Intensity Duration Frequency Relationships Research Articles

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.

Development of intensity–duration–frequency relationships in Khon Kaen City, Thailand under changing climate using GCMs and a simple scaling method

Abstract This study analyses the annual maximum (AM) rainfall series (1991–2022) in Khon Kaen City, Thailand. The AM rainfall series ranging from 3 to 24 h was best fitted to the Log-Pearson Type-III distribution. Notably, our findings reveal linear relationships between the moments of rainfall intensities and durations establishing the practicality of the simple scaling method for disaggregating 24-h AM rainfall data. Additionally, the results of this method are influenced by factors such as sample size, rainfall durations and the chosen probability distribution. Comparisons between intensity–duration–frequency (IDF) curves obtained through the simple scaling method and those derived from traditional frequency analysis provide valuable insights. Furthermore, this method was applied to bias-corrected rainfall data of 15 global climate models facilitating the generation of future IDF curves under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. Our results highlight that rainfall events in the SSP5-8.5 scenario are projected to exhibit higher intensities emphasizing the need to understand and prepare for increased rainfall extremes in the context of climate change. This research contributes valuable insights into rainfall analysis and prediction techniques, which are crucial for effective water resource management and climate adaptation strategies in the Khon Kaen region.

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

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.

Regional distribution of intensity–duration–frequency (IDF) relationships in Sultanate of Oman

Analysis of extreme rainfall parameters including rainfall intensities is a fundamental requisite in safe planning, designing, and operating various hydrologic and water engineering projects against storms and floods. In arid and semi-arid regions, such as Oman, sufficient long-term rainfall data with short aggregation are usually not available in most locations across the country. This paper presents the development of intensity duration frequency (IDF) curves using the available rainfall data from 65 meteorological stations situated at different elevations and regions throughout Oman. Gumbel distribution was fitted to the observed data and rainfall intensities were found for various return periods. Rainfall analysis showed the average annual rainfall of 109.21 mm with a standard deviation of 92.82 mm, Skewness coefficient of 1.62 and Kurtosis coefficient of 3.08 for all the studied stations from 1977 to 2017. The statistical analysis showed that the estimated rainfall intensities for various return periods are high in the mountainous region compared to the desert or interior region, and the coastal region of the country. Also, the empirical parameters of IDF formula for all studied stations were established using non-linear regression. Finally, the contour maps for all the parameters were drawn which could be used to determine the IDF relationships for ungauged locations. This study will be useful for the decision makers and practicing hydrologists for planning and design of water resources systems in Oman.

Implementation of a probability matching method in developing intensity–duration–frequency relationships for sub-daily durations using IMERG satellite-based data

ABSTRACT Reliable estimation of design rainfall in time and space can play a significant role in reducing flood-related losses. However, many parts of the world suffer from a lack of dense raingauge networks. Under this condition, the use of satellite-based data can be an alternative solution. In this research, using data from a dense ground-based raingauge network and Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite-based precipitation product, the performance of a probability matching method for satellite-based extreme values has been investigated. The results indicate that the correction model, especially at shorter durations, is able to reduce the error of extreme values of raw satellite-based estimates, and its performance is not affected significantly by the density of the available raingauge network. In conclusion, implementation of the proposed method in poorly gauged regions can play an effective role in providing a reliable estimate of the design rainfall and eventually reducing the financial losses caused by floods.

Hydrologic regionalization of non-stationary intensity–duration–frequency relationships for Indian mainland

Abstract Intensity–duration–frequency (IDF) curve is one of the important hydrologic tools used for the design of hydraulic infrastructure. The static return period assumption of precipitation extremes is invalid in a changing climate environment, and the underestimation of rainfall intensity may lead to the failure of infrastructure in extreme events. This study first developed the non-stationary (NS) IDF curves for six selected locations in India based on sub-daily station data based on time-dependent estimates of five combinations of Generalized Extreme Value (GEV) distribution parameters. Then, in order to identify the critical regions of rainfall non-stationarity, the IDF curves were developed for 357 grid points over India using the daily gridded data for the period 1951–2016 at 1° × 1° resolution. The comparison of spatial patterns of rainfall intensity estimates under stationary and non-stationary showed that about 23% of grids showed an overestimation of NS rainfall over their stationary counterparts by at least 15%. About 32 grid locations which showed at least 15% overestimation of rainfall under an NS case displayed a significantly increasing rainfall trend. The majority of the grids with larger deviation of non-stationary rainfall estimates over stationary values are located in India's eastern regions and coastal belts.

Estimation of regional intensity–duration–frequency relationships of extreme rainfall by simple scaling in Thailand

AbstractIn 2022, Thailand was subjected to extensive flooding all over the country in both urban and rural areas, which caused tremendous losses. Better design and construction of infrastructures for timely and sufficient drainage can help mitigate the problems. This requires accurate intensity–duration–frequency (IDF) relationships at or near the problem areas. To obtain an IDF curve, a continuous rain record from an automatic gauge of the area is needed. Some automatic rain-gauge stations are scattered all over the country and are much fewer in number than the daily-reading rain-gauge stations. By applying a simple scaling theory, we can construct IDF curves from the daily rain records. The 37 automatic stations distributed the scaling exponent over the country. Gumbel location and scale parameters, from 30-year rainfall records, were determined. These three parameters were mapped throughout the country and are ready to be used for creating an IDF curve at any location in the country. We verified these parameters to generate IDF curves for three sites in different regions and found very good agreements. The majority of the errors were less than 15%.

True interval non-stationary Intensity-Duration-Frequency curves under changing climate and effect of temporal discretisation on rainfall extremes

The extreme rainfall intensity-duration-frequency (IDF) curves are used to design stormwater infrastructure in the urban areas and need to be developed keeping in view of the warming climate. The current study develops the non-stationary IDF relationships for 11 class-I urban cities of India under changing climate. The total 11,528 generalized extreme value distribution (GEV) models are developed to quantify non-stationarity in extreme rainfall by introducing a clock hour correction factor (CHCF) as a covariate. The observed hourly rainfall data of the selected cities located in five different regions of India, namely the western region (Mumbai, Ahmedabad, Jaipur), northern region (New Delhi, Dehradun), north-eastern region (Imphal), eastern region (Bhubaneswar, Kolkata), and southern region (Madurai, Chennai, Bengaluru) are collected and converted in the fixed and sliding interval annual maximum rainfall series of eight different durations. The said data set is used in the computation of CHCF, evaluation of discretisation effect on extreme rainfall events, trend analysis, development of stationary and non-stationary IDF curves. The CHCF values of seven (two) cities are above (below) 1.15 respectively; the other two cities show a value ∼ 1.15. The effect of temporal discretisation on the computation of extreme rainfall for 24-h (FW and SW) maximum rainfall depth was found to be highest in Bhubaneswar (50%) and Imphal (44%). The trend analysis revealed no statistically significant increasing (decreasing) trend in extreme rainfall for all durations for all cities under consideration except in the case of Bengaluru for 2-,3-, and 6-h durations (Chennai for a 48-h duration). The current study concluded that, under a non-stationarity climate, the shorter (1-h) and longer (24-h) duration extreme rainfall is influenced by local and global processes, respectively. Further, the short-duration extreme events are intensifying with the decrease in the return period. The 1-h duration non-stationary rainfall intensity for a 2-year return period and stationary rainfall intensity for a 5-year return period of 62.9 mm/h and 57 mm/h for Bengaluru, 77.7 mm/h and 77.2 mm/h for Mumbai, 60.8 mm/h and 60.7 mm/h for Ahmedabad, 47.8 mm/h and 49.4 mm/h for Jaipur, 59.8 mm/h and 59.3 mm/h for Madurai, 73.6 mm/h and 73 mm/h for Bhubaneswar, and 39.6 mm/h and 40.1 mm/h for Imphal, respectively. The ready-to-use IDF curves developed under stationary and non- stationary climate can be useful to design engineers in designing stormwater drainage systems in the selected class-I urban cities of India.

Sub-Hourly to Daily Rainfall Intensity-Duration-Frequency Estimation Using Stochastic Storm Transposition and Discontinuous Radar Data

Frequency analysis of rainfall data is essential in the design and modelling of hydrological systems but is often statistically limited by the total observation period. With advances in weather radar technology, frequency analysis of areal rainfall data is possible at a higher spatial resolution. Still, the observation periods are short relative to established rain gauge networks. A stochastic framework, “stochastic storm transposition” shows great promise in recreating rainfall statistics from radar rainfall products, similar to rain gauge-derived statistics. This study estimates intensity–duration–frequency (IDF) relationships at both point and urban catchment scales. We use the stochastic storm transposition framework and a single high-resolution, 17-year long (however, discontinuous), radar rainfall dataset. The IDF relations are directly compared to rain gauge statistics with more than 40 years of observation, and rainfall extremes derived from the original, and untransposed, radar dataset. An overall agreement is discovered, however, with some discrepancies in short-duration storms due to scaling errors between gauge and radar.

Impacts of climate change on IDF curves for urban stormwater management systems design: the case of Dodola Town, Ethiopia.

Climate change impacts are one of the global challenges that change the intensity and frequency of rainfall. The Dodola town has previously experienced rainfall-induced flooding effects, and future floods may be more frequent and severe due to possible variations in rainfall intensity due to climate change. In this study, the rainfall intensity-duration-frequency (IDF) curves are updated for the design of urban stormwater drainage infrastructures under climate change to reduce flooding risks. To assess the variations in the rainfall intensity, the future IDF curves for the periods (2020-2100) and two GCMs (CanESM2 and HadGEM2-ES) were derived and compared to the current IDF curves. It was found that rainfall intensities for future climate conditions will differ from the current period for all durations and return periods. The comparison results show that the relative change between future rainfall intensities and historic rainfall ranges from 1.5 to 30.6%, 2.48 to 42.6%, and 3.7 to 23.24% for 2020-2040, 2041-2070, and 2071-2100, respectively. The IDF relationships revealed that as a result of climate change, urban flooding will increase in the future. This study will help to better understand the impacts of climate change on rainfall IDF relationships, as well as have implications for the design of current and future stormwater management systems in Dodola, Ethiopia.

Reconstruction of Urban Rainfall Measurements to Estimate the Spatiotemporal Variability of Extreme Rainfall

In recent decades, the impact of climate change on urban flooding has increased, along with an increase in urban population and urban areas. Hence, historical design storms require revisions based on robust intensity–duration–frequency (IDF) relationships. To this end, the development of an urban rain-gauge network is essential to yield the spatiotemporal attributes of rainfall. The present study addresses two objectives: (a) to reconstruct sub-daily rainfall time series for the historical period over an urban gauge network, and (b) to investigate the spatiotemporal variation in extreme rainfall distribution within a city. This study considers Bangalore, India, where rainfall has been historically monitored by two stations but a dense gauge network has recently been developed. The study applies random forest regression for rainfall reconstruction, finding that the performance of the model is better when the predictand and predictor stations are near to one another. Robust IDF relationships confirm significant spatial variations in extreme rainfall distribution at the station and the city-region levels. The areal reduction factor (ARF) is also estimated in order to understand the likely impact of the reconstructed time series on hydrological modeling. A significant decrease in the ARF is observed as the area grows beyond 450 km2, indicating a substantial reduction in the volume of the design floods.

Projected Rainfall Intensity Duration Frequency Relationships under Climate Change: A Case Study of Thane City

Climate change is the most important factor to increase in short-duration high-intensity rainfall and consequent flooding. Intensity-Duration Frequency (IDF) curves are commonly used tools in Stormwater design, so a method to derive future IDF curves including climate change effects could be necessary for mainstreaming climate change information into storm water planning. The objective of the present study is to define a mechanism to reflect the effect of climate change on the projected rainfall IDF relationships. For this, the continuously observed hourly rainfall data from 1969 to 2018 were divided into five subgroups. Then the IDF curve of each subgroup is defined. The rainfall intensity for the next 30 years was then estimated using a linear regression model. The obtained result indicates that for the same duration and for the same return period, the rainfall intensity is likely to increase over time: 17% (2019-2028), 25% (2029-2038) and 32% (2039-2048). However, the findings presented in this paper will be useful for local authorities and decision makers in terms of improving stormwater design and future flood damage will be avoided.

Changing Pattern of Intensity–Duration–Frequency Relationship of Precipitation due to Climate Change

Changing Pattern of Intensity–Duration–Frequency Relationship of Precipitation due to Climate Change

Developing Intensity-Duration-Frequency (IDF) Curves Based on Rainfall Cumulative Distribution Frequency (CDF) for Can Tho City, Vietnam

Information on the relationship between rainfall intensity, duration and accumulation frequency or return period (IDF) is commonly utilized in the design and management of urban drainage systems. Can Tho City, located in the Vietnamese Mekong Delta, is a city which has recently invested heavily in upgrading its stormwater drainage systems in the hope of preventing reoccurring flood events. Yet, much of these works were designed based on obsolete and outdated IDF rainfall curves. This paper presents an updated IDF curve for design rainfall for Can Tho City. For each duration and designated return period, a cumulative distribution function (CDF) was developed using the Pearson III, Log-Pearson III, and Log-Normal distribution functions. In order to choose the best IDF rainfall curve for Can Tho City, the CDF rainfall curve and empirical formulas used in Vietnam and Asia (Vietnamese standard 7957:2008, Department of Hydrology, Ministry of Transportation, Talbot, Kimijima, and Bermard) were compared. The goodness of fit between the IDF relationship generated by the frequency analysis (CDF curve), and that predicted by the IDF empirical formulas was assessed using the efficiency index (EI), and the root mean squared error (RMSE). The IDF built from Vietnam’s standard TCVN 7957:2008 with new parameters (A = 9594, C = 0.5, b = 26, n = 0.96) showed the best performance, with the highest values of EI (0.84 ≤EI≤ 0.93) and the lowest values of RMSE (2.5 ≤RMSE≤ 3.2), when compared to the other remnants.

A new method based on artificial bee colony programming for the regional standardized intensity–duration‒frequency relationship

A new method based on artificial bee colony programming for the regional standardized intensity–duration‒frequency relationship

A Novel Statistical Modeling Approach to Developing IDF Relations in the Context of Climate Change

Extreme rainfall intensity–duration–frequency (IDF) relations have been commonly used for estimating the design storm for the design of various urban water infrastructures. In recent years, climate change has been recognized as having a profound impact on the hydrologic cycle. Hence, the derivation of IDF relations in the context of a changing climate has been recognized as one of the most challenging tasks in current engineering practice. The main challenge is how to establish the linkages between the climate projections given by climate models at the global or regional scales and the observed extreme rainfalls at a local site of interest. Therefore, our overall objective is to introduce a new statistical modeling approach to linking global or regional climate predictors to the observed daily and sub-daily rainfall extremes at a given location. Illustrative applications using climate simulations from 21 different global climate models and extreme rainfall data available from rain gauge networks located across Canada are presented to indicate the feasibility, accuracy, and robustness of the proposed modeling approach for assessing the climate change impact on IDF relations.

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

Intensity duration frequency curve analysis for selected meteorological stations in North Shoa, Amhara Region, Ethiopia

The estimation of rainfall intensity is required for the design of hydraulic and water resources engineering control structures. Rainfall data of 11 – 23 years long from four stations was used to generate Intensity-Duration-Frequency (IDF) curves and parameters for selected stations in the study area. The daily rainfall data set obtained was then subjected to frequency analysis to determine the distribution which best characterizes the data set. The result showed that Log Person Type III was the best fit probability distribution function. The annual extreme values of rainfall depth were computed for different rainfall durations and return periods. Ranked rainfall depths for each return period were converted to rainfall intensities. The corresponding rainfall intensities were computed for different rainfall durations and return periods. IDF curves were developed using MIDUSS 2.25 software for return periods of 2, 5, 10, 25, 50, and 100 years and for durations of 1, 2, 3, 6, 12, and 24 h, and determined IDF parameters. The results shown that for shorter durations (1 hour and 2 hours), the IDF curves gave higher intensities for the same return period while for longer durations (3 h, 6 h, 12 h and 24 h), they gave low intensities for the same return period. The IDF relationship developed can be used as valuable tool for the designing of hydraulic structures in the region. This study could serve as a spring board for other studies that imply the calculation of a peak flow and designing of hydraulic structures as an input to help improve flood resilience in the region.

Time-varying Intensity-Duration-Frequency relationship through climate-informed covariates

The overwhelming increase and variations in the extreme rainfall events demand the use of a nonstationary Intensity-Duration-Frequency (IDF) curve for the design and management of water resource infrastructure. Generally, nonstationary IDF curves are developed by incorporating the trend in the distribution parameter using Generalized Extreme Value distribution (GEV) with time as a covariate. The physical processes influencing the variations in a hydrologic variable can be captured by utilizing relevant climatic variables (climate-informed) as covariates, since time alone cannot be the best covariate. Hence this study investigates the potential climate-informed covariates influencing the extreme rainfall and incorporates the best covariates to develop a realistic nonstationary IDF relationship. Unlike previous studies, a Time Sliding Window (TSW) approach is employed to detect the changing distribution parameters before performing Nonstationary Modeling (NSM). The proposed covariate based TSW-NSM is effectively used to construct IDF curves for seven major metropolitan cities of India. Several models are generated based on the detected changing parameters and combinations of covariates. Then, Bayesian Differential Evolutionary Monte Carlo (DE-MC) algorithm is employed to estimate the uncertainty bound of the nonstationary parameters, and the best model is chosen using the Deviance Information Criterion (DIC). The results reveal that the best covariate combinations for short duration events are dominated by local processes (i.e., Local temperature changes and diurnal temperature changes), whereas the same for longer duration events are dominated by the global processes (global warming, ENSO Modoki cycle, and IOD). However, the acceptable nonstationary models reveal that all the temperature-based covariates are capable of capturing the dynamic behavior. It is also observed that the local processes carry the signature of global processes. Finally, the return levels computed through the best nonstationary model show that the return periods are decreasing, and the short duration events have undergone drastic changes than the longer duration events. Thus, the results suggest that employing climate-informed covariates based nonstationary IDF curves is indispensable for devising long-term strategies to address the changing climate.