Rainfall Intensity Duration Frequency Curves Research Articles

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.

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.

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.

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

Remote Sensing for Development of Rainfall Intensity–Duration–Frequency Curves at Ungauged Locations of Yangon, Myanmar

This study aims to develop the intensity–duration–frequency (IDF) curves for Yangon, the economic center of Myanmar, using four satellite precipitation datasets, namely GPM IMERG, TRMM, GSMaP_NRT, and GSMaP_GC. Different probability distribution functions were used to fit the annual rainfall maximum series to determine the best-fit distribution. The estimated parameters of the best-fit distribution were used to fit the rainfall intensities of 2, 5, 10, 25, 50, and 100-year return periods for generating IDF curves using the Sherman equation. The IDF curves were bias-corrected based on the daily rainfall data available only at a location in Yangon. The bias correction factors were then used to estimate IDF curves from satellite rainfall at ungauged locations of Yangon. The results showed that the Generalized Extreme Value Distribution best fit the hourly rainfall distribution of satellite data. Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is the most suitable for constructing Yangon’s IDF curves. The bias-corrected IDF curve generated at four locations of greater Yangon indicates higher rainfall intensity at the coastal stations than the inland stations. The methodology presented in this study can be used to derive IDF curves for any location in Myanmar.

Comparative Analysis of Developed Rainfall Intensity–Duration–Frequency Curves for Erbil with Other Iraqi Urban Areas

Rainfall Intensity–Duration–Frequency (IDF) relationships are widely used in water infrastructure design and construction. IDF curves represent the relationship between rainfall intensity, duration, and frequency, and are obtained by analyzing observed data. These relationships are critical for the safe design of flood protection structures, storm sewers, culverts, bridges, etc. In this study, the IDF curves and empirical IDF formulas for the city of Erbil were developed for the first time by employing the annual maximum rainfall data for a period of 39 years (1980–2018), which is the only available recorded data. Statistical techniques such as Gumbel and Log-Pearson Type III (LPT III) were utilized to determine the IDF curves and empirical equations from daily rainfall data for several standard durations and return periods. The correlation between the rainfall intensities obtained from IDF curves and the empirical formula presented a reliable match, with a coefficient of determination of (R2 = 1). The results were compared to previously developed IDF curves and empirical formulas in Iraqi cities to show their reliability. Moreover, the results can be an initial step for authorities to establish required guidelines in the studied area, and in the design process of the storm water infrastructure of urban basins in the future.

Climate Change and Rainfall Intensity–Duration–Frequency Curves: Overview of Science and Guidelines for Adaptation

AbstractOne of the most important impacts of a future warmer climate is the projected increase in the frequency and intensity of extreme rainfall events. This increasing trend in extreme rainfall i...

High-Resolution Spatiotemporal Trend Analysis of Precipitation Using Satellite-Based Products over the United Arab Emirates

Current water demands are adequately satisfied in the United Arab Emirates (UAE) with the available water resources. However, the changing climate and growing water demand pose a great challenge for water resources managers in the country. Hence, there is a great need for management strategies and policies to use the most accurate information regarding water availability. Understanding the frequency and the short- and long-term trends of the precipitation by employing high-resolution data in both the spatial and temporal domains can provide invaluable information. This study examines the long-term precipitation trends over the UAE using 17 years of data from three of the most highly cited satellite-based precipitation products and rain gauge data observed at 18 stations. The UAE received, on average, 42, 51, and 120 wet hours in a year in the 21st century as recorded by CMORPH, PERSIANN, and IMERG, respectively. The results show that the areal average annual precipitation of the UAE is significantly lower in the early 21st century than that of the late 20th century, even though it shows an increasing trend by all the products. The Mann–Kendall trend test showed positive trends in six rain gauge stations and negative trends in two stations out of 18 stations, all of which are located in the wetter eastern part of the UAE. Results indicate that satellite products have great potential for improving the spatial aspects of rainfall frequency analysis and can complement rain gauge data to develop rainfall intensity–duration–frequency curves in a very dry region, where the installation of dense rain gauge networks is not feasible.

A robust method to develop future rainfall IDF curves under climate change condition in two major basins of Iran

Current rainfall Intensity-Duration-Frequency (IDFs) curves generally were developed based on stationary extreme value assumption. However, climate change is expected to alter extreme rainfalls and invalidate the stationary assumption. So, it is crucial to develop future rainfall IDFs taking into account the impacts of climate change. Difficulty in preparing reliable rainfall time series with fine time step and capturing extremes for future climate scenarios has led to limited studies aimed at investigating the change of IDF curves due to climate change. In this paper, a robust stochastic rainfall model (NSRP) is employed to produce future rainfall IDFs for five stations across Karkheh and Karun basins in Iran. NSRP can generate long-term realistic rainfall series containing extremes. Moreover, it applies changes in different rainfall statistical characteristics, projected by GCMs, in the downscaling procedure. For each station, the model was calibrated using observed rainfalls series. Then, 3000-year daily rainfall series were generated, and historical IDFs were developed. Consequently, NSRP parameters were perturbed based on GCM projections. Then, 3000-year future rainfall series were generated, and future IDFs were developed. The climate change uncertainties were represented by employing two GCM (CanESM2 and HadGem2) and three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results showed NSRP is able to reproduce observed extreme rainfalls in a wide range of time scales. Also, climate change will lead to a considerable increase in future extreme rainfall intensity in the study basins. As averages of all considered scenarios, rainfall intensities will increase between 22 and 206% in the future.

On the Rainfall Intensity–Duration–Frequency Curves, Partial-Area Effect and the Rational Method: Theory and the Engineering Practice

This research evaluates the partial-area effect and its relationship with the rainfall intensity–duration–frequency (IDF) equations. In the Rational Method, if the critical rainfall duration is shorter than the time of concentration, the partial-area effect occurs. We proved that the partial area could exist for the general ID equation i=a/(b+td)c, only when c>1. For these equations, in the application of the Rational Method, the maximum discharge at basin outlet occurs for rainfall duration (td) equal to b/(c−1). Nevertheless, for that case, the Depth Duration Frequency (DDF) has a maximum at that rainfall duration. These situations are present in engineering practice and will be discussed in this paper. Research was done to look for IDF equations with c>1 in hydrologic engineering practice. It found 640 inconsistent IDF equations (c>1) in four countries (Brazil, Mexico, India, and USA), which means that a fundamental principle for building consistent IDF equations (i.e., c>1), published in the scientific literature since 1998, did not reach the hydrologic engineering practice fully. We provided some analysis regarding this gap between theory and engineering practice.

Addressing uncertainty in extreme rainfall intensity for semi-arid urban regions: case study of Delhi, India

Classical approaches are used to develop rainfall intensity duration frequency curves for the estimation of design rainfall intensities corresponding to various return periods. The study modelled extreme rainfall intensities at different durations and compared the classical Gumbel and generalized extreme value (GEV) distributions in semi-arid urban region. The model and parameter uncertainties are translated to uncertainties in design storm estimates. A broader insight emerges that rainfall extremes in 1 h and 3 h are sensitive to the choice of frequency analysis (GEV in this case) and helps address anticipated intensification of extreme events for short duration at urban local scale. In comparison with Gumbel, GEV predicts higher extreme rainfall intensity corresponding to various return periods and duration (for 1-h duration the increase in extreme rainfall intensity is from 27 to 33% for return periods 10 years and higher, 3-h and 50-year return period—20%, 3-h and 100-year return period—20.6%, 24 h at similar return periods—10%). The Bayesian posterior distribution has a calibration effect on the GEV predictions and reduces the upper range of uncertainty in the GEV probability model prediction from a range of 16–31% to 10–28.4% for return period varying from 10 to 50 year for 1-h storms. In geographically similar areas these extreme intensities may be used to prepare for the rising flash flood risks.

A new approach to explore climate change impact on rainfall intensity–duration–frequency curves

Increasing the concentration of CO2 and other greenhouse gases in the atmosphere makes a large-scale change in atmospheric processes. Among the most dramatic effects of climate change, its impact on the type, amount, and intensity of rainfall is of particular importance. The goal of the present study was to provide a new method to investigate climate change impacts on rainfall intensity—duration—frequency (IDF) in the southwest of Iran. For this purpose, IDF curves were determined using recorded short-term rainfall from 15 stations. Then, the efficiency of the scale invariance theory was evaluated in determining IDFs by comparing IDFs resulting from observed data and IDFs estimated from the scaling model. Results demonstrated that two sets of IDFs had a good coincidence. In the next step, the data from three RCM models of the CORDEX project under two scenarios (RCP4.5 and RCP8.5) were used, and daily rainfall was downscaled by the musica package. Finally, IDFs were determined for two future periods including 2025–2049 as near future and 2075–2099 as far future. The result showed that rainfall intensity decreased in the north and central parts of the region.

Analysis of rainfall intensity-duration-frequency (IDF) curves of Baghdad city

An appropriate formula of rainfall data is the Intensity Duration Frequency (IDF) relationship. The rainfall IDF relationship is one of the tools used considerably in water resources engineering, either in planning, designing and operating projects of water resources, or in flood control projects. The purpose of this paper is to update rainfall IDF curves of Baghdad city developed in previously. The frequency analysis of available rainfall data is perform by three statistical methods, namely, the Gumbel Distribution Theory, the Log Pearson Type III and Log Normal Distribution to attain rainfall intensities for various short durations (0.25, 0.5, 1, 2, 3, 6, 12 and 24) in hours and return periods (2, 5, 10, 25, 50 and 100) in years. IDF equation was derived based on Bernard equation and the results of three distributions were compared using Kolmogorov-Smirnov goodness of fit test with help of the Easy fit software 5.6. The results of three methods were close and accepted at 10% significant level. The maximum rainfall intensity of 118.052mm/hr was happened at the duration 0.25hr of the return period 100yr, whereas the minimum rainfall intensity of 1.257mm/hr was happened at the duration 24hr of the return period 2yr.

Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios

This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006–2099 with that obtained using GCM hindcasts for the based period (1971–2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia.

A local-regional scaling-invariant Bayesian GEV model for estimating rainfall IDF curves in a future climate

Future changes in rainfall patterns induced by climate changes will affect society and ecosystems, and quantifying these changes is of utmost importance for the management of hydroclimate risk. In particular, the estimation of intensity-duration-frequency (IDF) curves for rainfall data is a routine procedure in urban hydrology and hydraulic studies and should be revisited to reflect future changes in rainfall variability. In this work, we propose a novel methodology based on the scaling-invariant property of rainfall duration versus intensity to estimate parameters of a generalized extreme value (GEV) distribution at sub-daily scales. A Bayesian inference framework is developed so that uncertainties are reduced and can be easily propagated to IDF curves. The proposed model can be employed to: (i) improve local (at-site) GEV estimates for sites with limited rainfall records; (ii) estimate GEV parameters at sub-daily scales and construct IDF curves for sites where only daily rainfall records are available (partially gauged sites); (iii) construct regional IDF curves for homogeneous hydrologic regions; and (iv) update local and regional IDF curves from simulations of future daily rainfall. The model is tested using historical rainfall data from 18 gauges located in the Han River Watershed in South Korea, and projected climate change scenarios RCP 6 and RCP 8.5 from the Met Office Hadley Centre HadGEM2-AO model. When considering historical data, the results show that the model satisfactorily estimate IDF curves for both gauged and partially gauged sites. In future scenarios, the model reveals a substantial increase in rainfall events of rare intensity (large return periods), mostly due to changes in the rainfall variability rather than changes in the average rainfall. Particularly, for a 100-year return period event, we expect an increase of about 23% in scenario RCP 6 and about 30% under scenario RCP 8.5 when projected using regional IDF curves. To the best of our knowledge, this is the first statistical approach in the literature to assess future changes in regional IDF curves, which in our opinion is more suitable than evaluating local estimates only.

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity–Duration–Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed.