Bias Correction to GEV Shape Parameters Used to Predict Precipitation Extremes

Carney, Matthew C.

doi:10.1061/(asce)he.1943-5584.0001416

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Meanwhile, the standard error and bias of the CS estimate are about 0.56 and 0.22 (based on a simulation study), respectively, for a record length of 50 years and a series with the average estimated moments of the entire dataset (MAF = 0.17 m 3 s −1 km −2 , CV = 0.52, CS = 1.28), which is about one-half and one-sixth of the underlying population moment (assuming a GEV distribution to be the datagenerating process). The bias and uncertainty for the estimation of skewness are well documented in Wallis et al (1974), Bobee and Robitaile (1975) and Carney (2016), for example. The estimation uncertainties need to be accounted for when interpreting the process controls on the flood moments.…”

Section: Analysis Methodsmentioning

confidence: 74%

Characteristics and process controls of statistical flood moments in Europe – a data-based analysis

Lun

Viglione

Bertola

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Many recent studies have sought to characterize variations of the annual maximum flood discharge series over time and across space in Europe, including some that have elucidated different process controls on different statistical properties of these series. To further support these studies, we conduct a pan-European assessment of process controls on key properties of this series, including the mean annual flood (MAF) and coefficients of variation (CV) and skewness (CS) of flood discharges. These annual maximum flood discharge series consist of instantaneous peaks and daily means observed in 2370 catchments in Europe without strong human modifications covering the period 1960–2010. We explore how the estimated moments MAF, CV and CS vary due to catchment size, climate and other controls across Europe, where their averages are 0.17 m3 s−1 km−2, 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, in the high-rainfall areas of the Mediterranean coast and in mountainous regions, while it is smallest in the sheltered parts of the East European Plain. The CV is largest in southern and eastern Europe, while it is smallest in the regions subject to strong Atlantic influence. The pattern of the CS is similar, albeit more erratic, in line with the greater sampling variability of CS. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area, suggesting that floods in small catchments are relatively very large, while in eastern Europe this dependence is much weaker, mainly due to more synchronized timing of snowmelt over large areas. The process controls on the flood moments in five predetermined hydroclimatic regions are identified through correlation and multiple linear regression analyses with a range of covariates, and the interpretation is aided by a seasonality analysis. Precipitation-related covariates are found to be the main controls of the spatial patterns of MAF in most of Europe except for regions in which snowmelt contributes to MAF, where air temperature is more important. The Aridity Index is, by far, the most important control on the spatial pattern of CV in all of Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, to provide a performance baseline for more local studies, we assess the estimation accuracy of regional multiple linear regression models for estimating flood moments in ungauged basins.

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Section: Analysis Methodsmentioning

confidence: 74%

Characteristics and process controls of statistical flood moments in Europe – a data-based analysis

Lun

Viglione

Bertola

et al. 2021

Hydrol. Earth Syst. Sci.

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“…The GEV method is used for the prediction of extreme events for each climatic factor, such as precipitation, temperature, wind and all those climatic factors, which could lead to hazards with their extreme manifestation [12,13]; however, the GEV is also widely used in economics, especially aimed at predicting rather negative economic scenarios or risks and in engineering [14,15]. In any case, the most widespread application of the GEV method is the definition of maximum rainfall return values due to the reliability of the forecast, although it sometimes seems to be penalised, as some research has shown, by estimates based on short time series because they make it difficult to estimate the shape parameter and also due to possible errors in the measurements [16]. It is well known that measurement errors are possible both when analysing weather stations, due to a lack of quality control, and also when using satellite data, which are not always reliable or calibrated in the study area [17].…”

Section: Aim Of the Study And State Of The Artmentioning

confidence: 99%

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

Gentilucci,

Rossi,

Pelagagge

et al. 2023

Sustainability

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The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.

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