Frequency Analysis for Predicting Extreme Precipitation in Changxing Station of Taihu Basin, China

Abstract

Zhou, Z.; Liu, S.; Hua H.; Chen, C.S.; Zhong G.; Lin, H., and Huang, C.W., 2014. Frequency analysis for predicting extreme precipitation in Changxing station of Taihu Rainfall induced flooding is one of the most severe natural disasters in coastal regions. In recent years, along with global warming and sea level rising, extreme hydrological events, such as extreme precipitation, are of high occurrence. Meanwhile, rapid urbanization makes the urban environment transformed dramatically and results in additional flood ventures. Taihu Basin, located in Yangtze Delta, is the richest basin in China and flood control planning in this region is of high significance. Therefore, precipitation analysis, as a basic work of flood design, should be accurate and precise. In this study, based on precipitation data at Changxing Station of Taihu Basin, precipitation frequency analysis using mixed methods is performed. Two of the most applied distribution models, Pearson-III (P3) and Generalized Extreme Value (GEV), are investigated. For parameter estimation method of probability distribution functions, maximum likelihood estimation (MLE) and L-moments (LM) are used. In addition, seeking-matrix curve fitting based on conventional moments (CM) is also investigated to compare the calculation results. The performance of mixed methods is tested by two classical goodness-of-fit tests, Chi-Square test and K-S test. Consequently, GEV distribution model based on LM is evaluated to be the best fitting model for identifying and predicting future precipitation occurrence. So precipitation estimations from different return periods at Changxing Station are identified. This study is a new attempt to precipitation frequency analysis in the stations of Taihu Basin and the result can provide a reference for flood risk and water resource management in Taihu Basin and even in more other regions in China. ADDITIONAL INDEX WORDS:Maximum daily precipitation, frequency analysis, parameter estimation, Taihu Basin.

The complexities of urban flood response: Flood frequency analyses for the Charlotte metropolitan region

“…The variability in space‐time rainfall structure is closely linked to different storm types. Organized thunderstorm systems, for instance, exhibit the largest short‐term rainfall rates and the largest spatial variability of rainfall rate [ Syed et al ., ; Smith et al ., ; Villarini et al ., ; Yang et al ., ; Zhou et al ., ; Llasat et al ., ]. Yang et al .…”

Quantifying Uncertainty of Probable Maximum Flood

“…Hershfield, 1961;Koutsoyiannis,1999;Douglas and Barros, 2003) utilize historical precipitation records and are based on frequency analysis or probability inference. Among the statistical methods, the Hershfield method has been most widely used because of the relatively large data sets it analyzed, the logical frequency analysis it derived, and the recognition by WMO ( 2009) (Casas et al, 2008;Desa and Rakhecha, 2007;Dhar et al, 1980;Mejia and Villegas, 1979;Nobilis et al, 1991;Rakhecha and Soma, 1994;Rezacova et al, 2005;Zhou et al, 2014;Sherif et al, 2014).…”

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

“…The list of stations included in the study are Silchar (1), Dholai (2), Goalpara (3), Guwahati (4), North Lakimpur (5), Choudhoughat (6), Batadighat (7), Kampur (8), Sibsagar (9), Beki Rd. Bridge (10), Dibrugarh (11), Jorhat (12), Neamatighat (13), Kherunighat (14), Bokajan (15), Gossaigaon (16), Kokrajhar (17), Tezpur (18), Mellabazar (19) Aie NH.Xing (20), Dharamtul (21), Golaghat (22), Dhollabazar (23), Margherita (24), Gharmura (25), Shillong (26), Cherrapunjee (27), Mawsynram (28), Kohima (29), Imphal (30), Aizwal (31), Agartala (32), and Kailashahar (33).…”

“…Maximum Likelihood Estimation (MLE) is a frequently used technique for estimating parameters of probability distributions, in which the parameter estimates generate the highest chance of occurrence for observations. With numerous application in extreme value models [12,30], it is considered in the present study for comparison with estimates of ITI based L-Moment estimates. GA was used for optimizing the MLE parameters to arrive at the likelihood of the real value.…”

“…With uncertainty and suitability involved in the selection of an appropriate probability distribution for any regional frequency analysis, it is crucial to have a performance comparison of fitted distributions against at-site analysis for the region. Numerous works on uncertainty analysis in regional frequency analysis of rainfall [10][11][12][13] have been published. Notable recent studies on the comparison of uncertainty analysis of rainfall from regional and at-site analysis can be found in the studies of [14][15][16], but very scarce studies are available for north east India.…”