Based on hourly rainfall observational data from 442 stations during 1960-2014, a regional frequency analysis of the annual maxima (AM) sub-daily rainfall series (1-, 2-, 3-, 6-, 12-, and 24-h rainfall, using a mov...Based on hourly rainfall observational data from 442 stations during 1960-2014, a regional frequency analysis of the annual maxima (AM) sub-daily rainfall series (1-, 2-, 3-, 6-, 12-, and 24-h rainfall, using a moving window approach) for eastern China was conducted. Eastern China was divided into 13 homogeneous regions: Northeast (NE1, NE2), Central (C), Central North (CN1, CN2), Central East (CE1, CE2, CE3), Southeast (SE1, SE2, SE3, SE4), and Southwest (SW). The generalized extreme value performed best for the AM series in regions NE, C, CN2, CE1, CE2, SE2, and SW, and the generalized logistic distribution was appropriate in the other regions. Maximum return levels were in the SE4 region, with value ranges of 80-270 mm (1-h to 24-h rainfall) and 108-390 mm (1-h to 24-h rainfall) for 20- and 100 yr, respectively. Minimum return levels were in the CN1 and NE1 regions, with values of 37-104 mm and 53-140 mm for 20 and 100 yr, respectively. Comparing return levels using the optimal and commonly used Pearson-III distribution, the mean return-level differences in eastern China for 1-24-h rainfall varied from -3-4 mm to -23-11 mm (- 10%-10%) for 20-yr events, reaching -6-26 mm (-10%-30%) and -10-133 mm (-10%-90%) for 100-yr events. In view of the large differences in estimated return levels, more attention should be given to frequency analysis of sub-daily rainfall over China, for improved water management and disaster reduction.展开更多
L-moments are defined as linear combinations of probability-weighted moments, They are, virtually unbiased for small samples, and perform well in parameter estimation, choice of the distribution type and regional anal...L-moments are defined as linear combinations of probability-weighted moments, They are, virtually unbiased for small samples, and perform well in parameter estimation, choice of the distribution type and regional analysis. The traditional methods of determining the design wave heights for planning marine structures use data only from the site of interest. Regional frequency analysis gives a new approach to estimate quantile by use of the homogeneous neighborhood informatian. A regional frequency analysis based on L-moments with a case study of the California coast is presented. The significant wave height data for the California coast is offered by NDBC. A 6-site region without 46023 is considered to be a homogeneous region, whose optimal regional distribution is Pearson Ⅲ. The test is conducted by a simulation process. The regional quantile is compared with the at-site quantile, and it is shown that efficient neighborhood information can be used via regional frequency analysis to give a reasonable estimation of the site without enough historical data.展开更多
Uncertainty exists widely in hydrological analysis, and this makes the process of uncertainty assessment very im- portant for making robust decisions. In this study, uncertainty sources in regional rainfall frequency ...Uncertainty exists widely in hydrological analysis, and this makes the process of uncertainty assessment very im- portant for making robust decisions. In this study, uncertainty sources in regional rainfall frequency analysis are identified for the first time. The numeral unite spread assessment pedigree (NUSAP) method is introduced and is first employed to quantify qual- itative uncertainty in regional rainfall frequency analysis. A pedigree matrix is particularly designed for regional rainfall frequency analysis, by which the qualitative uncertainty can be quantified. Finally, the qualitative and quantitative uncertainties are com- bined in an uncertainty diagnostic diagram, which makes the uncertainty evaluation results more intuitive. From the integrated diagnostic diagram, it can be determined that the uncertainty caused by the precipitation data is the smallest, and the uncertainty from different grouping methods is the largest. For the downstream sub-region, a generalized extreme value (GEV) distribution is better than a generalized logistic (GLO) distribution; for the south sub-region, a Pearson type III (PE3) distribution is the better choice; and for the north sub-region, GEV is more appropriate.展开更多
基金supported by the National Basic Research(973)Program of China(Grant Nos.2013CB430205 and 2012CB955903)the National Natural Science Foundation of China(Grant Nos.41171406,41375099,41561124014 and 91337108)
文摘Based on hourly rainfall observational data from 442 stations during 1960-2014, a regional frequency analysis of the annual maxima (AM) sub-daily rainfall series (1-, 2-, 3-, 6-, 12-, and 24-h rainfall, using a moving window approach) for eastern China was conducted. Eastern China was divided into 13 homogeneous regions: Northeast (NE1, NE2), Central (C), Central North (CN1, CN2), Central East (CE1, CE2, CE3), Southeast (SE1, SE2, SE3, SE4), and Southwest (SW). The generalized extreme value performed best for the AM series in regions NE, C, CN2, CE1, CE2, SE2, and SW, and the generalized logistic distribution was appropriate in the other regions. Maximum return levels were in the SE4 region, with value ranges of 80-270 mm (1-h to 24-h rainfall) and 108-390 mm (1-h to 24-h rainfall) for 20- and 100 yr, respectively. Minimum return levels were in the CN1 and NE1 regions, with values of 37-104 mm and 53-140 mm for 20 and 100 yr, respectively. Comparing return levels using the optimal and commonly used Pearson-III distribution, the mean return-level differences in eastern China for 1-24-h rainfall varied from -3-4 mm to -23-11 mm (- 10%-10%) for 20-yr events, reaching -6-26 mm (-10%-30%) and -10-133 mm (-10%-90%) for 100-yr events. In view of the large differences in estimated return levels, more attention should be given to frequency analysis of sub-daily rainfall over China, for improved water management and disaster reduction.
基金This research was financially supported bythe National Natural Science Foundation of China (Grant No.50279028)
文摘L-moments are defined as linear combinations of probability-weighted moments, They are, virtually unbiased for small samples, and perform well in parameter estimation, choice of the distribution type and regional analysis. The traditional methods of determining the design wave heights for planning marine structures use data only from the site of interest. Regional frequency analysis gives a new approach to estimate quantile by use of the homogeneous neighborhood informatian. A regional frequency analysis based on L-moments with a case study of the California coast is presented. The significant wave height data for the California coast is offered by NDBC. A 6-site region without 46023 is considered to be a homogeneous region, whose optimal regional distribution is Pearson Ⅲ. The test is conducted by a simulation process. The regional quantile is compared with the at-site quantile, and it is shown that efficient neighborhood information can be used via regional frequency analysis to give a reasonable estimation of the site without enough historical data.
基金the National Natural Science Foundation of China,the Zhejiang Provincial Natural Science Foundation of China
文摘Uncertainty exists widely in hydrological analysis, and this makes the process of uncertainty assessment very im- portant for making robust decisions. In this study, uncertainty sources in regional rainfall frequency analysis are identified for the first time. The numeral unite spread assessment pedigree (NUSAP) method is introduced and is first employed to quantify qual- itative uncertainty in regional rainfall frequency analysis. A pedigree matrix is particularly designed for regional rainfall frequency analysis, by which the qualitative uncertainty can be quantified. Finally, the qualitative and quantitative uncertainties are com- bined in an uncertainty diagnostic diagram, which makes the uncertainty evaluation results more intuitive. From the integrated diagnostic diagram, it can be determined that the uncertainty caused by the precipitation data is the smallest, and the uncertainty from different grouping methods is the largest. For the downstream sub-region, a generalized extreme value (GEV) distribution is better than a generalized logistic (GLO) distribution; for the south sub-region, a Pearson type III (PE3) distribution is the better choice; and for the north sub-region, GEV is more appropriate.