scholarly journals Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

Author(s):  
Hemin Sun ◽  
Tong Jiang ◽  
Cheng Jing ◽  
Buda Su ◽  
Guojie Wang

Abstract. Return period estimation plays an important role in the engineering practices of water resources and disaster management, but uncertainties accompany the calculation process. Based on the daily discharge records at two gauging stations (Cuntan and Pingshan) on the upper Yangtze River, three sampling methods (SMs; (annual maximum, peak over threshold, and decadal peak over threshold), five distribution functions (DFs; gamma, Gumbel, lognormal, Pearson III, and general extreme value), and three parameterization methods (PMs; maximum likelihood, L-Moment, and method of moment) were applied to analyze the uncertainties in return period estimation. The estimated return levels based on the different approaches were found to differ considerably at each station. The range of discharge for a 20-year return period was 63,800.8–74,024.1 m3 s−1 for Cuntan and 23,097.8–25,595.3 m3 s−1 for Pingshan, when using the 45 combinations of SMs, DFs, and PMs. For a 1000-year event, the estimated discharge ranges increased to 74,492.5–125,658.0 and 27,339.2–41,718.1 m3 s−1 for Cuntan and Pingshan, respectively. Application of the analysis of variance method showed that the total sum of the squares of the estimated return levels increased with the widening of the return periods, suggestive of increased uncertainties. However, the contributions of the different sources to the uncertainties were different. For Cuntan, where the discharge changed significantly, the SM appeared to be the largest source of uncertainty. For Pingshan, where the discharge series remained almost stable, the DF contributed most to the uncertainty. Therefore, multiple uncertainty sources in estimating return periods should be considered to meet the demands of different planning purposes. The research results also suggest that uncertainties of return level estimation could be reduced if an optimized DF were used, or if the decadal peak over threshold SM were used, which is capable of representing temporal changes of hydrological series.

2016 ◽  
Vol 10 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Michał Marosz

Abstract The paper presents the analysis of the anemological conditions variability over Poland with the usage of geostrophic wind vector as an objective (and homogenous) information concerning the airflow over the area of research. The geostrophic wind vector components are calculated using SLP and air temperature (at sigma 995 level) at selected gridpoints which were subsequently interpolated to a central point thus describing the average flow over the research area. The data originated from NCEP/NCAR Reanalysis and its temporal range was 1951-2014. The analysis covers statistical characteristics of the overall annual cycle as well as trend analysis of the airflow features over Poland: geostrophic wind vector module (V), and its zonal (u) and meridional (v) components. Aside from general statistical characteristics for averages and extremes (quantiles 10% and 90%) GEV distribution was fitted to maximum annual/monthly geostrophic wind speed values which allowed the estimation of return levels for selected return periods. For the period 1951-2014 average geostrophic wind velocity over Poland equals 7.4 ms−1 and the 99% quantile exceeds 21 ms−1. Maximum speed ever recorded equalled 37.6 ms−1. Geostrophic wind vector module (V) and its components (u, v) exhibit clear annual cycle with the highest V values in winter. Positive (westerly) u values dominate in the colder part of the year. In spring the dominance of eastern advection appears and in summer the prevalence of westerly flow is only minimal. There exists a distinctive variability of decadal directional structure and this is clearly visible in the substantial increase in the share of western sector frequencies in 1981-1990 and following decade. Monthly V averages do not exhibit (except October) statistically significant trends whereas in spring and summer months as well as for annual averages of u component trend is significant. There are virtually no significant changes in the v values. GEV analysis allowed the year to be divided into two parts. Warm one with relatively low return levels – for many months not exceeding 20 ms−1 even for 50y return period. On the other hand winter months return level values exceed 30 ms−1 even for relatively short return periods (20y) with upper estimates for 100y return period closing to 40 ms−1.


2013 ◽  
Vol 10 (5) ◽  
pp. 866-872 ◽  
Author(s):  
Xiao-guo Wang ◽  
Bo Zhu ◽  
Ke-ke Hua ◽  
Yong Luo ◽  
Jian Zhang ◽  
...  

2020 ◽  
Vol 5 (3) ◽  
pp. 121-128
Author(s):  
Ze’en Yu ◽  
Lixia Luo ◽  
Fang Zhang ◽  
Meiyan Hong ◽  
Xiangxiang Zhang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document