Rainfall Intensity Duration Research Articles

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

Triggering rainfall intensities for post-wildfire debris flows in the Sonoran Desertscrub plant community

Wildfire makes landscapes more vulnerable to debris flows by reducing soil infiltration capacity and decreasing vegetation cover. The extent to which fire affects debris-flow processes depends on the severity of the fire, the climatology of intense rainfall, the pre-fire plant community, and sediment supply, among other factors. As fire expands into new plant communities and geographic regions, there is a corresponding need to expand efforts to document fire-induced changes and their impacts on debris-flow processes. In recent years, several large wildfires have impacted portions of the Sonoran Desertscrub plant community in Arizona, USA, a plant community where fire has been historically infrequent. Following two of these fires, we monitored debris-flow activity at the watershed scale and quantified wildfire-driven changes in soil hydraulic properties using in-situ measurements with mini disk tension infiltrometers. Results indicate that rainfall intensity-duration thresholds for the initiation of post-fire debris flows in recently burned watersheds within the Sonoran Desertscrub plant community are substantially greater than those in nearby areas dominated by other plant communities, such as chaparral. Results provide insight into the impact of fire on debris-flow processes in a plant community where it is likely to be more impactful in the future and help expand existing post-fire debris flow databases into a plant community where there is a paucity of observations.

Objective definition of discharge thresholds for post-fire debris flows

Runoff-generated debris flows are a common post-fire hazard in the western United States and a growing number of regions around the world. As wildfire continues to emerge across a broader range of geographic regions and plant communities, there is an increasing need for generalizable methods to predict post-fire debris-flow initiation. The prediction of post-fire debris flow during intense rainstorms has traditionally relied upon empirical rainfall thresholds. Rainfall intensity-duration thresholds are often developed based on rainfall data and the hydrologic response to those rainstorms. They are most applicable to the specific regions where data are collected. Here, we present a new predictive approach that utilises processes-based models with fundamental physics and machine learning methods to estimate discharge thresholds for runoff-generated debris-flow initiation in four recently burned areas in the western United States. We assess the performance of the objectively defined discharge threshold-based predictions for post-fire debris-flow initiation from our hybrid framework, which utilises debris-flow timing within rainstorms, physically based numerical simulations of runoff, and the support vector machines method. The proposed thresholds have a good balance between true and false predictions for debris flow and floods. Importantly, our method permits the direct estimation of rainfall intensity-duration thresholds for areas where post-fire debris flow observations are limited.

Deriving rainfall intensity–duration–frequency (IDF) curves and testing the best distribution using EasyFit software 5.5 for Kut city, Iraq

Abstract The intensity of rainfall can be considered as an essential factor in designing and operating hydraulic structures. The intensity–duration–frequency (IDF) curve is used for designing hydraulic projects such as drainage networks, road culverts, bridges, and many other hydraulic structures. In the field of water resources engineering, IDF curve is dependent widely on the plan, designing, and operating the project. Additionally, it can be used for different flood engineering structures. The purpose of this research is to get the frequency of the intensity of rain duration for Al KUTcity, Iraq, and find curves. Three essential techniques of frequency analysis (Gumbel distribution, lognormal, and log Pearson Type III) were depended to formulate this relationship based on data of rainfall intensity during the period between 1992 and 2019. Distribution methods involving lognormal, Gumbel, and log Pearson Type III were applied by Indian Meteorological Department (IMD) for short periods of 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 12, and 24 h with 2, 5, 10, 25, 50, and 100 years return periods. The results showed that rainfall intensity reduced as the duration of the storm increased, and if the return period of the rainfall was large, rainfall of any specific duration showed a higher intensity. Using EasyFit 5.5 software, for all durations, the lognormal probability distribution showed the best fit for the data group and estimated intensities of precipitation for return periods of 2, 5, 10, 25, 50, and 100 years. According to the obtained results, one can notice that the intensity of rainfall increased with the increment in return periods, but decreased with the increment in duration. The resulting IDF models could be used to improve accuracy and results.

Slope Stability Numerical Analysis and Landslide Prevention of Coal Mine Waste Dump under the Impact of Rainfall—A Case Study of Janina Mine, Poland

In Poland, the mining waste from underground coal mines is commonly deposited in surface dump sites, forming slopes or piles of materials dozens of meters high. Because of the loose structure of a mine waste dump slope, landslides may occur after a heavy rainfall. This requires significant labor costs in reforming the mine waste dump sites and disturbs the continuity of the depositing operations. Moreover, if the mine waste dump sites located in the built-up areas, such as in the Janina mine waste dump, landslides apparently can threaten even lives and properties. Therefore, a mine waste dump stability analysis is necessary for ensuring safety. In this paper, slope stability analysis was conducted using numerical modeling under the impact of rainfall for the Janina mine waste dump, located in Libiąż, Poland. The results indicated that slope tends to loose stability in case of high rainfall intensity and short duration. Then, slope reinforcement using soil nailing and steel mesh was proposed to prevent landslide under the impact of high rainfall intensity. Once again, slope stability analysis was carried out with selected reinforcement. Meanwhile, slope monitoring was performed to assess the slope reinforcement implementation at the Janina mine waste dumps against the impact of high rainfall intensity. Based on the modeling and monitoring outcomes, assessments of slope stability and selected landslide prevention measures for the Janina mine waste dump under the impact of rainfall were presented.

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.

Landslide Threat Analysis and Slope Reinforcement Method with Sheet Pile on Singaraja City Boundary Road – Mengwitani Km 37+900 Right

The trigger factor for landslides is due to high rainfall intensity and long duration and the drainage system on the slopes is not good, causing the soil to become saturated. The Singaraja–Mengwitani City road section every rainy season always occurs landslides due to the geometry of the road adjacent to the slope because it is in a hilly area, as well as a drainage system that does not function during the rainy season. Based on the results of the previous soil investigation, the road section is a type of silty clay due to weathering of the ancient Buyan-Beratan volcanic rocks. The research was conducted through a field investigation with 2 drill points in a depth of 30 m, and slope stability analysis with the help of Geoslope software obtained. The value of the existing slope safety factor was 0.939, while at the time of moderate rain for 6 hours it was obtained SF<0.332, this condition indicates that the slope will fail so that it needs to be strengthened. The reinforcement is carried out with steel sheet piles is 4.49 m with a total length of sheet piles used is 6.49 ~ 7 m, based on the results of the analysis after being given reinforcement of steel sheet piles the safety factor value is 1.674, while when it rains it is moderate for 6 hours at 1,210, the slope is declared stable/safe because SF>1.0

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

Floods are one of the most frequent and destructive natural events which lead to lots of human and financial losses with damage to the houses, farms, roads, and other buildings. Intensity–duration–frequency (IDF) curves are the main and practical tools that have been used for flood control studies, including the design of the water structures. In many cases, there is no measuring device at the desired place, or their information is not helpful if there is any available. In this case, it is not possible to extract these curves through conventional methods. Regionalizing the IDF curves is a method that has solved the issues mentioned in the common methods. In this research, the regionalized IDF curves are extracted in Khuzestan province, Iran using 21 rain gauge stations through L-moments and neural gas networks. Clustering is one of the most effective steps and a prerequisite for regional frequency analysis (RFA) that divides the region and existing stations into hydrologically homogenous regions. In this study, clustering is done using two new models named neural gas (NG) and growing neural gas (GNG) network. Comparing the regional IDF curves with at-site curves, it was found that neural gas network models had a more accurate performance and higher efficiency, so they had the lowest estimate error amount among other models. Also, due to the acceptable difference between regional and at-site curves, the efficiency of L-moments in RFA was evaluated as appropriate.

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.

Critical Rainfall Thresholds as a Tool for Urban Flood Identification in Attica Region, Greece

Rainfall intensity–duration thresholds are commonly used to assess flood potential in both urban and rural environments. Derivation of these thresholds is one of the approaches commonly used for the development of flash flood warning systems that are mainly based on rainfall predictions. This research work presents a detailed analysis on these threshold estimations, implemented for the Attica region, Greece, as prior work in parts of the study area is limited and previous estimations regarding rainfall intensity–duration thresholds are based on a short period of available data. The analysis considers a large number of stations and takes into account all flood events occurred during the period between 2005 and 2017 in order to define two maximum intensity limits for various durations that denote three areas; conditions of flood occurrence, mixed conditions, and conditions linked to solely flood occurrence, respectively. Finally, limitations regarding the determination of specific spatiotemporal thresholds as observed through this analysis are also discussed. The application of this methodology as a tool to assess flood occurrence may contribute to minimize possible situations of pre-crisis or immediate crisis by reducing the flood consequences and the resources involved in emergency response to flood events.

Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold

Rainfall-induced landslide hazard warning, which refers to the prediction of the spatial-temporal probability of landslide occurrence in a certain area under the conditions of continuous rainfall processes, can be established based on landslide susceptibility mapping and critical rainfall threshold calculations. However, it is difficult to determine appropriate machine learning models for mapping landslide susceptibility. Additionally, it is significant to consider the influences of early effective rainfall on landslide instability in the critical rainfall threshold methods. Furthermore, the uncertainties of the critical rainfall threshold values generated by different calculation methods have not been well explored. To overcome these three drawbacks, first, frequency ratio analysis-based logistic regression (LR), support vector machine (SVM) and random forest (RF) models are adopted to predict landslide susceptibility for machine learning model comparison. Second, three different types of critical rainfall threshold methods, namely, cumulative effective rainfall-duration (EE-D), effective rainfall intensity-duration (EI-D) and cumulative effective rainfall-effective rainfall intensity (EE-EI) models, are proposed to calculate the temporal probabilities of landslide occurrence under rainfall conditions based on the concept of effective rainfall. The accuracies and uncertainties of these three critical rainfall threshold methods are discussed. Finally, the landslide susceptibility maps and the critical rainfall threshold values are coupled to predict the rainfall-induced landslide hazards. Xunwu County in China is selected as the study area, and several rainfall-induced landslides are used as the test samples of the proposed landslide hazard warning model. The results show that the RF model has remarkably higher susceptibility prediction accuracy than the SVM and LR models, and the prediction performance of the temporal probabilities of landslide occurrence using the EI-D values are higher than those of EE-D and EE-EI values. Furthermore, rainfall-induced landslide hazard warning is effectively implemented based on the coupling of the susceptibility map and EI-D model.

Warning Models for Landslide and Channelized Debris Flow under Climate Change Conditions in Taiwan

Climate change has caused numerous disasters around the world. It has also influenced the climate of Taiwan, with urban areas exhibiting a temperature increase by 1 °C between 1998 and 2020. In this study, climate change and landslides in Taiwan were statistically analyzed. Cumulative annual precipitation in mountain watersheds in central Taiwan exhibit a declining trend and is lower than that in urban areas. The relatively few typhoons reduced the distribution of rainfall in mountain watersheds and fewer landslides. From 2017 to 2020, typhoon-induced rains caused fewer landslides than did other climate events such as the meiyu front, tropical low pressure, and southwesterly flow events. Three rainfall characteristics of landslide initiation were identified: high rainfall intensity over a short duration (<12 h), high-intensity and prolonged rainfall, and high cumulative rainfall over a long duration (>36 h). Combinations of warning models for landslides in cumulative rainfall–duration plots with rainfall intensity classification and mean rainfall intensity–duration plots with cumulative rainfall classification were presented. In recent (2018–2020) years, climate change has resulted in higher temperatures, less rainfall in mountain watersheds, and a lower rainfall threshold at which landslides are initiated by non-typhoon climate events.

Model Selection and Design of Storm Water Drainage Systems at Oleh, Delta State, Nigeria

Rainfall Intensity-Duration-Frequency curves and equations present the exceedance probability of a given rainfall intensity and storm duration expected to occur at a particular location, for the design of storm water drainage systems and hydraulic structures. The aim of this study was to develop IDF curves/equations aim for Oleh, a municipality situated in the low-lying and flood prone region of Nigeria. The study was conducted using annual maxima rainfall series (ARMS) of 28 years obtained from Nigeria Meteorological Agency (NiMet). The AMRS were extracted into 15 Nos. storm durations between 10minutes and 600 minutes. The AMRS of each duration was fitted to Gumbel (EV1), LN2 and Normal (N) distributions using the K-S and A-D goodness-of-fit module of Easyfit software, Version 5.6. The graphical plots, development of IDF models and computations of performance measures of R2 NSE and RSR, were executed in Microsoft Excel, 2010. The results of GOF tests show that for K-S tests, Gumbel (EVI) is best-fit distribution in four durations, LN2 was best –fit in Nine out of fifteen durations, and Normal distribution durations scored 2. Similarly, for A-D GOF test, Gumbel (EVI) scored three (3), LN2 scored eleven (11), while Normal distribution scored one (1). Consequently, LN2 is the best-fit distribution, seconded by Gumbel (EVI). Accordingly, LN2 and Gumbel (EVI) distributions have been adopted in the development of the IDF models for Oleh municipality, while the performances of the IDF models may be presented the inequality; 0.984 ? R2 ? 0.998; 0.990 ? NSE ? 0.998 and 0.045 ? RSR ? 0.096. The graphical plots, IDF curves and models exhibited the peculiar attributes of IDF curves/equations reported in literature. The performance measures show that the equations are robust for practical application in the design of storm water drainage systems and hydraulic structures.

Temporal changes in rainfall intensity–duration thresholds for post-wildfire flash floods in southern California

Abstract. Rainfall intensity–duration (ID) thresholds are commonly used to assess flash flood potential downstream of burned watersheds. High-intensity and/or long-duration rainfall is required to generate flash floods as landscapes recover from fire, but there is little guidance on how thresholds change as a function of time since fire. Here, we force a hydrological model with radar-derived precipitation to estimate ID thresholds for post-fire flash floods in a 41.5 km2 watershed in southern California, USA. Prior work in this study area constrains temporal changes in hydrological model parameters, allowing us to estimate temporal changes in ID thresholds. The results indicate that ID thresholds increase by more than a factor of 2 from post-fire year 1 to post-fire year 5. Thresholds based on averaging rainfall intensity over durations of 15–60 min perform better than those that average rainfall intensity over shorter time intervals. Moreover, thresholds based on the 75th percentile of radar-derived rainfall intensity over the watershed perform better than thresholds based on the 25th or 50th percentile of rainfall intensity. Results demonstrate how hydrological models can be used to estimate changes in ID thresholds following disturbance and provide guidance on the rainfall metrics that are best suited for predicting post-fire flash floods.

Rainfall Triggering of Post-Fire Debris Flows over a 28-Year Period near El Portal, California, USA

ABSTRACT Wildfires frequently affect the steep hillslopes near El Portal, California (United States), a small community established during the California Gold Rush in the mid-1800s. In addition to the historical significance of El Portal, State Route 140 (SR 140) is a major transportation and economic corridor connecting the San Joaquin Valley to Yosemite National Park (YNP). In 2019, an estimated 4.5 million tourists visited and accessed YNP via SR 140. In the years after wildfires, the burned watersheds produced debris flows during intense rainfall, impacting the El Portal community and motorists traveling on SR 140 and local roads. The steepness of the hillslopes and confinement of the valley limit options for mitigating debris-flow risk. As such, emergency managers are left with evacuation orders or temporary road closures as the best options for risk reduction. The effectiveness of these options is highly dependent on establishing an accurate local rainfall intensity-duration threshold that officials can use to guide emergency response actions and timing. We present an overview of the rainfall conditions that initiated 12 post-fire debris-flow events near El Portal from 1991 to 2018 and objectively define rainfall intensity-duration thresholds from triggering rainfall rates. Our results highlight the modest rainfall rates that triggered debris flows in these steep watersheds, while radar data from more recent events (2012–2018) portray the spatial variability of intense rainfall in the area. Additional rainfall monitoring is needed to provide a robust rainfall threshold that will effectively mitigate risk for residents and motorists while minimizing the impact of road closures and evacuations.

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.

Role of Bimodal Water Retention Curve on the Unsaturated Shear Strength

Changes in climatic conditions are expected globally resulting in a higher rainfall intensity and longer duration of rainfall. The increase in the rainwater infiltration into the soil contributes to many geotechnical issues, such as excessive settlement, retaining wall failure and rainfall-induced slope failures. These geotechnical problems could be mitigated by the improvement of the problematic soil with the incorporation of the unsaturated soil mechanic principles. Dual-porosity soils or soils with bimodal water retention curve (WRC) are able to retain more water during prolonged drying and they would be able to drain out water faster during intense rainfall to maintain the slope stability. The objective of this study is to investigate the characteristics of the unsaturated shear strength of soil with bimodal WRC. In addition, the new mathematical equation is proposed to estimate the unsaturated shear strength of soils with a bimodal WRC. The results of the study indicated that the nonlinearity of the unsaturated shear strength is a function of the shape of bimodal WRC limited by the first and second air-entry value (AEV) of dual-porosity soils. The proposed equation agreed well with the experimental data of the unsaturated shear strength for dual-porosity soil.

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.