.
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Liao, Yifan, Bingzhang Lin, Xiaoyang Chen e Hui Ding. "A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas". Water 12, n. 4 (20 aprile 2020): 1177. http://dx.doi.org/10.3390/w12041177.

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Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.
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Bellerby, T. J. "Satellite Rainfall Uncertainty Estimation Using an Artificial Neural Network". Journal of Hydrometeorology 8, n. 6 (1 dicembre 2007): 1397–412. http://dx.doi.org/10.1175/2007jhm846.1.

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Abstract This paper describes a neural network–based approach to estimate the conditional distribution function (cdf) of rainfall with respect to multidimensional satellite-derived input data. The methodology [Conditional Histogram of Precipitation (CHIP)] employs a histogram-based approximation of the cdf. In addition to the conditional expected rainfall rate, it provides conditional probabilities for that rate falling within each of a fixed set of intervals or bins. A test algorithm based on the CHIP approach was calibrated against Goddard profiling algorithm (GPROF) rainfall data for June–August 2002 and then used to produce a 30-min, 0.5° rainfall product from global (60°N–60°S) composite geostationary thermal infrared imagery for June–August 2003. Estimated rainfall rates and conditional probabilities were validated against 2003 GPROF data. The CHIP methodology provides the means to extend existing probabilistic and ensemble satellite rainfall error models, conditioned on a single, scalar, satellite rainfall predictor or upon scalar rainfall-rate outputs, to make full use of multidimensional input data.
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Winters, Karl E. "Floods in Central Texas, September 7–14, 2010". Texas Water Journal 3, n. 1 (11 luglio 2012): 14–25. http://dx.doi.org/10.21423/twj.v3i1.3292.

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Severe flooding occurred near the Austin metropolitan area in central Texas September 7–14, 2010, because of heavy rainfall associated with Tropical Storm Hermine. The U.S. Geological Survey, in cooperation with the Upper Brushy Creek Water Control and Improvement District, determined rainfall amounts and annual exceedance probabilities for rainfall resulting in flooding in Bell, Williamson, and Travis counties in central Texas during September 2010. We documented peak streamflows and the annual exceedance probabilities for peak streamflows recorded at several streamflow-gaging stations in the study area. The 24-hour rainfall total exceeded 12 inches at some locations, with one report of 14.57 inches at Lake Georgetown. Rainfall probabilities were estimated using previously published depth-duration frequency maps for Texas. At 4 sites in Williamson County, the 24-hour rainfall had an annual exceedance probability of 0.002. Streamflow measurement data and flood-peak data from U.S. Geological Survey surface-water monitoring stations (streamflow and reservoir gaging stations) are presented, along with a comparison of September 2010 flood peaks to previous known maximums in the periods of record. Annual exceedance probabilities for peak streamflow were computed for 20 streamflow-gaging stations based on an analysis of streamflow-gaging station records. The annual exceedance probability was 0.03 for the September 2010 peak streamflow at the Geological Survey’s streamflow-gaging stations 08104700 North Fork San Gabriel River near Georgetown, Texas, and 08154700 Bull Creek at Loop 360 near Austin, Texas. The annual exceedance probability was 0.02 for the peak streamflow for Geological Survey´s streamflow-gaging station 08104500 Little River near Little River, Texas. The lack of similarity in the annual exceedance probabilities computed for precipitation and streamflow might be attributed to the small areal extent of the heaviest rainfall over these and the other gaged watersheds. Citation: Winters KE. 2012. Floods in Central Texas, September 7–14, 2010. Texas Water Journal. 3(1):14-25. Available from: https://doi.org/10.21423/twj.v3i1.3292.
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GORE, P. G., e V. THAPLIYAL. "Occurrence of dry and wet weeks over Maharashtra". MAUSAM 51, n. 1 (17 dicembre 2021): 25–38. http://dx.doi.org/10.54302/mausam.v51i1.1754.

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Based on the daily rainfall data of the past 90 years (1901-90), the initial and conditional probabilities of a wet week and the probabilities of 2 and 3 consecutive wet weeks have been computed for all the districts of Maharashtra during the southwest monsoon season by using Markov Chain model. A temporal and spatial distribution of probabilities of wet weeks have been studied in detail. Most of the districts show the highest probability of wet weeks during July. A few number of the districts show the second highest probability during August. The western and northeastern parts of the state show 10-16 wet weeks with high probability. The high rainfall districts along the west coast show high wet week probabilities during most of the period of the season. A few number of the districts from moderate rainfall zone, show high probability of a wet week during, July and August. A persistency in rainfall is noticed in only extreme western parts of the state. The east-west variation along 19° N shows 'L' shaped pattern for the high probability wet weeks. While, the north -south variation of the wet weeks with high probability shows a sinusoidal curve from north to south.
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DeGaetano, Arthur T., e Harrison Tran. "Recent Changes in Average Recurrence Interval Precipitation Extremes in the Mid-Atlantic United States". Journal of Applied Meteorology and Climatology 61, n. 2 (febbraio 2022): 143–57. http://dx.doi.org/10.1175/jamc-d-21-0129.1.

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Abstract Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p > 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals. Significance Statement Engineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the “2-yr storm” to the “100-yr storm”). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency.
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Adhikari, R. N., M. S. RAMA MOHAN RAO e P. BHASKAR RAO. "Analysis of rainfall data for water management In dry land zone of Karnataka". MAUSAM 44, n. 2 (1 gennaio 2022): 147–52. http://dx.doi.org/10.54302/mausam.v44i2.3812.

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Bellary region is characterized as one of the semi-arid zones of Karnataka, having only 508 mm of annual rainfall distributed over 35 rainy days. The ill-distribution of rainfall creates at least 5 drought years in every decade. The average rainfa1l distribution shows that there is a total failure in Kharif season. However, some assured rainfall received during September and October a better prospect which assumes for rabi season .This problem can be overcome to. certain extent by scientific management of crops and water. This calls for detailed analysis of any Important water resources Issues. Keeping this mind, an attempt made in this paper to analysis short and long period rainfall data. The probabilities analysis of. rainfall for shorter periods for identification of suitable periods for sowing, return period analysis for designing of soil and water conservation structures and determining the size of storage structures, the identification of number of Various rainfall events for designing water harvesting system for crop and water management are carried out and presented in this paper.
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Più fonti

Tesi sul tema "Rainfall probabilities"

1

Cung, Annie. "Statistical modeling of extreme rainfall processes in consideration of climate change". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100788.

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Abstract (sommario):
Extreme rainfall events may have catastrophic impacts on the population and infrastructures, therefore it is essential to have accurate knowledge of extreme rainfall characteristics. Moreover, both the scientific community and policymakers have recently shown a growing interest in the potential impacts of climate change on water resources management. Indeed, changes in the intensity and frequency of occurrence of extreme rainfall events may have serious impacts. As such, it is important to understand not only the current patterns of extreme rainfalls but also how they are likely to change in the future.
The objective of the present research is therefore to find the best method for estimating accurately extreme rainfalls for the current time period and future periods in the context of climate change. The analysis of extreme rainfall data from the province of Quebec (Canada) revealed that, according to L-moment ratio diagrams, the data may be well described by the Generalized-Extreme-Value (GEV) distribution. Results also showed that a simple scaling relationship between non-central moments (NCM) and duration can be established and that a scaling method based on NCMs and scaling exponents can be used to generate accurate estimates of extreme rainfalls at Dorval station (Quebec, Canada). Other results demonstrated that the method of NCMs can accurately estimate distribution parameters and can be used to construct accurate Intensity-Duration-Frequency (IDF) curves.
Furthermore, a regional analysis was performed and homogenous regions of weather stations within Quebec were identified. A method for the estimation of missing data at ungauged sites based on regional NCMs was found to yield good estimates.
In addition, the potential impacts of climate change on extreme rainfalls were assessed. Changes in the distribution of annual maximum (AM) precipitations were evaluated using simulations from two Global Climate Models (GCMs) under the A2 greenhouse gas emission scenario: the Coupled Global Climate Model version 2 (CGCM2A2) of the Canadian Centre for Climate Modelling and Analysis, and the Hadley Centre's Model version 3 (HadCM3A2). Simulations from these two models were downscaled spatially using the Statistical DownScaling Model (SDSM). A bias-correction method to adjust the downscaled AM daily precipitations for Dorval station was tested and results showed that after adjustments, the values fit the observed AM daily precipitations well. The analysis of future AM precipitations revealed that, after adjustments, AM precipitations downscaled from CGCM2A2 increase from current to future periods, while AM precipitations downscaled from HadCM3A2 show a mild decrease from current to future periods, for daily and sub-daily scales.
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Patron, Glenda G. "Joint probability distribution of rainfall intensity and duration". Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-06232009-063226/.

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Suyanto, Adhi. "Estimating the exceedance probabilities of extreme floods using stochastic storm transportation and rainfall - runoff modelling". Thesis, University of Newcastle Upon Tyne, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386794.

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Methods of estimating floods with return periods of up to one hundred years are reasonably well established, and in the main rely on extrapolation of historical flood data at the site of interest. However, extrapolating the tails of fitted probability distributions to higher return periods is very unreliable and cannot provide a satisfactory basis for extreme flood estimation. The probable maximum flood concept is an alternative approach, which is often used for critical cases such as the location of nuclear power plants, and is viewed as a consequence of a combination of a probable maximum precipitation with the worst possible prevailing catchment conditions. Return periods are not usually quoted although they are implicitly thought to be of the order of tens of thousand of years. There are many less critical situations which still justify greater flood protection than would be provided for an estimated one-hundred year flood. There is therefore a need for techniques which can be used to estimate floods with return periods of up to several thousand years. The predictive approach adopted here involves a combination of a probabilistic storm transposition technique with a physically-based distributed rainfall-runoff model. Extreme historical storms within a meteorologically homogeneous region are, conceptually, moved to the catchment of interest, and their return periods are estimated within a probabilistic framework. Known features of storms such as depth, duration, and perhaps approximate shape will, together with catchment characteristics, determine much of the runoff response. But there are other variables which also have an effect and these include the space-time distribution of rainfall within the storm, storm velocity and antecedent catchment conditions. The effects of all these variables on catchment response are explored.
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Vavae, Hilia. "A simple forecasting scheme for predicting low rainfalls in Funafuti, Tuvalu". The University of Waikato, 2008. http://hdl.handle.net/10289/2435.

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The development of some ability for forecasting low rainfalls would be helpful in Tuvalu as rainwater is the only source of fresh water in the country. The subsurface water is brackish and saline so the entire country depends totally on rainwater for daily domestic supplies, agricultural and farming activities. More importantly, these atolls are often influenced by droughts which consequently make inadequate drinking water an issue. A simple graph-based forecasting scheme is developed and presented in this thesis for forecasting below average mean rainfall in Funafuti over the next n-month period. The approach uses precursor ocean surface temperature data to make predictions of below average rainfall for n = 1, 2 12. The simplicity of the approach makes it a suitable method for the country and thus for the Tuvalu Meteorological Service to use as an operational forecasting tool in the climate forecasting desk. The graphical method was derived from standardised monthly rainfalls from the Funafuti manual raingauge for the period January 1945 to July 2007. The method uses lag-1 and-lag 2 NINO4 sea surface temperatures to define whether prediction conditions hold. The persistence of predictability tends to be maintained when the observed NINO4 ocean surface temperatures fall below 26.0oC. Although the developed method has a high success probability of up to 80 percent, this can only be achieved when conditions are within the predictable field. A considerable number of below average rainfall periods are not within the predictable field and therefore cannot be forecast by this method. However, the graphical approach has particular value in warning when an existing drought is likely to continue.
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Marx, Hester Gerbrecht. "The use of artificial neural networks to enhance numerical weather prediction model forecasts of temperature and rainfall". Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-02102009-161401/.

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Moatshe, Peggy Seanokeng. "Verification of South African Weather Service operational seasonal forecasts". Pretoria: [S.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-08112009-131703.

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Chen, Chia-Jeng. "Hydro-climatic forecasting using sea surface temperatures". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/48974.

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A key determinant of atmospheric circulation patterns and regional climatic conditions is sea surface temperature (SST). This has been the motivation for the development of various teleconnection methods aiming to forecast hydro-climatic variables. Among such methods are linear projections based on teleconnection gross indices (such as the ENSO, IOD, and NAO) or leading empirical orthogonal functions (EOFs). However, these methods deteriorate drastically if the predefined indices or EOFs cannot account for climatic variability in the region of interest. This study introduces a new hydro-climatic forecasting method that identifies SST predictors in the form of dipole structures. An SST dipole that mimics major teleconnection patterns is defined as a function of average SST anomalies over two oceanic areas of appropriate sizes and geographic locations. The screening process of SST-dipole predictors is based on an optimization algorithm that sifts through all possible dipole configurations (with progressively refined data resolutions) and identifies dipoles with the strongest teleconnection to the external hydro-climatic series. The strength of the teleconnection is measured by the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate ensemble hydro-climatic forecasts. The dipole teleconnection method is applied to the forecasting of seasonal precipitation over the southeastern US and East Africa, and the forecasting of streamflow-related variables in the Yangtze and Congo Rivers. These studies show that the new method is indeed able to identify dipoles related to well-known patterns (e.g., ENSO and IOD) as well as to quantify more prominent predictor-predictand relationships at different lead times. Furthermore, the dipole method compares favorably with existing statistical forecasting schemes. An operational forecasting framework to support better water resources management through coupling with detailed hydrologic and water resources models is also demonstrated.
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Roulin, Emmannuel. "Medium-range probabilistic river streamflow predictions". Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209270.

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River streamflow forecasting is traditionally based on real-time measurements of rainfall over catchments and discharge at the outlet and upstream. These data are processed in mathematical models of varying complexity and allow to obtain accurate predictions for short times. In order to extend the forecast horizon to a few days - to be able to issue early warning - it is necessary to take into account the weather forecasts. However, the latter display the property of sensitivity to initial conditions, and for appropriate risk management, forecasts should therefore be considered in probabilistic terms. Currently, ensemble predictions are made using a numerical weather prediction model with perturbed initial conditions and allow to assess uncertainty.

The research began by analyzing the meteorological predictions at the medium-range (up to 10-15 days) and their use in hydrological forecasting. Precipitation from the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. A semi-distributed hydrological model was used to transform these precipitation forecasts into ensemble streamflow predictions. The performance of these forecasts was analyzed in probabilistic terms. A simple decision model also allowed to compare the relative economic value of hydrological ensemble predictions and some deterministic alternatives.

Numerical weather prediction models are imperfect. The ensemble forecasts are therefore affected by errors implying the presence of biases and the unreliability of probabilities derived from the ensembles. By comparing the results of these predictions to the corresponding observed data, a statistical model for the correction of forecasts, known as post-processing, has been adapted and shown to improve the performance of probabilistic forecasts of precipitation. This approach is based on retrospective forecasts made by the ECMWF for the past twenty years, providing a sufficient statistical sample.

Besides the errors related to meteorological forcing, hydrological forecasts also display errors related to initial conditions and to modeling errors (errors in the structure of the hydrological model and in the parameter values). The last stage of the research was therefore to investigate, using simple models, the impact of these different sources of error on the quality of hydrological predictions and to explore the possibility of using hydrological reforecasts for post-processing, themselves based on retrospective precipitation forecasts.

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La prévision des débits des rivières se fait traditionnellement sur la base de mesures en temps réel des précipitations sur les bassins-versant et des débits à l'exutoire et en amont. Ces données sont traitées dans des modèles mathématiques de complexité variée et permettent d'obtenir des prévisions précises pour des temps courts. Pour prolonger l'horizon de prévision à quelques jours – afin d'être en mesure d'émettre des alertes précoces – il est nécessaire de prendre en compte les prévisions météorologiques. Cependant celles-ci présentent par nature une dynamique sensible aux erreurs sur les conditions initiales et, par conséquent, pour une gestion appropriée des risques, il faut considérer les prévisions en termes probabilistes. Actuellement, les prévisions d'ensemble sont effectuées à l'aide d'un modèle numérique de prévision du temps avec des conditions initiales perturbées et permettent d'évaluer l'incertitude.

La recherche a commencé par l'analyse des prévisions météorologiques à moyen-terme (10-15 jours) et leur utilisation pour des prévisions hydrologiques. Les précipitations issues du système de prévisions d'ensemble du Centre Européen pour les Prévisions Météorologiques à Moyen-Terme ont été utilisées. Un modèle hydrologique semi-distribué a permis de traduire ces prévisions de précipitations en prévisions d'ensemble de débits. Les performances de ces prévisions ont été analysées en termes probabilistes. Un modèle de décision simple a également permis de comparer la valeur économique relative des prévisions hydrologiques d'ensemble et d'alternatives déterministes.

Les modèles numériques de prévision du temps sont imparfaits. Les prévisions d'ensemble sont donc entachées d'erreurs impliquant la présence de biais et un manque de fiabilité des probabilités déduites des ensembles. En comparant les résultats de ces prévisions aux données observées correspondantes, un modèle statistique pour la correction des prévisions, connue sous le nom de post-processing, a été adapté et a permis d'améliorer les performances des prévisions probabilistes des précipitations. Cette approche se base sur des prévisions rétrospectives effectuées par le Centre Européen sur les vingt dernières années, fournissant un échantillon statistique suffisant.

A côté des erreurs liées au forçage météorologique, les prévisions hydrologiques sont également entachées d'erreurs liées aux conditions initiales et aux erreurs de modélisation (structure du modèle hydrologique et valeur des paramètres). La dernière étape de la recherche a donc consisté à étudier, à l'aide de modèles simples, l'impact de ces différentes sources d'erreur sur la qualité des prévisions hydrologiques et à explorer la possibilité d'utiliser des prévisions hydrologiques rétrospectives pour le post-processing, elles-même basées sur les prévisions rétrospectives des précipitations.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Tirivarombo, Sithabile. "Climate variability and climate change in water resources management of the Zambezi River basin". Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002955.

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Water is recognised as a key driver for social and economic development in the Zambezi basin. The basin is riparian to eight southern African countries and the transboundary nature of the basin’s water resources can be viewed as an agent of cooperation between the basin countries. It is possible, however, that the same water resource can lead to conflicts between water users. The southern African Water Vision for ‘equitable and sustainable utilisation of water for social, environmental justice and economic benefits for the present and future generations’ calls for an integrated and efficient management of water resources within the basin. Ensuring water and food security in the Zambezi basin is, however, faced with challenges due to high variability in climate and the available water resources. Water resources are under continuous threat from pollution, increased population growth, development and urbanisation as well as global climate change. These factors increase the demand for freshwater resources and have resulted in water being one of the major driving forces for development. The basin is also vulnerable due to lack of adequate financial resources and appropriate water resources infrastructure to enable viable, equitable and sustainable distribution of the water resources. This is in addition to the fact that the basin’s economic mainstay and social well-being are largely dependent on rainfed agriculture. There is also competition among the different water users and this has the potential to generate conflicts, which further hinder the development of water resources in the basin. This thesis has focused on the Zambezi River basin emphasising climate variability and climate change. It is now considered common knowledge that the global climate is changing and that many of the impacts will be felt through water resources. If these predictions are correct then the Zambezi basin is most likely to suffer under such impacts since its economic mainstay is largely determined by the availability of rainfall. It is the belief of this study that in order to ascertain the impacts of climate change, there should be a basis against which this change is evaluated. If we do not know the historical patterns of variability it may be difficult to predict changes in the future climate and in the hydrological resources and it will certainly be difficult to develop appropriate management strategies. Reliable quantitative estimates of water availability are a prerequisite for successful water resource plans. However, such initiatives have been hindered by paucity in data especially in a basin where gauging networks are inadequate and some of them have deteriorated. This is further compounded by shortages in resources, both human and financial, to ensure adequate monitoring. To address the data problems, this study largely relied on global data sets and the CRU TS2.1 rainfall grids were used for a large part of this study. The study starts by assessing the historical variability of rainfall and streamflow in the Zambezi basin and the results are used to inform the prediction of change in the future. Various methods of assessing historical trends were employed and regional drought indices were generated and evaluated against the historical rainfall trends. The study clearly demonstrates that the basin has a high degree of temporal and spatial variability in rainfall and streamflow at inter-annual and multi-decadal scales. The Standardised Precipitation Index, a rainfall based drought index, is used to assess historical drought events in the basin and it is shown that most of the droughts that have occurred were influenced by climatic and hydrological variability. It is concluded, through the evaluation of agricultural maize yields, that the basin’s food security is mostly constrained by the availability of rainfall. Comparing the viability of using a rainfall based index to a soil moisture based index as an agricultural drought indicator, this study concluded that a soil moisture based index is a better indicator since all of the water balance components are considered in the generation of the index. This index presents the actual amount of water available for the plant unlike purely rainfall based indices, that do not account for other components of the water budget that cause water losses. A number of challenges were, however, faced in assessing the variability and historical drought conditions, mainly due to the fact that most parts of the Zambezi basin are ungauged and available data are sparse, short and not continuous (with missing gaps). Hydrological modelling is frequently used to bridge the data gap and to facilitate the quantification of a basin’s hydrology for both gauged and ungauged catchments. The trend has been to use various methods of regionalisation to transfer information from gauged basins, or from basins with adequate physical basin data, to ungauged basins. All this is done to ensure that water resources are accounted for and that the future can be well planned. A number of approaches leading to the evaluation of the basin’s hydrological response to future climate change scenarios are taken. The Pitman rainfall-runoff model has enjoyed wide use as a water resources estimation tool in southern Africa. The model has been calibrated for the Zambezi basin but it should be acknowledged that any hydrological modelling process is characterised by many uncertainties arising from limitations in input data and inherent model structural uncertainty. The calibration process is thus carried out in a manner that embraces some of the uncertainties. Initial ranges of parameter values (maximum and minimum) that incorporate the possible parameter uncertainties are assigned in relation to physical basin properties. These parameter sets are used as input to the uncertainty version of the model to generate behavioural parameter space which is then further modified through manual calibration. The use of parameter ranges initially guided by the basin physical properties generates streamflows that adequately represent the historically observed amounts. This study concludes that the uncertainty framework and the Pitman model perform quite well in the Zambezi basin. Based on assumptions of an intensifying hydrological cycle, climate changes are frequently expected to result in negative impacts on water resources. However, it is important that basin scale assessments are undertaken so that appropriate future management strategies can be developed. To assess the likely changes in the Zambezi basin, the calibrated Pitman model was forced with downscaled and bias corrected GCM data. Three GCMs were used for this study, namely; ECHAM, GFDL and IPSL. The general observation made in this study is that the near future (2046-2065) conditions of the Zambezi basin are expected to remain within the ranges of historically observed variability. The differences between the predictions for the three GCMs are an indication of the uncertainties in the future and it has not been possible to make any firm conclusions about directions of change. It is therefore recommended that future water resources management strategies account for historical patterns of variability, but also for increased uncertainty. Any management strategies that are able to satisfactorily deal with the large variability that is evident from the historical data should be robust enough to account for the near future patterns of water availability predicted by this study. However, the uncertainties in these predictions suggest that improved monitoring systems are required to provide additional data against which future model outputs can be assessed.
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Akil, Nicolas. "Etude des incertitudes des modèles neuronaux sur la prévision hydrogéologique. Application à des bassins versants de typologies différentes". Electronic Thesis or Diss., IMT Mines Alès, 2021. http://www.theses.fr/2021EMAL0005.

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Les crues et les sécheresses sont deux des risques majeurs en France et nécessitent une attention particulière. Dans ces conditions où le changement climatique engendre des phénomènes extrêmes de plus en plus fréquents, la modélisation de ces risques est désormais un élément incontournable pour la gestion de la ressource en eau.Actuellement, les débits ou hauteurs d’eau sont principalement anticipés à partir de modèles à base physique ou conceptuelle. Bien qu’efficaces et nécessaires, la calibration et la mise en œuvre de ces modèles nécessitent la réalisation d’études longues et coûteuses.Dans ce contexte, cette thèse, soutenue par l’IMT Mines Alès et conjointement financée par la société aQuasys et l’ANRT, a pour objectif de développer des modèles issus du paradigme systémique. Ceux-ci nécessitent uniquement des connaissances a priori basiques sur la caractérisation physique du bassin étudié, et qui peuvent être calibrés à partir des seules informations d’entrées et de sorties (pluies et débits/hauteurs).Les modèles les plus utilisés dans le monde environnemental sont les réseaux neuronaux, qui sont utilisés sur ce projet. Cette thèse cherche à répondre à trois objectifs principaux :1. Élaboration d’une méthode de conception de modèle adaptée aux différentes variables (débits/hauteur des eaux de surface) et à des bassins de types très différents : bassins versants ou bassins hydrogéologiques (hauteur des eaux souterraines)2. Évaluation des incertitudes liées à ces modèles en fonction des types de bassins visés3. Réduction de ces incertitudesPlusieurs bassins sont utilisés pour répondre à ces problématiques : la nappe du bassin du Blavet en Bretagne et le bassin de la nappe de la Craie de Champagne sud et Centre
Floods and droughts are the two main risks in France and require a special attention. In these conditions, where climate change generates increasingly frequent extreme phenomena, modeling these risks is an essential element for water resource management.Currently, discharges and water heights are mainly predicted from physical or conceptual based models. Although efficient and necessary, the calibration and implementation of these models require long and costly studies.Hydrogeological forecasting models often use data from incomplete or poorly dimensioned measurement networks. Moreover, the behavior of the study basins is in most cases difficult to understand. This difficulty is thus noted to estimate the uncertainties associated with hydrogeological modeling.In this context, this thesis, supported by IMT Mines Alès and financed by the company aQuasys and ANRT, aims at developing models based on the systemic paradigm. These models require only basic knowledge on the physical characterization of the studied basin, and can be calibrated from only input and output information (rainfall and discharge/height).The most widely used models in the environmental world are neural networks, which are used in this project. This thesis seeks to address three main goals:1. Development of a model design method adapted to different variables (surface water flows/height) and to very different types of basins: watersheds or hydrogeological basins (groundwater height)2. Evaluation of the uncertainties associated with these models in relation to the types of targeted basins3. Reducing of these uncertaintiesSeveral basins are used to address these issues: the Blavet basin in Brittany and the basin of the Southern and Central Champagne Chalk groundwater table
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Libri sul tema "Rainfall probabilities"

1

Robertson, George W. Rainfall probabilities in [name of area]. Islamabad: Barani Agricultural Research and Development Project, National Agricultural Research Centre, 1985.

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Gore, P. G. Study of dry and wet spells for meteorological subdivisions of India. Pune: Drought Research Unit, Office of the Additional Director General of Meteorology (Research), 2001.

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Huygen, J. Estimation of rainfall in Zambia using METEOSAT-TIR data. Wageningen (Netherlands): Winand Staring Centre, 1989.

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R, Kulkarni J., e Indian Institute of Tropical Meteorology., a cura di. Multimodel scheme for prediction of monthly rainfall over India. Pune: Indian Institute of Tropical Meteorology, 2003.

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Hunter, John P. Rainfall and temperature probability statistics for Lesotho agriculture: Selected stations. Maseru, Lesotho: Farming Systems Research Project, Research Division, Ministry of Agriculture, 1986.

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Kumar, Avadhesh. Overland flow in mountainous areas. Roorkee: National Institute of Hydrology, 1987.

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California Weather Symposium (1994 Sierra College). Predicting heavy rainfall events in California: A symposium to share weather pattern knowledge : Sierra College, Rocklin, California, June 25, 1994. Rocklin, Calif: Sierra College Science Center, 1994.

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Bernard, Guillot, African Center for Meteorology Applied to Development. e France. Ministère de la coopération., a cura di. Problèmes de validation des méthodes d'estimation des précipitations par satellite en Afrique intertropical: Actes de l'atelier de Niamey du 1er au 3 décembre 1994. Paris: Orstom, 1996.

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Johnson, Michelle L. Estimating precipitation over the Amazon Basin from satellite and in-situ measurements. Middleton, Del: Legates Consulting, 2003.

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Khaladkar, R. M. Performance of NCMRWF models in predicting high rainfall spells during SW monsoon season: A study for some cases in July 2004. Pune: Indian Institute of Tropical Meteorology, 2007.

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Capitoli di libri sul tema "Rainfall probabilities"

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Gariano, Stefano Luigi, Massimo Melillo, Maria Teresa Brunetti, Sumit Kumar, Rajkumar Mathiyalagan e Silvia Peruccacci. "Challenges in Defining Frequentist Rainfall Thresholds to Be Implemented in a Landslide Early Warning System in India". In Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, 409–16. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16898-7_27.

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AbstractIn India, rainfall-induced landslides cause a high toll in terms of fatalities and damages. Therefore, the adoption of tools to predict the occurrence of such phenomena is urgent. For the purpose, the LANDSLIP project aimed at developing a landslide early warning system (LEWS) to forecast the occurrence of rainfall-induced landslides in two Indian pilot areas: Darjeeling and Nilgiris. Rainfall thresholds are a widely used tool to define critical probability levels for the possible occurrence of landslides in large areas, and are particularly suitable to be implemented in LEWSs.In this work, we exploited two catalogues of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively. Adopting a frequentist statistical method and using an automatic tool, we determined rainfall thresholds at different non-exceedance probabilities for the two pilot areas. Despite the daily temporal resolution of rainfall data and the spatial and temporal distribution of the documented landslides, the thresholds calculated for the two areas have acceptable uncertainties and were implemented in the LANDSLIP LEWS prototype. We expect that the new thresholds and the whole system will contribute to mitigate the landslide risk in the study areas.
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Engelbrecht, Francois A., Jessica Steinkopf, Jonathan Padavatan e Guy F. Midgley. "Projections of Future Climate Change in Southern Africa and the Potential for Regional Tipping Points". In Sustainability of Southern African Ecosystems under Global Change, 169–90. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-10948-5_7.

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AbstractSouthern Africa is a climate change hotspot with projected warming and drying trends amplifying stresses in a naturally warm, dry and water-stressed region. Despite model-projected uncertainty in rainfall change over the eastern escarpment of South Africa, strong model agreement in projections indicates that southern African is likely to become generally drier. Sharply increased regional warming and associated strong reductions in soil-moisture availability and increases in heat-waves and high fire-danger days are virtually certain under low mitigation futures. Changes are detectible in observed climate trends for the last few decades, including regional warming, drying in both the summer and winter rainfall regions, and increases in intense rainfall events. The southern African climate is at risk of tipping into a new regime, with unprecedented impacts, such as day-zero drought in the Gauteng province of South Africa, collapse of the maize and cattle industries, heat-waves of unprecedented intensity and southward shifts in intense tropical cyclone landfalls. Many of these adverse changes could be avoided if the Paris Accord’s global goal were to be achieved, but research is urgently required to quantify the probabilities of such tipping points in relation to future levels of global warming. Adaptation planning is an urgent regional priority.
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Pabreja, Kavita. "Artificial Neural Network for Markov Chaining of Rainfall Over India". In Research Anthology on Artificial Neural Network Applications, 1130–45. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch053.

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Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.
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Khakzad, Nima. "Vulnerability Assessment of Process Vessels in the Event of Hurricanes". In Natural Hazards - New Insights [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.109430.

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Hurricanes are multi-hazard natural hazards that can cause severe damage to chemical and process plants via individual or combined impact of strong winds, torrential rainfall, floods, and hitting waves especially in coastal areas. To assess and manage the vulnerability of process plants, failure modes and respective failure probabilities both before and after implementing safety measures should be assessed. However, due to the uncertainties arising from interdependent failure modes and lack of accurate and sufficient historical data, most conventional quantitative risk assessment techniques deliver inaccurate results, which in turn lead to inaccurate risk assessment and thus ineffective or non-cost-effective risk management strategies. Bayesian network (BN) is a probabilistic technique for reasoning under uncertainty with a variety of applications is system safety, reliability engineering, and risk assessment. In this chapter, applications of BN to vulnerability assessment and management of process vessels in the event of hurricanes are demonstrated and discussed.
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Atti di convegni sul tema "Rainfall probabilities"

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Yamano, Hidemasa, Hiroyuki Nishino e Kenichi Kurisaka. "Development of Probabilistic Risk Assessment Methodology of Decay Heat Removal Function Against Combination Hazards of Strong Wind and Rainfall for Sodium-Cooled Fast Reactors". In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-66059.

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This paper describes probabilistic risk assessment (PRA) methodology development against combination hazard of strong wind and rainfall. In this combination hazard PRA, a hazard curve has been evaluated in terms of maximum instantaneous wind speed, hourly rainfall, and rainfall duration. A scenario analysis provided event sequences resulted from the combination hazard of strong wind and rainfall. The event sequence was characterized by the function loss of auxiliary cooling system, of which heat transfer tubes could crack due to cycle fatigue by cyclic contact of rain droplets. This situation could occur if rain droplets ingress into air cooler occurs after the air cooler roof failure due to strong-wind-generated missile impact. This event sequence was incorporated into an event tree which addressed component failure by the combination hazard. Finally, a core damage frequency has been estimated the order of 10−7/year in total by multiplying discrete hazard frequencies by conditional decay heat removal failure probabilities. A dominant sequence is the failure of the auxiliary cooling system by the missile impact after the failure of external fuel tank by the missile impact. A dominant hazard is the maximum instantaneous wind speed of 40–60 m/s, the hourly rainfall of 20–40 mm/h, and the rainfall duration of 0–10 h.
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Bălăuță MINDA, Codruța. "Gumbel’s Extreme Value Distribution for Flood Frequency Analyses of Timis River". In Air and Water – Components of the Environment 2024 Conference Proceedings. Casa Cărţii de Ştiinţă, 2024. http://dx.doi.org/10.24193/awc2024_02.

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One of the major problems in the engineering design of water resource is the estimation of peak flood flows. In probability theory and statistics, flood frequency analysis is used to obtain the distribution of flood probabilities. Gumbel distribution represents distributions of extreme values used in hydrological studies to predict flood peak, maximum rainfall, etc. This paper aims to analyses the frequency of floods, Gumbel's frequency distribution method, based on the maximum annual flows in the Timis River for the period of 30 years (1993 – 2022). For this analysis the return period (T) used is 5 years, 10 years, 50 years, 100 years, 150 years.
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Hommadi, Ali, Ali Al-Fawzy e Fadhil Al-Mohammed. "Flood Forecasting for the Greater Zabb Tributary of Tigris River Using the Probability Techniques". In 4th International Conference on Architectural & Civil Engineering Sciences. Cihan University-Erbil, 2023. http://dx.doi.org/10.24086/icace2022/paper.875.

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The climate change in Iraq from 1933 until 1992 that caused fluctuation of rainfall led to fluctuation of rivers discharge. The increasing of rainfall intensity may cause flood which will impact on human by flooding the neighboring town that lead to damage the houses and human loss; while the decreasing of rainfall causes the scarcity. Therefore accurate forecasting must be done to measure the maximum incomes of discharge by statically analysis in rivers through 56 years to predict the maximum discharge through recurrence interval (T) 10 ,25 ,50 ,100 ,200 ,1000 ,10000, and 105years. In this study the data of Greater Zabb river in Aski Kelek station will be used because of it is not controlled by dams. In this research analyzing river flood is done by utilizing of Gamble, Log person type III., Normal distribution, Log normal distribution, Weibull distribution and GEV(General extreme value) that used by PWM (Power weighted method) distribution. The obtained results of this research were close in all used methods of T = 1000 year except the log normal distribution and GEV, whereas they were higher than the other distributions. Moreover; the outcomes of previous study for suggested dam (Bakhma dams) on the river which are Japanese report 1979, the study of Swiss consultants 1985, study and water resource design 2005 were close to t the outcomes of this research. The many distributions give a lot of probabilities of flood to obtain a clear view of maximum inflow (flood) to river and best design of reservoir.
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Cheung, R. W. M., Cheung, H. W. M. Li e E. K. H. Chu. "Exploratory Study of using Artificial Intelligence for Landslide Predictions". In The HKIE Geotechnical Division 43rd Annual Seminar. AIJR Publisher, 2023. http://dx.doi.org/10.21467/proceedings.159.17.

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Riding on the comprehensive inventories of landslide-related data maintained by the Geotechnical Engineering Office (GEO) over the years, the GEO has initiated an exploratory study to enhance the existing landslide prediction models (i.e. Model A – landslide susceptibility model for natural terrain, and Model B – rainfall-landslide correlations for reported landslides on man-made slopes) with the application of machine learning (ML) and big data analytics. Model A adopted seven common ML algorithms to correlate the multitude of features (e.g. rainfall, geology, and some terrain-related features) with landslide in the natural terrain on the Lantau Island non-linearly. Domain knowledge of geotechnical and geological engineering was incorporated in the course of developing the ML model. The training and testing of the ML models used most of the available data as an approach to acquire realistic prediction of landslide probabilities out of an inherently acutely-imbalanced dataset. The applicability of some common evaluation metrics to this approach, and grid size effect were examined. Promising results with about three orders of magnitude enhancement to the model resolution were achieved. The use of ML on Model B is ongoing based on the knowledge and experience gained from Model A. This paper presents the latest progress of the exploratory study.
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Paralska, Katia, Petko Tsarev, Rositsa Stefanova e Georgy Koshinchanov. "ANALYSIS OF HIGH WAVE DURING HYDROLOGICAL EXTREME EVENT ON 10-15 DECEMBER 2021 WITH SIGNIFICANT RAINFALL IN SOUTHERN BULGARIA". In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/3.1/s12.04.

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Manual measurement of flow velocity and streamflow discharge during high waters is often impossible because of different factors: night events, harsh meteorological conditions, very high dangerous river velocities and levels or infrastructure destruction. The purpose of this study is to determine maximums of river flow velocity and streamflow discharge with hydraulic engineering methods, as well to determine the modulus of runoff at some stations in the East Aegean River Basin in South Bulgaria for the period between 10 and 15 December 2021 characterized with heavy rain. At that time significant precipitations were observed in South Bulgaria as result of a large cyclone over the East Mediterranean area. As a result of these precipitations, the river water levels in the whole country have raise, but the most significant increases were observed in the rivers of the Rhodope Mountains: upper streams of Arda River, the rivers Varbitsa, Krumovitsa, Vacha, Shirokolashka, Chepelarska, etc. On Arda River a large pedestrian bridge altogether with the telemetric station for quantitative river flow monitoring were destroyed and engulfed by water. We determined analytically the maximum streamflow discharge and water levels of Arda River at the village of Kitnitsa and of Vacha River at the station of Zabral. Computed data shows statistic probabilities of maximum streamflow discharge of about 20 year return period (5%). Hydrographs from stations for water levels in the upper or downstream of the river flow are used for revision and verification. We also use information about the calculated surface velocity with the LSPIV method and we show results computed with the coupled SURFEX-RAPID hydrological model. A retrospective analysis is made using data on high waters that passed on 12th December 1991, during which similar water quantities were observed along Varbitsa River.
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Li, H. W. M., R. H. L. Li, C. C. J. Wong e F. L. C. Lo. "Machine Learning-based Natural Terrain Landslide Susceptibility Analysis – A Pilot Study". In The HKIE Geotechnical Division 42nd Annual Seminar. AIJR Publisher, 2022. http://dx.doi.org/10.21467/proceedings.133.8.

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Recently, the Geotechnical Engineering Office has initiated a pilot study on data-driven landslide susceptibility analysis (LSA) using a machine learning (ML) approach. A study area covering about one-fifth of the total natural hillside area of Hong Kong on and around the Lantau Island was considered. Three common tree-type ML classifiers: Decision Tree, Random Forest and XGBoost have been used. Conditioning factors (or features) including rainfall, geological and topography-related features were considered. In the study, the domain knowledge on natural terrain landslides in Hong Kong were critically incorporated into the susceptibility models through feature engineering to ensure that the resulted models are physically meaningful. In addition, an approach proposed to resolve the serious data imbalance problem, which is common in LSA, will be highlighted. Under this approach, the predicted probabilities of the positive class (i.e., landslide) can also be taken as a proxy to the landslide probability. This paper reports the methodology and key findings of this pilot study. The approach can be extended to cover other ML algorithms and features, and to a territory-wide scale with a view to enhancing the resolution and accuracy of the current susceptibility model of natural hillsides in Hong Kong.
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Karatvuo, Helena, Michael Linde, Azam Dolatshah e Simon Mortensen. "Improved Climate Change Adaptation in Port of Brisbane Using a Digital Twin Cloud-Based Modelling Approach". In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-79613.

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Abstract Due to their low-lying coastal location, ports are vulnerable to climate change induced increases to flooding, waves, extreme winds, and the associated costly damages to port infrastructure and operational disruptions. For these reasons, there is an increasing need for ports to undertake regular risk assessments of the vulnerability of their infrastructure and operations due to the impacts of climate change. A digital twin, cloud-based climate change modelling solution has been developed to enable in-house risk assessments of climate change vulnerability to be undertaken for any port. Once set-up, the system supports the continued sustainable operation of ports and enhancing stakeholder confidence in corporate sustainability strategies by allowing in-house re-evaluation of the ports climate risk as new predictions are released. The basis of the digital twin model of the port are numerical wave and hydrodynamic models, configured with the actual port geography and bathymetry enabling highly detailed simulations of the ports physical environment. The numerical model simulations are supplemented with observations of wind, rainfall, and sea level to identify trends and extreme event probabilities under the historic climate conditions. Scenarios describing the predicted impacts of climate change can be superimposed on the historical climate via a web-based interface where the user (port) selects a planning horizon (e.g., 2050), storm event frequency (e.g., 100-year storm), and climate change predictions (e.g. RCP8.5). The resulting climate change simulations shows great potential to enable port-specific predictions of future impacts of extreme occurrences of wind, waves, water levels, and currents. The ports asset portfolio is incorporated in the risk assessment through dynamic GIS layouts and damage curves identifying the damage cause and cost for each vulnerable port asset. As new climate science becomes available, this cloud-based digital twin model enables ports to rapidly complete updated risk assessments and respond to stakeholder queries and concerns. The capability of the tool was validated by comparing the model results against a large conventional study of the region, and a historical flood event of 2011. Both validation exercises displayed a reasonable agreement increasing confidence in the model’s capacity as a predictive tool. Additionally, six climate change scenarios were modelled for one of Australia’s fastest growing container ports, Port of Brisbane and the results were successfully incorporated in the ports overall sustainability strategy.
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