The formation of groups for regional flood frequency analysis

Abstract

A new technique is developed for identifying groups for regional flood frequency analysis. The technique uses a clustering algorithm as a starting point for partitioning the collection of catchments. The groups formed using the clustering algorithm are subsequently revised to improve the regional characteristics based on three requirements that are defined for effective groups. The result is overlapping groups that can be used to estimate extreme flow quantiles for gauged or ungauged catchments. The technique is applied to a collection of catchments from India and the results indicate that regions with the desired characteristics can be identified using the technique. The use of the groups for estimating extreme flow quantiles is demonstrated for three example sites.     

Delineation of precipitation regions in two Canadian study areas: the role of the temporal resolution of the precipitation data

The identification of homogeneous precipitation regions has value in many water resources engineering applications (infrastructure planning, design, operations; climate forecasting, modelling). The objective of this paper is to assess the sensitivity of precipitation regions to the temporal resolution (monthly, seasonal, annual and the annual maximum series) of the data. The presented method uses the fuzzy c-means clustering algorithm to partition climate sites into statistically homogeneous precipitation regions. The regions are validated using an approach based on L-moment statistics. The method is conducted in two climatically different study areas in western and eastern Canada. There does not appear to be a relationship between the spatial distributions of the regions formed using different temporal resolutions of the precipitation data. It is recommended to delineate precipitation regions that are specific to the task at hand, and to select a temporal resolution that is consistent with the final application of the regional precipitation dataset.

EDITOR A. Castellarin; ASSOCIATE EDITOR T. Kjeldsen     

Characterizing and understanding the variability of streamflow drought indicators within the USA

ABSTRACT

Low streamflow conditions can have adverse consequences for society and river ecology. The variability and drivers of streamflow drought indicators within the USA were investigated using observed streamflow records from 603 gauges across the USA. The analysis was based on two main approaches: (i) low-flow magnitude indicators, and (ii) streamflow deficit indicators. First, we examined how streamflow drought indicators vary spatially across the USA. Second, we used a data-driven clustering method to identify spatial clusters for each indicator. Finally, we assessed the association with regional climate drivers. The results show that the spatial variability of low-flow magnitude indicators is significantly different from the deficit indicators. Further, our clustering approach identifies regions of spatial homogeneity, which can be linked to the extreme regional climate drivers and land–atmosphere interactions. The influence of regional climate on streamflow drought indicators varies more between clusters than between indicators.     

Nonparametric catchment clustering using the data depth function

ABSTRACT

The clustering of catchments is important for prediction in ungauged basins, model parameterization and watershed development and management. The aim of this study is to explore a new measure of similarity among catchments, using a data depth function and comparing it with catchment clustering indices based on flow and physical characteristics. A cluster analysis was performed for each similarity measure using the affinity propagation clustering algorithm. We evaluated the similarity measure based on depth–depth plots (DD-plots) as a basis for transferring parameter sets of a hydrological model between catchments. A case study was developed with 21 catchments in a diverse New Zealand region. Results show that clustering based on the depth–depth measure is dissimilar to clustering on catchment characteristics, flow, or flow indices. A hydrological model was calibrated for the 21 catchments and the transferability of model parameters among similar catchments was tested within and between clusters defined by each clustering method. The mean model performance for parameters transferred within a group always outperformed those from outside the group. The DD-plot based method was found to produce the best in-group performance and second-highest difference between in-group and out-group performance.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR A. Viglione     

Missing precipitation data estimation using optimal proximity metric-based imputation,nearest-neighbour classification and cluster-based interpolation methods

Abstract

New optimal proximity-based imputation, K-nearest neighbour (K-NN) classification and K-means clustering methods are proposed and developed for estimation of missing daily precipitation records. Mathematical programming formulations are developed to optimize the weighting, classification and clustering schemes used in these methods. Ten different binary and real-valued distance metrics are used as proximity measures. Two climatic regions, Kentucky and Florida, (temperate and tropical) in the USA, with different gauge density and network structure, are used as case studies to evaluate the new methods. A comprehensive exercise is undertaken to compare the performances of the new methods with those of several deterministic and stochastic spatial interpolation methods. The results from these comparisons indicate that the proposed methods performed better than existing methods. Use of optimal proximity metrics as weights, spatial clustering of observation sites and classification of precipitation data resulted in improvement of missing data estimates.

Editor D. Koutsoyiannis; Associate editor C. Onof     

Regionalization of precipitation characteristics in the Canadian Prairie Provinces using large-scale atmospheric covariates and geophysical attributes

Observed data at most stations are often inadequate to obtain reliable estimates of many hydro-meteorological variables that not only define water availability across a region but also the vulnerability of social infrastructure to climatic extremes. To overcome this, data from neighboring sites with similar statistical characteristics are often pooled. The pooling process is based on partitioning of a larger region into smaller sub-regions with homogeneous features of interest. The established approaches rely heavily on statistics computed from observed precipitation data rather than the covariates that play a significant role in modulating the regional and local climate patterns at various temporal and spatial scales. In this study, a new approach for identifying homogeneous regions for regionalization of precipitation characteristics is proposed for the Canadian Prairie Provinces. This approach incorporates information about large-scale atmospheric covariates, teleconnection indices and geographical site attributes that impact spatial patterns of precipitation in order to delineate homogeneous precipitation regions through combined use of multivariate approaches—principal component analysis, canonical correlation analysis and fuzzy C-means clustering. Results of the analyses suggest that the study area can be partitioned into five homogeneous regions. These partitions are validated independently for homogeneity using statistics computed from monthly and seasonal precipitation totals, and seasonal extremes from a network of observation stations. Furthermore, based on the identified regions, precipitation magnitude-frequency relationships of warm and cold season single- and multi-day precipitation extremes, developed through regional frequency analysis, are mapped spatially. Such estimates are important for numerous water resources related activities.     

Basin-scale analysis of rainfall and runoff in Peru (1969–2004): Pacific,Titicaca and Amazonas drainages

Abstract

We present a procedure for estimating Q95 low flows in both gauged and ungauged catchments where Q95 is the flow that is exceeded 95% of the time. For each step of the estimation procedure, a number of alternative methods was tested on the Austrian data set by leave-one-out cross-validation, and the method that performed best was used in the final procedure. To maximise the accuracy of the estimates, we combined relevant sources of information including long streamflow records, short streamflow records, and catchment characteristics, according to data availability. Rather than deriving a single low flow estimate for each catchment, we estimated lower and upper confidence limits to allow local information to be incorporated in a practical application of the procedure. The components of the procedure consist of temporal (climate) adjustments for short records; grouping catchments into eight seasonality-based regions; regional regressions of low flows with catchment characteristics; spatial adjustments for exploiting local streamflow data; and uncertainty assessment. The results are maps of lower and upper confidence limits of low flow discharges for 21 000 sub-catchments in Austria.     

How well do indicator variograms capture the spatial connectivity of soil moisture?

Indicators are binary transforms of a variable and are 1 or 0, depending on whether the variable is above or below a threshold. Indicator variograms can be used for a similar range of geostatistical estimation techniques as standard variograms. However, they are more flexible as they allow different ranges for small and large values of a hydrological variable. Indicator geostatistics are also sometimes used to represent the connectivity of high values in spatial fields. Examples of connectivity are connected high values of hydraulic conductivity in aquifers, leading to preferential flow, and connected band-shaped saturation zones in catchments. However, to the authors' knowledge the ability of the indicator approach to capture connectivity has never been shown conclusively. Here we analyse indicator variograms of soil moisture in a small south-east Australian catchment and examine how well they can represent connectivity. The indicator variograms are derived from 13 soil moisture patterns, each consisting of 500–2000 point TDR (time domain reflectometry) measurements. Winter patterns are topographically organized with long, thin, highly connected lines of high soil moisture in the drainage lines. In summer the patterns are more random and there is no connectivity of high soil moisture values. The ranges of the 50^th and 90^th percentile indicator semivariograms are approximately 110 and 75 m, respectively, during winter, and 100 and 50 m, respectively, during summer. These ranges indicate that, compared with standard semivariograms, the indicator semivariograms provide additional information about the spatial pattern. However, since the ranges are similar in winter and in summer, the indicator semivariograms were not able to distinguish between connected and unconnected patterns. It is suggested that new statistical measures are needed for capturing connectivity explicitly. © 1998 John Wiley & Sons, Ltd.     

Performance of flood frequency pooling analysis in a low CV context

Abstract

Pooling of flood data is widely used to provide a framework to estimate design floods by the Index Flood method. Design flood estimation with this approach involves derivation of a growth curve which shows the relationship between X_T and the return period T, where X_T ?=?Q_T /Q_I and Q_I is the index flood at the site of interest. An implicit assumption with the Index Flood procedure of pooling analysis is that the X_T –T relationship is the same at all sites in a homogeneous pooling group, although this assumption would generally be violated to some extent in practical cases, i.e. some degree of heterogeneity exists. In fact, in only some cases is the homogeneity criterion effectively satisfied for Irish conditions. In this paper, the performance of the index-flood pooling analysis is assessed in the Irish low CV (coefficient of variation) hydrology context considering that heterogeneity is taken into account. It is found that the performance of the pooling method is satisfactory provided there are at least 350 station years of data included. Also it is found that, in a highly heterogeneous group, it is more desirable to have many sites with short record lengths than a smaller number of sites with long record lengths. Increased heterogeneity decreases the advantage of pooling group-based estimation over at-site estimation. Only a heterogeneity measure (H1) less than 4.0 can render the pooled estimation preferable to that obtained for at-site estimation for the estimation of 100-year flood. In moderately to highly heterogeneous regions it is preferable to conduct at-site analysis for the estimation of 100-year flood if the record length at the site concerned exceeds 50.

Editor Z.W. Kundzewicz; Associate editor A. Carsteanu

Citation Das, S. and Cunnane, C., 2012. Performance of flood frequency pooling analysis in a low CV context. Hydrological Sciences Journal, 57 (3), 433–444.     

The role of science in setting water resource use limits: case studies from New Zealand

Abstract

Water resource use limits ensure protection of environmental values and define the availability and reliability of water supply for out-of-channel use. We examined how three types of scientific tools (environmental flow setting methods, hydrological analyses for setting total allocations and spatial frameworks) have been used to define limits across jurisdictional regions comprising multiple catchments in New Zealand. We found that recently developed minimum flow and total allocation setting tools are widely used. Spatial frameworks are increasingly used to discriminate and account for variation in environmental characteristics, thereby increasing the specificity of water resource use limits. The uptake of scientific tools has enabled improvements in the clarity of water management objectives and the transparency of limits defined by regional water management plans. We argue that more integrated use of scientific tools could improve the clarity and transparency of regional limits by explicitly demonstrating the trade-off between out-of-channel use and protection of environmental values.

Editor D. Koutsoyiannis; Guest editor M. Acreman

Citation Snelder, T.H., Rouse, H.L., Franklin, P.A., Booker, D.J., Norton, N., and Diettrich, J., 2014. The role of science in setting water resource use limits: case studies from New Zealand. Hydrological Sciences Journal, 59 (3–4), 844–859.     

A canonical correlation approach to the determination of homogeneous regions for regional flood estimation of ungauged basins

Abstract

A canonical correlation method for determining the homogeneous regions used for estimating flood characteristics of ungauged basins is described. The method emphasizes graphical and quantitative analysis of relationships between the basin and flood variables before the data of the gauged basins are used for estimating the flood variables of the ungauged basin. The method can be used for both homogeneous regions, determined a priori by clustering algorithms in the space of the flood-related canonical variables, as well as for “regions of influence” or “neighbourhoods” centred on the point representing the estimated location of the ungauged basin in that space.     

Spatial patterns and temporal stability of soil moisture across a range of scales in a semi‐arid environment

The antecedent soil moisture status of a catchment is an important factor in hydrological modelling. Traditional Hortonian infiltration models assume that the initial moisture content is constant across the whole catchment, despite the fact that even in small catchments antecedent soil moisture exhibits tremendous spatial heterogeneity. Spatial patterns of soil water distribution across three transects (two in a burnt area and one in an unburnt area) in a semi‐arid area were studied. At the transect scale, when the factors affecting soil moisture were limited to topographical position or local topography, spatial patterns showed time stability, but when other factors, such as vegetation, were taken into account, the spatial patterns became time unstable. At the point scale, and in the same areas, topographical position was the main factor controlling time stability. Scale dependence of time stability was studied and local topography and vegetation presence were observed to play an important role for the correlation between consecutive measures depending on the scale. Copyright © 2000 John Wiley & Sons, Ltd.     

Detection of trends in the 7-day sustained low-flow time series of Irish rivers

A combination of statistical hypothesis testing methods (Mann-Whitney, Mann-Kendall and Spearman’s rho) and visual exploratory analysis were used to investigate trends in Irish 7-day sustained low-flow (7SLF) series possibly driven by changes in summer rainfall patterns. River flow data from 33 gauging stations covering most major Irish rivers were analysed, after excluding catchments where low flows are influenced by significant human interventions. A statistically significant increasing trend in the 7SLF series was identified by all three tests at eight gauging stations; in contrast, a statistically significant decreasing trend was identified by all three tests at four stations. The stations with increasing trends are mainly located within the western half of the country, while there is no particular spatial clustering of the stations showing a decreasing trend. Further analysis suggests that the increasing trend in the 7SLF time series persists regardless of the starting year of analysis. However, the decreasing trend occurs only when years prior to 1970 are included in the analysis, and disappears, or is reversed, if only the data from 1970 and onwards are considered. There is strong evidence that the direction of the trends in the 7SLF series is determined mainly by trends in total summer rainfall amounts, i.e. is linked to weather.

EDITOR Z.W. Kundzewicz

ASSOCIATE EDITOR not assigned     

Monthly spatial rainfall correlation functions and interpretations for Turkey

Abstract

Monthly spatial rainfall distribution features and their effects on spatial correlation patterns are significant in any regional study. In this paper, first a number of statistical terms and properties are explained with reference to the spatial correlation functions (SCFs). This is followed by the analysis of a theoretical spatial correlation model and its parameter estimation. Monthly empirical SCFs are examined in relation to spatial rainfall characteristics. In order to obtain a definite pattern, the SCF values are averaged in successive equal-distance groups. This average spatial correlation function shows a decreasing pattern with distance. Some interpretations of these spatial correlation functions are given for Turkey with discussion of the results obtained.     

Simulated precipitation fields with variance-consistent interpolation

Abstract

Gridded meteorological data are available for all of Norway as time series dating from 1961. A new way of interpolating precipitation in space from observed values is proposed. Based on the criteria that interpolated precipitation fields in space should be consistent with observed spatial statistics, such as spatial mean, variance and intermittency, spatial fields of precipitation are simulated from a gamma distribution with parameters determined from observed data, adjusted for intermittency. The simulated data are distributed in space, using the spatial pattern derived from kriging. The proposed method is compared to indicator kriging and to the current methodology used for producing gridded precipitation data. Cross-validation gave similar results for the three methods with respect to RMSE, temporal mean and standard deviation, whereas a comparison on estimated spatial variance showed that the new method has a near perfect agreement with observations. Indicator kriging underestimated the spatial variance by 60–80% and the current method produced a significant scatter in its estimates.

Citation Skaugen, T. & Andersen, J. (2010) Simulated precipitation fields with variance-consistent interpolation. Hydrol. Sci. J. 55(5), 676–686.     

Characterization of regional variability of seasonal water balance within Omo-Ghibe River Basin,Ethiopia

In this study we quantify the spatial variability of seasonal water balances within the Omo-Ghibe River Basin in Ethiopia using methods proposed within the Prediction in Ungauged Basins initiative. Our analysis consists of: (1) application of the rainfall–runoff model HBV-Light to several sub-catchments for which runoff data are available, and (2) estimation of water balances in the remaining ungauged catchments through application of the model with regionalized parameters. The analyses of the resulting water balance outcomes reveal that the seasonal water balance across the Omo-Ghibe Basin is driven by precipitation regimes that change with latitude, from being strongly “seasonal” in the north to “precipitation spread throughout the year, but with a definite wetter season” in the south. The basin is divided into two distinct regions based on patterns of seasonal water balance and, in particular, seasonal patterns of soil moisture storage.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR A. Efstratiadis     

A model of a homogeneous isotropic turbulent flow and its application for the simulation of cloud drop tracks

Abstract

A model of a homogeneous isotropic turbulent flow is presented. The model provides different realizations of the random velocity field component with given correlation latitudinal and lateral functions and a spatial structure which obeys the Kolmogorov theory of homogeneous and isotropic turbulence. For the generation of the turbulent flow the structural function of the flow in the form suggested by Batchelor (Monin and Yaglom, 1975) was used. This function describes the spectrum of turbulence both in the viscous and inertial ranges. The isotropy and homogeneity of the velocity field of the model are demonstrated.

The model is aimed at simulating the ‘‘fine'’ features of drop's (aerosol particles') motion, such as the deviations of drops’ velocity from the velocity of the flow, detailed structures of drops’ tracks, related to drops’ (particles') inertia. The model is intended also for the purpose of studying cloud drops’ and aerosol particles’ motion and their diffusional spreading utilizing the Monte Carlo methods.

Some examples of drop tracks for drops of different size are presented. Drops’ tracks are very sophisticated, so that the relative position of drops falling initially from the same point can vary drastically. In some cases drops’ tracks diverge very quickly, in other cases all drops move within a turbulent eddy along nearly the same closed tracks, but with different speed. The concentration of drop tracks along isolated paths is found in spite of the existence of a large number of velocity harmonics. It is shown that drops (aerosol particles) tend to leave some areas of the turbulent flow apparently due to their inertia. These effects can possibly contribute to inhomogeneity of drops’ concentration in clouds at different spatial scales.     

How can a few streamflow measurements help to predict daily hydrographs at almost ungauged sites?

Abstract

Available data from nearby gauging stations can provide a great source of hydrometric information that is potentially transferable to an ungauged site. Furthermore, streamflow measurements may even be available for the ungauged site. This paper explores the potential of four distance-based regionalization methods to simulate daily hydrographs at almost ungauged pollution-control sites. Two methods use only the hydrological information provided by neighbouring catchments; the other two are new regionalization methods parameterized with a limited number of streamflow data available at the site of interest. Based on a network of 149 streamgauges and 21 pollution-control sites located in the Upper Rhine-Meuse area, the comparative assessment demonstrates the benefit of making available point streamflow measurements at the location of interest for improving quantitative streamflow prediction. The advantage is moderate for the prediction of flow types (stormflow, recession flow, baseflow) and pulse shape (duration of rising limb and falling limb).

Editor Z.W. Kundzewicz; Associate editor A. Viglione