Intensity-duration-frequency Curves 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.

Flood risk assessment of the Narmada Basin, India, under climate change scenarios

ABSTRACT Climate change has led to a significant rise in the occurrence of extreme events such as floods and droughts. It is essential to accurately assess the hazards linked to climate change to effectively tackle flood risks in the future. This study investigates the risk of flooding in the Narmada Basin under climate change scenarios. Future precipitation data of global climate models are used to create intensity–duration–frequency curves and peak flows are estimated using a hydrological model. These peak flows serve as inputs for the hydraulic model to simulate floods across various return periods. The fuzzy inference system (FIS) is employed to optimize releases and reduce peak flows to mitigate flood impacts. The findings indicate that the extent of flooding and maximum water depths increase with higher return periods and future scenarios, and optimizing the reservoir releases, decreases the extent of flooding. This decrease in inundation area ranges from approximately 9 to 10% in the Bargi-to-Indira Sagar section to 20–23% in the Indira Sagar-to-Omkareshwar Sagar segment. In the Omkareshwar Sagar-to-Sardar Sarovar segment, the flooded area diminishes by 17–18% under the Radioactive Concentration Pathway (RCP) 4.5 and by 12–14% under the RCP 8.5 scenarios.

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.

Wastewater flooding risk assessment for coastal communities: Compound impacts of climate change and population growth

The study introduces a wastewater modeling framework that evaluates the compound impacts of intense rainfall, groundwater infiltration, sewer aging based roughness and population growth on wastewater systems. It integrates property and city-level flooding risk assessment and wastewater treatment plant (WWTP) capacity analysis into a single methodological approach. The framework applied to the coastal city of Charlottetown, whose population increased from 30,887 in 1981 to 42,440 in 2023, but the wastewater system did not expand accordingly, resulting in frequent sewer backups, street and basement flooding, as witnessed during the extreme wet weather event of September 2, 2021. Using PCSWMM (Personal Computer Storm Water Management Model), the study assessed that the city-wide wastewater flooding risk in Charlottetown, based on 2023 population data and historical IDF curves, affects 13.31% of the network during a 2-year storm and 18.38% during a 100-year storm. These risks increase to 14.5% and 22.6% under future IDF scenarios, reaching 17.89% and 26.4% by 2060 with projected population growth. The WWTP capacity is exceeded by 27.8% during peak wet weather flows from a 2-year storm and by 86.3% during a 100-year storm, based on 2023 population and historic IDFs. Under future IDF scenarios for 2060 population, exceedances rise to 103.6% and 169.1% respectively, for a 2-year and 100-year storm. Basement flooding risk affects 13.35% of basements during a 2-year storm and 18.31% during a 100-year storm, for 2023 population and historic IDFs. Future IDF scenarios indicate risk increasing to 17.77% and 25.80% by 2060 for a 2-year and 100-year storm respectively. The hydraulic modeling results indicate that GWI is not currently impacting the study area, nor is it expected to in near future, because the groundwater table is over 10 m deep, while wastewater pipes are no deeper than 6 m. The framework and study have significant social implications and benefits, including protecting public health, enhancing the resilience of urban infrastructure, and safeguarding the environment, ultimately improving the quality of life for residents in coastal communities like Charlottetown.

ANALISIS INTENSITAS CURAH HUJAN DAN KURVA IDF (INTENSITY-DURATION-FREQUENCY) METODE MONONOBE DI KOTA SALATIGA

Extreme rainfall in Indonesia has led to natural disasters in many areas including Salatiga City. Many human activities depend on the amount of rainfall that falls on the earth's surface. In the field of civil engineering, research on rainfall is very common because many activities related to civil engineering use rainfall data such as for water resource management, drainage development, dam construction and other building construction. This study aims to determine the amount of rainfall intensity and IDF curves in Salatiga City for return periods of 2 years, 5 years, 10 years, 25 years, 50 years and 100 years. The data used in this study is the maximum daily rainfall data collected from Salatiga Central Bureau of Statistics (BPS). The type of distribution selected in this study is the Log Pearson Type III probability distribution. The results of the analysis show that the highest rainfall intensity occurs at a short duration (5 minutes) in the 100-year return period of 106.66 mm/hour and the results of the IDF curve show that the shorter the rainfall time, the higher the rainfall intensity while the longer the rainfall time, the smaller the rainfall intensity at each return period T.

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.

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

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

Impacts of climate change on urban stormwater runoff quantity and quality in a cold region

Climate change poses significant challenges to urban environments affecting both flood risks and stormwater pollutant loadings. However, studies on variations in stormwater runoff quantity and quality in cold regions, which are highly sensitive to climate change, are notably limited. Integrating climatic, hydrologic, and hydraulic modelling, the study assesses the potential impacts of climate change on stormwater runoff volume and pollutant dynamics in a Canadian urban watershed (Calgary). A two-year field program was conducted to support the calibration and validation of the Storm Water Management Model (SWMM). Intensity–duration–frequency curves were employed to evaluate the impacts of climate change on peak flow rate and flooding duration. In addition, typical dry, average, and wet years were applied to continuously simulate stormwater runoff quantity and quality during the 2050s and 2080s. The results suggest substantial increases in peak flow rates and flooding durations, particularly for the 5-year return period rainfall, with 1-h, 4-h, and 24-h peak inflow rates increasing by 74.3% (170.7%), 89.2% (158.4%), and 64.1% (102.8%) in the 2050s (2080s) Furthermore, the runoff quantity is projected to rise by 2.4–10.2% in the 2050s and 11.8–17.5% in the 2080s. Total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) loadings are anticipated to increase by 2.0–36.1%, 3.1–21.4%, and 4.1–20.7%, respectively. As a result, the current stormwater system could overload and stormwater quality is likely to deteriorate under the impact of climate change. The findings are beneficial for cold regions to develop adaptive strategies that enhance urban water security and environmental sustainability under climate change.

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.

Statistical downscaling model for future projection of daily IDF relationship by Markov chain and kernel density estimator

ABSTRACT Changes in climate might have a significant impact on rainfall characteristics, including extreme rainfall. This study aims to project the future daily rainfall, preserving most of the rainfall characteristics, including extreme rainfall incorporating climate changes. This paper presents two hybrid semi-parametric statistical downscaling models for future projection of IDF curves. The precipitation flux from seven scenarios of ten GCMs and observed daily rainfall data are considered as predictors and predictand variables, respectively. At site, daily rainfall occurrence is modeled using a two-state first-order Markov chain. Rainfall amounts on each wet day are modelled using a univariate nonparametric kernel density estimator. Two types of amount generation models are presented in this study. The bounded model (KDE-SP) is developed, considering the support for the kernel distribution as positive. In the unbounded model (KDE-Ext), the wet days are reclassified as extreme and non-extreme rainy days. A significant increasing trend can be observed in the future projected intensity–duration–frequency relationships. The maximum increment using empirical distribution is observed as 93.21 and 80.93% on a 5-year return period in the far future for the SSP5-8.5 scenario, using KDE-Ext and KDE-SP models, respectively. Although both methods show similar results, the KDE-Ext model performs better in simulating extreme rainfall.

The Necessity of Updating IDF Curves for the Sharjah Emirate, UAE: A Comparative Analysis of 2020 IDF Values in Light of Recent Urban Flooding (April 2024)

In the arid Arabian Peninsula, particularly within the United Arab Emirates (UAE), the perception of rainfall has shifted from a natural blessing to a significant challenge for infrastructure and community resilience. The unprecedented storm on 17 April 2024, exposed critical vulnerabilities in the UAE’s urban infrastructure and flood management practices, revealing substantial gaps in handling accumulated precipitation. This study addresses the necessity of updating the Intensity–Duration–Frequency (IDF) curves for the Sharjah Emirate by utilizing recent precipitation data from 2021 to April 2024, alongside previously published 2020 data. By recalibrating the IDF curves based on data from three meteorological stations, this study reveals a substantial increase in rainfall intensities across all durations and return periods. Rainfall intensities increased by an average of 36.76% in Sharjah, 26.52% in Al Dhaid, and 17.55% in Mleiha. These increases indicate a trend towards more severe and frequent rainfall events, emphasizing the urgent need to revise hydrological models and infrastructure designs to enhance flood resilience. This study contributes valuable insights for policymakers, urban planners, and disaster management authorities in the UAE and similar regions worldwide.

A multi-decadal analysis of U.S. and Canadian wind and solar energy droughts

The spatial and temporal characteristics of wind and solar energy droughts across the contiguous U.S. and most of Canada for the period 1959–2022 are investigated using bias-corrected values of daily wind and solar power generation derived from the ERA5 meteorological reanalysis. The analysis domain has been divided into regions that correspond to four major interconnects and nine sub-regions. Droughts are examined for wind alone, solar alone, or a mix of wind and solar in which each provides 50% of the long-term mean energy produced, for durations of 1–90 days. Wind and solar energy droughts and floods are characterized on a regional basis through intensity–duration–frequency curves. Wind and solar generation are shown to be weakly anti-correlated over most of the analysis domain, with the exception of the southwest U.S. The intensities of wind and solar droughts are found to be strongly dependent on region. In addition, the wind resource in the central U.S. and the solar resource in the southwestern U.S. are sufficiently good that over-weighting capacity in those areas would help mitigate droughts that span the contiguous United States for most duration lengths. The correlation of droughts for the 50%–50% mix of wind and solar generation with temperature shows that the most intense droughts occur when temperatures exhibit relatively moderate values, not when energy demand will be largest. Finally, for all regions except the southeast U.S., winter droughts will have a larger impact on balancing the electric grid than summer droughts.

Investigation the Impact of the Climate Change on Intensity Duration Frequency (IDF) Curve Development: Case Study at Hulu Terengganu, Malaysia

The intensity-duration-frequency (IDF) curves are the most common form of design rainfall data used for peak discharge estimation. Thus, the IDF curve needs to be improved with the expectation that rainfall intensity and frequency have increased as a result of climate change. The main purpose of this study was to investigate the changes of IDF curves considering the climate change impacts on Hulu Terengganu. The climate projection from MRI-ESM2-0, CMCC-CM2-SR5, and GFDL-ESM4 under 3 different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used to provide the climate changes pattern in the future year (∆2050). In order to downscale the climate projection, the statistical downscaling method (SD-LS) was employed to correct the biases of these three GCMs. The IDF curves for the return periods of 2, 5, 10, 20, 50, 100, and 200 year were then developed based on the maximum rainfall intensity that were projected by the SD-LS model. The results clearly indicated that there are possibilities for increasing patterns in the projected annual and monthly rainfall for both time periods compared to historical data. Thus, the future extreme rainfall events for various durations with different return periods are all likely to increase over time. The largest potential increase is predicted at Sg. Gawi (+2.0% to +86.0%) based on the different return periods and rainfall durations. It could change the pattern of IDF curve that been developed based on projected rainfall by various SSPs. The developed IDF curves shows higher rainfall intensities in a shorter duration under the same return periods. Therefore, comprehensive action must be taken immediately to regulate and manage the effects of climate change.

Derivation of Rainfall Intensity-Duration- Frequency (IDF) Curves of Gainesville, Georgia, United States

This research presents a set of Intensity-Duration-Frequency (IDF) curves of Gainesville, Georgia, USA for duration of 15 minutes to 24 hours, and return periods of 2 to 100 years. The objective of this research focuses on the development of IDF curves for the Gainesville, Georgia, USA. Gumbel and Log Pearson Type III models were used to develop the IDF curves. The best rainfall model between Log Pearson III and Gumbel model for the predication of rainfall in the study area was determined for the different return periods and durations. The Gumbel and Log Pearson Type III distributions give maximum intensities of 138.17 mm/hr and 137.75 mm/hr at return period of 100 years with duration of 0.25 hours, respectively. The result of this study can be used by water resource planners and managers for the city of Gainesville, Georgia.

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.

Open-Source Design of Infiltration Trenches for Sustainable Soil and Water Conservation in Rural Areas of Central Chile

Specific algorithms are developed to solve the equations that define the physical dimensions under various conditions. In this sense, the storm index method was incorporated for the variable precipitation intensity, expanding the number of rainfall stations with the intensity duration frequency (IDF) curves from 9 to 31 within the considered territory (the Biobio Region of Central Chile). Likewise, the infiltration values and runoff coefficients necessary for calculating the dimensions of the trenches were obtained using the Python programming language. The results show that an open-source Python solution allows high reliability and efficiency based on the tests developed. For this reason, this prototype is expected to add new mathematical expressions that may arise to better account for an efficient design of soil and water conservation works or infiltration trenches. In this way, it is concluded that it is possible to develop simulation models for the efficient design of trenches based on well-defined and limited theoretical modeling, adding to computer language tools. This allows for a virtuous synergy that can help address efficient public policies to conserve soil and water in Chile and elsewhere.

Design storm parameterisation for urban drainage studies derived from regional rainfall datasets: A case study in the Spanish Mediterranean region

ABSTRACT A significant amount of information on regional rainfall characteristics is available nowadays, allowing its use in hydrological applications. This article is motivated by the availability of regional studies regarding maximum daily rainfall and intensity–duration–frequency curves that can be coupled with the design storm concept for urban hydrology studies. This is accomplished through a convenient index describing temporal variability of rainfall. More precisely, a methodology for regionalising the two parameters (i0, φ) of the two-parameter gamma design storm (G2P) is developed herein. A three-step methodology is proposed for obtaining the two parameters (i0, φ) for a given location. The results obtained in a case study show coherence with previous studies concerning maximum rainfall statistics.

Modeling nonstationary intensity-duration-frequency curves for urban areas of India under changing climate

Enhancing stormwater drainage systems is paramount amid evolving climate dynamics, necessitating robust design and continual upgrades to address changing environmental conditions. The present work constructs the nonstationary Intensity-Duration-Frequency (IDF) curves for prominent urban areas of India. It develops 2313 nonstationary Generalized Extreme Value (GEV) models in annual and seasonal timeframes by integrating the influence of local and global climate-informed covariates, including time covariates. The work involves analyzing 1, 2, 3, 4, 6, 12, 24, 36, and 48 hourly maximum rainfall series with return periods of 2, 5, 10, 25, 50, and 100 years. Among the 16 urban areas examined, there's a significant shift from stationary to nonstationary extreme rainfall intensities, marked by a 38.7% increase in shorter duration series with a 5-year return period in New Delhi and Visakhapatnam. AMO, DMI, GTA, and LTA in New Delhi play significant roles. Similarly, in Visakhapatnam, SST in Niño 3.4 and DMI are significant covariates influencing nonstationarity. Recently, in the 2023 monsoon, the 25-year flood wreaked havoc in New Delhi, Rajkot, Surat, and Visakhapatnam. Generating nonstationary IDF curves for the annual and seasonal timeframes offers a comprehensive approach to stormwater design and infrastructure upgradation and effective adaptation strategies across sixteen Indian cities.

Intensity-Duration-Frequency Curves for Manicaragua city, Cuba

The Intensity-Duration-Frequency (IDF) curves are a way to visualize and represent extreme hydrometeorological rainfall events. In this article, an analysis of convective rainfall events recorded at the La Piedra Meteorological Station, Villa Clara, Cuba, was conducted. To develop IDF curves, the 2006- 2019 time series was analyzed. A partial duration series was generated, including intervals from 20 minutes to 4320 minutes, subjected to an outlier detection process. The series was divided into two categories: one for durations ≤ 720 minutes and another for durations > 720 minutes. The resulting series underwent nonparametric tests to assess their independence, randomness, homogeneity, and seasonality. Subsequently, they were fitted to the Generalized Pareto probability distribution and to a parametric equation of the Montana model, and then the curves were plotted for return periods of 10, 50 and 100. The Montana model led to obtaining correlation coefficients greater than 0.90 compared to the other methods used, significantly improving the quality of the fit in both categories. This research provides information to understand and plan the management of intense climatic phenomena and adequate risk management in an area where such studies are lacking, facilitating access to crucial data essential in the design and execution of hydraulic engineering projects in the region.

Development of intensity–duration–frequency curves for Herat, Afghanistan: enhancing flood risk management and implications for infrastructure and safety

Development of intensity–duration–frequency curves for Herat, Afghanistan: enhancing flood risk management and implications for infrastructure and safety