Effect of Uncertainty in Historical Data on Flood Frequency Analysis Using Bayesian Method

Shang, Xiaosan; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Liu, Jiufu; Zou, Ying; He, Ronghai

doi:10.1061/(asce)he.1943-5584.0002075

Cited by 3 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the abrupt change cases, the non-parametric Mann-Kendall test was employed to test abrupt changes in rainfall erosivity [12,13]. Traditional statistical methods such as the moving T/F test [14,15], the rank sum test [16], Bayesian analysis [17], and the Pettitt test method [18] are regularly used for abrupt change detection. Regarding the nonlinear and nonstationary rainfall erosivity, Lü et al [19] used the heuristic segmentation method in abrupt change detection.…”

Section: Introductionmentioning

confidence: 99%

Detection Framework of Abrupt Changes and Trends in Rainfall Erosivity in Three Gorges Reservoir, China

Feng

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this study, a detection framework is introduced to identify temporal changes in rainfall erosivity time series as follows :(i) The significance of time series variation of rainfall erosivity is assessed based on the Hurst coefficient and divided into three levels: None, medium, and high. (ii) The detection of abrupt changes (Mann–Kendall, Moving T, and Bayesian tests) and trends (Spearman and Kendall rank correlation tests) of variate series and the correlation coefficient between the variation component and the original series is calculated. (iii) The modified series is obtained by preferentially eliminating the variation component (trend or change point) with larger correlation coefficients. (iv) We substituted the modified series into steps i to iii until the correlation coefficient was not significant. This framework is used to analyze the variation of rainfall erosivity in the Three Gorges Reservoir, China. The results showed that by using traditional methods, both an increasing trend and an upward change point were observed in Zigui station. However, after the upward change point was deducted from the annual rainfall erosivity series R(t), the resultant Rm(t) showed no statistically significant trend. Trend analysis should be performed considering the existence of an abrupt change to assess the long-term changes in rainfall erosivity series; otherwise, it would result in the wrong conclusion. In addition, the change points detected in the Rm(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection Framework of Abrupt Changes and Trends in Rainfall Erosivity in Three Gorges Reservoir, China

Feng

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

Effect of streamflow measurement error on flood frequency estimation

Velásquez,

Krajewski

2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Significant errors often arise when measuring streamflow during high flows and flood events. Such errors conflated by short records of observations may induce bias in the flood frequency estimates, leading to costly engineering design mistakes. This work illustrates how observational (measurement) errors affect the uncertainty of flood frequency estimation. The study used the Bulletin 17 C (US standard) method to estimate flood frequencies of historical peak flows modified to represent the measurement limitations. To perform the modifications, we explored, via Monte Carlo simulation, four hypothetical scenarios that mimic measurement errors, sample size limitations, and their combination. We used a multiplicative noise from a log-normal distribution to simulate the measurement errors and implemented a bootstrap approach to represent the sampling error. Then, we randomly selected M samples from the total N records of the observed peak flows of four gauging stations in Iowa in central USA. The observed data record ranges between 76 and 119 years for watersheds with drainage areas between 500 and 16,000 km2. According to the results, measurement errors lead to more significant differences than sampling limitations. The scenarios exhibited differences with median magnitudes of up to 50%, with some cases reaching differences up to 100% for return periods above 50 years. The results raise a red flag regarding flood frequency estimation that warrants looking for further research on observational errors.

show abstract