Trend and Return Level Analysis of Extreme Rainfalls in Senegal

In recent years, Senegal has been confronted with increasingly frequent and damaging extreme events. In the context of climate change, we conducted this study to characterize the trends of rainfall extremes in Senegal. In this work, we used daily rainfall data from 27 stations in Senegal from the period 1951 to 2005 (55 years). To study their linear trends, non-stationary extreme value models with time as a covariate are fitted to evaluate them. Our results indicate a decreasing trend of extreme rainfalls at most of the stations except for 5 stations. However, the decreasing trends are only significant for two stations (Thiès and Kidira), however, this can only be taken as information that climate change may have already impacted extreme rainfalls. For the 20-year and 30-year return periods, the results show that they have undergone changes, in fact for almost all stations, the trends in return periods are decreasing.

Research on Flood Control Design Based on Joint Distribution of Flood Levels and Flood Peak Discharges

The return level of the joint distribution of flood levels and flood peak discharges was analyzed by Archimedean Gumbel-Hougaard copula and Kendall distribution functions.Using the annual maximum level(H)and discharge(Q)of flood peak at Boluo Hydrologic Station in the Dongjiang River in last 56 years,the"OR"return period,"AND"return period and Kendall return period of their joint distribution and the most likely design flood value were calculated.The main conclusions of this study can be summarized as follows:the Kendall return period can more accurately reflect the risk rate of the combination of flood elements,relative to"OR"return period and"AND"return period.The design value of univariate flood element based on the current specification can meet the design standard.While the design value calculated according to"OR"return period was on the high side,and the design value calculated by the"AND"return period was on low side.Based on the principle of maximum probability,the calculated design value of Kendall return period under the different combinations of flood peak discharge and water level can provide new options for flood control project safety and risk management.

Regional Frequency Analysis of Observed Sub-Daily Rainfall Maxima over Eastern China

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.

Extreme air pollution events:Modeling and prediction

In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.

Daily extreme precipitation and trends over China

Based on daily precipitation data of more than 2000 Chinese stations and more than 50 yr, we constructed time series of extreme precipitation based on six different indices for each station: annual and summer maximum(top-1) precipitation,accumulated amount of 10 precipitation maxima(annual, summer; top-10), and total annual and summer precipitation.Furthermore, we constructed the time series of the total number of stations based on the total number of stations with top-1 and top-10 annual extreme precipitation for the whole data period, the whole country, and six subregions, respectively. Analysis of these time series indicate three regions with distinct trends of extreme precipitation:(1) a positive trend region in Southeast China,(2) a positive trend region in Northwest China, and(3) a negative trend region in North China. Increasing(decreasing)ratios of 10–30% or even >30% were observed in these three regions. The national total number of stations with top-1 and top-10 precipitation extremes increased respectively by 2.4 and 15 stations per decade on average but with great inter-annual variations.There have been three periods with highly frequent precipitation extremes since 1960:(1) early 1960 s,(2) middle and late 1990 s,and(3) early 21 st century. There are significant regional differences in trends of regional total number of stations with top-1 and top-10 precipitation. The most significant increase was observed over Northwest China. During the same period, there are significant changes in the atmospheric variables that favor the decrease of extreme precipitation over North China: an increase in the geopotential height over North China and its upstream regions, a decrease in the low-level meridional wind from South China coast to North China, and the corresponding low moisture content in North China. The extreme precipitation values with a50-year empirical return period are 400–600 mm at the South China coastal regions and gradually decrease to less than 50 mm in Northwest China. The mean increase rate in comparison with 20-year empirical return levels is 6.8%. The historical maximum precipitation is more than twice the 50-year return levels.