Rainfall Frequency Analysis Research Articles

Modelling of Intensity-Duration Frequency curves for Upper Cauvery Karnataka through Normal Distribution

The IDF Curves accessible are for the most part done by fitting arrangement of yearly greatest precipitation force to parametric dispersions. Intensity-duration-frequency (IDF) curves represent the relationship between storm intensity, storm duration and return period. Environmental change is relied upon to intensify the boundaries in the atmosphere factors. Being prone to harsh climate impacts, it is very crucial to study extreme rainfall-induced flooding for short durations over regions that are rapidly growing. One way to approach the extremes is by the application of the Intensity-Duration-Frequency (IDF) curves. The annual maximum rainfall intensity (AMRI) characteristics are often used to construct these IDF curves that are being used in several infrastructure designs for urban areas. Thus, there is a necessity to obtain high temporal and spatial resolution rainfall information. Many urban areas of developing countries lack long records of short-duration rainfall. The shortest duration obtained is normally at a daily scale/24 h. Thus, it is very crucial to find a methodology to construct IDF curves for short-duration rainfall (sub-daily) for these urban areas. The fast extension of urban area that does not have adequate preparedness to cope with climate change is certainly a big risk to life and economy. The study region lies in Karnataka India. The sub-daily IDF curves for current and future climate for the region were constructed from 1 to 24 h based on the Normal Distribution approach. Rainfall data of 23 (Twenty three) hydrological years of all stations were used. Maximum rainfall frequency analysis was made by Normal Distribution method. Finally Equations were developed for different return periods.

Estimation of extreme quantiles at ungaged sites based on region-of-influence and weighting approaches to regional frequency analysis of maximum 24-h rainfall

Lack of adequate and reliable data for estimating the extreme values of hydrological variables at ungaged sites has always been one of the issues facing hydrologists in designing and planning water resource projects. Regionalizing the considered hydrological variable, finding an acceptable relationship for estimating its extreme values at ungaged sites using given data of other stations, and applying their available attributes are the solutions for the mentioned issue. In this study, historical data of maximum 24-h rainfall (M24-hR) covering the statistical period of 30 years (1979–2008) were collected and used from 63 rainfall gaging stations situated at Lake Urmia basin, northwestern Iran. Afterwards, using the method of region-of-influence (ROI) regionalization, the study area was regionalized through the geographic attributes of the stations (including latitude, longitude, elevation above mean sea level, and distance to the center of Lake Urmia). Then, all possible situations were considered for providing an appropriate regression relationship to estimate the extreme quantiles of M24-hR at ungaged sites by defining various scenarios of weighting to the geographic attributes and rainfall quantiles. The results showed that among different defined weighting scenarios, weighting to both stations and attributes in the at-site situation had an effective impact on forming an appropriate regression relationship for the estimation of extreme quantiles at ungaged sites. However, in the regional situation, a scenario considering no weight for both stations and attributes resulted in the most acceptable estimation of the quantiles with the lowest error (MSE = 1.29 mm). Further, the study showed that in most scenarios, the extreme quantiles estimated by means of regional regression relationships at ungaged sites (MSE = 1.29~1.75 mm) resulted in lower errors than the at-site ones (MSE = 1.35~7.64 mm).

Evaluation of the extreme rainfall predictions and their impact on landslide susceptibility in a sub-catchment scale

Geohazards induced by extreme-rainfall events have considerably affected Taiwan throughout the last decade. Because of differences in rainfall behavior, the hazards of rainfall-induced landslides differ according to the catchment. In addition to have detailed analyses, comparisons and discussions, a rainfall-frequency analysis was conducted and an atmospheric general circulation model (AGCM) was used to assess the temporal and spatial rainfall behavior in an adopted catchment in Central Taiwan. To assess the future spatial hazard of landslides in the study area, landslide susceptibility analysis methods, including logistic regression (LR) and support vector machines (SVMs), were trained before being applied. The predictions of rainfall extreme and their Impact on landslide susceptibility were evaluated, compared, and discussed. The results suggest the AGCM predictions are sensitive to the route of typhoon and the local topography. Due to uncertainties of rainfall prediction and the prediction limitation of landslide susceptibility models, a multi-method analysis is necessary for the rainfall-induce landslide susceptibility predictions.

A development of nonstationary rainfall frequency analysis model based on mixture distribution

A development of nonstationary rainfall frequency analysis model based on mixture distribution

Uncertainty of stationary and nonstationary models for rainfall frequency analysis

AbstractThe development of nonstationary frequency analysis models is gaining popularity in the field of hydro‐climatology. Such models account for nonstationarities related to climate change and climate variability but at the price of added complexity. It has been debated if such models are worth developing considering the increase in uncertainty inherent to more complex models. However, the uncertainty associated to nonstationary models is rarely studied. The objective of this article is to compare the uncertainties in stationary and nonstationary models based on objective criteria. The study is based on observed rainfall data in the United Arab Emirates (UAE) where strong nonstationarities were observed. In this study, a nonstationary frequency analysis introducing covariates into the distribution parameters was carried out for total and maximum annual rainfalls observed in the UAE. The generalized extreme value (GEV) distribution was used to model annual maximum rainfalls and the gamma (G) distribution was used to model total annual rainfalls. A number of nonstationary models, using time and climate indices as covariates, were developed and compared to classical stationary frequency analysis models. Two climate oscillation patterns having strong impacts on precipitation in the UAE were selected: the Oceanic Niño Index and the Northern Oscillation Index. Results indicate that the inclusion of a climate oscillation index generally improves the fit of the models to the observed data and the inclusion of two covariates generally provides the overall best fits. Uncertainties of estimated quantiles were assessed with confidence intervals (CIs) computed with the parametric bootstrap method. Results show that for the small sample sizes in this study, the width of the CIs can be very large for extreme nonexceedance probabilities and for the most extreme values of the climate index covariates. The weaknesses of nonstationary models revealed by the bootstrap uncertainties are discussed and words of caution are formulated.

Flood Frequency Analysis Using Participatory GIS and Rainfall Data for Two Stations in Narok Town, Kenya

Flood management requires in-depth computational modelling through assessment of flood return period and river flow data in order to effectively analyze catchment response. The participatory geographic information system (PGIS) is a tool which is increasingly used for collecting data and decision making on environmental issues. This study sought to determine the return periods of major floods that happened in Narok Town, Kenya, using rainfall frequency analysis and PGIS. For this purpose, a number of statistical distribution functions were applied to daily rainfall data from two stations: Narok water supply (WS) station and Narok meteorological station (MS). The first station has a dataset of thirty years and the second one has a dataset of fifty-nine (59) years. The parameters obtained from the Kolmogorov–Smirnov (K–S) test and chi-square test helped to select the appropriate distribution. The best-fitted distribution for WS station were Gumbel L-moment, Pareto L-moment, and Weibull distribution for maximum one day, two days, and three days rainfall, respectively. However, the best-fitted distribution was found to be generalized extreme value L-moment, Gumbel and gamma distribution for maximum one day, two days, and three days, respectively for the meteorological station data. Each of the selected best-fitted distribution was used to compute the corresponding rainfall intensity for 5, 10, 25, 50, and 100 years return period, as well as the return period of the significant flood that happened in the town. The January 1993 flood was found to have a return period of six years, while the April 2013, March 2013, and April 2015 floods had a return period of one year each. This study helped to establish the return period of major flood events that occurred in Narok, and highlights the importance of population in disaster management. The study’s results would be useful in developing flood hazard maps of Narok Town for different return periods.

MultiRain: a GIS-based tool for multi-model estimation of regional design rainfall for scientists and practitioners

Abstract Extreme rainfall estimation is a long-standing challenge for hydrological hazard assessment and infrastructure design, particularly if considering the need to deal with climate change. Advances in statistical methods and in rainfall data availability allow for frequent updates of regional rainfall frequency analyses. These allow for new estimates that, however, cannot simply replace older ones in the risk management, due to technical, socio-economic and legislative reasons. To preserve compatibility between old and new regional estimates a multi-model approach could be used, where model uncertainties can be combined to help reach a final decision. To make this possible, one has to face the uneasy retrieval of data and results of older analyses and, quite often, non-trivial areal rainfall estimates are needed with all methods. To give an answer to these technical needs, a tool named MultiRain has been developed. The tool computes depth–duration–frequency (DDF) curves, both related to a point and integrated over an area, from multiple regional statistical analyses. The MultiRain procedure is based on Python scripting, geographic information system (GIS) functions and web technologies, and can be performed via web browser or in a desktop GIS environment. A demonstration version has been built using four different regional analyses proposed in a 20-year period for the north-west of Italy.

On the choice of the optimal frequency analysis of annual extreme rainfall by multifractal approach

Intensity Duration Frequency (IDF) curves are important tools for hydraulic and hydrologic design. Considering that there are different approaches to obtain the rainfall quantiles on which the IDF curves are based, the availability of a method to evaluate their reliability is of great importance. With this aim, in this work the multifractal properties of hourly rainfall data recorded at 23 rain gauges in the Umbria Region (Italy) have been used to select the most appropriate frequency analysis method of extreme annual rainfall at each location. Three methods have been tested: Local Frequency Analysis (by fitting at each station extreme annual rainfall data of different durations by a probability distribution function), Regional Frequency Analysis approach based on L-Moments and flood index method (considering the extreme annual rainfall data from all the stations), and a variant of the latter method known as In-site Regional Frequency Analysis (based on the consideration of the station as a region). Therefore, quantiles of rainfall for different durations and return periods have been obtained. These quantiles have been fitted to the Montana Intensity-Duration-Frequency (IDF) curve. The scaling properties of rainfall have been obtained through out the empirical moments scaling function K(q). Their comparison with some scaling behavior properties of the IDF curves has let the selection of the most adequate quantile estimation method at each site, being the Regional Frequency Analysis the most appropriate one for 14 out of the 23 sites included in the study.

Pooled frequency analysis for intensity–duration–frequency curve estimation

AbstractRainfall intensity–duration–frequency (IDF) curves are used in the design of urban infrastructure. Their estimation is based on rainfall frequency analysis, usually performed on rainfall records from a single gauged station. However, available at‐site record length is often too short to provide accurate estimates for long return periods. In the present study, a general framework for pooled rainfall frequency analysis based on the index‐event model is proposed for IDF estimation at gauged stations. Pooling group formation is defined by the region of influence approach on the basis of the geographical distance similarity measure. Several pooled approaches are defined and evaluated by a procedure through which quantile estimation and uncertainty are assessed. Alternate approaches for the definition of a pooling group are based on different criteria regarding initial pooling group size (and the relationship between size and return period), approaches for assessing pooling group homogeneity, and the use of macroregions in pooling group formation. The proposed framework is applied to identify the preferred approach for pooled rainfall intensity frequency analysis in Canada. Pooled approaches are found to provide more precise estimates than the at‐site approach, especially for long return periods. Pooled parent distribution selection supported the use of the generalized extreme value distribution across the country. Recommendations for pooling group formation include increasing the pooling group size with increases in return period and identifying an appropriate trade‐off between pooling group homogeneity and size for long return periods.

Probable maximum precipitation in tropical zone (Thailand) as estimated by generalized method and statistical method

AbstractThe objective of this research was to estimate probable maximum precipitation (PMP) for the duration of 1 to 3 days in Thailand. Rainfall data were collected from 217 meteorological stations. Two methods for estimating PMP were applied: the generalized method which analysed actual storms data using meteorological process, and the Hershfield's statistical method which applied the frequency analysis of the annual maximum rainfall. Both methods were commonly used and were recommended by the World Meteorological Organization (WMO) for estimating PMP. PMP estimation results were presented as isohyetal maps. For the generalized method, most PMP values were found higher in the east and decreased towards the west. However, there was no clear trend with the statistical method in the PMP's spatial distribution. An analysis of the 3‐day PMP averages showed that PMP estimation using the generalized method is 10% higher than that of the statistical method. The study results were then applied to estimate PMP of six large dams in Thailand and compared with the ones used in the design of the spillways. It was found that PMP estimation based on the generalized method was closer to the value used in the spillway design of those dams than PMP estimation based on the statistical method.

Comparison of dynamical and statistical rainfall downscaling of CMIP5 ensembles at a small urban catchment scale

Downscaling of future projections of climatic variables from global climate models (GCMs) to urban catchment scales is required for stormwater studies as existing GCMs are unable to predict the rainfall at high temporal and spatial resolutions. In the current study, the capability of dynamical and statistical downscaling methods is evaluated and compared using the rainfall data of current climate and future extreme events at a small urban catchment scale. Regional climate model (RCM) and statistical downscaling model (SDSM) are utilized to downscale rainfall data from twelve GCMs under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP 4.5 and RCP 8.5). The daily rainfall data (1985–2015) of Lucas Creek catchment located in Auckland, New Zealand is used as a baseline/current climate. The future downscaled rainfall data is analyzed in the 2090s (2071–2100). The results showed that both the methods performed well in downscaling the current climate. For future projections, SDSM underestimated mean daily rainfall at the start of the annual cycle and overestimated towards the middle of the year compared to RCM. Similarly, monthly variance and skewness were overestimated for some months by SDSM. The GCMs of both the methods also showed variations in the future rainfall projections amongst themselves. However, significant alterations in the future rainfall were observed compared to the current climate. Rainfall frequency analysis was performed by applying the Gumbel distribution to the baseline and downscaled data for 1, 2, 3, 4, 5, 10, 20, 30, 50 and 100 years return periods. The investigation revealed that RCM and SDSM show similar results for low return periods and different results for high return periods for the current and future climate. Both the methods forecasted an increase in magnitudes of future events however, RCM projections were lower compared to SDSM. The results illustrate the downscaling abilities of both the methods at a small urban catchment scale however, the contrasting implication in downscaling the rainfall data is related to their different downscaling mechanisms.

Sedimentation Analysis Based on River Hydrological Discharge at Cipunagara Estuary, Subang, Indonesia

Coastal area is very dynamic. The sedimentation in estuary causes coastal morphological changes. Sediment which is transported by the river flow tends to accumulate in the estuary. Sedimentation also occurs in Cipunagara, Subang and causes the accretion. Based on this Due to this occurrence of sedimentation, the objective of study are to calculate the hydrological discharge, calculate the sediment transport rate, and analyze the added area of sedimentation in the Cipunagara estuary. The method of the calculation of hydrological discharge has been reffered to SNI 2145-2016 while the analysis of sedimentation referred to Van Rijn's analytical equations. The frequency analysis of maximum daily rainfall in Cipunagara watershed was done to obtain the value of rainfall plan. The rainfall plan was then used to calculate flood discharge plan with using HSS Nakayasu method. The flood discharge plan with 2-years return period was chosen to determine the flow rate at STA 1 in Cipunagara River. Furthermore, the flowrate and sediment characteristic at STA 1 is used as the parameter to determine the sediment transport rate at STA 1. The sediment transport rate from January to December are calculated to obtain the volumetric value of the transported sediment which then used for the added area of sedimentation analysis at STA6 and STA 9 in Cipunagara estuary. In this study, the value of flood discharge plan from January to December was obtained as, 328,41 m3/s, 275,96 m3/s, 247,62 m3/s, 300,25 m3/s, 212,19 m3/s, 129,84 m3/s, 142,14 m3/s, 67,83 m3/s, 52,93 m3/s, 160,33 m3/s, 266,60 m3/s, and 299,44 m3/s. The added area of sedimentation over one year at STA 6 and STA 9 are 320,04 ha/year and 182,88 ha/year with scheme A and 337,33 ha/year and 203,36 ha/year with sheme B.

Rainfall variability on the southern slope of the Bambouto mountain (West-Cameroon) and impact on the crop cultivation calendar

A study on rainfall variability and its impact on the cultural cycle was carried out on the southern slope of the Bambouto mountain by using a long term data bank collected from 1941 to 2013 in the area. Statistical tests, including Pettitt's test, were used for the analysis. The study revealed that despite the average high rainfall (annual average rainfall of Dschang, 1846 mm and Djuttitsa 1639 mm), some drought recently occurred. The cumulated total approach indicated that these stations are located in the same climatic region. Annual rainfall for the Dschang meteorological station showed two distinct periods namely: a period of moisture excess from 1941 to 1976, and a period of moisture deficit from 1977 to 2013. The drought experienced in 1977 was characterised by a decrease in annual and monthly rainfall as well as the number of rainy days. This decrease, however, was not uniform in the year and from one year to another. There were also changes in the seasonal occurrence. Daily rainfall frequency analyses indicated that the growing season covers the period between 14 March and 12 November in Dschang, and between 22 March and 11 November in Djuttitsa. The appropriate planting date for the first season would be around 15 March in Dschang and 23 March in Djuttitsa.

Storm Catalog‐Based Analysis of Rainfall Heterogeneity and Frequency in a Complex Terrain

AbstractUrban development, topographic relief, and coastal boundaries can all exert influences on storm hydroclimatology, making rainfall and flood frequency analysis a major challenge. This study explores heterogeneity in extreme rainfall in the Baltimore Metropolitan region at small spatial scales using hydrometeorological analyses of major storm events in combination with hydroclimatological analyses based on storm catalogs developed using a 16‐year record of high‐resolution bias‐corrected radar rainfall fields. Our analyses demonstrate the potential for rainfall frequency methods using storm catalogs combined with stochastic storm transposition (SST); procedures are implemented for Dead Run, a small (14.3 km2) urban watershed located within the Baltimore Metropolitan area. The results point to the pronounced impact of complex terrain (including the Chesapeake Bay to the east, mountainous terrain to the west and urbanization in the region) on the regional rainfall climatology. Warm‐season thunderstorm systems are shown to be the dominant mechanism for generating extreme, short‐duration rainfall that leads to flash flooding. The SST approach is extended through the implementation of a multiplier field that accounts for spatial heterogeneities in extreme rainfall magnitude. SST‐based analyses demonstrate the need to consider rainfall heterogeneity at multiple scales when estimating the rainfall intensity‐duration‐frequency relationships.

Isohyetal Maps of Daily Maximum Rainfall for Different Return Periods for the Colombian Caribbean Region

In Colombia, daily maximum multiannual series are one of the main inputs for design streamflow calculation, which requires performing a rainfall frequency analysis that involves several prior steps: (a) requesting the datasets, (b) waiting for the information, (c) reviewing the datasets received for missing or data different from the requested variable, and (d) requesting the information once again if it is not correct. To tackle these setbacks, 318 rain gauges located in the Colombian Caribbean region were used to first evaluate whether or not the Gumbel distribution was indeed the most suitable by performing frequency analyses using three different distributions (Gumbel, Generalized Extreme Value (GEV), and Log-Pearson 3 (LP3)); secondly, to generate daily maximum isohyetal maps for return periods of 2, 5, 10, 20, 25, 50, and 100 years; and, lastly, to evaluate which interpolation method (IDW, spline, and ordinary kriging) works best in areas with a varying density of data points. GEV was most suitable in 47.2% of the rain gauges, while Gumbel, in spite of being widely used in Colombia, was only suitable in 34.3% of the cases. Regarding the interpolation method, better isohyetals were obtained with the IDW method. In general, the areal maximum daily rainfall estimated showed good agreement when compared to the true values.

Non-stationary frequency analysis of annual extreme rainfall volume and intensity using Archimedean copulas: A case study in eastern China

Owing to hydrological change and human activities, increasing attention is being paid to the evolution of dependence structure of hydrological characteristics, especially for non-stationary multivariate frequency analysis. In this study, we performed a non-stationary frequency analysis of annual extreme rainfall of four study regions in eastern coastal China. Two indexes separately accounting for rainfall volume and intensity were derived from daily precipitation data recorded by four pairs of gauges. In particular, based on the compatibility of data sets examined by the copula equality test, index series sharing similar dependence structures were merged to enrich data bases for copula modelling. A moving time window of 30 years was applied to the index series for non-stationarity analysis. An investigation of dependence evolution was conducted by detecting trends and change-points in the correlation coefficient series. Time-dependent Archimedean copulas and Generalized Extreme Value (GEV) distribution were employed to model the joint and marginal distributions, respectively. Kendall’s return period was employed to compute the design values of the indexes. Results showed an intensifying tendency of extreme rainfall volume and/or intensity in three of the study regions and uneven spatial and temporal distribution of precipitation. Finally, the occurrence risk of annual extreme rainfall corresponding to different joint return periods (JRPs) was analyzed based on the observed index series. The study highlights incorporating non-stationarity in multivariate hydrological analysis and merging compatible data sets to improve copula inference.

Data-based bivariate uncertainty assessment of extreme rainfall-runoff using copulas: comparison between annual maximum series (AMS) and peaks over threshold (POT).

Bivariate frequency analysis of extreme rainfall and runoff is crucial for water resource planning and management in a river basin. This study is aimed at accounting for uncertainties in bivariate analysis of extreme rainfall-runoff frequency in the Taleghan watershed, one of the major watersheds in northern Iran, using copulas. Two types of paired rainfall and runoff data, including annual maximum series (AMS) and peaks over threshold (POT) are adopted to investigate the uncertainties that arose due to the input data. The Cramer von-Mises goodness-of-fit test and Akaike information criteria (AIC) reveal that the Student's t copula is the best-fit copula for PAMS-QAMS with Gaussian-Pearson III (P3) margins, while the Plackett copula is the best-fit copula for PPOT-QPOT with generalized Pareto (GPAR-GPAR) margins. A nonparametric bootstrapping method for sampling from p-level curves is established to investigate the effects of univariate and bivariate models selection and uncertainties induced by input data. The results indicated that the sampling uncertainty reduces POT data compared to AMS data due to the increased sample size. However, the parameterization uncertainty of the POT data increases because of the weaker dependence structure between rainfall and runoff for the POT data. The results also reveal that the larger sampling uncertainties are associated with higher p-level curves for both AMS and POT data which are induced by lower data density in the upper tail. For the study area, the input-data uncertainty is most significant in bivariate rainfall-runoff frequency analysis and quantile estimation, while the uncertainty induced by probabilistic model selection is least significant.

Rainfall frequency analysis of Nainital District (Uttarakhand)

Rainfall frequency analysis of Nainital District (Uttarakhand)

Regional and Temporal Variations of Intensity–Duration–Frequency Relationships: A Case Study

Regional rainfall intensity–duration–frequency (IDF) curves provide information on the likelihood of heavy rainfall events of various amounts and durations. Hourly rainfall data at Ananthapur, Arogyavaram and Kurnool located in the drought-prone area of Rayalaseema region, Kakinada, Machilipatnam and Nellore located in Coastal region of Andhra Pradesh, and Hyderabad, Nizamabad and Ramagundam raingauge stations of Telangana State have been used in the present study for regional rainfall frequency analysis to develop IDF relationships for different durations and time periods. Higher rainfall intensities have been observed in the regions in the order Telangana, Rayalaseema and Coastal regions during 1979–2008 barring a few exceptions. The intensities also increased significantly with the time period in Telangana and Rayalaseema regions.

Correction to: Selection of the best fit probability distribution in rainfall frequency analysis for Qatar

The original article was published with two typographical errors found in the unit of Y-axis in Figs. 4 and 5 of the original article. The author would like readers to know that the units should be changed from “mm/hr” to “mm/day”. This correction stands to correct the original article.