Journal articles: 'Rainfall frequency analysi'

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Jun, Changhyun, Xiaosheng Qin, Yeou-Koung Tung, and Carlo De Michele. "Storm event-based frequency analysis method." Hydrology Research 49, no. 3 (November 9, 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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Nguyen, V.-T.-V., T. D. Nguyen, and F. Ashkar. "Regional frequency analysis of extreme rainfalls." Water Science and Technology 45, no. 2 (January 1, 2002): 75–81. http://dx.doi.org/10.2166/wst.2002.0030.

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This study proposes two alternative methods for estimating the distribution of extreme rainfalls for sites where rainfall data are available (gaged sites) and for locations without data (ungaged sites). The first method deals with the estimation of short-duration rainfall extremes from available rainfall data for longer durations using the “scale-invariance” concept to account for the relationship between statistical properties of extreme rainfall processes for different time scales. The second method is concerned with the estimation of extreme rainfalls for ungaged sites. This method relies on a new definition of homogeneous sites. Results of the numerical application using data from a network of 10 recording rain gauges in Quebec (Canada) indicate that the proposed methods are able to provide extreme rainfall estimates that are comparable with those based on observed at-site rainfall data.

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Boardman, John, and David Favis-Mortlock. "Frequency-magnitude distributions for soil erosion, runoff and rainfall - a comparative analysis." Zeitschrift für Geomorphologie Supplement Volumes 115 (July 1, 1999): 51–70. http://dx.doi.org/10.1127/zfgsuppl/115/1999/51.

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Fontanazza, C. M., G. Freni, G. La Loggia, and V. Notaro. "Uncertainty evaluation of design rainfall for urban flood risk analysis." Water Science and Technology 63, no. 11 (June 1, 2011): 2641–50. http://dx.doi.org/10.2166/wst.2011.169.

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A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aims to assess the uncertainty of such an approach and for suggesting improvements in the definition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copula method was applied to rainfall features (total depth, duration and maximum intensity) to generate synthetic rainfalls that are more consistent with historical events. The procedure was applied to a real case study, and the results were compared with those obtained by simulating other typical synthetic rainfall events linked to intensity–duration–frequency (IDF) curves. The copula-based multi-variate analysis is more robust and adapts well to experimental flood locations even if it is more complex and time-consuming. This study demonstrates that statistical correlations amongst rainfall frequency, duration, volume and peak intensity can partially explain the weak reliability of flood-frequency analyses based on synthetic rainfall events.

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MALEKINEZHAD, HOSSEIN, and ARASH ZARE-GARIZI. "Regional frequency analysis of daily rainfall extremes using L-moments approach." Atmósfera 27, no. 4 (January 13, 2015): 411–27. http://dx.doi.org/10.20937/atm.2014.27.04.07.

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Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures

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Lee, Chang-Hwan, Jae-Hyun Ahn, and Tae-Woong Kim. "Evaluation of Probability Rainfalls Estimated from Non-Stationary Rainfall Frequency Analysis." Journal of Korea Water Resources Association 43, no. 2 (February 28, 2010): 187–99. http://dx.doi.org/10.3741/jkwra.2010.43.2.187.

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Kim, Dong-IK, Dawei Han, and Taesam Lee. "Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall." Atmosphere 12, no. 2 (January 31, 2021): 191. http://dx.doi.org/10.3390/atmos12020191.

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Nonstationarity is one major issue in hydrological models, especially in design rainfall analysis. Design rainfalls are typically estimated by annual maximum rainfalls (AMRs) of observations below 50 years in many parts of the world, including South Korea. However, due to the lack of data, the time-dependent nature may not be sufficiently identified by this classic approach. Here, this study aims to explore design rainfall with nonstationary condition using century-long reanalysis products that help one to go back to the early 20th century. Despite its useful representation of the past climate, the reanalysis products via observational data assimilation schemes and models have never been tested in representing the nonstationary behavior in extreme rainfall events. We used daily precipitations of two century-long reanalysis datasets as the ERA-20c by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 20th century reanalysis (20CR) by the National Oceanic and Atmospheric Administration (NOAA). The AMRs from 1900 to 2010 were derived from the grids over South Korea. The systematic errors were downgraded through quantile delta mapping (QDM), as well as conventional stationary quantile mapping (SQM). The evaluation result of the bias-corrected AMRs indicated the significant reduction of the errors. Furthermore, the AMRs present obvious increasing trends from 1900 to 2010. With the bias-corrected values, we carried out nonstationary frequency analysis based on the time-varying location parameters of generalized extreme value (GEV) distribution. Design rainfalls with certain return periods were estimated based on the expected number of exceedance (ENE) interpretation. Although there is a significant range of uncertainty, the design quantiles by the median parameters showed the significant relative difference, from −30.8% to 42.8% for QDM, compared with the quantiles by the multi-decadal observations. Even though the AMRs from the reanalysis products are challenged by various errors such as quantile mapping (QM) and systematic errors, the results from the current study imply that the proposed scheme with employing the reanalysis product might be beneficial to predict the future evolution of extreme precipitation and to estimate the design rainfall accordingly.

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Yilmaz, A. G., I. Hossain, and B. J. C. Perera. "Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne." Hydrology and Earth System Sciences 18, no. 10 (October 15, 2014): 4065–76. http://dx.doi.org/10.5194/hess-18-4065-2014.

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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity–frequency–duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

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Yilmaz, A. G., I. Hossain, and B. J. C. Perera. "Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne." Hydrology and Earth System Sciences Discussions 11, no. 6 (June 16, 2014): 6311–42. http://dx.doi.org/10.5194/hessd-11-6311-2014.

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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of, threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary Generalized Pareto Distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on Intensity–Frequency–Duration (IFD) relationships. The developed methodology was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, and 3 and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

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Shimizu, Keita, Tadashi Yamada, and Tomohito J. Yamada. "Uncertainty Evaluation in Hydrological Frequency Analysis Based on Confidence Interval and Prediction Interval." Water 12, no. 9 (September 12, 2020): 2554. http://dx.doi.org/10.3390/w12092554.

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The shortage of extreme rainfall data gives substantial uncertainty to design rainfalls and causes predictions for torrential rainfall to deviate strongly from adopted probability distributions used in river planning. These torrential rainfalls are treated as outliers which existing studies do not evaluate. However, probability limit method test which its acceptance region expresses with high accuracy the range where observed ith order statistics could realize. Confidence interval which quantifies uncertainty of adopted distributions can be constructed by assuming that these critical values in both sides of the adopted region follow the same function form applied to actual observed data. Furthermore, its validity is proved through comparison of confidence interval derived from ensemble downscaling calculations. In addition, these critical values are almost in accordance with outliers in samples from the ensemble downscaling calculations. Therefore, prediction interval which expresses the range that an unknown observed datum can take is constructed by extrapolating the critical values for limit estimation of a future datum. In this paper, quantification method of uncertainty of design rainfall and occurrence risk of outliers in the traditional framework, using the proposed confidence interval and prediction interval, is shown. Moreover, their application to future climate by using Bayesian statistics is explained.

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Ramos, M. H., E. Leblois, and J. D. Creutin. "From point to areal rainfall: linking the different approaches for the frequency characterisation of rainfalls in urban areas." Water Science and Technology 54, no. 6-7 (September 1, 2006): 33–40. http://dx.doi.org/10.2166/wst.2006.613.

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In urban water design and management, many hydrologic problems involve the frequency characterisation of rainfalls. Hydrologists are commonly asked to evaluate rainfall intensities for given recurrence frequencies or to indicate how rare an observed event is by estimating its return period. This study aims to improve the characterisation of rainfall hazard over a city by linking point to areal rainfall frequency analysis. We use a stochastic rainfall field generator based on the turning-bands method directly to assess areal rainfall distributions and to illustrate the link between different approaches. The simulating algorithm is applied to rainfall data from the city of Marseilles. The frequency analysis of simulated fields provides the elements to deal with the notions of return period and severity of observed storm events. The study concludes on the importance of a unified approach to assess rainfall better.

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Park, Cheol-Soon, Chul-Sang Yoo, and Chang-Hyun Jun. "Bivariate Rainfall Frequency Analysis and Rainfall-runoff Analysis for Independent Rainfall Events." Journal of Korea Water Resources Association 45, no. 7 (July 31, 2012): 713–27. http://dx.doi.org/10.3741/jkwra.2012.45.7.713.

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Chen, Lu-Hsien, and Yu-Ting Hong. "Regional Taiwan rainfall frequency analysis using principal component analysis, self-organizing maps and L-moments." Hydrology Research 43, no. 3 (June 1, 2012): 275–85. http://dx.doi.org/10.2166/nh.2012.032.

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The objective of this paper is to propose an approach, which consists of principal component analysis (PCA), self-organizing maps (SOM) and the L-moment method, for improving estimation of desired rainfall quantiles of ungauged sites. Firstly, PCA is applied to obtain the principal components. Then SOM is applied to group the rain gauges into specific clusters and the number of clusters can be objectively decided by visual inspection. Moreover, the L-moment based discordancy and heterogeneity are used to test whether clusters may be acceptable as being homogeneous. After the gauges are grouped into specific clusters, the homogeneous regions are then delineated. Finally, goodness-of-fit measure is used to select the regional probability distributions and the design rainfall quantiles with various return periods for each region can be estimated. The proposed approach is applied to analyze and quantify regional rainfalls in Taiwan. The proposed approach is a robust and efficient way for regional rainfall frequency analysis. Moreover, one can easily assign an ungauged site to a previously defined cluster according to a map of homogeneous regions. Therefore, the proposed approach is expected to be useful for providing the design rainfall quantiles with various return periods at ungauged sites.

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Dariusz, Młyński, Petroselli Andrea, and Walega Andrzej. "Flood frequency analysis by an event-based rainfall-runoff model in selected catchments of southern Poland." Soil and Water Research 13, No. 3 (July 2, 2018): 170–76. http://dx.doi.org/10.17221/153/2017-swr.

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The study evaluated the applicability of Event-Based Approach for Small and Ungauged Basins (EBA4SUB) for calculating annual peak flows with a specific return period (QT) in southern Poland. Data used in the calculations in a form of observation series of annual peak flows were derived from the Institute of Meteorology and Water Management in Warsaw and covered a multi-year period 1971–2015. The data were statistically verified for their homogeneity, significance of monotonic trends, outliers and equality of variances. Peak flows with a given return period were estimated by a statistical method of Pearson Type III distribution, and by EBA4SUB model. The analysis showed that QT for the investigated catchments was the most accurately matching the values derived from the statistical method when EBA4SUB model was employed. This was evidenced by the values of average relative errors that reached 34% for EBA4SUB model (with beta hyetograph). The results of the study demonstrated usefulness of EBA4SUB model for the estimation of QT quantiles in catchments of the upper Vistula water region.

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Johnson, K. A., J. C. Smithers, and R. E. Schulze. "A review of methods to account for impacts of non-stationary climate data on extreme rainfalls for design rainfall estimation in South Africa." Journal of the South African Institution of Civil Engineering 63, no. 3 (November 11, 2021): 1–7. http://dx.doi.org/10.17159/2309-8775/2021/v63n3a5.

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Frequency analysis of extreme rainfall and flood events are used to determine design rainfalls and design floods which are needed to design hydraulic structures such as dams, spillways and culverts. Standard methods for frequency analysis of extreme events are based on the assumption of a stationary climate. However, this assumption in rainfall and flood frequency analysis is being challenged with growing evidence of climate change. As a consequence of a changing climate, the frequency and magnitude of extreme rainfall events are reported to have increased in parts of South Africa, and these and other changes in extreme rainfall occurrences are expected to continue into the future. The possible non-stationarity in climate resulting in changes in rainfall may impact on the accuracy of the estimation of extreme rainfall quantities and design rainfall estimations. This may have significant consequences for the design of new hydraulic infrastructure, as well as for the rehabilitation of existing infrastructure. Hence, methods that account for non-stationary data, such as caused by climate change, need to be developed. This may be achieved by using data from downscaled global circulation models in order to identify non-stationary climate variables which affect rainfall, and which can then be incorporated into extreme value analysis of a non-stationary data series.

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Shou, K. J., C. C. Wu, and J. F. Lin. "Predictive analysis of landslide susceptibility in the Kao-Ping watershed, Taiwan under climate change conditions." Natural Hazards and Earth System Sciences Discussions 3, no. 1 (January 20, 2015): 575–606. http://dx.doi.org/10.5194/nhessd-3-575-2015.

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Abstract. Among the most critical issues, climatic abnormalities caused by global warming also affect Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary Typhoon Morakot hit Southern Taiwan on 8 August 2009 and induced serious flooding and landslides. In this study, the Kao-Ping River watershed was adopted as the study area, and the typical events 2007 Krosa Typhoon and 2009 Morakot Typhoon were adopted to train the susceptibility model. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the Kao-Ping River watershed. The rainfall estimates were introduced in the landslide susceptibility model to produce the predictive landslide susceptibility for various rainfall scenarios, including abnormal climate conditions. These results can be used for hazard remediation, mitigation, and prevention plans for the Kao-Ping River watershed.

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KOTHYARI, U. C., S. K. VERMA, and R. J. GARDE. "Rainfall intensity duration frequency analysis." MAUSAM 41, no. 3 (February 24, 2022): 147–50. http://dx.doi.org/10.54302/mausam.v41i3.2750.

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In the present study the analysis of rainfall data compiled for eighty stations spread over several parts of India has been carried out for developing a general relationship for the estimation of short duration rainfall intensity.

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Back, Álvaro J., Augusto C. Pola, Nilzo I. Ladwig, and Hugo Schwalm. "Erosive rainfall in the Rio do Peixe Valley in Santa Catarina, Brazil: Part II - Characteristics and temporal distribution pattern." Revista Brasileira de Engenharia Agrícola e Ambiental 21, no. 11 (November 2017): 780–84. http://dx.doi.org/10.1590/1807-1929/agriambi.v21n11p780-784.

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ABSTRACT Exploring the characteristics of erosive rain is an important aspect of studying erosive processes, and it allows researchers to create more natural and realistic hydrological simulations. The objective of this study was to analyse the characteristics of erosive rain and to determine the temporal distribution pattern of erosive rainfall in the Valley of Rio do Peixe in the state of Santa Catarina, Brazil. Daily pluviograms from the meteorological stations located in the cities Campos Novos, Videira, and Caçador in Santa Catarina from 1984 to 2014 were utilized for this study. By studying rainfall that is classified as erosive, the values of kinetic energy, maximum intensity in thirty minutes, and the value of EI30 erosivity index were determined. The rainfall was also classified according to the temporal distribution of rainfall in advanced, intermediate, and delayed patterns. Erosive rainfalls occur at a frequency of 53.3% advanced, 31.1% intermediate, and 15.6% delayed patterns. Erosive rainfall has an average precipitation amount of 25.5 mm, duration of 11.1 h, kinetic energy of 5.6 MJ ha-1, maximum intensity of 30 min of 17.7 mm h-1, and erosivity of 206.4 MJ mm ha-1 h-1. The highest frequency of erosive rainfall occurred in rainfalls lasting from 6 to 12 h (36.1%), followed by rainfalls lasting from 4 to 6 h (22.4%).

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Sabino, Marlus, Adilson Pacheco de Souza, Eduardo Morgan Uliana, Luana Lisboa, Frederico Terra de Almeida, and Cornélio Alberto Zolin. "Intensity-duration-frequency of maximum rainfall in Mato Grosso State." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 15, no. 1 (February 3, 2020): 1. http://dx.doi.org/10.4136/ambi-agua.2373.

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Intensive rainfall is an important meteorological variable that is of technical interest in hydraulic projects. This study therefore generated Intensity-Duration-Frequency equations (IDF) for 14 weather stations in Mato Grosso State, based on pluviograph analysis. Annual maximum rainfall data regarding 10-to-1440-minute long rainfall events were collected from digitized daily pluviographs. Data adherence to the generalized extreme value distribution (GEV) was checked through the Kolmogorov-Smirnov test at a 20% significance level. Next, the maximum probable rainfall for return periods such as 2, 5, 10, 20, 30, 50 and 100 years was calculated and the IDF equations were adjusted. The performance of the IDF equations was evaluated based on mean absolute error (MAE), root mean square error (RMSE), bias, Willmott's concordance index and Nash-Sutcliffe efficiency index (ENS). Adjusting the IDF equations was only possible for rainfall durations ranging from 10 to 360 min at each station due to the low frequency of longer rainfalls. High variation was present in parameters of the IDF equation and in maximum rainfall intensity between stations. The satisfactory performance of the models, as attested to by statistical indices, allows using IDF equations adjusted for rainfall durations from 10 to 360 min, and return periods from 2 to 100 years, in the regions of the Mato Grosso weather stations.

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Kim, Sang-Ug, Kil-Seong Lee, and Young-Jin Park. "Analysis of Uncertainty of Rainfall Frequency Analysis Including Extreme Rainfall Events." Journal of Korea Water Resources Association 43, no. 4 (April 30, 2010): 337–51. http://dx.doi.org/10.3741/jkwra.2010.43.4.337.

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Singh, Vishal, and Xiaosheng Qin. "Rainfall variability in Malay Peninsula region of Southeast Asia using gridded data." E3S Web of Conferences 81 (2019): 01002. http://dx.doi.org/10.1051/e3sconf/20198101002.

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Southeast Asia is recognized as a climate-change vulnerable region as it has been significantly affected by many extreme events in the past. This study carried out a rainfall analysis over the Malay Peninsula region of Southeast Asia utilizing historical (1981-2007) gridded rainfall datasets (0.5°×0.5°). The rainfall variability was analyzed in an intra-decadal time series duration. The uncertainty involved in all datasets was also checked based on the comparison of multiple global rainfall datasets. Rainfall gap filling analysis was conducted for producing more accurate rainfall time series after testing multiple mathematical functions. Frequency-based rainfall extreme indices such as Dry Days and Wet days are generated to assess the rainfall variability over the study area. Our results revealed a notable variation existed in the rainfalls over Malay Peninsula as per the long historical duration (1981-2007).

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Madsen, H., I. B. Gregersen, D. Rosbjerg, and K. Arnbjerg-Nielsen. "Regional frequency analysis of short duration rainfall extremes using gridded daily rainfall data as co-variate." Water Science and Technology 75, no. 8 (February 16, 2017): 1971–81. http://dx.doi.org/10.2166/wst.2017.089.

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A regional partial duration series (PDS) model is applied for estimation of intensity duration frequency relationships of extreme rainfalls in Denmark. The model uses generalised least squares regression to relate the PDS parameters to gridded rainfall statistics from a dense network of rain gauges with daily measurements. The Poisson rate is positively correlated to the mean annual precipitation for all durations considered (1 min to 48 hours). The mean intensity can be assumed constant over Denmark for durations up to 1 hour. For durations larger than 1 hour, the mean intensity is significantly correlated to the mean extreme daily precipitation. A Generalised Pareto distribution with a regional constant shape parameter is adopted. Compared to previous regional studies in Denmark, a general increase in extreme rainfall intensity for durations up to 1 hour is found, whereas for larger durations both increases and decreases are seen. A subsample analysis is conducted to evaluate the impacts of non-stationarities in the rainfall data. The regional model includes the non-stationarities as an additional source of uncertainty, together with sampling uncertainty and uncertainty caused by spatial variability.

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Abang Uthman, Dayang Nazihah, and Onni Suhaiza Selaman. "REGIONAL RAINFALL FREQUENCY ANALYSIS FOR SAMARAHAN RIVER BASIN." Journal of Civil Engineering, Science and Technology 8, no. 2 (October 5, 2017): 89–95. http://dx.doi.org/10.33736/jcest.442.2017.

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In planning to mitigate flood, it is essential for engineers to determine the magnitude and frequency of rainfall. The rainfall frequency and magnitude can be determined by rainfall frequency analysis. This study analyzes the regional rainfall frequency of the Samarahan River basin. There are 12 rainfall stations over the 508km2 of basin area, of which 11 are included in this study. The rainfall frequency analyses of each individual station in Samarahan River basin are conducted using Gumbel distribution and Weibull plotting position formulas. The curves that are close to each other are grouped into the same region. Other factors such as topography, station elevation, type of rainfall distribution and isohyet are also considered in determining the region. Subsequently, a regional rainfall frequency map of Samarahan River basin is established. The findings show that Samarahan River basin can be divided into three homogenous regions. In comparison to previous research, there are changes in grouping the rainfall stations selected into regions. These changes may be due to different years of data used and number of rainfall stations selected since the data is limited. Dissimilar outcomes may also be caused by other factors such as nature change over time. This research updates the rainfall analysis of the Samarahan River basin using more adequate data compared to previous research.

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Jeong, Minsu, Taesam Lee, JooHeon Lee, Hyeonseok Choi, and Sunkwon Yoon. "Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul." Journal of the Korean Society of Hazard Mitigation 20, no. 1 (February 29, 2020): 19–29. http://dx.doi.org/10.9798/kosham.2020.20.1.19.

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In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

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Mahjabin, Tasnuva, and Omar I. Abdul-Aziz. "Trends in the Magnitude and Frequency of Extreme Rainfall Regimes in Florida." Water 12, no. 9 (September 16, 2020): 2582. http://dx.doi.org/10.3390/w12092582.

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Trends in the extreme rainfall regimes were analyzed at 24 stations of Florida for four analysis periods: 1950–2010, 1960–2010, 1970–2010, and 1980–2010. A trend-free pre-whitening approach was utilized to correct data for autocorrelations. Non-parametric Mann-Kendall test and Theil-Sen approach were employed to detect and estimate trends in the magnitude of annual maximum rainfalls and in the number of annual above-threshold events (i.e., frequency). A bootstrap resampling approach was used to account for cross-correlations across sites and evaluate the global significance of trends at the 10% level (p-value ≤ 0.10). Dominant locally significant (p-value ≤ 0.10) increasing trends were found in the magnitudes of 1–12 h extreme rainfalls for the longest period, and in 6 h to 7 day rainfalls for the shortest period. The trends in 2–12 h rainfalls were also globally significant (i.e., exceeded the trends that could occur by chance). In contrast, globally significant decreasing trends were noted in the annual number of 1–3 h, 1–6 h, and 3–6 h extreme rainfalls during 1950–2010, 1960–2010, and 1980–2010, respectively. Trends in the number of 1–7 day extreme rainfalls were mixed, lacking global significance. Our findings would guide stormwater management in tropical/subtropical environments of Florida and around the world.

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Nam, Woo-Sung, Tae-Soon Kim, Ju-Young Shin, and Jun-Haeng Heo. "Regional Rainfall Frequency Analysis by Multivariate Techniques." Journal of Korea Water Resources Association 41, no. 5 (May 25, 2008): 517–25. http://dx.doi.org/10.3741/jkwra.2008.41.5.517.

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Rai, Mahendra, R. K. Mehta, R. B. Gautam, and K. K. Maurya. "Rainfall frequency analysis of Nainital District (Uttarakhand)." South Asian Journal of Food Technology and Environment 04, no. 02 (December 31, 2018): 748–52. http://dx.doi.org/10.46370/sajfte.2018.v04i02.11.

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Liu, Chenglin, Yuwen Zhou, Jun Sui, and Chuanhao Wu. "Multivariate frequency analysis of urban rainfall characteristics using three-dimensional copulas." Water Science and Technology 2017, no. 1 (March 7, 2018): 206–18. http://dx.doi.org/10.2166/wst.2018.103.

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Abstract Urban runoff is a major cause of urban flooding and is difficult to monitor in the long term. In contrast, long term continuous rainfall data are generally available for any given region. As a result, it has become customary to use design rainfall depth as a proxy for runoff in urban hydrological analyses, with an assumption of the same frequency for runoff and rainfall. However, this approach has lack of overall coordination and cannot fully reflect the variability of rainfall characteristics. To address this issue, this study presents a three-dimensional copula-based multivariate frequency analysis of rainfall characteristics based on a long term (1961–2012) rainfall data from Guangzhou, China. Firstly, continuous rainfall data were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall (design rainfall depth, DRD; total rainfall depth, TRD; peak rainfall depth, PRD) were sampled using the annual maximum method. Finally, a copula method was used to develop the multivariate joint probability distribution and the conditional probability distribution of rainfall characteristics. The results showed that the copula-based method is easy to implement and can better reflect urban rainstorm characteristics. It can serve a scientific reference for urban flood control and drainage planning.

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Soldini, Luciano, and Giovanna Darvini. "Extreme rainfall statistics in the Marche region, Italy." Hydrology Research 48, no. 3 (April 6, 2017): 686–700. http://dx.doi.org/10.2166/nh.2017.091.

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A statistical analysis of the rainfalls is carried out for detecting a possible trend in the observed data. The rainfall dataset refers to the historical series collected in the hydrographic basins of the Marche region. On the one hand, the annual maximum daily, hourly and sub-hourly rainfalls have been analysed, on the other hand Climate Change Indices by Expert Team on Climate Change Detection and Indices (ETCCDI) (R1 mm, Rx1day, R20 mm, R95pTOT, PRCPTOT) have been computed to verify an eventual variation of the frequency of the rainfall regime in the Marche region. The time series, selected in the reference period 1951–2013, have been processed by using the non-parametric Mann–Kendall test. The results confirm that most of the series relating to the annual maximum rainfalls do not exhibit any trend. The absence of trend or the presence of negative trend prevail also in the analysis of the ETCCDI indices. The annual average anomalies of the same indices computed with respect to the climatological reference period 1961–1990 are negative since the mid-1980s, but they appear to show an increasing behaviour in the period 2009–2013.

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Kang, Dong-Ho, Dong-Ho Nam, Se-Jin Jeung, and Byung-Sik Kim. "Impact Assessment of Flood Damage in Urban Areas Using RCP 8.5 Climate Change Scenarios and Building Inventory." Water 13, no. 6 (March 10, 2021): 756. http://dx.doi.org/10.3390/w13060756.

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Korea has frequent flood damage due to localized torrential rain and typhoons as a result of climate change, which causes many casualties and property damage. In particular, much damage occurs due to urban inundation caused by stream flooding as a result of climate change. Thus, this study aims to analyze the effect of climate change on flood damage targeting the Wonjucheon basin, which is an urban stream flowing the city. For future rainfall data, RCP (Representative Concentration Pathways) 8.5 climate change scenario data was used, statistical detailed using SDQDM (Spatial Disaggregation with Quantile Delta Mapping) techniques, and daily data was downscaled using Copula model. In general, the flood damage rate is calculated by using the area ratio according to the land use in the administrative district, but in this study, the flood damage rate is calculated using the flood damage rate proposed in the multi-dimensional flood damage analysis using Building Inventory. Using the created future rainfall data and current data, the runoff in the Wonjucheon basin, Wonju-si, South Korea, by rainfall frequency was calculated through the Spatial Runoff Assessment Tool (S-RAT) model, which was a distributed rainfall-runoff model. The runoff was calculated using 100-year and 200-year frequency rainfalls for a four-hour duration and the flood damage area was calculated by applying the calculated runoff to the Flo-2D model, was developed by Federal Emergency Management Agency (FEMA) in United State of America, which was a flood inundation model. As a result of calculating the amount of discharge, it was analyzed that the average amount of discharge increased by 16% over the 100-year, 200-year frequency. The calculated result of the flood damage area was analyzed and the analysis results showed that the future flood damage area increased by around 30% at the 100-year frequency and around 15% at the 200-year frequency. The estimated flood damage by rainfall frequency was calculated using the flood damage area by frequency and multi-dimensional analysis, and the analysis result exhibited that the damage increased by around 23% at the 100-year frequency and around 45% at the 200-year frequency.

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Yoon, Philyong, Tae-Woong Kim, and Chulsang Yoo. "Rainfall frequency analysis using a mixed GEV distribution: a case study for annual maximum rainfalls in South Korea." Stochastic Environmental Research and Risk Assessment 27, no. 5 (October 7, 2012): 1143–53. http://dx.doi.org/10.1007/s00477-012-0650-5.

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Faulkner, D. S., and C. Prudhomme. "Mapping an index of extreme rainfall across the UK." Hydrology and Earth System Sciences 2, no. 2/3 (September 30, 1998): 183–94. http://dx.doi.org/10.5194/hess-2-183-1998.

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Abstract. Distance from the sea, proximity of mountains, continentality and elevation are all useful covariates to assist the mapping of extreme rainfalls. Regression models linking these and other variables calculated from a digital terrain model have been built for estimating the median annual maximum rainfall, RMED. This statistic, for rainfall durations between 1 hour and 8 days, is the index variable in the rainfall frequency analysis for the new UK Flood Estimation Handbook. The interpolation of RMED between raingauge sites is most challenging in mountainous regions, which combine the greatest variation in rainfall with the sparsest network of gauges. Sophisticated variables have been developed to account for the influence of topography on extreme rainfall, the geographical orientation of the variables reflecting the prevailing direction of rain-bearing weather systems. The different processes of short and long-duration extreme rainfall are accounted for by separate regression models. The technique of georegression combines estimates from regression models with a map of correction factors interpolated between raingauge locations using the geostatistical method of kriging, to produce final maps of RMED across the UK.

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Koh, Deuk-Koo, Tai-Ho Choo, Seung-Jin Maeng, and Chanda Trivedi. "Regional Frequency Analysis for Rainfall using L-Moment." Journal of the Korea Contents Association 8, no. 3 (March 31, 2008): 252–63. http://dx.doi.org/10.5392/jkca.2008.8.3.252.

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Joo, Kyung-Won, Ju-Young Shin, and Jun-Haeng Heo. "Bivariate Frequency Analysis of Rainfall using Copula Model." Journal of Korea Water Resources Association 45, no. 8 (August 31, 2012): 827–37. http://dx.doi.org/10.3741/jkwra.2012.45.8.827.

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Ciupak, Maurycy, Bogdan Ozga-Zieliński, Tamara Tokarczyk, and Jan Adamowski. "A Probabilistic Model for Maximum Rainfall Frequency Analysis." Water 13, no. 19 (September 28, 2021): 2688. http://dx.doi.org/10.3390/w13192688.

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As determining the probability of the exceedance of maximum precipitation over a specified duration is critical to hydrotechnical design, particularly in the context of climate change, a model was developed to perform a frequency analysis of maximum precipitation of a specified duration. The PMAXΤP model (Precipitation MAXimum Time (duration) Probability) harbors a pair of computational modules fulfilling different roles: (i) statistical analysis of precipitation series, and (ii) estimation of maximum precipitation for a specified duration and its probability of exceedance. The input data consist of homogeneous 30-element series of precipitation values for 16 different durations: 5, 10, 15, 30, 45, 60, 90, 120, 180, 360, 720, 1080, 1440, 2160, 2880, and 4320 min, obtained through Annual Maximum Precipitation (AMP) and Peaks-Over-Threshold (POT) approaches. The statistical analysis of the precipitation series includes: (i) detecting outliers using the Grubbs-Beck test; (ii) checking for the random variable’s independence using the Wald-Wolfowitz test and the Anderson serial correlation coefficient test; (iii) checking the random variable’s stationarity using nonparametric tests, e.g., the Kruskal-Wallis test and Spearman rank correlation coefficient test for trends of mean and variance; (iv) identifying the trend of the random variables using correlation and regression analysis, including an evaluation of the form of the trend function; and (v) checking for the internal correlation of the random variables using the Anderson autocorrelation coefficient test. To estimate maximum precipitations of a specified duration and with a specified probability of exceedance, three-parameter theoretical probability distributions were used: a shifted gamma distribution (Pearson type III), a log-normal distribution, a Weibull distribution (Fisher-Tippett type III), a log-gamma distribution, as well as a two-parameter Gumbel distribution. The best distribution was selected by: (i) maximum likelihood estimation of parameters; (ii) tests of the hypothesis of goodness of fit of the theoretical probability distribution function with the empirical distribution using Pearson’s χ2 test; (iii) selection of the best-fitting function within each type according to the criterion of minimum Kolmogorov distance; (iv) selection of the most credible probability distribution function from the set of various types of best-fitting functions according to the Akaike information criterion; and (v) verification of the most credible function using single-dimensional tests of goodness of fit: the Kolmogorov-Smirnov test, the Anderson-Darling test, the Liao-Shimokawa test, and Kuiper’s test. The PMAXTP model was tested on data from two meteorological stations in northern Poland (Chojnice and Bialystok) drawn from a digital database of high-resolution precipitation records for the period of 1986 to 2015, available for 100 stations in Poland (i.e., the Polish Atlas of Rainfall Intensities (PANDa)). Values of maximum precipitation with a specified probability of exceedance obtained from the PMAXTP model were compared with values obtained from the probabilistic Bogdanowicz-Stachý model. The comparative analysis was based on the standard error of fit, graphs of the density function for the probability of exceedance, and estimated quantile errors. The errors of fit were lower for the PMAXTP compared to the Bogdanowicz-Stachý model. For both stations, the smallest errors were obtained for the quantiles determined on the basis of maximum precipitation POT using PMAXTP.

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Song, Chang Woo, Yon Soo Kim, Na Rae Kang, Dong Ryul Lee, and Hung Soo Kim. "Regional Frequency Analysis for Rainfall Under Climate Change." Journal of Wetlands Research 15, no. 1 (February 28, 2013): 125–37. http://dx.doi.org/10.17663/jwr.2013.15.1.125.

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Sharma, Nitish Kumar, and Ashish Kumar. "Frequency Analysis of Rainfall Data of Dharamshala Region." MATEC Web of Conferences 57 (2016): 03013. http://dx.doi.org/10.1051/matecconf/20165703013.

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Yu, Pao‐Shan, and Chia‐Jung Chen. "Regional analysis of rainfall intensity‐duration‐frequency relationship." Journal of the Chinese Institute of Engineers 19, no. 4 (June 1996): 523–32. http://dx.doi.org/10.1080/02533839.1996.9677815.

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Wilson, Larry L., and Efi Foufoula‐Georgiou. "Regional Rainfall Frequency Analysis via Stochastic Storm Transposition." Journal of Hydraulic Engineering 116, no. 7 (July 1990): 859–80. http://dx.doi.org/10.1061/(asce)0733-9429(1990)116:7(859).

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Parodi, A., and G. Boni. "Phenomenological validation of a regional rainfall frequency analysis." Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 26, no. 9 (January 2001): 649–54. http://dx.doi.org/10.1016/s1464-1909(01)00064-8.

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Zhang, L., and Vijay P. Singh. "Gumbel–Hougaard Copula for Trivariate Rainfall Frequency Analysis." Journal of Hydrologic Engineering 12, no. 4 (July 2007): 409–19. http://dx.doi.org/10.1061/(asce)1084-0699(2007)12:4(409).

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Lee, Soon H., and Sung J. Maeng. "Frequency analysis of extreme rainfall using L-moment." Irrigation and Drainage 52, no. 3 (2003): 219–30. http://dx.doi.org/10.1002/ird.90.

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Al Saji, M., J. J. O'Sullivan, and A. O'Connor. "Design impact and significance of non-stationarity of variance in extreme rainfall." Proceedings of the International Association of Hydrological Sciences 371 (June 12, 2015): 117–23. http://dx.doi.org/10.5194/piahs-371-117-2015.

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Abstract. Stationarity in hydro-meteorological records is often investigated through an assessment of the mean value of the tested parameter. This is arguably insufficient for capturing fully the non-stationarity signal, and parameter variance is an equally important indicator. This study applied the Mann-Kendall linear and Mann-Whitney-Wilcoxon step change trend detection techniques to investigate the changes in the mean and variance of annual maximum daily rainfalls at eight stations in Dublin, Ireland, where long and high quality daily rainfall records were available. The eight stations are located in a geographically similar and spatially compact region (< 950 km2) and their rainfalls were shown to be well correlated. Results indicate that while significant positive step changes were observed in mean annual maximum daily rainfalls (1961 and 1997) at only two of the eight stations, a significant and consistent shift in the variance was observed at all eight stations during the 1980's. This period saw a widely noted positive shift in the winter North Atlantic Oscillation that greatly influences rainfall patterns in Northern Europe. Design estimates were obtained from a frequency analysis of annual maximum daily rainfalls (AM series) using the Generalised Extreme Value distribution, identified through application of the Modified Anderson Darling Goodness of Fit criterion. To evaluate the impact of the observed non-stationarity in variance on rainfall design estimates, two sets of depth-frequency relationships at each station for return periods from 5 to 100-years were constructed. The first was constructed with bootstrapped confidence intervals based on the full AM series assuming stationarity and the second was based on a partial AM series commencing in the year that followed the observed shift in variance. Confidence intervals distinguish climate signals from natural variability. Increases in design daily rainfall estimates obtained from the depth-frequency relationship developed from the truncated AM series, as opposed to those using the full series, ranged from 5 to 16% for the 5-year event and from 20 to 41% for the 100-year event. Results indicate that the observed trends exceed the envelopes of natural climate variability and suggest that the non-stationarity in variance is associated with a climate change signal. Results also illustrate the importance of considering trends in higher order moments (e.g. variance) of hydro-meteorological variables in assessing non-stationarity influences.

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McEwen, L. J. "The use of long-term rainfall records for augmenting historic flood series: a case study on the upper Dee, Aberdeenshire, Scotland." Transactions of the Royal Society of Edinburgh: Earth Sciences 78, no. 4 (1987): 275–85. http://dx.doi.org/10.1017/s0263593300011214.

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ABSTRACTEstablishing the magnitude and frequency of floods within upland catchments on the basis of short-term gauged runoff records is crucially dependent upon the extent to which the record is truly representative. In the case of the River Dee, upstream of Crathie in Aberdeenshire, gauged discharge records are limited in length. Although the middle Dee has been gauged since 1929, the gauge within the upper catchment has only ten years of record. Thus, reliable estimates of the return intervals of extreme floods for this part of the Dee can only be obtained by using a variety of historical sources to extend the flood series.Long-term rainfall records, where available, provide a valuable independent check on the reconstructed flood series. Such rainfall records, when analysed in terms of the magnitude, frequency and duration of major events, should, in general terms, correspond with the flood series. In this paper, the recurrence interval of extreme rainfalls of varying magnitude and duration in upper Deeside is estimated by extreme value analysis of the annual maximum series. The frequency of rainfall events above varying thresholds is also assessed. The existence of climatic fluctuations giving highly variable recurrence intervals for rainfall events of the same magnitude is demonstrated. Finally, the seasonality of frequent flood-producing storms is analysed. Patterns observed within the rainfall record are compared with those previously established within the historic flood series to substantiate and augment the flood record.

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Libertino, Andrea, Daniele Ganora, and Pierluigi Claps. "Technical note: Space–time analysis of rainfall extremes in Italy: clues from a reconciled dataset." Hydrology and Earth System Sciences 22, no. 5 (May 7, 2018): 2705–15. http://dx.doi.org/10.5194/hess-22-2705-2018.

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Abstract. Like other Mediterranean areas, Italy is prone to the development of events with significant rainfall intensity, lasting for several hours. The main triggering mechanisms of these events are quite well known, but the aim of developing rainstorm hazard maps compatible with their actual probability of occurrence is still far from being reached. A systematic frequency analysis of these occasional highly intense events would require a complete countrywide dataset of sub-daily rainfall records, but this kind of information was still lacking for the Italian territory. In this work several sources of data are gathered, for assembling the first comprehensive and updated dataset of extreme rainfall of short duration in Italy. The resulting dataset, referred to as the Italian Rainfall Extreme Dataset (I-RED), includes the annual maximum rainfalls recorded in 1 to 24 consecutive hours from more than 4500 stations across the country, spanning the period between 1916 and 2014. A detailed description of the spatial and temporal coverage of the I-RED is presented, together with an exploratory statistical analysis aimed at providing preliminary information on the climatology of extreme rainfall at the national scale. Due to some legal restrictions, the database can be provided only under certain conditions. Taking into account the potentialities emerging from the analysis, a description of the ongoing and planned future work activities on the database is provided.

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Hussain, Rukaia A. "Intensity – Duration – Frequency Analysis for Rainfall in Baiji Station." Tikrit Journal of Engineering Sciences 13, no. 4 (December 31, 2006): 116–35. http://dx.doi.org/10.25130/tjes.13.4.06.

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In the present study, modified relations were obtained to relate rainfall intensity with frequency using Weibull method for 15, 30 and 60 minutes duration. This was done to estimate the designed value for rainfall intensity for any mentioned duration at any observation interval. The obtained results show that the rainfall intensity increases with the increasing of return period. Also, for every duration rainfall, the intensity decreases with the increasing of duration. Two types of probability distribution tests were applied for any duration, they are Gumble and Person (III) tests, also, statistical tests were applied to determine the optimum and suitable distribution for the studied data. The obtained relations can be usefull as a design equation for small weirs, culverts and rain downsrtream systems in the case of construction of them in the studied area.

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ARTHIRANI, B., N. MANIKANDAN, and N. MARAGATHAM. "Trend analysis of rainfall and frequency of rainy days over Coimbatore." MAUSAM 65, no. 3 (July 1, 2014): 379–84. http://dx.doi.org/10.54302/mausam.v65i3.1044.

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Rainfall is very crucial for the economic development, disaster management, hydrological planning for the country. In the context of climate change, it is pertinent to ascertain whether the characteristic of Indian rainfall is also changing. Using daily rainfall data for the period of 1907-2012 analysis were carried out, to find out the change in rainfall and frequency of rainfall intensity. Results indicated that the annual rainfall is not dependable. Contribution of NEM to the total rainfall is 50.3 percent which was followed by SWM (26.3%). Contribution of NEM during every 30 years of periods was constantly increasing and the increasing trend was statistically significant at 95% confidence level. Total number of rainy days (very light, light, moderate, rather heavy, heavy, very heavy rainy days) during the study period was 80.2 days, in which the frequency of very light rainy days (35.0 days) was highest followed by light (20.7 days) and moderate rainy days (20.8 days). Trend analysis was done for all categories of rainfall to find out the presence of increasing or decreasing trend. Total number of rainy days slightly gets decreasing in all the seasons except NEM where the rainy days are increasing but the changes were not statistically significant. The results showed that there is no change in long term of monthly, seasonal, annual rainfall and frequency of rainy days. Hence, it can be concluded that there is no climate change observed over Coimbatore.

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PASUPALAK, S., G. PANIGRAHI, T. PANIGRAHI, S. MOHANTY, and K. K. SINGH. "Extreme rainfall events over Odisha state, India." MAUSAM 68, no. 1 (November 30, 2021): 131–38. http://dx.doi.org/10.54302/mausam.v68i1.442.

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Extreme rainfall events are a significant cause of loss of life and livelihoods in Odisha. Objectives of the present study are to determine the trend of the extreme rainfall events during 1991-2014 and to compare the events between two periods before and after 1991. Block level daily rainfall data were used in identifying the extreme rainfall events, while district level aggregation was used in analysing the trend in three categories, viz., heavy, very heavy and extremely heavy rainfall as per criteria given by India Meteorological Department (IMD). The state as a whole received one extremely heavy, nine very heavy, and forty heavy rainfall events in a year. When percentage of occurrence of each category out of the total extreme events over different districts was considered, maximum % of extremely heavy rainfall occurred in Kalahandi (5.8%), very heavy rainfall in Bolangir (23.8%) and heavy rainfall in Keonjhargarh (85.4%). Trend analysis showed that number of extreme rainfall events increased in a few districts, namely, Bolangir, Nuapada, Keonjhargarh, Koraput, Malkangiri, and Nawarangapur and did not change in other districts. In Puri district, extremely heavy rainfall frequency decreased. New all-time record high one-day rainfall events were observed in twenty districts during 1992 to 2014, surpassing the earlier records, which could be attributed to climate change induced by global warming. Interior south Odisha was found as the hot spot for extreme rainfalls.

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Yoo and Cho. "Effect of Multicollinearity on the Bivariate Frequency Analysis of Annual Maximum Rainfall Events." Water 11, no. 5 (April 29, 2019): 905. http://dx.doi.org/10.3390/w11050905.

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A rainfall event, simplified by a rectangular pulse, is defined by three components: the rainfall duration, the total rainfall depth, and mean rainfall intensity. However, as the mean rainfall intensity can be calculated by the total rainfall depth divided by the rainfall duration, any two components can fully define the rainfall event (i.e., one component must be redundant). The frequency analysis of a rainfall event also considers just two components selected rather arbitrarily out of these three components. However, this study argues that the two components should be selected properly or the result of frequency analysis can be significantly biased. This study fully discusses this selection problem with the annual maximum rainfall events from Seoul, Korea. In fact, this issue is closely related with the multicollinearity in the multivariate regression analysis, which indicates that as interdependency among variables grows the variance of the regression coefficient also increases to result in the low quality of resulting estimate. The findings of this study are summarized as follows: (1) The results of frequency analysis are totally different according to the selected two variables out of three. (2) Among three results, the result considering the total rainfall depth and the mean rainfall intensity is found to be the most reasonable. (3) This result is fully supported by the multicollinearity issue among the correlated variables. The rainfall duration should be excluded in the frequency analysis of a rainfall event as its variance inflation factor is very high.

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Vinagre, Marco, Claudio Blanco, and André Amarante Mesquita. "A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments." Journal of Hydrology and Hydromechanics 59, no. 3 (September 1, 2011): 145–56. http://dx.doi.org/10.2478/v10098-011-0012-x.

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A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments The objective of this paper is to simulate flow frequency distribution curves for Amazon catchments with the aim of scaling power generation from small hydroelectric power plants. Thus, a simple nonlinear rainfall-runoff model was developed with sigmoid-variable gain factor due to the moisture status of the catchment, which depends on infiltration, and is considered a factor responsible for the nonlinearity of the rainfall-runoff process. Data for a catchment in the Amazon was used to calibrate and validate the model. The performance criteria adopted were the Nash-Sutcliffe coefficient (R2), the RMS, the Q95% frequencyc flow percentage error, and the mean percentage errors ranging from Q5% to Q95%.. Calibration and validation showed that the model satisfactorily simulates the flow frequency distribution curves. In order to find the shortest period of rainfall-runoff data, which is required for applying the model, a sensitivity analysis was performed whereby rainfall and runoff data was successively reduced by 1 year until a 1.5-year model application minimum period was found. This corresponds to one hydrological year plus the 6-month long "memory". This analysis evaluates field work in the ungauged sites of the region.

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Journal articles on the topic 'Rainfall frequency analysi'

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