Rainfall Intensity Duration Frequency Research Articles

Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method

Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration.

Utilizing statistical distribution tests to develop rainfall intensity–duration–frequency curves for enhanced hydrological analysis in Kirkuk city, Iraq

ABSTRACT Rainfall intensity is considered one of the basic factors in designing hydrological models based on rainstorm data. The objective of this research is to employ novel intensity–duration–frequency curves and develop empirical equations for rainfall intensity in the city of Kirkuk. The reduction formula adopted by the Indian Meteorological Department was used to divide the maximum daily rainfall for short periods of 0.5, 1, 2, 4, 6, 12, and 24 h. Three key methods of frequency analysis (lognormal distribution, log Pearson type III distribution, and Gumbel extreme value distribution) were utilized to formulate a statistical relationship based on rainfall intensity data from between the years 1981 and 2023 for a gauging station upstream of the Kirkuk city basin to provide the best data set for all periods of 100, 50, 25, 10, 5, and 2 years. Research has shown that the logarithmic distribution is the best fit for modelling the relationship between the annual maximum rainfall at Kirkuk station and duration of the rainfall. The goodness-of-fit results indicate that the lognormal distribution statistically outperforms other distribution models. Hence, the generated rainfall intensity, duration, and frequency curves that were developed led to estimating the intensity of precipitation to build forecasting and hydrological behaviour of the Kirkuk city basin.

Rainfall Intensity–Duration–Frequency Curves Using Cluster and Regional Frequency Analyses

Rainfall Intensity–Duration–Frequency Curves Using Cluster and Regional Frequency Analyses

Deriving rainfall IDF curves using modified Bartlett-Lewis rectangular pulses (BLRP) model for Babylon City, Iraq

In this study, daily rainfall data spanning 32 years, from 1991 to 2022, were collected from the Babylon City Meteorological Station (BCMS). The modified Bartlett-Lewis Rectangular Pulses (BLRP) model with seven parameters was used to generate the intensity-duration-frequency (IDF) curve for durations of 2, 5, 10, 25, 50, and 100 years. The estimated extreme values obtained from BCMS were fitted with EasyFit software using three statistical continuous distributions: the Gumbel (EV-1), Generalized Extreme Value (GEV), and Log-Pearson type III (LP-3) to determine the optimal distribution model for the IDF curve. The AIC and BIC criteria were employed to rank the generated IDF curves for each duration to select the optimal distribution model. The results of the AIC criteria ranked the PDFs as LP-3 first model, EV-1 second model, and GEV third model with sums of weighted rank values as 16, 17, and 27, respectively. In contrast, the BIC criteria ranked the PDFs as EV-1 first model, LP-3 second model, and GEV third model with sums of weighted rank values as 16, 17, and 27, respectively. The EV-1 and LP-3 models were selected as the most suitable results for simulating the maximum rainfall in Babylon City for various return periods. This decision was based on the total score of the special rankings of the two tests, with EV-1 and LP-3 having the lowest values of AIC and BIC.

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

Developing Rainfall Intensity- Frequency- Duration Curves for Three Selected Sites in the Kurdistan Region, Iraq

Developing Rainfall Intensity- Frequency- Duration Curves for Three Selected Sites in the Kurdistan Region, Iraq

Climate change impacts on rainfall intensity–duration–frequency curves in local scale catchments

The increasing intensity and frequency of rainfall events, a critical aspect of climate change, pose significant challenges in the construction of intensity–duration–frequency (IDF) curves for climate projection. These curves are crucial for infrastructure development, but the non-stationarity of extreme rainfall raises concerns about their adequacy under future climate conditions. This research addresses these challenges by investigating the reasons behind the IPCC climate report’s evidence about the validity that rainfall follows the Clausius-Clapeyron (CC) relationship, which suggests a 7% increase in precipitation per 1 °C increase in temperature. Our study provides guidelines for adjusting IDF curves in the future, considering both current and future climates. We calculate extreme precipitation changes and scaling factors for small urban catchments in Barranquilla, Colombia, a tropical region, using the bootstrapping method. This reveals the occurrence of a sub-CC relationship, suggesting that the generalized 7% figure may not be universally applicable. In contrast, our comparative analysis with Illinois, USA, an inland city in the north temperate zone, shows adherence to the CC relationship. This emphasizes the need for local parameter calculations rather than relying solely on the generalized 7% figure.

Flash flood detection via copula-based intensity–duration–frequency curves: evidence from Jamaica

Abstract. Extreme rainfall events frequently cause hazardous floods in many parts of the world. With growing human exposure to floods, studying conditions that trigger floods is imperative. Flash floods, in particular, require well-defined models for the timely warning of the population at risk. Intensity–duration–frequency (IDF) curves are a common way to characterize rainfall and flood events. Here, the copula method is employed to model the dependence between the intensity and duration of rainfall events flexibly and separately from their respective marginal distribution. Information about the localization of 93 flash floods in Jamaica was gathered and linked to remote-sensing rainfall data, and additional data on location-specific yearly maximum rainfall events were constructed. The estimated normal copula has Weibull and generalized extreme value (GEV) marginals for duration and intensity, respectively. Due to the two samples, it is possible to pin down above which line in the intensity duration space a rainfall event likely triggers a flash flood. The parametric IDF curve with an associated return period of 216 years is determined as the optimal threshold for flash flood event classification. This methodology delivers a flexible approach to generating rainfall IDF curves that can directly be used to assess flash flood risk.

Study rainfall intensity duration frequency relationships under climate change for design of efficient urban stormwater drainage systems in Dodola town, Ethiopia

Study rainfall intensity duration frequency relationships under climate change for design of efficient urban stormwater drainage systems in Dodola town, Ethiopia

Development of Synthetic Unit Hydrographs Using Spatial Proximity Regionalization for the Makiling Forest Reserve, Philippines

There is a dearth of streamflow data in the Philippines to generate hydrographs needed for flood forecasting and water resources assessment. A method of generating and calibrating synthetic hydrographs using model simulations and spatial proximity regionalization is presented. Synthetic unit hydrographs of four storm events in the gauged watershed of Makiling Forest Reserve were generated using Soil Conservation Service Unit Hydrograph, Clark Unit Hydrograph, and Snyder’s Unit Hydrograph methods. The generated synthetic hydrographs for each runoff modeling technique were calibrated with the actual hydrographs of the watershed. Snyder’s Unit Hydrograph model results were the most acceptable based on the Nash-Sutcliffe Efficiency, Index of Volumetric Fit, and Relative Error of Peak Flow. The weighted values of the calibrated watershed parameters computed using spatial proximity regionalization technique and the hyetographs derived from the Rainfall Intensity Duration Frequency Curve of the University of the Philippines Los Baños-National Agrometeorological Station were used to generate the synthetic unit hydrographs for three neighboring ungauged watersheds at 2-, 5-, 10-, 15-, 20-, 25-, 50, 100-year return periods. Total runoff volume, the magnitude of peak flows, and time to peak derived from the generated hydrographs can be used in watershed planning, water resources management, flood forecasting and the design of various water control structures.

Climate Change Effects on Rainfall Intensity–Duration–Frequency (IDF) Curves for the Lake Erie Coast Using Various Climate Models

This study delved into the analysis of hourly observed as well as future precipitation data in the towns of Willoughby and Buffalo on the Lake Erie Coast to examine the variations in IDF relationships over the 21st century. Several regional climate models (RCMs) and general circulation models (GCMs) from the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 were used. The study evaluated three RCMs with historical and Representative Concentration Pathway (RCP) 8.5 scenarios for each CMIP5 and three GCMs with historical and Shared Socioeconomic Pathways (SSPs) (126, 245, 370, and 585) scenarios for each CMIP6. The results suggested that the town of Willoughby would experience an increase of 9–46%, whereas Buffalo would experience an upsurge of 6–140% in the hourly precipitation intensity under the worst-case scenarios of RCP8.5 for CMIP5 and SSP585 for CMIP6. This increase is expected to occur in both the near (2020–2059) and far future (2060–2099), with a return period as low as 2 years and as high as 100 years when compared to the baseline period (1980–2019). The analysis indicated an increased range of 9–39% in the near future and 20–55% in the far future for Willoughby, while the Buffalo region may experience an increase of 2–95% in the near future and 3–192% in the far future as compared to the baseline period. In contrast to CMIP6 SSP585 models, CMIP5 RCP8.5 models predicted rainfall with an intensity value that is up to 28% higher in the town of Willoughby, while the reverse was true for the Buffalo region. The findings of this study are expected to be helpful for the design of water resource infrastructures.

Extreme Rainfall Analysis for Development of Rainfall Intensity Duration Frequency Curves for Semiarid Region of Andhra Pradesh in India

Extreme Rainfall Analysis for Development of Rainfall Intensity Duration Frequency Curves for Semiarid Region of Andhra Pradesh in India

Frequency analysis of rainfall events in Karbala city, Iraq, by creating a proposed formula with eight probability distribution theories

ABSTRACT Due to its impact on the derivation of hydrological models and the safe design of sewer and storm networks, the anticipation of rainfall events should be intensively studied. In this regard, studying the rainfall events in Karbala, Iraq, has specific importance after the rapid increase in population in this city and the impact of climate change beyond 2003. Herein, the maximum daily rainfall depth of Karbala city has been analyzed to investigate and extract its probability of future occurrence using frequency analysis. Eight theoretical probability distributions, which are Weibull, Gamma, Gumbel, Log normal, Generalized Extreme Value, Normal, Exponential, and Log-Pearson Type III, were fitted using Hyfran Plus software to simulate the characteristics of the observed probability of rainfall depth. Based on the results of four statistical indicators, the exponential distribution revealed the best performance as compared with other distributions. Accordingly, ‎the synthetic storms of 24 hr were derived‎ for cumulative and incremental distribution of rainfall depth using the Soil Conservation Service method type ΙΙ for the arid and semiarid region to recurrence periods ‎‎(T = 2, 5, 10, 15, 25, 50, 100, 200, and 500), also the rainfall intensity duration frequency curves were developed. As an essential finding, new validated empirical formula has been proposed to optimize the coefficients of the location of Karbala city which may fill the gap for predicting the rainfall intensity and improving storm management strategies in Iraq.

Development of Rainfall Intensity – Duration – Frequency (RIDF) Using Statistical Package for The Social Sciences (SPSS) as a Numerical Tool

Information and adequate data on the intensity–duration–frequency of rainfall are regular requirements to reduce and correct variations in hydrologic, environmental and hydraulic applications, which are vital elements in Civil and Environmental engineering works. This paper focuses on the utilization of the Statistical Package for the Social Sciences (SPSS) as a numerical tool for the development of accurate Rainfall Intensity-Duration-Frequency (RIDF) equations for Makurdi, Nigeria as a follow-up to our previous studies. Rainfall intensities from Makurdi were used to establish empirically derived constants (C, m, n, and a) for three different Rainfall Intensity-Duration-Frequency equations. These equations were evaluated statistically (Analysis of Variance (ANOVA), total error, model of’ selection criterion (MSC), Coefficient of Determination (CD), Correlation coefficient (R) and Akaike Information Criterion (A IC), with the main objective of utilizing SPSS in the selection of the best equation for the location. The study revealed that C” ranges from 281.456 to 5160.64, “m” was between 0.446 and 0.577, “n” was in the form of -1.142 to 10.8887 and the value of “a” was between 14.486 and 52.793. The CD and R values for models A, B and C were 0.816, 0.854 and 0.8159, 0.903, 0.924 and 0.903 respectively. It was concluded that SPSS can be used as a numerical tool in the design of storm drains.

The NRCS curve number equation derived from an instantaneous unit hydrograph: Some consequences

The NCRS-curve number equation allows calculating the storm runoff from a rainfall event for specific types of land use. It was based on an analysis of direct runoff data using baseflow corrected hydrographs and rainfall. Given this basis, the curve number equation can be derived assuming a constant effective rainfall intensity and a cubic reciprocal function as the instantaneous unit hydrograph. The instantaneous unit hydrograph and the resulting curve number equation are further generalized by adding a lag time. The equation for a curve number related hydrograph is presented, allowing to fit this curve number-based hydrograph to event data. The curve number itself is shown be a function of a catchment response time and the average event rainfall intensity. As the catchment response time is linked to the time of concentration the curve number equation and the storage index can be linked to catchment- and flow type characteristics. First results suggest that including the rainfall intensity duration frequency function in the curve number equation may explain systematic deviations observed when fitting the NCRS curve number equation to measured data.

Correction to: Regionalization of rainfall intensity–duration–frequency (IDF) curves with L-moments method using neural gas networks

Correction to: Regionalization of rainfall intensity–duration–frequency (IDF) curves with L-moments method using neural gas networks

Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar

Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.

Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar

Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.

Rainfall intensity duration frequency curve statistical analysis and modeling for Patna, Bihar

Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on aweekly, seasonal, and annual basis (19812020). First, utilizing the intensity-duration-frequency (IDF) curve and therelationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period(19812020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhousegas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. Onestrategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal,normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and returntimes of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall andrecurrence interval. Findings:Based on findings, the Gumbel approach produced the highest intensity values, whereas the otherapproaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell duringthe monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall,92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that theyearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examinerainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence intervalmathematical correlations were also developed. Further regression analysis revealed that short wave irrigation,wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and value:The results of the rainfall IDF curves can provide useful information to policymakers inmaking appropriate decisions in managing and minimizing floods in the study area