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A comparison of six procedures for estimating the linear trend parameter β in annual maximum 1 day river flows at five sites in southern Brazil showed marked differences between, on the one hand, estimates obtained by incorporating trend into the generalized extreme value (GEV) location parameter with all parameters estimated by maximum likelihood (ML) and on the other hand, estimates found by least squares, trend removal prior to fitting the GEV by ML, boot-strap sampling, and Theil-Sen estimation. ML estimates of trend were considerably smaller than those given by all other procedures. The same was true where trend had been incorporated into the Gumbel location parameter. Where 95% confidence intervals were calculated for the 'true' trend β by different procedures, some confidence intervals bracketed zero (indicating that the trend was not 'significant' at the 5% level), but there was no consistency between results from different procedures; Theil-Sen confidence intervals always bracketed zero, confidence intervals given by detrending never did. It is concluded that not only do different estimation procedures give different measures of trend uncertainty, as reported elsewhere, but the estimated trends themselves may differ, and the paper suggests an explanation why this may occur. Some philosophical issues relating to estimation of trend in climatological and hydrological extremes are discussed, and it is concluded that selection of a method to estimate trend must depend on context. [ABSTRACT FROM AUTHOR]

Summary: The analysis of annual extremes of hydrological and meteorological variables is frequently complicated by the presence of gaps in record, and when records are not only fragmented but also short, it is necessary to utilize to the full the information contained in them. One method is to abstract for statistical analysis all extreme events whose peaks exceed a pre-selected threshold value, but the threshold must be carefully chosen if “clumps” of peaks are to be avoided. A common alternative is a statistical analysis of maxima in years that are complete, possibly including in the analysis values from incomplete years according to some empirical rule. A plausible probability distribution has been proposed by [Jones, D.A., 1997. Plotting positions via maximum likelihood for a non-standard situation. Hydrol. Earth Syst. Sci. 1, 357–366] for the extremes observed in incomplete years, which takes into account not only the proportion of record that is missing within an incomplete year, but also the effect of seasonality. As part of a larger study on the hydrology of the Amazon basin, this paper uses 484 records with length not less than 12years from an extensive network of 750 rain gauges, to compare the method proposed by Jones (termed the DAJ method) with the following alternative procedures: (i) using only complete years of record and (ii) including years with less than 20% missing record, as if they were complete. Using the large-sample variance calculated for the annual maximum one-day rainfall with 100-year return period (P 100), the method proposed by Jones is shown to give smaller standard errors than either of the alternatives. Using the number of years in each record to calculate weighted mean variances over the 484 records, the mean standard errors of P 100 obtained by methods (i) and (ii) were 1.25 and 1.06 times the mean standard error given by the DAJ method. The precision of estimates obtained by the latter method was therefore better than either alternative. [Copyright &y& Elsevier]

This paper presents results of analyses by parametric methods of annual means of temperature, precipitation and discharge, and of seasonal maximum precipitation at 17, 28 and 10 Icelandic stations, respectively, for the period 1961-2000. Trends in mean seasonal temperature and precipitation are in broad agreement with results found by other authors using other methods. A positive trend appears in both mean annual temperature and mean temperatures in most seasons. Annual mean precipitation trends are positive in most seasons except for negative trends in the September-November season in the south. Additionally, positive trends appear in maximum one-, three- and five-day precipitation, both during the spring and autumn, except at a group of stations in central Iceland. Some of the positive trends in mean annual and seasonal precipitation may, however, be attributed to the positive trend in temperature which may have influenced gauge catch. Trends in mean annual and seasonal discharge are small and statistically insignificant; the trends found in temperature and precipitation do not all relate directly to trends in discharge but suggest hypotheses for further study of the relationships between them. [ABSTRACT FROM AUTHOR]

Gaps in measured discharge series are common in Iceland, especially during winter, but reconstructed series are needed for a number of applications. Modeled series are therefore sometimes used to fill the gaps to create a continuous series, but the modeled inserts may have different statistical characteristics from the observed series, whilst their initial and final values rarely coincide with the corresponding measured values. Where gaps are numerous, individual treatment of each gap is time-consuming and often impractical. A method for adjusting modeled sequences to be inserted into gaps was therefore designed with the objective of reducing the difference between model-generated inserts and recorded series. This paper reports on the performance of the method and shows how an improved fit can be attained by adjusting the model-generated inserts so as to smoothly match the discharges observed before and after a gap. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with a non-standard application of frequency analysis of extremes of hydrologic variables in which some years of record are incomplete. If F(y) is the cumulative distribution function (cdf) of the extreme variable in a complete year (or ‘block’), the cdf of the variable in an incomplete year is taken as F(y) p , where 0< p <1 is the proportion of observations existing for that year. In this model, which can also take into account whether the missing observations occurred in the wet or dry season, the extreme values in different years no longer have the same parent distribution, so that where parameters of F(y) are estimated by maximum likelihood (ML), the usual large-sample characteristics of ML estimators (consistency, asymptotic Normality) may be modified. The paper examines the consistency, bias and approach to Normality of ML estimates of Gumbel parameters for position and scale, and of the Gumbel extreme event y 100 with 100-year return period. In the non-standard model F(y) p , consistency of ML estimates of Gumbel position and scale parameters is considerably modified, but the consistency of the estimated y 100 much less so. Estimates of y 100 are negatively biased, but the bias is similar to that found in the standard (no missing data) case. The results are relevant where hydrologic records are short and incomplete, such that all existing data must be fully utilized. [Copyright &y& Elsevier]

The paper discusses evidence that common assumptions in the analysis of hydrological time series (homogeneous variability in random fluctuations about a constant mean value) may not be appropriate for some South American drainage basins. Relatively rapid changes have occurred, and are occurring, as a consequence of replacing mature forest by short crops and urban development. Some research claims to have detected non-linear trends in streamflow in rivers draining the south-eastern part of the sub-continent, together with decadal fluctuations and interannual peaks at ENSO timescales. The paper discusses the implications of such changes for hydrological practices now in widespread and largely unquestioned use. [ABSTRACT FROM AUTHOR]