Region Of Influence Approach Research Articles

Comparison between observed and remotely sensed attributes to include in the region-of-influence approach of extreme precipitation estimation: a case study in the Yangtze River basin, China

ABSTRACT The application of remotely sensed (RS) data at ungauged locations is well recognized in hydrological studies; however, its suitability for use as a descriptor in the region-of-influence (ROI) approach is hardly assessed. This study compares two types of at-site attributes, observed and RS, to include in the ROI approach for the estimation of extreme precipitation, in particular at ungauged locations in China. The performance of the method against the fixed-group-based regional approach is also examined. The results, which are based on data for the Yangtze River basin, showed that the ROI scheme that used physical proximity and elevation combined produced the lowest error and performed better than that containing RS data. The scheme also outperformed the fixed regional models in terms of error. Overall, although the application of RS data is intuitively attractive, its inclusion is unable to outperform the observed site descriptors for the study region.

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

AbstractVarious regionalization approaches are in use to delineate watersheds in an area into homogeneous groups/regions for regional frequency analysis of hydrologic extremes (floods, droughts). Each approach differs in the underlying assumptions and strategy for regionalization and yields regions that differ in composition. There is ambiguity in the choice of approaches, as none is established to be universally superior in yielding true regions that are unknown. This article proposes a novel fuzzy ensemble clustering (FEC) approach to deal with uncertainty in the composition of regions obtained using different regionalization approaches. It forms fuzzy meta‐regions by integrating information on similarities in watershed groupings found in an ensemble of regions derived using several regionalization approaches. The FEC approach's potential to form effective regions (than those found in the ensemble) and their utility in regional flood frequency analysis to predict flood quantiles at ungauged sites is demonstrated through Monte Carlo simulation experiments and a case study on peninsular India. An ensemble of regions for use with FEC is formed using the region of influence approach, clustering approach based on Gaussian mixture model (GMM), and a hybrid approach which combines canonical correlation analysis with GMM based clustering. The FEC is shown to be effective in grouping similar watersheds even when the ensemble comprises regions having some improperly/wrongly grouped watersheds.

Flood frequency estimation in New Zealand using a region of influence approach and statistical depth functions

A region of influence approach in which statistical depth functions are used to select gauged catchments having similar flood generation behaviour to a given ungauged basin coupled with nonparametric regression on catchment characteristics is employed to estimate mean annual and a quantile flood of 100 year return period in New Zealand basins. This approach may be applied internationally. Relative and root mean square errors assessed by jack-knife resampling are too large to be useful for detailed design in ungauged catchments but are much smaller than those resulting from a conventional multiple nonlinear regression approach. The distribution of errors for samples of sites indicates that large error values result from contributions from a relatively small number of statistical outliers Although these are all natural catchments, the outlier basins have particular physical characteristics such as moraines, karst features and pumice sediments which affect their flood generation processes. Relative errors in estimates for an ungauged basin may be predicted using a relationship involving differences in area, rainfall and statistical depth between the ungauged catchment and gauged similar catchments. Improved analytical techniques and selection and measurement of catchment characteristics should reduce error magnitude but not perhaps sufficiently for use in detailed design. Parallel development based on rainfall-runoff models is desirable but the present lack of requisite information largely concerning rainfalls means that attainment of desired accuracy is a distant prospect.

Regionalization of potential evapotranspiration using a modified region of influence

This study examined the effect of different attributes on regionalization of potential evapotranspiration (ETp) in Urmia Lake Basin (ULB), Iran, using the region of influence (RoI) framework. Data for the period 1997–2016 from 30 weather stations were selected for the analysis. To achieve similarity between stations, climate, geographical, and statistical attributes were selected. To determine the effect of each attribute, the Shannon entropy weighting method was used. The results showed that attribute weighting had a significant impact on ETp clustering. Among the groups studied, the most significant effect of weighting was observed in the statistical attributes category. Among all attributes, skewness coefficient (CS) was the most useful in determining similarity between stations. Based on the results, ULB can be divided into three homogeneous regions. Proximity of weather stations did not always indicate similarity between them, but by weighting the stations in addition to weighting the attributes, more accurate estimates of ETp in the basin were obtained. Overall, the results demonstrate potential for application of the RoI approach in regionalization of ETp, by assigning a weight to weather stations and to influencing attributes.

Pooled frequency analysis for intensity–duration–frequency curve estimation

AbstractRainfall intensity–duration–frequency (IDF) curves are used in the design of urban infrastructure. Their estimation is based on rainfall frequency analysis, usually performed on rainfall records from a single gauged station. However, available at‐site record length is often too short to provide accurate estimates for long return periods. In the present study, a general framework for pooled rainfall frequency analysis based on the index‐event model is proposed for IDF estimation at gauged stations. Pooling group formation is defined by the region of influence approach on the basis of the geographical distance similarity measure. Several pooled approaches are defined and evaluated by a procedure through which quantile estimation and uncertainty are assessed. Alternate approaches for the definition of a pooling group are based on different criteria regarding initial pooling group size (and the relationship between size and return period), approaches for assessing pooling group homogeneity, and the use of macroregions in pooling group formation. The proposed framework is applied to identify the preferred approach for pooled rainfall intensity frequency analysis in Canada. Pooled approaches are found to provide more precise estimates than the at‐site approach, especially for long return periods. Pooled parent distribution selection supported the use of the generalized extreme value distribution across the country. Recommendations for pooling group formation include increasing the pooling group size with increases in return period and identifying an appropriate trade‐off between pooling group homogeneity and size for long return periods.

Regional frequency analysis with development of region-of-influence approach for maximum 24-h rainfall (case study: Urmia Lake Basin, Iran)

In this study, region-of-influence (ROI) approach was used for modeling extreme values of 24-h maximum rainfall using seven groups of defined attributes including climatic, geographical, and statistical attributes and their hybrids. According to the degree of importance and the role of each defined attribute, different weighting scenarios were defined for each of the attributes relating to the corresponding group. The goal of this paper is to select the best weighting scenario for attributes in the ROI model for estimating more accurate and reliable quantiles of 24-h maximum rainfall over Urmia Lake Basin. The investigation of 140 pooling groups built with the ROI approach showed that each of the seven groups of defined attributes has a different performance in the estimation of quantiles. Moreover, results showed that, in most cases, performance of the models with weighted attributes was better than that of the non-weighted ones. Among the seven categories, the statistical attributes and their combination with climatic and geographical attributes had the best estimation of quantiles in terms of low relative error. In addition, results showed that skewness plays an effective role in the estimation of quantiles. In general, it can be concluded that by applying appropriate weights for available attributes in regions without data, the acceptable and reliable results of extreme values can be earned at ungauged stations. Furthermore, results showed that the use of median of metric distance as one of the threshold options can obtain better results in the estimation of quantiles.

Extreme snow hazard and ground snow load for China

The ground snow load is used as the reference snow load to estimate the design snow load on roofs. The ground snow load is recommended in Chinese load code for the design of building structures in the applicable jurisdiction; this load needs to be updated regularly by integrating new available snow measurements and new analysis techniques. This study is concentrated on the estimation of extreme snow depth and ground snow load and on snow hazard mapping in China by using historical snow measurement data. A probabilistic model of the snowpack bulk density was developed. For the extreme value analysis of annual maximum snow depth, both the at-site analysis and region of influence approach were applied. Also, several commonly used probabilistic models and distribution fitting methods were considered for the extreme value analysis. For the annual maximum snow depth, it was identified from the at-site analysis results that the number of sites where the lognormal distribution is preferred is greater than that where the Gumbel distribution is preferred. The 50-year return period value obtained from the ROI approach is insensitive to whether the three-parameter lognormal distribution or the generalized extreme value distribution is adopted. Maps of annual maximum snow depth and ground snow load were developed. Comparison of the estimated ground snow load to that recommended in the design code was presented, and potential updating to the ground snow load in the design code was suggested.

Regional flood frequency analysis method for Tasmania, Australia: a case study on the comparison of fixed region and region-of-influence approaches

A new regional flood frequency analysis method for Tasmania is developed using data from 53 gauged catchments. The Bayesian generalized least squares regression (BGLSR) approach is used to developing prediction equations for selected flood quantiles using a quantile regression technique (QRT) and the first three moments of the log Pearson Type 3 distribution by means of a parameter regression technique (PRT). Regions are formed in three ways: (a) a fixed-region approach is examined in which all the sites in Tasmania are assumed to form one region; (b) a region of influence (ROI) approach is investigated in which a region is formed around each of the sites based on the criterion of minimum model error variance; and (c) the state of Tasmania is divided into two fixed regions (east Tasmania and west Tasmania) based on their distinct rainfall regimes. Independent testing showed that the BGLSR-ROI approach can deal quite well with the uncertainty in the regional estimation, as shown by the various regression diagnostics. Furthermore, the PRT-ROI approach was found to provide quantile estimates that are generally more accurate and consistent than the QRT. Generally, the ROI approach outperformed the fixed-region approach for Tasmania.

Regionalisation of the parameters of the log-Pearson 3 distribution: a case study for New South Wales, Australia

The index flood method is widely used in regional flood frequency analysis (RFFA) but explicitly relies on the identification of ‘acceptable homogeneous regions’. This paper presents an alternative RFFA method, which is particularly useful when ‘acceptably homogeneous regions’ cannot be identified. The new RFFA method is based on the region of influence (ROI) approach where a ‘local region’ can be formed to estimate statistics at the site of interest. The new method is applied here to regionalize the parameters of the log-Pearson 3 (LP3) flood probability model using Bayesian generalized least squares (GLS) regression. The ROI approach is used to reduce model error arising from the heterogeneity unaccounted for by the predictor variables in the traditional fixed-region GLS analysis. A case study was undertaken for 55 catchments located in eastern New South Wales, Australia. The selection of predictor variables was guided by minimizing model error. Using an approach similar to stepwise regression, the best model for the LP3 mean was found to use catchment area and 50-year, 12-h rainfall intensity as explanatory variables, whereas the models for the LP3 standard deviation and skewness only had a constant term for the derived ROIs. Diagnostics based on leave-one-out cross validation show that the regression model assumptions were not inconsistent with the data and, importantly, no genuine outlier sites were identified. Significantly, the ROI GLS approach produced more accurate and consistent results than a fixed-region GLS model, highlighting the superior ability of the ROI approach to deal with heterogeneity. This method is particularly applicable to regions that show a high degree of regional heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd.

Design Flood Estimation in Ungauged Catchments: A Comparison Between the Probabilistic Rational Method and Quantile Regression Technique for NSW

Design flood estimation for ungauged catchments is often required in hydrologie design. The most commonly adopted regional flood frequency analysis methods used for this purpose include the index flood method, regression based techniques and various forms of the rational method. This paper first examines the similarities and differences between the probabilistic rational method (PRM) (the currently recommended method for Victoria and eastern NSW in Australian Rainfall and Runoff) and the generalised least squares (GLS) based quantile regression technique (QRT). It then uses data from 107 catchments in NSW to compare the performance of these two methods. To make a valid comparison, the same predictor variables and data set have been used for both methods. Based on one-at-a-time and split sample validation tests and a range of evaluation statistics, it has been found that the GLS-based QRT performs better than the PRM. The particular advantage of the QRT is that it does not require a contour map of the runoff coefficient as with the PRM. The QRT also offers potential for improvement through inclusion of additional predictor variables. The QRT can also be integrated with the region of influence approach, where a region can be formed around an ungauged catchment by selecting an “appropriate number” of neighbouring gauged catchments. Overall, the QRT offers much greater flexibility and potential in terms of error analysis and further development as compared to the PRM.

Region-of-influence approach to a frequency analysis of heavy precipitation in Slovakia

Abstract. The paper compares different approaches to regional frequency analysis with the main focus on the implementation of the region-of-influence (ROI) technique for the modelling of probabilities of heavy precipitation amounts in the area of the Western Carpathians. Unlike the conventional regional frequency analysis where the at-site design values are estimated within a fixed pooling group (region), the ROI approach as a specific alternative to focused pooling techniques makes use of flexible pooling groups, i.e. each target site has its own group of sufficiently similar sites. In this paper, various ROI pooling schemes are constructed as combinations of different alternatives of sites' similarity (pooling groups defined according to climatological characteristics and geographical proximity of sites, respectively) and pooled weighting factors. The performance of the ROI pooling schemes and statistical models of conventional (regional and at-site) frequency analysis is assessed by means of Monte Carlo simulation studies for precipitation annual maxima for the 1-day and 5-day durations in Slovakia. It is demonstrated that a) all the frequency models based on the ROI method yield estimates of growth curves that are superior to the standard regional and at-site estimates at most individual sites, and b) the selection of a suitable ROI pooling scheme should be adjusted to the dominant character of the formation of heavy precipitation.

Regional Flood Frequency with Hierarchical Region of Influence

Regional flood frequency analysis involves combining extreme-flow information from numerous stations to enhance the reliablity of the temporal characterization of extreme-flow data at sites with short periods of record. The region of influence (ROI) approach used herein, defines regions such that each site has a potentially unique combination of stations. The similarity of the selected stations is ensured by incorporating a homogeneity test into the regionalization process. A hierarchical feature is added to the ROI approach to further enhance the efficiency of the spatial information transfer. This is done by taking advantage of the different spatial similarity scales that have been observed for different orders of moments for a flood frequency distribution. The incorporation of this concept into the ROI framework results in a set of ROIs for a site as opposed to a single ROI. The hierarchical ROI approach is evaluated through the use of a Monte Carlo experiment applied to data representative of a collection of unregulated catchments in the midwest portion of Canada. The simulation experiment shows that an improvement in flood quantile estimation is achieved.

Flood frequency analysis for ungauged sites using a region of influence approach

A framework is presented for regional flood frequency analysis that is applicable for estimating extreme flow quantiles at ungauged catchments. The methodology uses the Region of Influence (ROI) approach to regionalization and explicitly incorporate a homogeneity test in the process of selecting the collection of stations that comprise the ‘region’ for an ungauged site. The relative merits of the methodology are demonstrated through an application to extreme flow data for sites in Newfoundland, Canada. The new approach is compared with results obtained from regression analysis and is shown to provide improved estimates of extreme quantiles at sites which are considered to be ungauged.

Evaluation of regional flood frequency analysis with a region of influence approach

A novel approach to regional flood frequency analysis is presented and evaluated. The technique is referred to as the region of influence approach in that every site can have a potentially unique set of gauging stations for use in the estimation of at‐site extremes. The rationale for the methodology is discussed, and several options for incorporating the approach into regional flood frequency analysis are developed and compared with traditional regional estimation procedures. Through a Monte Carlo experiment, the region of influence approach is demonstrated to provide improved at‐site estimates of extreme flow quantlies in terms of network average root mean squared error and comparable results for bias. The method is further shown to have attractive features for estimating extremes for unusual sites in a network of gauging stations.

Evaluation of regional flood frequency analysis with a region of influence approach

Evaluation of regional flood frequency analysis with a region of influence approach