Gotowe bibliografie tematyczne / Rainfall

Gotowa bibliografia na temat „Rainfall”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 8 czerwca 2024

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Artykuły w czasopismach na temat "Rainfall"

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Mazurkiewicz, Karolina, i Marcin Skotnicki. "A determination of the synthetic hyetograph parameters for flow capacity assessment concerning stormwater systems". E3S Web of Conferences 45 (2018): 00053. http://dx.doi.org/10.1051/e3sconf/20184500053.

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The paper presents the results of an analysis of the influence of synthetic rainfall duration and location of rainfall intensity peak on the rate of the flood volume in surcharged storm sewers. The analyzed rainfalls had durations from 15 minutes to 180 minutes. It was assumed, that the rainfall peak location would change between the beginning and the end of the rainfall with increments of 10% of the rainfall duration. Outflow simulations were performed with the use of SWMM5.1.012 for three models of real urban catchments with surfaces from 1.6 km2 to 6.7 km2. An assessment of the influence of rainfall parameters was made on the basis of the flood volume rates. Short rainfalls with peaks located at the beginning of rainfall duration do not generate flooding. For other rainfalls it was found that for a specified rainfall duration the flood volume increases with the increase of time of the rainfall peak location. The maximum flood volume varied from 5% to 12% of the total runoff volume, depending on the catchment area, and is generated by the rainfall, whose intensity peak occurs right after the time corresponding to the flow time through the catchment.
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Brychta, Jiří, i Miloslav Janeček. "Determination of erosion rainfall criteria based on natural rainfall measurement and its impact on spatial distribution of rainfall erosivity in the Czech Republic". Soil and Water Research 14, No. 3 (27.05.2019): 153–62. http://dx.doi.org/10.17221/91/2018-swr.

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Rainfall erosivity is the main factor of the USLE or RUSLE equations. Its accuracy depends on recording precision and its temporal resolution, number of stations and their spatial distribution, length of recorded period, recorded period, erosion rainfall criteria, time step of rainfall intensity and interpolation method. This research focuses on erosion rainfall criteria. A network of 32 ombrographic stations, 1-min temporal resolution rainfall data, 35.6-year period and experimental runoff plots were used. We analysed 8951 rainfalls from ombrographic stations, 100 rainfalls and caused soil losses and runoffs from experimental runoff plots. Main parameter which influenced the number of erosion rainfalls was the precondition AND/OR which determines if conditions of rainfall total (H) have to be fulfilled simultaneously with rainfall intensity (I<sub>15</sub> or I<sub>30</sub>) or not. We proved that if parameters I<sub>15 </sub>&gt; 6.25 mm/15 min AND H &gt; 12.5 mm were fulfilled, then 84.2% of rainfalls caused soil loss &gt; 0.5 t/ha and 73.7% ≥ 1 t/ha. In the case of precondition OR only 44.6% of rainfalls caused soil loss &gt; 0.5 t/ha and 33.9% ≥ 1 t/ha. If the precondition AND was fulfilled, there were on average 75.5 rainfalls, average R factor for each rainfall was 21 MJ/ha·cm/h (without units below in the text, according international unit: 210 MJ/ha·mm/h) and average annual R factor was 45.4. In the case of precondition OR there were on average 279 rainfalls but average R factor for each rainfall was only 9.1 and average annual R factor was 67.4. Therefore if the precondition OR is used, R factor values are overestimated due to a high number of rainfalls with no or very low erosive potential. The resulting overestimated soil losses calculated using USLE/RUSLE subsequently cause an overestimation of financial expenses for erosion-control measures.
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RAGHAVENDRA, V. K. "Trends and periodicities of rainfall in sub-divisions of Maharashtra State". MAUSAM 25, nr 2 (7.02.2022): 197–210. http://dx.doi.org/10.54302/mausam.v25i2.5194.

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The Maharashtra State of India is divided into four meteorological sub-divisions, viz., Konkan, Madhya Maharashtra, Marathwada and Vidarbha. Of these, Madhya Maharashtra and Marathwada are prone to droughts. The principal rainy season is the monsoon season of June to September when over 80 per cent of the annual rainfall is received. The coefficient of variation is about 20 per cent for the annual and monsoon rainfall except in Marathwada where it is 25 per cent. The annual and monsoon rainfalls follow the normal distribution for their yearly frequencies. In this region the annual and the monsoon rainfall series are highly correlated. In the loss drought prone sub-division of Konkan, the annual and monsoon rainfalls show a 100 year cycle. In all the sub-divisions the successive years' rainfalls are not dependent. The trend as revealed by fitting of orthogonal polynomials is shown as a quadratic curve for the annual and monsoon rainfalls of Konkan and Madhya, Maharashtra, the sub-divisions on either side of the Western Ghats. The low pass filter and Mann-Kendall test against randomness confirmed the trend in Konkan rainfall. The power spectral analysis of the data indicates the existence of long term trend for monsoon rainfall of Konkan, 60 year cycle for the annual rainfall of Konkan and Madhya Maharashtra, 30.year cycle for the annual and monsoon rainfall or Vidarbha, 20-year cycle for the monsoon rainfall of Marathwada, 15-year cycle for the monsoon rainfall of Madhya Maharashtra, 7.5-year cycle for the annual and monsoon rainfall of Marathwada.
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Stefanidis, Stefanos, i Dimitrios Stathis. "Spatial and Temporal Rainfall Variability over the Mountainous Central Pindus (Greece)". Climate 6, nr 3 (6.09.2018): 75. http://dx.doi.org/10.3390/cli6030075.

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In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to Mann–Kendall tests to assess the possible upward or downward statistically significant trends and to change-point analyses to detect whether a change in the rainfall time series mean had taken place. Additionally, Sen’s slope method was used to estimate the trend magnitude, whereas multiple regression models were developed to determine the relationship between rainfall and geomorphological factors. The results showed decreasing trends in annual, winter, and spring rainfalls and increasing trends in autumn and summer rainfalls, both not statistically significant, for most stations. Rainfall non-stationarity started to occur in the middle of the 1960s for the annual, autumn, spring, and summer rainfalls and in the early 1970s for the winter rainfall in most of the stations. In addition, the average magnitude trend per decade is approximately −1.9%, −3.2%, +0.7%, +0.2%, and +2.4% for annual, winter, autumn, spring, and summer rainfalls, respectively. The multiple regression model can explain 62.2% of the spatial variability in annual rainfall, 58.9% of variability in winter, 75.9% of variability in autumn, 55.1% of variability in spring, and 32.2% of variability in summer. Moreover, rainfall spatial distribution maps were produced using the ordinary kriging method, through GIS software, representing the major rainfall range within the mountainous catchment of the study area.
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Jun, Changhyun, Xiaosheng Qin, Yeou-Koung Tung i Carlo De Michele. "Storm event-based frequency analysis method". Hydrology Research 49, nr 3 (9.11.2017): 700–710. http://dx.doi.org/10.2166/nh.2017.175.

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Abstract In this study, a storm event-based frequency analysis method was proposed to mitigate the limitations of conventional rainfall depth–duration–frequency (DDF) analysis. The proposed method takes the number, rainfall depth, and duration of rainstorm events into consideration and is advantageous in estimation of more realistic rainfall quantiles for a given return period. For the purpose of hydraulics design, the rainfall depth thresholds are incorporated to retrieve the rainstorm events for estimating design rainfalls. The proposed method was tested against the observed rainfall data from 1961 to 2010 at Seoul, Korea and the computed rainfall quantiles were compared with those estimated using the conventional frequency analysis method. The study results indicated that the conventional method was likely to overestimate the rainfall quantiles for short rainfall durations. It represented that the conventional method could reflect rainfall characteristics of actual rainstorm events if longer durations (like 24 hours) were considered for estimation of design rainfalls.
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Li, Liye, i Fan Zhang. "Hourly and Sub-Hourly Rainfall under Synoptic Patterns during the Anomalous Meiyu Season 2020". Atmosphere 14, nr 4 (18.04.2023): 727. http://dx.doi.org/10.3390/atmos14040727.

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The 2020 Meiyu season has received extensive attention due to its record-breaking rainfall in the Yangtze–River Huai Basin (YHRB) region of China. Although its rainfall features have been well studied on various time scales, the sub-hourly/hourly rainfall features are unknown. In this study, a wavelet analysis was applied to 1 min rainfall data from 480 national rain gauges across the YHRB, and hourly synoptic patterns during the Meiyu season were grouped using an obliquely rotated principal component analysis in T-mode (PCT). The results suggest that variances on the sub-hourly and hourly scales contributed 63.4% of the 2020 Meiyu rainfall. The hourly synoptic variations in the Meiyu season can be categorized into three major patterns: weak synoptic forcing (P1), a convergence line (P2), and a vortex (P3). The rainfalls under P1 were spatially dispersed over the YHRB and on the shortest time scale, with a 70.4% variance from sub-hourly to hourly rainfalls. P2 had a peak wavelet variance around 30 min–1 h, with rainfalls concentrated to the south of the convergent line. The rainfalls under P3 were locally distributed with a longer duration of around 1–4 h. Compared with the climate mean, hourly rainfall frequencies are indispensable to understanding the 2020 accumulated Meiyu rainfall anomaly. This research highlights the dominant role of synoptic patterns on the temporal and spatial features of the Meiyu rainfall.
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Thompson, S., I. M. Sanni, U. A. Abubakar i B. S. Sani. "Preliminary Analysis of Daily Rainfall Data from Kano State using Statistical Techniques". October 2022 6, nr 2 (1.10.2022): 317–24. http://dx.doi.org/10.36263/ijest.2022.02.0366.

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A 35-year annual rainfall was collected from NIMET to determine the Standard Precipitation Index (SPI) and also to study the trend of rainfall parameter in the Kano State, Northern Nigeria. The paper captures the average rainfall (normal rainfall) data for thirty-five years period which implies a rainfall of about 1081.56mm. The positive values (above zero) signify rainfalls that were higher than normal (wet); while the negative values (below zero) imply rainfalls that were lower than normal (dry). From the rainfall anomaly, 18 years (52.9%) recorded wet due to the fact that rainfall occurred in those years were greater than the normal rainfall of 1081.56; while 16 years (47.1%) recorded dry because the rainfalls that occurred in those years were below normal rainfall. In the study, Weilbul method was used to determine the return period in order to predict the year of occurrence of maximum rainfall. In addition, Standard Precipitation Index was used to determine periods of dry, normal or wet temperatures. September 1991 recorded the minimum SPI value of -0.86 (moderately dry), while October 2011 recorded the maximum SPI value of 1.88 (moderately wet). This study is carried out because of the importance of agriculture in the region and to Nigeria at large. Kano state is well known to support food production in the country. Also, the presence of dams further buttresses this study. Dams have many purposes; one is agriculture during dry and wet season. It is observed that the rainfall in the basin has no definite pattern.
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Thompson, S., I. M. Sanni, U. A. Abubakar i B. S. Sani. "Preliminary Analysis of Daily Rainfall Data from Kano State using Statistical Techniques". October 2022 6, nr 2 (1.10.2022): 317–24. http://dx.doi.org/10.36263/nijest.2022.02.0366.

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A 35-year annual rainfall was collected from NIMET to determine the Standard Precipitation Index (SPI) and also to study the trend of rainfall parameter in the Kano State, Northern Nigeria. The paper captures the average rainfall (normal rainfall) data for thirty-five years period which implies a rainfall of about 1081.56mm. The positive values (above zero) signify rainfalls that were higher than normal (wet); while the negative values (below zero) imply rainfalls that were lower than normal (dry). From the rainfall anomaly, 18 years (52.9%) recorded wet due to the fact that rainfall occurred in those years were greater than the normal rainfall of 1081.56; while 16 years (47.1%) recorded dry because the rainfalls that occurred in those years were below normal rainfall. In the study, Weilbul method was used to determine the return period in order to predict the year of occurrence of maximum rainfall. In addition, Standard Precipitation Index was used to determine periods of dry, normal or wet temperatures. September 1991 recorded the minimum SPI value of -0.86 (moderately dry), while October 2011 recorded the maximum SPI value of 1.88 (moderately wet). This study is carried out because of the importance of agriculture in the region and to Nigeria at large. Kano state is well known to support food production in the country. Also, the presence of dams further buttresses this study. Dams have many purposes; one is agriculture during dry and wet season. It is observed that the rainfall in the basin has no definite pattern.
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S, Dhamodaran. "Implementing Dynamic Rainfall Prediction Algorithm (DRPA) For Forecasting Rainfall by Analyzing World Rainfall Records". Journal of Advanced Research in Dynamical and Control Systems 12, nr 01-Special Issue (13.02.2020): 317–23. http://dx.doi.org/10.5373/jardcs/v12sp1/20201078.

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Rahardjo, H., T. T. Lee, E. C. Leong i R. B. Rezaur. "Response of a residual soil slope to rainfall". Canadian Geotechnical Journal 42, nr 2 (1.04.2005): 340–51. http://dx.doi.org/10.1139/t04-101.

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Rainfall-induced landslides are a common problem in residual soil slopes of the tropics. It is widely known that rainfall-induced slope failures are mainly caused by infiltration of rainwater; however, the response of a residual soil slope to infiltration is not fully understood. The difficulties lie in the quantification of the flux boundary condition across the slope surface with respect to infiltration and its effect on the pore-water pressure conditions in the slope. Therefore, it is important to understand the response of a slope to different rainfall conditions and the resulting changes in pore-water pressures and water contents. A residual soil slope in Singapore was instrumented with pore-water pressure, water content, and rainfall measuring devices, and studies were carried out under natural and simulated rainfalls. Results indicate that significant infiltration may occur in a residual soil slope during a rainfall. Small total rainfalls can contribute a larger infiltration percentage than large total rainfalls. The percentage of infiltration usually decreases with increasing total rainfalls. The study has indicated the existence of a threshold rainfall of about 10 mm for runoff generation to commence. Infiltration during wet periods may lead to the development of positive pore-water pressures as a consequence of a perched water table condition. Matric suctions are recovered gradually during dry periods due to redistribution. Soil water contents tend to be higher near the toe of the slope than at the crest irrespective of rainfall events, indicating subsurface movement of water in the downslope direction. The study has also indicated a correlation between rainfall amount and relative increase in pore-water pressure. The results can be used to quantify the flux boundary conditions required for the seepage analyses associated with rainfall-induced slope failures.Key words: infiltration, pore-water pressure, water content, residual soil, rainfall-induced slope failures.
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Rozprawy doktorskie na temat "Rainfall"

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HELLIES, MATTEO. "Extreme rainfall regime characterization in Sardinia using daily rainfall data". Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266863.

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For the design of hydraulic structures for flood conveyance and discharge, or protection of territory against flood is fundamental the knowledge of the ``extreme rainfall regime'' in the area where the hydraulic structures must be set up. Indeed the design flood is commonly evaluated as output of rainfall-runoff models that receive as input the quantitative description of a rainfall extreme event with a given exceedance probability. This dissertation assesses the performance of different statistical approaches in characterizing extreme rainfall in the island of Sardinia (Italy). After a detailed review of the theoretical bases of existing methodologies, we compare the results obtained from the use of: a) a Generalized Extreme value (GEV) distribution model, and a Two component Extreme Value (TCEV) distribution model, both applied to yearly maxima of daily rainfall, and b) a Generalized Pareto (GP) distribution model applied to rainfall excesses above a properly specified threshold. For the latter purpose, we use the Multiple Threshold Method (MTM) developed by Deidda(2010), which demonstrate good performance also in the case of quantized records. In order to describe the spatial variation of TCEV, GEV and GP model parameters a regional approach based on homogeneous regions, and two versions of Kriging (a commonly used geostatistical approach) i.e. ordinary Kriging (OK), and Kriging for uncertain Data (KUD), are compared. The obtained results are very promising, pointing towards the use of: a)a GEV distribution model for yearly rainfall maxima, and a KUD model to describe the spatial variation of model parameters, and b)a GP model for rainfall excesses and either an OK or a KUD model for the spatial variation of model parameters. The reason why the OK and KUD approaches lead to the same results in the GP case, is attributed to the robustness of the MTM method.
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Welles, Edwin. "Comparison of rainfall sampling schemes using a calibrated Stochastic Rainfall Generator". Thesis, The University of Arizona, 1994. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0062_m_sip1_w.pdf&type=application/pdf.

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Cramer, Sam. "New genetic programming methods for rainfall prediction and rainfall derivatives pricing". Thesis, University of Kent, 2017. https://kar.kent.ac.uk/69471/.

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Rainfall derivatives is a part of an umbrella concept of weather derivatives, whereby the underlying weather variable determines the value of derivative, in our case the rainfall. These financial contracts are currently in their infancy as they have started trading on the Chicago Mercantile Exchange (CME) since 2011. Such contracts are very useful for investors or trading firms who wish to hedge against the direct or indirect adverse effects of the rainfall. The first crucial problem to focus on in this thesis is the prediction of the level of rainfall. In order to predict this, two techniques are routinely used. The first most commonly used approach is Markov chain extended with rainfall prediction. The second approach is Poisson-cluster model. Both techniques have some weakness in their predictive powers for rainfall data. More specifically, a large number of rainfall pathways obtained from these techniques are not representative of future rainfall levels. Additionally, the predictions are heavily influenced by the prior information, leading to future rainfall levels being the average of previously observed values. This motivates us to develop a new algorithm to the problem domain, based on Genetic Programming (GP), to improve the prediction of the underlying variable rainfall. GP is capable of producing white box (interpretable, as opposed to black box) models, which allows us to probe the models produced. Moreover, we can capture nonlinear and unexpected patterns in the data without making any strict assumptions regarding the data. The daily rainfall data represents some difficulties for GP. The difficulties include the data value being non-negative and discontinuous on the real time line. Moreover, the rainfall data consists of high volatilities and low seasonal time series. This makes the rainfall derivatives much more challenging to deal with than other weather contracts such as temperature or wind. However, GP does not perform well when it is applied directly on the daily rainfall data. We thus propose a data transformation method that improves GP's predictive power. The transformation works by accumulating the daily rainfall amounts into accumulated amounts with a sliding window. To evaluate the performance, we compare the prediction accuracy obtained by GP against the most currently used approach in rainfall derivatives, and six other machine learning algorithms. They are compared on 42 different data sets collected from different cities across the USA and Europe. We discover that GP is able to predict rainfall more accurately than the most currently used approaches in the literature and comparably to other machine learning methods. However, we find that the equations generated by GP are not able to take into account the volatilities and extreme periods of wet and dry rainfall. Thus, we propose decomposing the problem of rainfall into 'sub problems' for GP to solve. We decompose the time series of rainfall by creating a partition to represent a selected range of the total rainfall amounts, where each partition is modelled by a separate equation from GP. We use a Genetic Algorithm to assist with the partitioning of data. We find that through the decomposition of the data, we are able to predict the underlying data better than all machine learning benchmark methods. Moreover, GP is able to provide a better representation of the extreme periods in the rainfall time series. The natural progression is to price rainfall futures contracts from rainfall prediction. Unlike other pricing domains in the trading market, there is no generally recognised pricing framework used within the literature. Much of this is due to weather derivatives (including rainfall derivatives) existing in an incomplete market, where the existing and well-studied pricing methods cannot be directly applied. There are two well-known techniques for pricing, the first is through indifference pricing and the second is through arbitrage free pricing. One of the requirements for pricing is knowing the level of risk or uncertainty that exists within the market. This allows for a contract price free of arbitrage. GP can be used to price derivatives, but the risk cannot be directly estimated. To estimate the risk, we must calculate a density of proposed rainfall values from a single GP equation, in order to calculate the most probable outcome. We propose three methods to achieve the required results. The first is through the procedure of sampling many different equations and extrapolating a density from the best of each generation over multiple runs. The second proposal builds on the first considering contract-specific equations, rather than a single equation explaining all contracts before extrapolating a density. The third method is the proposition of GP evolving and creating a collection of stochastic equations for pricing rainfall derivatives. We find that GP is a suitable method for pricing and both proposed methods are able to produce good pricing results. Our first and second methods are capable of pricing closer to the rainfall futures prices given by the CME. Moreover, we find that our third method reproduces the actual rainfall for the specified period of interest more accurately.
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Langousis, Andreas 1981. "Extreme rainfall intensities and long-term rainfall risk from tropical cyclones". Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47737.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.
Includes bibliographical references (leaves 78-85).
We develop a methodology for the frequency of extreme rainfall intensities caused by tropical cyclones (TCs) in coastal areas. The mean rainfall field associated with a TC with maximum tangential wind speed Vmax, radius of maximum winds Rmax, and translation speed Vmax, is obtained using a physically-based model, whereas rainfall variability at both large scales (from storm to storm) and small scales (due to rainbands and local convection) is modeled statistically. The statistical component is estimated using precipitation radar (PR) data from the TRMM mission. Taylor's hypothesis is used to convert spatial rainfall intensity fluctuations to temporal fluctuations at a given location A. The combined physical-statistical model gives the distribution of the maximum rainfall intensity at A during a period of duration D for a TC with characteristics (Vmax, Rmax, Vt) that passes at a given distance from A. To illustrate the use of the model for long-term rainfall risk analysis, we formulate a recurrence model for tropical cyclones in the Gulf of Mexico that make landfall between longitudes 85°-95°W. We then use the rainfall and recurrence models to assess the rainfall risk for New Orleans. For return periods of 100 years or more and long averaging durations (D around 12-24 hours), tropical cyclones dominate over other rainfall event types, whereas the reverse is true for shorter return periods or shorter averaging durations.
by Andreas Langousis.
Ph.D.
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Rudberg, Olov, i Daniel Bezaatpour. "Regional Rainfall Frequency Analysis". Thesis, Stockholms universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-186813.

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Frequency analysis is a vital tool when nding a well-suited probability distributionin order to predict extreme rainfall. The regional frequency approach have beenused for determination of homogeneous regions, using 11 sites in Skane, Sweden. Todescribe maximum annual daily rainfall, the Generalized Logistic (GLO), GeneralizedExtreme Value (GEV), Generalized Normal (GNO), Pearson Type III (PE3),and Generalized Pareto (GPA) distributions have been considered. The method ofL-moments have been used in order to nd parameter estimates for the candidatedistributions. Heterogeneity measures, goodness-of-t tests, and accuracy measureshave been executed in order to accurately estimate quantiles for 1-, 5-, 10-, 50- and100-year return periods. It was found that the whole province of Skane could beconsidered as homogeneous. The GEV distribution was the most consistent withthe data followed by the GNO distribution and they were both used in order toestimate quantiles for the return periods. The GEV distribution generated the mostprecise estimates with the lowest relative RMSE, hence, it was concluded to be thebest-t distribution for maximum annual daily rainfall in the province.
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Puvaneswaran, Manickam. "A study of rainfall fluctuations in the homogeneous rainfall regimes in Sri Lanka". Thesis, University of Sheffield, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296059.

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Zagrodnik, Joseph P. "Comparison and Validation of Tropical Rainfall Measuring Mission (TRMM) Rainfall Algorithms in Tropical Cyclones". FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/903.

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Tropical Rainfall Measuring Mission (TRMM) rainfall retrieval algorithms are evaluated in tropical cyclones (TCs). Differences between the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) retrievals are found to be related to the storm region (inner core vs. rainbands) and the convective nature of the precipitation as measured by radar reflectivity and ice scattering signature. In landfalling TCs, the algorithms perform differently depending on whether the rainfall is located over ocean, land, or coastal surfaces. Various statistical techniques are applied to quantify these differences and identify the discrepancies in rainfall detection and intensity. Ground validation is accomplished by comparing the landfalling storms over the Southeast US to the NEXRAD Multisensor Precipitation Estimates (MPE) Stage-IV product. Numerous recommendations are given to algorithm users and developers for applying and interpreting these algorithms in areas of heavy and widespread tropical rainfall such as tropical cyclones.
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To, Chun-hung. "Stochastic model of daily rainfall". Click to view the E-thesis via HKUTO, 1989. http://sunzi.lib.hku.hk/hkuto/record/B31976098.

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Stein, Daniel. "Rainfall index insurance in India". Thesis, London School of Economics and Political Science (University of London), 2011. http://etheses.lse.ac.uk/167/.

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This thesis provides three works which each contribute to understanding of the promising yet struggling market for rainfall index insurance in India. The first chapter contains an analysis of the willingness-to-pay (WTP) for rainfall insurance by poor farmers in Gujarat, India. It develops a theoretical model to predict individual WTP and contrasts it with emprical estimates of WTP using the Becker-DeGroot-Marshalk (BDM) mechanism. We find that BDM works well as a predictor of WTP, but that our model significantly overestimates WTP. The second chapter seeks to provide a possible explanation for demand being lower than theoretical predictions by looking at the dynamics of insurance demand. Using a panel dataset of insurance purchasers in India, it shows that people who receive an insurance payout are 9-22% more likely to purchase insurance the following year. The results are consistent with a dynamic model of insurance demand featuring loss aversion, in which receiving an insurance payout shifts the reference point such that people become more risk averse the following season. I provide evidence against other possible explanations, such as increased trust and learning about insurance, and direct effects of bad weather. The final chapter explores the possibility that combining rainfall insurance with savings may result in a more attractive financial product than insurance on its own. We conduct a laboratory experiment with Indian farmers that uses the BDM mechanism to assess the valuation of various insurance/savings combinations, which we title WISAs (Weather Insured Savings Accounts). We find that, contrary to theoretical predictions, most people prefer both pure savings and pure insurance to any combination of the two. This paper hopefully provides valuable contibutions to solving the puzzle of how to shield poor farmers from uncertain rainfall.
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To, Chun-hung, i 杜振雄. "Stochastic model of daily rainfall". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31976098.

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Książki na temat "Rainfall"

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Office, Great Britain Meteorological. Rainfall. Bracknell: Meteorological Office., 1989.

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Office, Great Britain Meteorological, red. Rainfall. Bracknell: Meteorological Office, 1990.

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Office, Great Britain Meteorological, red. Rainfall. Bracknell: Meteorological Office, 1993.

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Office, Great Britain Meteorological. Rainfall 1987. Bracknell: Meteorological Office, 1989.

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Rainfall forecasting. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Beven, Keith. Rainfall-Runoff Modelling. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119951001.

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Loague, Keith M. Rainfall-runoff modelling. Wallingford, UK: IAHS Press, 2010.

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S, Theon J., Fugono Nobuyoshi i United States. National Aeronautics and Space Administration., red. Tropical rainfall measurements. Hampton, Va: A. Deepak Pub., 1988.

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Anglian Water Authority. Oundle Division. Rainfall Statistics 1987. Oundle: Anglian Water Authority, 1988.

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Anglian Water Authority. Oundle Division. Rainfall Statistics 1984. Oundle: Anglian Water Authority, 1985.

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Części książek na temat "Rainfall"

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Ferraro, Ralph. "Rainfall". W Encyclopedia of Remote Sensing, 640–53. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_154.

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Chan, Irene. "Rainfall". W New Masters of Flash, 258–85. Berkeley, CA: Apress, 2000. http://dx.doi.org/10.1007/978-1-4302-5145-3_10.

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Yoo, Kyung H., i Claude E. Boyd. "Rainfall". W Hydrology and Water Supply for Pond Aquaculture, 37–62. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2640-7_3.

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Mbuya, O. S., T. D. Tsegaye i L. McCowan. "Rainfall". W Soil Science Step-by-Step Field Analysis, 201–10. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2015. http://dx.doi.org/10.2136/2008.soilsciencestepbystep.c15.

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Sene, Kevin. "Rainfall Forecasting". W Flash Floods, 101–32. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5164-4_4.

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Guo, James C. Y. "Rainfall analysis". W Urban Flood Mitigation and Stormwater Management, 15–48. Boca Raton, FL : CRC Press, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/b21972-2.

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Sharma, Ashish, i Raj Mehrotra. "Rainfall generation". W Geophysical Monograph Series, 215–46. Washington, D. C.: American Geophysical Union, 2010. http://dx.doi.org/10.1029/2010gm000973.

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Aron, Gert. "Rainfall Abstractions". W Water Resources Monograph, 69–86. Washington, D. C.: American Geophysical Union, 2013. http://dx.doi.org/10.1029/wm007p0069.

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Gregory, S. "Rainfall reliability". W Environment and Land Use in Africa, 57–82. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003383925-5.

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Morin, Joseph. "Rainfall Analysis". W Soil Erosion, Conservation, and Rehabilitation, 23–40. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003418177-2.

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Streszczenia konferencji na temat "Rainfall"

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BEILICCI, Erika Beata Maria, i Robert BEILICCI. "Influence of Rainfall Characteristics on Runoff in a Small Watershed". W 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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Streszczenie:
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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Cleveland, Theodore G., Xin He i David B. Thompson. "Simple Rainfall Loss Models for Rainfall-Runoff Modeling". W World Environmental and Water Resources Congress 2008. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)55.

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William T Gilmore, Allen L Thompson i Neil I Fox. "Comparison of Rainfall Energy and Soil Erosion Parameters from a Rainfall Simulator and Natural Rainfall". W 2007 Minneapolis, Minnesota, June 17-20, 2007. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2007. http://dx.doi.org/10.13031/2013.23501.

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Jyothi, K. Amar, D. Devajyoti, D. Preveen Kumar, E. N. Rajagopal i T. Narayana Rao. "Gridded radar rainfall product for comparison with model rainfall". W SPIE Asia-Pacific Remote Sensing, redaktorzy Eastwood Im, Raj Kumar i Song Yang. SPIE, 2016. http://dx.doi.org/10.1117/12.2223802.

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Habib, Emad, Ananda V. Aduvala i Ehab A. Meselhe. "Effect of Radar-Rainfall Errors on Rainfall-Runoff Modeling". W World Environmental and Water Resources Congress 2007. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40927(243)285.

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Fisler, Kathi, i Francisco Enrique Vicente Castro. "Sometimes, Rainfall Accumulates". W ICER '17: International Computing Education Research Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3105726.3106183.

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Glazner, Michael K., Saša Tomić i Benjamin White. "Redefining Rainfall Classification". W 29th Annual Water Resources Planning and Management Conference. Reston, VA: American Society of Civil Engineers, 1999. http://dx.doi.org/10.1061/40430(1999)5.

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Roy, Venkat, Shahzad Gishkori i Geert Leus. "Spatial rainfall mapping from path-averaged rainfall measurements exploiting sparsity". W 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2014. http://dx.doi.org/10.1109/globalsip.2014.7032131.

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Blodgett, D. L., i J. A. Hoopes. "Impacts of Radar Indicated Rainfall on Distributed Rainfall-Runoff Modeling". W Watershed Management Conference 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41143(394)113.

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Alonge, A. A., i T. J. Afullo. "Rainfall rate modeling for various rainfall types in South Africa". W AFRICON 2011. IEEE, 2011. http://dx.doi.org/10.1109/afrcon.2011.6072002.

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Raporty organizacyjne na temat "Rainfall"

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Research Institute (IFPRI), International Food Policy. Rainfall and rainfall variability. Washington, DC: International Food Policy Research Institute, 2014. http://dx.doi.org/10.2499/9780896298460_17.

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Research Institute (IFPRI), International Food Policy. Rainfall data comparison. Washington, DC: International Food Policy Research Institute, 2014. http://dx.doi.org/10.2499/9780896298460_22.

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Welles, E. Comparison of rainfall sampling schemes using a calibrated stochastic rainfall generator. Office of Scientific and Technical Information (OSTI), grudzień 1994. http://dx.doi.org/10.2172/671862.

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Judi, David R., i Byron A. Tasseff. Rainfall-driven Flooding Capability Development Report. Office of Scientific and Technical Information (OSTI), czerwiec 2013. http://dx.doi.org/10.2172/1086768.

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Peters, John C., i Daniel J. Easton. Runoff Simulation Using Radar Rainfall Data. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1996. http://dx.doi.org/10.21236/ada316115.

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Rao, A., i Shih-Chieh Kao. Statistical Analysis of Indiana Rainfall Data. West Lafayette, IN: Purdue University, 2006. http://dx.doi.org/10.5703/1288284313446.

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Research Institute (IFPRI), International Food Policy. Effects of Rainfall Variability on Maize Yields. Washington, DC: International Food Policy Research Institute, 2014. http://dx.doi.org/10.2499/9780896298460_19.

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Ogden, Fred L., i Hatim O. Sharif. Propagation of Radar-Rainfall Uncertainty in Runoff Predictions. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2001. http://dx.doi.org/10.21236/ada394770.

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Coe, Lauren, i Christine Moore. SPDAT rainfall and streamflow analysis at Mobile, Alabama. Engineer Research and Development Center (U.S.), marzec 2020. http://dx.doi.org/10.21079/11681/35793.

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Peters, John C. Application of Rainfall-Runoff Simulation for Flood Forecasting. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1993. http://dx.doi.org/10.21236/ada273140.

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