Annual Maximum Rainfall Research Articles

Estimation of probable maximum precipitation 24-h (PMP 24-h) through statistical methods over Iran

Abstract Estimating the probable maximum precipitation (PMP) is necessary to calculate the probable maximum flood (PMF). It is of high importance in checking the adequacy of dam overflow capacity and other development and water transfer plans of a given area. In this research, using the for an annual 24-hour maximum rainfall data set of 45 synoptic stations throughout the country, the PMP values were calculated through the original Hershfield method and then the Hershfield-Desa method. By comparing the obtained results of 24-hour PMP estimation through the two mentioned methods, it is found that the estimation of PMP values in the original/first Hershfield method is well higher than the expected value (2.54 to 4.03). While in the modified method (Desa method), PMP values are significantly reduced and seem more reasonable (1.02 to 1.3). Meanwhile, the calculated variability and skewness coefficients also indicated more variability of PMP values in the southern stations of the country compared to rainy regions, which makes the estimation of PMP in the southern regions of the country considerably unreliable.

Trend Analysis of Rainfall, Land Cover, and Flow Discharge in the Citarum Hulu–Majalaya Basin

Floods and drought have been essential aspects of the water issue for over the past 50 years. Several studies have found that they are caused by massive land cover and climate changes. Many climate change studies have been conducted to establish their characteristics using historical data. However, land cover change is a human need that inevitably has to be undertaken to improve the standard of living. Changes in land cover affect the response of land to rainfall, which inevitably affect the amount of run-off generated into rivers. Discharge, land cover, and rainfall are variables that are related to each other. By understanding one variable, we can understand the condition of others, which can describe the situation of the catchment area. This study aims to determine the rain, land cover, and discharge trends in the Citarum Hulu - Majalaya Basin. The historical data obtained by the ground station were used to analyze the rainfall and discharge. A hydrology model was used to establish the change in the land cover parameters, specifically a semi-distributed SWAT model. The non-parametric Mann-Kendall test was employed for the trend analysis and was applied to the annual maximum rainfall and discharge data and the curve number (CN) values. The results show the three are positive trends in maximum rainfall and CN value which affect the discharge value.

Trend Analysis of Annual Rainfall in Bastar Plateau and Northern Hill Zones of Chhattisgarh, India

This study was to find out the trend of annual rainfall for 11 districts of Bastar plateau and the Northern hill zone of Chhattisgarh. Long-term rainfall data of (27 years) in respective districts were collected from the Department of Agro-meteorology. IGKV, Raipur (Chhattisgarh). The trend analysis of rainfall was computed with the help of the Mann-Kendall method and linear trend graph method. Results revealed that the annual rainfall of two districts i.e. Sukma and Kondagaon of the Bastar plateau zone recorded a significant increasing trend at a 5 % level of significance whereas two districts i.e. Bastar and Bijapur were showing a significant increasing trend at a 1 % level of significance. Jashpur district of Northern hill zone shows a significantly decreasing trend at a 5 % level of significance whereas rest five districts i.e. Dantewada, Narayanpur, Surguja, Surajpur, and Koria districts reported non-significant. Two districts showed a non-significant increasing trend i.e. Narayanpur and Balrampur districts. The maximum number of districts reported more or less stable rainfall during the years. On an annual basis, the Bijapur district recorded the maximum amount of rainfall i.e. 3144.5 mm in a year while the lowest rainfall was 511.4 mm in the Dantewada district. The maximum average annual rainfall over the study period was 1624.7 mm in the Sukma district and the lowest average annual rainfall was 1029.5 mm (in 17 years data set) in the Balrampur district.

Non-parametric estimator of a multivariate madogram for missing-data and extreme value framework

The modeling of dependence between maxima is an important subject in several applications in risk analysis. To this aim, the extreme value copula function, characterized via the madogram, can be used as a margin-free description of the dependence structure. From a practical point of view, the family of extreme value distributions is very rich and arise naturally as the limiting distribution of properly normalized component-wise maxima. In this paper, we investigate the nonparametric estimation of the madogram where data are completely missing at random. We provide the functional central limit theorem for the considered multivariate madrogram correctly normalized, towards a tight Gaussian process for which the covariance function depends on the probabilities of missing. Explicit formula for the asymptotic variance is also given. Our results are illustrated in a finite sample setting with a simulation study. Our method is also illustrated on a sparse dataset of annual maxima rainfall in Central Eastern Canada.

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.

Hazard Characterization of the Annual Maximum Daily Precipitation in the Southwestern Iberian Peninsula (1851–2021)

High-intensity rainfall can raise fluvial channel levels, increasing the risk of flooding. Maximum precipitation depths are used to estimate return periods and, thus, calculate the risk of this type of event. To improve these estimates in Southwest Europe, we studied the behavior of extreme rainfall using the historical records of San Fernando (Cádiz, southwest Spain), obtaining the maximum daily annual rainfall (period 1851–2021). Local risk levels for intense precipitation were established based on the mean values and standard deviation of daily precipitation. In this series, 38% of the years had some type of risk (>53.7 mm), of which 13% of these years had high risk (>73.2 mm) or disaster risk (>92.7 mm). In these risk thresholds, the maximum daily precipitation is mostly concentrated in the autumn months. The SQRT-ETMax model used fits well with the instrumental historical records for return periods of up to 25 years, although it may present appreciable deviations for longer return periods. Using a 170-year secular series, a more precise understanding of extreme periods and precipitation variability was obtained.

Application of Large-scale Climate Simulation Data to Evaluate the Scale of Extreme Rainfall Events: A Case of the 2018 Hiroshima Extreme-scale Rainfall Event

This study aims to apply large-scale climate ensemble simulation data to evaluate the magnitude of extreme rainfall events. It was conducted on the basis of extreme rainfall that occurred in the Hiroshima region in 2018. This event recorded an extreme rainfall magnitude corresponding to a return period of 1,000 years at a 24-h rainfall duration; it is difficult to evaluate this magnitude with the concept of probability rainfall based on frequency analysis due to the short observation period. To overcome this limitation, the data for policy decision making for future (d4PDF) climate change database based on a large-scale climate ensemble simulation was used. These data provided 3,000 annual maximum daily rainfall values, which were used to empirically estimate the probability rainfall with a return period of 10-1,000 years based on a non-parametric approach without statistical methods. The estimated probability rainfall of the d4PDF was compared with the estimated probability obtained from the observed rainfall and frequency analysis. The difference between the two probability rainfall values was 3.53% for the return period of 50 years. However, as the return period increases, the error increases to more than 10%. This indicates that the estimation of the probability rainfall with a long-term return period using the observed data of a relatively short period may present uncertainties. Regarding the probability rainfall using d4PDF under present climate conditions, the Hiroshima event represented a return period of nearly 300 years. Meanwhile, for the probability rainfall using the d4PDF under future climate conditions, the Hiroshima event had a return period of nearly 100 years. Consequently, the magnitude of the probability rainfall increased in future climate conditions, and the probability of the occurrence of extreme rainfall corresponding to the Hiroshima event increased from 0.33% to 1%. Therefore, d4PDF can be used to quantitatively evaluate the magnitude of extreme rainfall events under present and future climate conditions.

National‐scale predictions of plant assemblages via community distribution models: Leveraging published data to guide future surveys

Abstract Species distribution models (SDMs) have been widely used to create maps of expected species incidence, often using citizen science (CS) occurrence data as inputs. Environmental policy is informed by knowledge of community distributions, but there have been fewer attempts to utilise the potential of community distribution models (CDMs) to predict these. Many countries have vegetation community classification systems which include phytosociological information on individual species. Within Great Britain, the National Vegetation Classification (NVC) is the primary standard for vegetation communities, and while maps have been produced at regional scales, cost‐effective techniques are required for national scales. Published NVC occurrence records of 22 upland NVC communities in England and Wales were used as observed occurrences (presence‐only data). Predictors for the CDMs were enhanced vegetation index (EVI), elevation, slope, aspect, temperature and rainfall. Five modelling methods were investigated: generalised linear models (GLMs), support vector machines (SVMs), random forests (RFs), maximum entropy (MaxEnt) and maximum likelihood (MaxLike). Model quality was assessed via bootstrapping via area under the curve (AUC), true skill statistic (TSS) and Kappa index. There were only small differences in the accuracy of the models (median TSS model accuracy 0.742; range 0.280 to 0.873) with RF models the best overall CDM method. Across all NVC communities, summer and winter maximum temperatures and annual rainfall were the most important predictor variables. NVCs with spatially disjunct distributions in both lowlands and uplands, or that responded to localised management or environmental conditions, were poorly predicted. Synthesis and applications. Vegetation communities can be reliably predicted at large spatial scales using CDMs from extant datasets. Management practitioners can use community‐level predictions to design targeted field surveys for individual species typically associated with specific communities. Most existing CS survey schemes focus on species rather than communities. Hence future development of new CS schemes similar to the National Plant Monitoring Scheme (NPMS), that aligns with the NVC, will enable CS data to generate up‐to‐date maps of both communities and species.

Regional Ombrian Curves: Design Rainfall Estimation for a Spatially Diverse Rainfall Regime

Ombrian curves, i.e., curves linking rainfall intensity to return period and time scale, are well-established engineering tools crucial to the design against stormwaters and floods. Though the at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modeling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modeling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure, except for a spatially varying scale parameter which is itself modeled by a spatial smoothing model for the 24 h average annual rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13,700 km2 water district of Greece characterized by varying topography and hydrometeorological properties.

Runoff Reduction Effects at Installation of LID Facilities under Different Climate Change Scenarios

In order to prepare for floods and droughts that occur as a result of climate change, various studies in water-related fields are being carried out in various countries around the world. Among them, special attention is being paid to the low-impact development (LID) technique. This study measured the annual maximum daily rainfall data from 2019 to 2100 by climate change scenario, which is the annual maximum daily rainfall series observed for rainfall stations, and tested the hydrological data using statistical analysis. After determining whether the data could be analyzed, the probability distribution was selected, and the parameters of the selected probability distribution were calculated using the L-moment method for each rainfall station. The probabilities of rainfall data were derived using GEV distribution, and the United States Environmental Protection Agency Storm Water Management Model (SWMM), a runoff simulation program, was used to compare and analyze the runoff reduction rate before and after the installation of a permeable pavement as an LID facility. The results of the analysis showed that representative concentration pathway (RCP) 4.5 and RCP 8.5 had the effect of reducing the runoff for more than 100 years at a 30% reduction rate compared with before installation.

Flood Hazard Mapping and Flood Preparedness Literacy of the Elderly Population Residing in Bangkok, Thailand

This research aimed at assessing flood hazard areas and flood literacy of the elderly population in Bangkok, Thailand and analyzing their flood preparedness through SWOT analysis. Expert interviews and a community survey were conducted. Using the analytic hierarchy process (AHP) and GIS technique, the results indicated that land-use, drainage density, and annual maximum rainfall were the most heavily weighted factors in flood hazard mapping in Bangkok. About half (50.32%) of Bangkok’s total area was defined as high flood hazard area. A total of 736 questionnaires were distributed in flood-prone areas and in the areas with the highest percentage of elderly population. The results of both SWOT and survey analysis found that many senior citizens have low digital and media literacy and limited experience in using information technology for flood preparedness. Lack of integration of disaster risk reduction and aging population policy, ineffective warning system, and lack of access to disaster preparedness training were the key barriers in reducing vulnerability to flood hazard. The survey revealed that the majority of elderly respondents (75%) have neither used online applications for their flood hazard management both before and during flood disaster nor shared/communicated information via online platforms. Some respondents (13%) used Facebook and Line applications to obtain information before a flood event. Very few of the elderly respondents (<2%) accessed the national/provincial web-based platform to find out flood-related information. Almost all respondents, especially who are living in high-risk flood zones, had never participated in the community training of flood preparedness and management. Therefore, effective strategies in enhancing social engagement of the elderly and their literacy skills in flood risk preparedness and management are urgently needed.

Regional Assessment of Sub-Hourly Annual Rainfall Maxima

The assessment of rainfall extremes at sub-hourly scales is generally hindered by a lack of rainfall data at small timescale resolutions. This study proposes a methodology for assessing mean annual maximum rainfall at the sub-hourly scale by blending historical time series of annual maxima recorded by mechanical stations (operating at hourly scales) up to the end of the past century with newer time series of annual maxima at higher time resolutions recorded by automatic stations installed over the past twenty years. A linear correlation was found at the regional scale between the shape parameter controlling the dependency of rainfall maxima with a duration longer than one hour and the shape parameter of the dependency of rainfall maxima with the durations shorter than one hour. Thanks to this correlation, data recorded at the mechanical stations could be exploited to assess sub-hourly mean annual maxima. The proposed hybrid procedure was verified and was found to provide estimates with an accuracy close to those obtained with the high-resolution data, i.e., our best estimates. Moreover, the proposed procedure outperforms what could be achieved by spatially interpolating the best estimates at those locations where only hourly data are available.

Influence of changes in extreme daily rainfall distribution on the stability of residual soil slopes

ABSTRACT Many landslides triggered by intense rainfall have occurred in mountainous areas in Thailand, causing major economic losses and infrastructure damage. Extreme daily rainfall is a significant trigger for hillslope instability. Increases in extreme daily rainfall intensity due to climate change may be one of the key factors responsible for the increased landslides. Thus, in this context, changes in the intensity of extreme daily rainfall in Chiang Mai Province in North Thailand and their effects on hillslope stability are analyzed. Extreme rainfall is modeled using a generalized extreme value distribution and estimated for various return periods. A numerical analysis of seepage and an infinite slope stability model are combined to understand the hillslope response under extreme rainfall conditions. The analysis period is divided into two periods of 34 years: 1952 to 1985 and 1986 to 2019. According to the analysis results, the distribution of extreme daily rainfall changes in terms of location. The average annual daily maximum rainfall increased by approximately 11.13%. The maximum decrease in the safety factor is approximately 4.5%; therefore, these changes in extreme daily rainfall should be considered in future landslide prevention policies.

Process Controls on Flood Seasonality in Brazil

AbstractA coincidence in the timing of floods and their drivers can be used as a proxy for the causality of flood generation. Here, we investigate the relationship between the seasonality of floods, maximum annual rainfall, and maximum annual soil moisture data of 886 basins in Brazil for 1980–2015 to shed light on process controls of flood generation. Floods tend to occur at the same time of year as soil moisture peaks and lag behind rainfall peaks by 3 weeks. In Amazonia, central and northern Brazil, flood timing is more correlated with the timing of soil moisture peaks than with that of rainfall peaks, which is interpreted as resulting from high subsurface water storage capacities. In southern and southeastern Brazil, on the other hand, flood timing is highly correlated with both soil moisture and rainfall because of low subsurface water storage capacities. These findings can support flood forecasting and climate impact studies.

Spatio‐Temporal Changes in Extreme Rainfall Events Over Different Indian River Basins

AbstractDuring recent decades, India experienced more frequent and severe floods due to increasing extreme rainfall events over different Indian River Basins (IRBs). The present study uses Generalized Extreme Value distribution, Expert Team on Climate Change and Detection Indices, and Standardized Precipitation Index to examine the trend in extreme rainfall events over the IRBs using long‐term observed high‐resolution gridded rainfall data (1901‐2019) obtained from India Meteorological Department. The analysis depicts a marked shifting trend in extreme rainfall events from northeastern Indian river basins toward the western Indian river basins during the recent decades of 1981‐2019. The spatial variations in the annual maximum rainfall for the 10‐, 30‐, and 100‐year return levels show statistically significant increasing trends over the IRBs. The observed decadal changes of rainfall during wet and dry conditions showed the shifting and increasing (15%–58.74%) pattern in extreme rainfall events during the last decades of the 20th and current twenty first century over the west‐flowing river basins. This research highlights the significant increasing trend in extreme rainfall events, which may pose a grave risk to agriculture, human life, and infrastructure, predominantly on the vulnerable sections of the society.

STUDY ON ESTIMATING INITIAL ABSTRACTION (Ia) AND PONDING TIME (tp) BASED ON RAINFALL INTENSITY-INFILTRATION RATE

Initial abstraction (Ia) is one of the key parameters used in the well-known SCS-CN rainfall-runoff relationship method. The Ia parameter, which is recognized to have a relationship with runoff time concentration (tc) is frequently associated with ponding time (tp). The present study described a new approach to estimate Ia and tp through overlaying between rainfall intensity (i) and infiltration rate (f) curves. The infiltration rate measurement was established at thirteen (13) measurement points using double-ring infiltrometer while the hourly duration (t) and annual maximum daily rainfall data for 2009 – 2019 were used to determine rainfall hyetograph (i). The observed initial abstraction (Iaobs) and ponding time (tpobs) were obtained from the measured infiltration data at each point of infiltration measurement while the empirical initial abstraction (Iaemp) and ponding time (tpemp) were estimated by the method of overlaying between rainfall hyetograph and infiltration rate curves. The study found that the variabilities of Ia and tp were highly induced by the varying of infiltration rate (f) and rainfall intensity (i). The reliability analysis demonstrated that there is an intense association between Iaobs and Iaemp, as well as tpobs and tpemp which was indicated by the high value of the determination coefficient (R2). The results confirmed that the values of Iaemp and tpemp derived by overlaying curve of rainfall hyetograph and infiltration rate show a good agreement with Ia and tp from the observation (Iaobs, tpobs). Thus, it is reliable for estimating Ia and tp in the research location.

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.

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.

Impacts of Rainfall Data Aggregation Time on Pluvial Flood Hazard in Urban Watersheds

Pluvial floods occur when heavy rainstorms cause the surcharge of the sewer network drainage, representing one of the most impacting natural hazard in urban watersheds. Pluvial flood hazard is usually assessed considering the effect of annual maxima rainfall of short duration, comparable with the typical concentration times of small urban watersheds. However, short duration annual maxima can be affected by an error of underestimation due to the time resolution as well as the aggregation time of the rainfall time series. This study aims at determining the impact of rainfall data aggregation on pluvial flood hazard assessment. Tuscany region (Central Italy) is selected as a case study to perform the assessment of the annual maxima rainfall underestimation error, since the entire region has the same temporal aggregation of rainfall data. Pluvial flood hazard is then evaluated for an urban watershed in the city of Florence (Tuscany) comparing the results obtained using observed (uncorrected) and corrected annual maxima rainfall as meteorological forcing. The results show how the design of rainfall events with a duration of 30 min or shorter is significantly affected by the temporal aggregation, highlighting the importance of correcting annual maxima rainfall for a proper pluvial flood hazard evaluation.

Revisiting Flood Hazard Assessment Practices under a Hybrid Stochastic Simulation Framework

We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modeling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms on antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modeling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically derived quantiles for given return periods. Lastly, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.