Design Rainfall Estimates Research Articles

Uncertainty analysis for design rainfall estimation using peaks-over-threshold model and specially formulated pivotal quantities

Uncertainty analysis for design rainfall estimation using peaks-over-threshold model and specially formulated pivotal quantities

Comparison of Future Design Rainfall with Current Design Rainfall: A Case Study in New South Wales, Australia

Climate change impacts have the potential to alter the design rainfall estimates around the world. Decreasing trends in the summer and winter rainfall in New South Wales (NSW), Australia have already been observed due to climate variability and change. The derivation of design rainfall from historical rainfall, which is required for the design of stormwater management infrastructure, may be ineffective and costly. It is essential to consider climate change impacts in estimating design rainfall for the successful design of stormwater management infrastructure. In this study, the probability of the occurrence of daily extreme rainfall has been assessed under climate change conditions. The assessment was performed using data from 29 meteorological stations in NSW, Australia. For the evaluation of future design rainfall, the probability of the occurrence of extreme rainfall for different recurrence intervals was developed from daily extreme rainfall for the periods of 2020 to 2099 and compared with the current Australian Bureau of Meteorology (BoM) design rainfall estimates. The historical mean extreme rainfall across NSW varied from 37.71 mm to 147.3 mm, indicating the topographic and climatic influences on extreme rainfall. The outcomes of the study suggested that the future design rainfall will be significantly different from the current BoM estimates for most of the studied stations. The comparison of the results showed that future rainfall in NSW will change from −4.7% to +60% for a 100-year recurrence interval. However, for a 2-year recurrence interval, the potential design rainfall change varies from an approximately 8% increase to a 40% decrease. This study revealed that the currently designed stormwater management infrastructure will be idle in the changing climate.

A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

Rainfall frequency analysis using event-maximum rainfalls – An event-based mixture distribution modeling approach

Rainfall frequency analysis, an essential work for water resources management, is often conducted by using the annual maximum rainfall series. For rainfall stations with short record lengths and outliers presence, the use of annual maximum series for rainfall frequency analysis may yield design rainfall estimates of higher uncertainties. Moreover, for regions with cyclostationary climate patterns, the annual maximum rainfalls may be caused by different prevalent storm types, which differ in terms of their occurrence frequency and storm rainfall characteristics. In this study, we propose a novel event-maximum-rainfall-based mixture distribution modeling approach for rainfall frequency analysis. By considering the event-maximum rainfalls of individual storm events, the sample size for parameter estimation increases, and the uncertainty of design rainfall estimates reduces. Mixture distribution modeling enables a thorough investigation of the contributing probabilities of different storm types to the annual maximum rainfall. Through rigorous stochastic simulation, we demonstrated the superiority of the proposed approach over the conventional annual maximum rainfall approach. The proposed approach was applied to four representative rainfall stations in Taiwan, and the results revealed that the proposed approach is more robust than the conventional annual maximum rainfall approach. The results provide insights into the contributions of individual storm types to the annual maximum rainfall.

Derivation of long duration geographically-centred areal reduction factors for catchment design rainfall estimation in South Africa

Study regionSouth Africa. Study focusThe use of Areal Reduction Factors (ARFs) to convert average design point rainfall into areal catchment design rainfall is regarded as fundamental input to event-based deterministic design flood estimation methods. The ARF methods currently used in South Africa are outdated and were neither developed nor verified using local data. This paper presents the development and assessment of a regionalised approach to estimate long duration (≥ 1-day) geographically-centred ARFs using daily rainfall data representative of the 78 homogeneous long duration rainfall clusters associated with the Regional Linear Moment Algorithm and Scale Invariance (RLMA&SI) regionalisation scheme in South Africa. In order to meet the recommended World Meteorological Organisation rainfall station density criteria, the latter clusters were merged into 46 clusters. Subsequently, long duration geographically-centred sample ARFs were estimated using 2 053 circular catchments and 1 779 daily rainfall stations. New hydrological insights for the regionFive ARF regions were deduced from the 46 clusters and a single regional empirical ARF equation, with unique regional calibration coefficients, was derived. The ARF methodology developed and the subsequent findings are not only new to the South African flood hydrology community and practice, but are also probabilistically correct given that the ARF estimates decrease with an increasing catchment area and decrease with a decreasing storm duration and return period, unlike many other methods used internationally.

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

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

Estimation of daily design rainfall in Argentina from satellite-derived data

Se presentan los resultados de la estimación de las precipitaciones diarias de diseño en Argentina para el 97 % del territorio nacional continental. Este análisis se basó en el procesamiento de los datos de precipitación derivados de satélite (DPDS) del producto CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), con una resolución espacial de 5 km y una cobertura temporal de 37 años. La extracción de los mapas de precipitación diaria máxima anual (pdMa) se efectuó por medio de códigos ad hoc desarrollados a través de la plataforma Google Earth Engine. Se compararon las series de pdMa provenientes de CHIRPS con las obtenidas a partir de 64 estaciones pluviométricas provistas por el Servicio Meteorológico Nacional (SMN), determinando las pendientes de la regresión lineal. Estos parámetros de la regresión fueron interpolados al territorio bajo estudio para corregir los mapas de pdMa. Los mapas corregidos se procesaron a través de códigos desarrollados en Python para asociar probabilidades empíricas de no excedencia y ajustar la distribución teórica óptima de probabilidades a cada uno de los 106 778 pixeles considerados. A partir de estos resultados se generaron mapas de precipitaciones diarias de diseño para periodos de retorno comprendidos entre 2 y 100 años. Los resultados se validaron contra 30 estaciones provenientes del Sistema Nacional de Información Hídrica, obteniéndose un acuerdo aceptable. Se considera que los resultados obtenidos serán de utilidad en tareas de diseño hidrológico, en especial en las regiones que carecen de registros pluviométricos adecuados para un análisis estadístico tradicional.

Estimation of Daily Design Rainfall in Argentina from Satellite-Derived Data

Estimation of Daily Design Rainfall in Argentina from Satellite-Derived Data

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.

Estimation of Scottish Pluvial Flooding Expected Annual Damages Using Interpolation Techniques

Flood modelling and mapping, underpinned by hydraulic modelling, are typically used to define flood hazard and allow a quantification of risk and associated Expected Annual Damages (EAD). At a regional or national scale, such modelling is often a lengthy process, which does not allow changes in risk resulting from new science such as revised rainfall frequency estimates or climate projections to be readily quantified by policy makers. A framework of interpolation and extrapolation methods has been developed in the R language via practical application to the city of Perth in central Scotland. These methods allow existing flood mapping, design rainfall estimates and property receptor datasets combined with revised design rainfall estimates to be used to rapidly assess the consequences of change in risk and EAD. The results are evaluated against detailed hydraulic modelling and are shown to provide a good approximation of changes in flood depth and EAD for properties previously modelled as at risk of flooding, particularly residential properties, with lower confidence for non-residential properties. In the Scottish context, the methods are considered to be robust for regional and national scale application and would allow policy makers with a means to rapidly determine the consequence of changes in design rainfall estimates without the immediate requirement to undertake complex hydraulic modelling.

A review of methods to account for impacts of non-stationary climate data on extreme rainfalls for design rainfall estimation in South Africa

Frequency analysis of extreme rainfall and flood events are used to determine design rainfalls and design floods which are needed to design hydraulic structures such as dams, spillways and culverts. Standard methods for frequency analysis of extreme events are based on the assumption of a stationary climate. However, this assumption in rainfall and flood frequency analysis is being challenged with growing evidence of climate change. As a consequence of a changing climate, the frequency and magnitude of extreme rainfall events are reported to have increased in parts of South Africa, and these and other changes in extreme rainfall occurrences are expected to continue into the future. The possible non-stationarity in climate resulting in changes in rainfall may impact on the accuracy of the estimation of extreme rainfall quantities and design rainfall estimations. This may have significant consequences for the design of new hydraulic infrastructure, as well as for the rehabilitation of existing infrastructure. Hence, methods that account for non-stationary data, such as caused by climate change, need to be developed. This may be achieved by using data from downscaled global circulation models in order to identify non-stationary climate variables which affect rainfall, and which can then be incorporated into extreme value analysis of a non-stationary data series.

Credibility of design rainfall estimates for drainage infrastructures: extent of disregard in Nigeria and proposed framework for practice

Credibility of design rainfall estimates for drainage infrastructures: extent of disregard in Nigeria and proposed framework for practice

Analyses of flood peak discharge in Cimadur river basin, Banten Province, Indonesia

Cimadur river basin is one of the most important catchment areas in Lebak District, Banten Province. For the past few years, the catchment has experienced floods during the rainy season. The big issue of flooding has been recorded recently in December 2019 which has caused damage and negative impacts to the local people and surrounding community. This study aims to analyze the possibility of flood peak discharges in the catchment area of the Cimadur river. The flood discharges are calculated for 2, 5, 10, 25, 50, and 100 years return period based on the daily rainfall data from the year 2011 to 2020. The rainfall and land use data are obtained from PT Saeba Consultant. In this study, the hydrological analyses are including 1) analyses of average annual rainfall using the Thiessen method; 2) analyses of rainfall distribution and estimation of design rainfall by considering three methods involving: Log-Normal, Log Pearson Type III, and Gumbel Type 1; and 3) analyses of flood discharges by adopting Nakayasu Synthetic Hydrograph Unit (SHU). The rainfall distribution analyses show that the Log Pearson Type III provided the best fit. Based on the flood peak discharges analyses, the results show that the flood discharges for the 5, 10, 25, and 50 years return period in the Cimadur river basin are 470.71 m3/s, 560.16 m3/s, 698 m3/s, and 820.4 m3/s, respectively.

Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach

Abstract This study aims to explore possible distributional changes in annual daily maximum rainfalls (ADMRs) over South Korea using a Bayesian multiple non-crossing quantile regression model. The distributional changes in the ADMRs are grouped into nine categories, focusing on changes in the location and scale parameters of the probability distribution. We identified seven categories for a distributional change in the selected stations. Most of the stations (28 of 50) are classified as Category III, which is characterized by an upward trend with an increase in variance in the distribution. Moreover, stations with a downward trend with a decrease in the variance pattern (Category VII) are mainly distributed on the southern Korean coast. On the other hand, Category I stations are mostly located in eastern Korea and primarily show a statistically significant upward trend with a decrease in variance. Moreover, this study explored changes in design rainfall estimates for different categories in terms of distributional changes. For Categories I, II, III, and VI, a noticeable increase in design rainfall was observed, while Categories IV, V, and VII showed no evidence of association with risk of increased extreme rainfall.

Exploration of Daily Rainfall Intensity Change in South Korea 1900–2010 Using Bias-Corrected ERA-20C

AbstractRainfall frequency analysis has been routinely adopted for the estimation of design rainfall for a specific return period. Annual maximum rainfall data are generally used for frequency anal...

Quantifying the relative impact of hydrological and hydraulic modelling parameterizations on uncertainty of inundation maps

ABSTRACTFlood risk management strongly relies on inundation models for river basin zoning in flood-prone and risk-free areas. Floodplain zoning is significantly affected by the diverse and concurrent uncertainties that characterize the modelling chain used for producing inundation maps. In order to quantify the relative impact of the uncertainties linked to a lumped hydrological (rainfall–runoff) model and a FLO-2D hydraulic model, a Monte Carlo procedure is proposed in this work. The hydrological uncertainty is associated with the design rainfall estimation method, while the hydraulic model uncertainty is associated with roughness parameterization. This uncertainty analysis is tested on the case study of the Marta coastal catchment in Italy, by comparing the different frequency, extent and depth of inundation simulations associated with varying rainfall forcing and/or hydraulic model roughness realizations. The results suggest a significant predominance of the hydrological uncertainty with respect to the hydraulic one on the overall uncertainty associated with the simulated inundation maps.

Estimation of areai reduction factors using daily rainfall data and a geographically centred approach

This paper presents the estimation of geographically centred and probabilistically correct areal reduction factors (ARFs) from daily rainfall data to explain the unique relationship between average design point rainfall and average areal design rainfall estimates at a catchment level in the C5 secondary drainage region in South Africa as a pilot case study. The methodology adopted is based on a modified version of Bell's geographically centred approach. The sample ARF values estimated varied with catchment area, storm duration and return period, hence confirming the probabilistic nature. The derived algorithms also provided improved probabilistic ARF estimates in comparison to the geographically and storm-centred methods currently used in South Africa. At a national level, it is envisaged that the implementation and expansion of the methodology will ultimately contribute towards improved ARF estimations at a catchment level in South Africa. Consequently, the improved ARF estimations will also result in improved design flood estimations.

Distribution fitting on rainfall data in Jakarta

The estimation of design flood or rainfall for various return periods is not easy due to the difficulty in finding the fitting distribution for the corresponding data. This study focuses on selecting the best distribution in representing rainfall data from Kemayoran rain gauge (Jakarta, Indonesia) for the last 20 years. The objective is to probe the most fitting distribution for Jakarta. This study applies goodness of fit tests to Kemayoran rainfall data. The result is quite unexpected as the best fitting distributions are not commonly used in flood frequency analysis (FFA) subject and most of them are distributions with three parameters. The referred distributions in Indonesia national standard for flood estimation (SNI 2415:2016) produce mixed results. The classic distributions in FFA e.g. Lognormal 3P and Log-Pearson 3 show good compatibility to the corresponding data while the less prevalent distributions such as Lognormal and Normal are not as compatible.

Review: Methods for the estimation of extreme rainfall events

The estimation of design rainfalls is necessary to estimate the exceedance probabilities of extreme floods required to design hydraulic structures and to quantify the risk of failure of these structures. New approaches to estimating extreme rainfall events are being developed internationally. This paper reviews methods for estimating design rainfalls, particularly extreme events, in South Africa and internationally, and highlights the need to update methods used for estimating extreme rainfall events in South Africa as a platform for future research.

MultiRain: a GIS-based tool for multi-model estimation of regional design rainfall for scientists and practitioners

Abstract Extreme rainfall estimation is a long-standing challenge for hydrological hazard assessment and infrastructure design, particularly if considering the need to deal with climate change. Advances in statistical methods and in rainfall data availability allow for frequent updates of regional rainfall frequency analyses. These allow for new estimates that, however, cannot simply replace older ones in the risk management, due to technical, socio-economic and legislative reasons. To preserve compatibility between old and new regional estimates a multi-model approach could be used, where model uncertainties can be combined to help reach a final decision. To make this possible, one has to face the uneasy retrieval of data and results of older analyses and, quite often, non-trivial areal rainfall estimates are needed with all methods. To give an answer to these technical needs, a tool named MultiRain has been developed. The tool computes depth–duration–frequency (DDF) curves, both related to a point and integrated over an area, from multiple regional statistical analyses. The MultiRain procedure is based on Python scripting, geographic information system (GIS) functions and web technologies, and can be performed via web browser or in a desktop GIS environment. A demonstration version has been built using four different regional analyses proposed in a 20-year period for the north-west of Italy.