Optimal design of rain gauge network in the Middle Yarra River catchment,Australia

Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well‐designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging‐based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.     

Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and L-moments approaches

Rainfall extremes often result in the occurrence of flood events with associated loss of life and infrastructure in Malawi. However, an understanding of the frequency of occurrence of such extreme events either for design or disaster planning purposes is often limited by data availability at the desired temporal and spatial scales. Regionalisation, which involves “trading time for space” by pooling together observations for stations with similar behavior, is an alternative approach for more accurate determination of extreme events even at ungauged areas or sites with short records. In this study, regional frequency analysis of rainfall extremes in Southern Malawi, large parts of which are flood prone, was undertaken. Observed 1-, 3-, 5- and 7-day annual maximum rainfall series for the period 1978–2007 at 23 selected rainfall stations in Southern Malawi were analysed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. L-moments were applied to derive regional index rainfall quantiles. The procedure also validated the three rainfall regions identified through homogeneity and heterogeneity tests based on Monte Carlo simulations with regional average L-moment ratios fitted to the Kappa distribution. Based on assessments of the accuracy of the derived index rainfall quantiles, it was concluded that the performance of this regional approach was satisfactory when validated for sites not included in the sample data. The study provides an estimate of the regional characteristics of rainfall extremes that can be useful in among others flood mitigation and engineering design.     

Development of regionalized joint probability approach to flood estimation: a case study for Eastern New South Wales,Australia

Estimation of design flood in ungauged catchments is a common problem in hydrology. Methods commonly adopted for this task are limited to peak flow estimation, e.g. index flood, rational and regression‐based methods. To estimate complete design hydrograph, rainfall–runoff modelling is preferred. The currently recommended method in Australia known as Design Event Approach (DEA) has some serious limitations since it ignores the probabilistic nature of principal model inputs (such as temporal patterns (TP) and initial loss) except for design rainfall depth. A more holistic approach such as Joint Probability Approach (JPA)/Monte Carlo Simulation Technique (MCST) can overcome some of the limitations associated with the DEA. Although JPA/MCST has been investigated by many researchers, it has been proved to be difficult to apply since its routine application needs readily available regional design data such as stochastic rainfall duration, TP and losses, which are largely unavailable for Australian states. This paper presents regionalization of the model inputs/parameters to the JPA/MCST for eastern New South Wales (NSW) in Australia. This uses data from 86 pluviograph stations and six catchments from NSW to regionalize the input distributions for application with the JPA/MCST. The independent testing to three test catchments shows that the regionalized JPA/MCST generally outperforms the at‐site DEA. The developed regionalized JPA/MCST can be applied at any arbitrary location in eastern NSW. The method and design data developed here although primarily applicable to eastern NSW can be adapted to other Australian states and countries. Copyright © 2013 John Wiley & Sons, Ltd.     

Stochastic multi-site generation of daily rainfall occurrence in south Florida

This paper presents a stochastic model to generate daily rainfall occurrences at multiple gauging stations in south Florida. The model developed in this study is a space–time model that takes into account the spatial as well as temporal dependences of daily rainfall occurrence based on a chain-dependent process. In the model, a Markovian method was used to represent the temporal dependence of daily rainfall occurrence and a direct acyclic graph (DAG) method was introduced to encode the spatial dependence of daily rainfall occurrences among gauging stations. The DAG method provides an optimal sequence of generation by maximizing the spatial dependence index of daily rainfall occurrences over the region. The proposed space–time model shows more promising performance in generating rainfall occurrences in time and space than the conventional Markov type model. The space–time model well represents the temporal as well as the spatial dependence of daily rainfall occurrences, which can reduce the complexity in the generation of daily rainfall amounts.     

Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario,Canada

Abstract

Southern Ontario, Canada, has been impacted in recent years by many heavy rainfall and flooding events that have exceeded existing historical estimates of infrastructure design rainfall intensity–duration–frequency (IDF) values. These recent events and the limited number of short-duration recording raingauges have prompted the need to research the climatology of heavy rainfall events within the study area, review the existing design IDF methodologies, and evaluate alternative approaches to traditional point-based heavy rainfall IDF curves, such as regional IDF design values. The use of additional data and the regional frequency analysis methodology were explored for the study area, with the objective of validating identified clusters or homogeneous regions of extreme rainfall amounts through Ward's method. As the results illustrate, nine homogeneous regions were identified in Southern Ontario using the annual maximum series (AMS) for daily and 24-h rainfall data from climate and rate-of-rainfall or tipping bucket raingauge (TBRG) stations, respectively. In most cases, the generalized extreme value and logistic distributions were identified as the statistical distributions that provide the best fit for the 24-h and sub-daily rainfall data in the study area. A connection was observed between extreme rainfall variability, temporal scale of heavy rainfall events and location of each homogeneous region. Moreover, the analysis indicated that scaling factors cannot be used reliably to estimate sub-daily and sub-hourly values from 24- and 1-h data in Southern Ontario.

Citation Paixao, E., Auld, H., Mirza, M.M.Q., Klaassen, J. & Shephard, M.W. (2011) Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario, Canada. Hydrol. Sci. J. 56(7), 1067–1089.     

The 20-second regional magnitude <Emphasis Type="Italic">M</Emphasis><Subscript><Emphasis Type="Italic">S</Emphasis></Subscript>(20R) for the Russian Far East

A new modified magnitude scale M _S (20R) is elaborated. It permits us to extend the teleseismic magnitude scale M _S (20) to the regional epicenter distances. The data set used in this study contains digital records at 12 seismic stations of 392 earthquakes that occured in the northwest Pacific Ocean in the period of 1993–2008. The new scale is based on amplitudes of surface waves of a narrow range of the periods (16–25 s) close to the period of 20 s, for distances of 80–3000 km. The digital Butterworth filter is used for processing. On the basis of the found regional features concerning distance dependence for seismic wave attenuation, all the stations of the region have been subdivided into two groups, namely, “continental” and “island-arc.” For each group of stations, its own calibration function is proposed. Individual station corrections are used to compensate for the local features.     

Constructing rainfall depth-frequency curves considering a linear trend in rainfall observations

Comprehensive flood prevention plans are established in large basins to cope with recent abnormal floods in South Korea. In order to make economically effective plans, appropriate design rainfalls are critically determined from the rainfall depth-frequency curves which take the occurrence of abnormal floods into consideration. Conventional approaches to construct the rainfall depth-frequency curves are based on the stationarity assumption. However, this assumption has a critical weak aspect in that it cannot reflect non-stationarities in rainfall observations. As an alternative, this study suggests the non-stationary Gumbel model (NSGM) which incorporates a linear trend of rainfall observations into rainfall frequency analysis to construct the rainfall depth-frequency curves. A comparison of various schemes employed in the model found that the proposed NSGM permits the estimation of the distribution parameters even when shifted in the future by using linear relationships between rainfall statistics and distribution parameters, and produces more acceptable estimates of design rainfalls in the future than the conventional model. The NSGM was applied at several stations in South Korea and then expected the design rainfalls to increase by up to 15–30% in 2050.     

Derivation and assessment of a bivariate IDF relationship using paired rainfall intensity–duration data

A procedure is presented for developing a rainfall intensity–duration–frequency (IDF) relationship that is consistent with bivariate normal distribution modeling. The Box–Cox transformation was used to derive the relation and two methods of determining the parameters of this transformation were evaluated. To assess the uncertainty of the parameters, a confidence interval was constructed and verified with the non-parametric bootstrap method. Additionally, the effect of sample size on the bivariate normality assumption was examined. Case studies, based on data from significant gauge stations in Korea, were performed. The result shows that the use of the bivariate normal model as an IDF relationship is particularly recommended when the available data size is small.     

Using a rainfall stochastic generator to detect trends in extreme rainfall

An original approach is proposed to estimate the impacts of climate change on extreme events using an hourly rainfall stochastic generator. The considered generator relies on three parameters. These parameters are estimated by average, not by extreme, values of daily climatic characteristics. Since climate changes should result in parameters instability in time, the paper focuses on testing the presence of linear trends in the generator parameters. Maximum likelihood tests are used under a Poisson–Pareto-Peak-Over-Threshold model. A general regionalization procedure is also proposed which offers the possibility to work on both local and regional scales. From the daily information of 139 rain gauge stations between 1960 and 2003, changes in heavy precipitations in France and their impacts on quantile predictions are investigated. It appears that significant changes occur mainly between December and May for the rainfall occurrence which increased during the four last decades, except in the Mediterranean area. Using the trend estimates, one can deduced that these changes, up to now, do not affect quantile estimations.     

Annual runoff assessment in arid and semiarid Mediterranean watersheds under the Budyko's framework

The solution of many practical water problems is strictly connected to the availability of reliable and widespread information about runoff. The estimation of mean annual runoff and its interannual variability for any basin over a wide region, even if ungauged, would be fundamental for both water resources assessment and planning and for water quality analysis. Starting from these premises, the main aim of this work is to show a new approach, based on the Budyko's framework, for mapping the mean annual surface runoff and deriving the probability distribution of the annual runoff in arid and semiarid watersheds. As a case study, the entire island of Sicily, Italy, is here proposed. First, time series data of annual rainfall, runoff, and reconstructed series of potential evapotranspiration have been combined within the Budyko's curve framework to obtain regional rules for rainfall partitioning between evapotranspiration and runoff. Then this knowledge has been used to infer long‐term annual runoff at the point scale by means of interpolated rainfall and potential evapotranspiration. The long‐term annual runoff raster layer has been obtained at each pixel of the drainage network, averaging the upstream runoff using advanced spatial analysis techniques within a GIS environment. Furthermore, 2 alternative methods are here proposed to derive the distribution of annual runoff, under the assumption of negligible interannual variations of basin water storage. The first method uses Monte Carlo simulations, combining rainfall and potential evapotranspiration randomly extracted from independent distributions. The second method is based on a simplification of the Budyko's curve and analytically provides the annual runoff distribution as the derived distribution of annual rainfall and potential evapotranspiration. Results are very encouraging: long‐term annual runoff and its distribution have been derived and compared with historical records at several gauged stations, obtaining satisfactory matching.     

Estimation of rainfall intensity–duration–frequency curves at ungauged locations using quantile regression methods

Intensity–duration–frequency (IDF) curves of extreme rainfall are used extensively in infrastructure design and water resources management. In this study, a novel regional framework based on quantile regression (QR) is used to estimate rainfall IDF curves at ungauged locations. Unlike standard regional approaches, such as index-storm and at-site ordinary least-squares regression, which are dependent on parametric distributional assumptions, the non-parametric QR approach directly estimates rainfall quantiles as a function of physiographic characteristics. Linear and nonlinear methods are evaluated for both the regional delineation and IDF curve estimation steps. Specifically, delineation by canonical correlation analysis (CCA) and nonlinear CCA (NLCCA) is combined, in turn, with linear QR and nonlinear QR estimation in a regional modelling framework. An exhaustive comparative study is conducted between standard regional methods and the proposed QR framework at sites across Canada. Overall, the fully nonlinear QR framework, which uses NLCCA for delineation and nonlinear QR for estimation of IDF curves at ungauged sites, leads to the best results.     

P wave amplitude measurements at the Kaapvaal network: do they help in resolving deep earth structure?

Measurements of the amplitudes of seismic body waves at teleseismic distances have rarely resolved significant features mainly because of the large scatter of the data. However, amplitudes are easy to measure and may provide additional constraints on structure to supplement times and waveforms. A new approach to analysing body wave amplitudes at a regional network of similar instruments seeks to minimize scatter by first deriving amplitude station corrections analogous to station corrections for times. After correction for station effects, amplitudes from several events can be combined to give regional amplitude–distance curves without using information on event magnitudes. However, the earthquakes providing the observations must lie in a restricted range of azimuths from the stations of the network and provide considerable overlap in the range of distances between adjacent events, with no gaps in distance coverage. The advantages of the method are explored using P wave amplitudes from two sets of earthquakes in the Indonesian and South American regions recorded by the Kaapvaal network deployed across southern Africa. In the first example, high amplitudes near 88° distance suggest the presence of a small discontinuity at the top of D″ that causes constructive interference between the closely separated arrivals of a small triplication in the travel times. The second example, supplemented by calculations using synthetic data, shows how long-wavelength regional variations in amplitudes can be resolved to assist the interpretation of times and waveforms. However, the limited range of distances in the observations and lateral heterogeneities at any depths can result in bias or tilt of the amplitude–distance relationships. Constraining the depths of the structure causing the long-wavelength variations is a subject for future research.     

Rainfall trend and its implications for water resource management within the Yarra River catchment,Australia

Rainfall is the key climatic variable that governs the regional hydrologic cycle and availability of water resources. Recent studies have analysed the changes in rainfall patterns at global as well as regional scales in Australia. Recent studies have also suggested that any analysis of hydroclimatic variables should be performed at the local scale rather than at a large or global scale because the trends and their effects may be different from one location to the other. Because no studies were found specific to the Yarra River catchment, which is an important catchment in Victoria, Australia, this study performs a spatiotemporal trend analysis on long‐term rainfall records at 15 measuring stations within the catchment. The Mann–Kendall test was used to detect trends, and Sen's slope estimator was used to calculate the slopes in both monthly and annual rainfall. Moreover, a cumulative summation technique was used to identify the trend beginning year, and prewhitening criteria were tested to check for autocorrelation in the data. The results showed that the monthly rainfall has generally decreasing trends except in January and June. Significant decreasing rainfall trends were observed in May (among the autumn months of March, April and May) at most stations and also in some other months at several stations. A decreasing trend was also observed in the annual rainfall at all stations. This study indicates that there has been a consistent reduction in rainfall over the catchment, both spatially and temporally over the past 50 years, which will have important implications for the future management of water resources. Copyright © 2012 John Wiley & Sons, Ltd.     

Validation and use of rainfall radar data to simulate water flows in the Rio Escondido basin

This paper presents a combined validation method of radar-sensed rainfall, using rain gauge data and hydrologic closure, with an application to the Rio Escondido basin (North-East of Mexico). The space–time scaling behavior of rainfall between rain gauge and radar scales is compared with the intrinsic variability of rainfall, for a statistical validation of space–time variability. For hydrological validation purposes, the CEQUEAU model is used to perform rainfall-runoff routing. It provides a basin-wide water balance, to be compared with the measured water flow at the Villa de Fuentes hydrometric station, for mean-value gauging closure. A good qualitative agreement in terms of hydrograph shape and timing is obtained between the simulated and the observed water flows, and a multiplicative correction factor of an initially proposed Z–R relationship is adopted for the watershed under study, which agrees approximately with other authors’ findings about that relationship. The results are considered particularly useful as a validation-and-correction methodology of radar rainfall estimates for areas sparsely covered by rain gauges.     

Rainfall frequency analysis using a mixed GEV distribution: a case study for annual maximum rainfalls in South Korea

Extreme rainfalls in South Korea result mainly from convective storms and typhoon storms during the summer. A proper way for dealing with the extreme rainfalls in hydrologic design is to consider the statistical characteristics of the annual maximum rainfall from two different storms when determining design rainfalls. Therefore, this study introduced a mixed generalized extreme value (GEV) distribution to estimate the rainfall quantile for 57 gauge stations across South Korea and compared the rainfall quantiles with those from conventional rainfall frequency analysis using a single GEV distribution. Overall, these results show that the mixed GEV distribution allows probability behavior to be taken into account during rainfall frequency analysis through the process of parameter estimation. The resulting rainfall quantile estimates were found to be significantly smaller than those determined using a single GEV distribution. The difference of rainfall quantiles was found to be closely correlated with the occurrence probability of typhoon and the distribution parameters.     

Entropy,consistency in rainfall distribution and potential water resource availability in Australia

Understanding the variability in monthly rainfall amounts is important for the management of water resources. We use entropy, a measure of variability, to quantify the rainfall variability in Australia. We define the entropy of stable rainfall (ESR) to measure the long‐term average rainfall variability across the months of the year. The stations in northern Australia observe substantially more variability in rainfall distributions and stations in southern Australia observe less variability in rainfall distribution across the months of the year. We also define the consistency index (CI) to compare the distribution of the monthly rainfall for a given year with the long‐term average monthly rainfall distribution. Higher value of the CI indicates the rainfall in the year is consistent with the overall long‐term average rainfall distribution. Areas close to the coastline in northern, southern and eastern Australia observe more consistent rainfall distribution in individual years with the long‐term average rainfall distribution. For the studied stations, we categorize the years into different potential water resource availability on the basis of annual rainfall amount and CI. For almost all Australian rainfall stations, El Niño years have a greater risk of having below median and relatively inconsistent rainfall distribution than La Niña years. The results may be helpful for developing area‐specific water usage strategies. Copyright © 2011 John Wiley & Sons, Ltd.     

The implications of regional variations in rainfall for reconstructing rainfall patterns using tree rings

In Australia, multidecadal periods of floods and droughts have major economic consequences. Due to the short duration of Australian instrumental precipitation records, it is difficult to determine the patterns of these multidecadal periods. Proxy records can be used to create long‐term rainfall reconstructions for regions that are lacking instrumental data. However, the spatial extent over which single‐site proxy records can be applied is poorly understood. Southeast Queensland (SEQ) is an area where tree rings can be used to reconstruct long‐term rainfall patterns, but their regional representation is unknown. In this study, the spatial variability in rainfall across SEQ is investigated from 1908 to 2007 using 140 instrumental rainfall stations. Pearson correlation analysis between stations is used to create groups at the r = 0.80, 0.85, and 0.90 correlation levels, and then annual deviations from the mean are determined. These patterns indicate that rainfall is not uniform across SEQ but can be broken into 2 main spatially consistent groups. Each of these groups is broken down into several subgroups with higher correlation levels. Long‐term streamflow records are found to be correlated to rainfall patterns local to the streamflow stations, indicating that analysis of extreme events should consider spatial precipitation variability. Finally, the only currently available proxy rainfall reconstruction for the region, a 140‐year Toona ciliata tree ring width record from Lamington National Park, is compared to rainfall groups at different correlation levels across all of SEQ. The correlation between the reconstruction and the rainfall station groupings is best for the groups within which the tree‐ring record is spatially located, and this correlation improves as rainfall group correlation increases. Correlation is nearly nonexistent for groupings located at a distance from the tree‐ring site. These results demonstrate the importance of assessing the spatial variability of precipitation so that the spatial applicability of proxy records can be assessed.     

Bayesian comparison of different rainfall depth–duration–frequency relationships

Depth–duration–frequency curves estimate the rainfall intensity patterns for various return periods and rainfall durations. An empirical model based on the generalized extreme value distribution is presented for hourly maximum rainfall, and improved by the inclusion of daily maximum rainfall, through the extremal indexes of 24 hourly and daily rainfall data. The model is then divided into two sub-models for the short and long rainfall durations. Three likelihood formulations are proposed to model and compare independence or dependence hypotheses between the different durations. Dependence is modelled using the bivariate extreme logistic distribution. The results are calculated in a Bayesian framework with a Markov Chain Monte Carlo algorithm. The application to a data series from Marseille shows an improvement of the hourly estimations thanks to the combination between hourly and daily data in the model. Moreover, results are significantly different with or without dependence hypotheses: the dependence between 24 and 72 h durations is significant, and the quantile estimates are more severe in the dependence case.     

Development of isopluvial map using L-moment approach for Tehri-Garhwal Himalaya

Estimation of design rainfall intensity is crucial for design and planning of water resources engineering projects. The intent of the present study is to develop regional IDF curves for Tehri-Garhwal Himalayan region in India, wherein numbers of hydropower projects are in planning and execution stage. Self Recording Rain Gauge (SRRG) stations are generally not so frequent in the project locations. Under this situation, the engineers are forced to use regional intensity duration frequency (IDF) curves. Under this study, four stations viz. Tehri M.T.Lab, Mukhim, Pilkhi and Dhuttu were available with SRRG data. These data are used to develop the regional IDF curve for entire Tehri-Garwal region. After selection of the most intensive storms, return periods has been determined using regionalized L-moment method. After developing IDF curves for above four raingauge stations, Thiessen Ploygon method is applied to find out average IDF curve. To show the spatial variability, Isopluvial maps have been generated using ArcGIS and a relation equation has been developed.     

Variation in curve numbers derived from plot runoff data for New South Wales (Australia)

The curve number method is a simple one parameter (the curve number) rainfall runoff model. While its theoretical underpinning has been questioned it remains a powerful hydrological tool in the absence of detailed data and is therefore used extensively in hydrological models. This study aims to characterize the variation in maximum retention values (S), which underlie curve numbers, for a range of agricultural treatments across a large spatial area in New South Wales (NSW), Australia. The data used for the analysis spans several decades of rainfall runoff observations. A range of different derivation methods result in variation in mean and variance of S. In particular, methods that emphasize the larger storms result in greater S and thus lower runoff. For larger spatial scales, emphasis on larger storms gives more reliable estimates of S. Systematic variation in S arises from variations in treatment, pre‐runoff soil moisture, rainfall depth, and variations in cover. On the basis of the analysis, a table of curve number values for different land uses found in NSW is presented. The resulting distributions of S and curve numbers provide guidance for rainfall runoff modelling studies in the agricultural important areas of NSW. Copyright © 2011 John Wiley & Sons, Ltd.