Intensity Duration Frequency Research Articles

The Influence of Typhoon Events on the Design Storm for the Shanghai Metropolitan Area in the Yangtze River Delta, China

Given the fact that the high frequency of extreme weather events globally, in particular typhoons, has more of an influence on flood forecasting, there is a great need to further understand the impact of typhoon events on design storms. The main objectives of this paper are to examine the magnitude, occurrence, and mechanism of typhoon events in southeast coastal China and their contribution to the design storm study. We take Shanghai, which is a typical metropolitan region in the Yangtze River Delta, China, as an example. The impact of typhoons on the rainfall frequency analysis is quantitatively evaluated using stochastic storm transposition (SST)-based intensity–duration–frequency (IDF) estimates with various temporal and spatial structures under different return periods. The results show that there is significant variability in the storm magnitude within the transposition domain across different durations, highlighting the spatiotemporal heterogeneity over the coastal area. Moreover, the probability of random storm transposition exhibits an uneven distribution. The frequency of typhoon rainfall events within the transposition domain is notably high, and there is considerable variability in the structure of rainfall. Typhoon rainfall amplifies the intensity of design storms, and its contribution increases with return periods. The variability in design storms increases accordingly. Based on the advantages of SST, which retains the spatiotemporal structure of the rainfall in the generated scenarios, the overall framework provides an effective way to examine the impact of diverse characteristics of typhoon rainfall on frequency analysis and facilitate a deeper exploration of the direct impact of various types of extreme storms on the intensity, spatial, and temporal distributions of design storms amidst evolving environmental conditions over this metropolitan region.

Review: Fractal Geometry in Precipitation

Rainfall, or more generally the precipitation process (flux), is a clear example of chaotic variables resulting from a highly nonlinear dynamical system, the atmosphere, which is represented by a set of physical equations such as the Navier–Stokes equations, energy balances, and the hydrological cycle, among others. As a generalization of the Euclidean (ordinary) measurements, chaotic solutions of these equations are characterized by fractal indices, that is, non-integer values that represent the complexity of variables like the rainfall. However, observed precipitation is measured as an aggregate variable over time; thus, a physical analysis of observed fluxes is very limited. Consequently, this review aims to go through the different approaches used to identify and analyze the complexity of observed precipitation, taking advantage of its geometry footprint. To address the review, it ranges from classical perspectives of fractal-based techniques to new perspectives at temporal and spatial scales as well as for the classification of climatic features, including the monofractal dimension, multifractal approaches, Hurst exponent, Shannon entropy, and time-scaling in intensity–duration–frequency curves.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Development of intensity – duration – frequency (IDF) curves using combined rain gauge (RG) and remote sense (TRMM) datasets for Weta Traditional Area in Ghana

This research developed IDF curves and derived empirical equations for the Weta Area to estimate design rainfall. Daily rainfall data of Rain Gauge and Tropical Rainfall Measuring Mission (TRMM) 3B42 over 22 years for Weta were obtained from the Ghana Meteorological Services and National Aeronautics and Space Administration, respectively. The Indian Meteorological Department formula was used to disaggregate the daily data into hourly synthetic series to develop the IDF curves. The combined dataset was subjected to frequency analysis to determine the distribution which best characterize the dataset. The results demonstrated that the IDF curves provided higher intensities for shorter durations (0.08-h, 0.17-h, 0.25 h, and 0.5 h) while lower intensities for longer durations (1 h, 2-h, 6-h, 12 h and 24 h). The rainfall intensities and return periods are parallel and high intensities correlated with brief durations for the same return period. TRMM overestimated rainfall compared to Rain Gauge data. The IDF curves and derived empirical equations are useful scientific tools for hydraulic infrastructure design and water resources planning and management.

Rainfall Intensity-Duration-Frequency Relationships: A Robust Foundation for Urban Decision-Making and Flood Management: A Case Study

Ahmedabad, a city in India, experiences varying climatic patterns and is prone to extreme rainfall and relies on established intensity-duration-frequency (IDF) relationships for critical infrastructure planning and flood management. While the city has existing IDF relationships, these curves were established some time ago and still lack well-established IDF Curves. To fill this research gap this study is dedicated to the development of IDF curves for Ahmedabad City, utilizing 45 years of rainfall data (1978 to 2022) sourced from the Indian Meteorological Department (IMD) making our study a pioneering effort in this direction. Employing frequency analysis methodologies, the relationships between rainfall intensity, storm duration, and return periods were established. IDF curves were generated for durations of 5, 10, 15, 30 minutes, and 1, 2, 6, 12 and 24 hours for Ahmedabad City. Short-term rainfall intensities were derived from daily rainfall values using the IMD formula. The empirical approach, Gumbel distribution, and Log Pearson Type-III distribution were applied. The Gumbel method tends to produce slightly higher rainfall intensities for shorter return periods, while the LPT III method exhibits the opposite pattern for longer return periods, such as 25, 50, and 100 years. In general, both approaches yield closely matching results across most return periods. The IDF curves produced in this study offer a versatile set of rainfall scenarios for diverse applications, including the design and modification of hydraulic structures, stormwater management infrastructure, and flood mitigation systems in Ahmedabad City. The outcomes of this research contribute not only to filling a crucial research gap but also empower decision-makers and urban planners with updated and reliable data crucial for enhancing the city’s resilience against extreme rainfall events.

Precipitation Study in Flood Zones: San Mateo Atenco, State of Mexico, Mexico Case Study

This work is focused on analyzing the causes of flooding in the sub-basin belonging to the municipality of San Mateo Atenco: Almoloya-Otzolotepec, because the municipal territory is constantly threatened by hydrometeorological phenomena, such as hailstorms, frost and torrential rains. The maximum precipitation was analyzed in neighboring climatological stations and the precipitation at different return periods (2 to 1000) was determined through the Gumbel distribution, and its impact according to the Thiessen polygon method. With the information of the 24-hour intensities, the Intensity Duration Frequency (IDF) curves and the hietogram of the study area were plotted using the alternating block method. The above procedure was applied to the precipitation data from the 9 stations that affect the basin. The approach proposed in this work considers all the precipitation variables for the best approximation of the phenomena that may occur in the basin; it is an innovative model because it considers the weighted averages of precipitation for each area of influence and its surroundings; this information is useful for the implementation of flood protection projects, adequate design of drainage and sewage systems, and a rational use of the resource to guarantee better living conditions for the inhabitants. According to the results obtained, there is a slow hydrological response and the areas with the greatest runoff potential (8383<CN<100) are located towards the central part of the basin where there is a significant concentration of bare soil.

Equações universais para as distribuições de chuva dos métodos SCS e Huff

ABSTRACT The temporal distribution of design rainfall hyetographs can be obtained through various procedures. One such method involves using temporal distribution curves to disaggregate a rainfall value of longer duration into chronological values within shorter time intervals. This is demonstrated by SCS distribution curves for durations of 6 and 24 hours and the well-known Huff curves. Another approach involves deriving temporal distribution using design hyetograph calculation methods, such as the Chicago and alternating block methods, based on Intensity-Duration-Frequency (IDF) equations. This paper aims to combine these approaches and develop universal equations capable of accurately fitting smooth rainfall time distribution curves. The advantage is the replacement of tables, like SCS and Huff, with analytical equations, enhancing usability in spreadsheets. Furthermore, these universal equations can determine the temporal rainfall distribution using an IDF equation or fitting a specified design hyetograph like Euler type II. Four numerical examples are provided.

Applicability of Utilizing Remote Sensing Rainfall Products Data in Arid and Semi-Arid Poorly Gauged Catchments: Study of Wadi Ghoweiba Watershed, Egypt

AbstractDue to the scarcity of established rain gauge stations, obtaining continuous time series daily rainfall observation data is a major challenge. The availability of these data is very essential to conduct hydrological studies and predicting flood events. Nowadays, several satellites could be used to provide such data. This study aims to investigate the appropriate remote sensing (RS) rainfall product and use its data to develop frequency analysis and intensity–duration–frequency curve (IDF curve) at 5-, 10-, 25-, 50-, 100-, and 200-year return periods in the study area. The evaluation of the PERSIANN family products (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now) with NASA-POWER datasets compared to the available annual maximum of daily observation rainfall in Suez Rain Gauge (SRG) station as a reference was conducted. The results show that the PERSIANN-CDR product is the appropriate satellite-based rainfall dataset product based on the outcomes of the comparison criteria (RMSE, Bias, CC, and R2 error measures) of rainfall characteristics analysis and Chi-squared test of distributions. The frequency analysis results confirmed that the values of about 30.3–38.6 and 40–53.3 mm/day corresponding to the biggest flood reported in 1965 and 2020 in SRG station and PERSIANN-CDR product have a return period of 50–100 years, classifying it as an extreme event in Wadi Ghoweiba. These findings would considerably benefit decision-makers in estimating flood risks and planning the appropriate structure protections in the Wadi Ghoweiba Watershed.

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.

Simple scaling of extreme precipitation regime in Senegal

Extreme precipitation exhibits high temporal and spatial variability, and understanding this variability is crucial for designing hydraulic infrastructures and assessing the impacts of natural risks such as floods and droughts. The Simple Scaling (SS) model, which describes the dependence of extreme rainfall statistics on timescales, is used to estimate Intensity Duration Frequency (IDF) curves. This study aims to evaluate the validity of the Simple Scaling (SS) hypothesis for 14 rain gauges in Senegal and explore the existence of breaks in temporal scaling regimes (transition regimes). For the analysis of extreme characteristics, we considered the Annual Maximum Series (AMS) of precipitation in sub-hourly and supra-daily durations ranging from 10 min to 7 days. The empirical validation of the SS models was confirmed for the majority of the scaling intervals. We identified two scaling breaks, indicating the presence of three scaling regimes: short durations (SD), intermediate durations (ID), and long durations (LD). The spatial variation of scaling exponents reveals the existence of different scaling regimes across Senegal. The results are valuable for modeling the spatial distribution of scaling exponents, which can aid in characterizing IDF curves at ungauged locations.

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

The study examines the characteristics and variability of monsoon rainfall in Myanmar, focusing on the relationship between heavy rainfall, floods, and earthquakes, which impact agriculture, hydrology, and the environment. Generally, heavy rainfall can cause flooding, economic losses, and water table changes. Northern Myanmar floods occur mainly during the monsoon season from June to October and can be classified into widespread floods along major rivers like Ayeyarwady, Thanlwin, Chindwin, and Sittoung and flash floods in small streams and rivers. Climate change is expected to increase the frequency and intensity of extreme weather events, including heavy rainfall, which can trigger floods or landslides, which also can in turn cause earthquakes. Heavy rainfall over northern Myanmar and the Sagaing faults, which are the main triggers of earthquakes, has been the subject of several studies. The study uses the Copernicus 5 database of global climate model (GCM) simulations with two scenario analyses on climate change detection and indices (ETCCDI) to study changes in climatic extremes. Results show high intensity in the northern region and monsoon core regions, while the central region shows less intensity. The study also uses intensity-duration-frequency (IDF) curves to analyze the relationship between rainfall duration, intensity, and return time in major risk zones. The study finds that as short duration lengthens, rainfall intensity increases for future rainfall patterns. This information is expected to be convenient for local authorities and flood protection projects in rural and urban basins.

Development of Intensity–Duration–Frequency (IDF) Curves over the United Arab Emirates (UAE) Using CHIRPS Satellite-Based Precipitation Products

The recent flooding events in the UAE have emphasized the need for a reassessment of flood frequencies to mitigate risks. The exponential urbanization and climatic changes in the UAE require a reform for developing and updating intensity–duration–frequency (IDF) curves. This study introduces a methodology to develop and update IDF curves for the UAE at a high spatial resolution using CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. A bias correction was applied to the CHIRPS data, resulting in an improved capture of extreme events across the country. The Gumbel distribution was the most suitable theoretical distribution for the UAE, exhibiting a strong fit to the observed data. The study also revealed that the CHIRPS-derived IDF curves matched the shape of IDF curves generated using rain gauges. Due to orographic rainfall in the northeastern region, the IDF intensities were at their highest there, while the aridity of inland regions resulted in the lowest intensities. These findings enhance our understanding of rainfall patterns in the UAE and support effective water resource management and infrastructure planning. This study demonstrates the potential of the CHIRPS dataset for IDF curve development, emphasizes the importance of performing bias corrections, and recommends tailoring adjustments to the intended application.

Quantification of climate change impact on rainfall-induced shallow landslide susceptibility: a case study in central Norway

ABSTRACT Climate change impact on rainfall-induced landslide susceptibility of a certain region is often implied based on expected changes in rainfall patterns and rarely explicitly quantified. This study aims to address this gap by implementing coupled landslide and climate modelling chains to explicitly assess the effects of changing rainfall patterns on rainfall-induced landslide susceptibility. The effects of climate change are integrated into the landslide modelling chain via Intensity-Duration-Frequency (IDF) curves for the present and future climate conditions for a landslide-prone study area located in central Norway. The effects of climate change on landslide susceptibility are examined by using a physical-based landslide prediction model with rainfall events of varying duration and intensity that are simulated based on the climate-dependent IDF curves. The novelty of this study is the proposition of a novel probabilistic framework to assess the climate change impact on landslide susceptibility for rainfall events with a given duration. The proposed framework accounts for both the uncertainties of rainfall events through probabilistic interpretation of IDF curves and the uncertainties in the landslide model with the Monte Carlo method. Compared to results based on only intense rainfall events, the proposed framework leads to a lower increase in the probability of landslide initiation and landslide-susceptible extents due to climate change.

Flood Estimation and Control in a Micro-Watershed Using GIS-Based Integrated Approach

Flood analyses when using a GIS-based integrated approach have been successfully applied around the world in large-sized watersheds. This study employed hydrological-hydraulic modeling to analyze flash floods by integrating HEC-HMS, HEC-RAS, and ArcGIS software for flood evaluation and control in a micro-watershed in the Samaru River, Nigeria. The watershed boundaries, its characteristics (soil and land use), the topographical survey, and the intensity duration frequency curve (IDF) of the study area were produced using data-driven techniques. The HEC-HMS model was used to derive the peak discharges for 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods with the frequency storm method. Afterward, the water surface profiles for the respective return periods were estimated using the HEC-RAS hydrodynamic model. The simulated design flood for the 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods at the reference location (the NUGA gate culvert) were 3.5, 6.8, 9.1, 12.1, 14.3, 16.6, and 19.0 m3/s, respectively, while those at the watershed outlet for the respective return periods were 7.5, 14.9, 20.3, 27.3, 32.6, 38.0, and 43.5 m3/s, respectively (with a water height of 0.9 m, 1.1 m, 1.3 m, 1.33 m, 1.38 m, 1.5 3m, and 1.8 m, respectively), at the NUGA gate culvert cross-section. The maximum water depths of about 0.9 m and 1.0 m were recorded in the right and left overbanks, which were similar to the simulated water depth for the 2- and 5-year return periods. Hence, for the smart control of floods passing through the river and major hydraulic structures, a minimum design height of 1.50 m is recommended. For the most economic trapezoidal channel section, a normal depth of 1.50 m, a bottom width of 1.73 m, a top width of 3.50 m, and a free board of 0.30 m is proposed to curb the overtopping of floods along the channel sub-sections. The findings of this study could help hydraulic engineers minimize flooding in streams and rivers overbanks in a micro-watershed.

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

GENERATION OF IDF EQUTION CASE STUDY AL- SHUWAIJA WATERSHEDS/(IRAQ-IRAN)

One of the most useful tools in water resources engineering is the Intensity-Duration-Frequency (IDF) relationship, which may be used in the planning, design, and operation of water resource projects and a wide range of engineering initiatives to mitigate the effects of flooding and torrents Al- Shuwaija in addition to the designs of rain networks within Cities nearby.
 This research aims to find the curves of intensity, duration and frequency (IDF), determine the intensity of rainfall in Shuwaija, and derive an experimental method for estimating rainfall intensity. Rainfall records dating back 20 years were relied on as well as the processing of missing data for five plants. A set of tests was used for probability distributions using programs (ESY FIT, HYFRAN-PLUS), and Log-person type 3 was found to be the appropriate option for the probability distribution. For the AL- Shuwaija area, any return time with a given storm length may be predicted using a median empirical formula. The research found that the highest intensities occur over the shortest periods of time and that these intensities vary greatly with the return period, but that over longer periods, there is no fluctuation in intensities.

Artificial neural network-empowered projected future rainfall intensity-duration-frequency curves under changing climate

With the increasing trend of greenhouse gases in the atmosphere, by 2052 the temperature is expected to rise by 1.5 °C from the Pre-industrial Period, affecting future extreme rainfall events. This necessitates quantifying extreme hydrologic events to plan and design hydrologic and hydraulic structures using rainfall Intensity-Duration-Frequency (IDF) curves to adapt to future climate scenarios. This study developed future projected IDF curves for the Southeast United States using disaggregated sub-hourly (15-, 30-, and 45-min) monthly maximum rainfall from 2030 to 2059 using five climate models under the Representative Concentration Pathway 8.5 scenario. A computationally efficient feed-forward back-propagation Artificial Neural Network (ANN)-based approach was found to be significantly superior for disaggregating rainfall to a stochastic model with an average Nash–Sutcliffe efficiency (NSE) ranging from 0.67 to 0.84. The study found that there is an increasing rate of future projected annual maximum rainfall intensities in the range of 7% to 36% with reference to the historical period. The spatial variation in future projected extreme rainfall depths showed that the Gulf-Atlantic coast and the Appalachian Mountains are expected to receive more extreme rainfalls.

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.

Analysis of Extreme Rainfall Characteristics in 2022 and Projection of Extreme Rainfall Based on Climate Change Scenarios

In this study, we analyzed the characteristics of the heavy rainfall events that occurred in Seoul in 2022 and compared them with the projections of the representative concentration pathway (RCP). The analysis results indicated that climate change is ongoing. In this era of climate crisis, based on the shared socioeconomic pathway (SSP) data, we projected the 20-year frequency rainfall for South Korea at intervals of 1 day/24 h. Our results indicate that the maximum rainfall (with a 24 h duration) will increase by ~18% in the second half of the 21st century, compared to the current maximum rainfall. Finally, we projected the intensity–duration–frequency (IDF) curve for the infrastructure design of Seoul. According to the projected IDF curve, across all durations, the rainfall intensity was the strongest in the early 21st century, indicating that at present, we are in the midst of a climate crisis. Thus, it is important to develop and implement effective urban and river flood management measures to mitigate the current effects of climate change. Notably, our study can serve as a reference for future studies on climate change and help policymakers and decision-makers develop relevant policies and mitigation strategies related to the effects of climate change.