A quantitative model for danger degree evaluation of staged operation of earth dam reservoir in flood season and its application

Based on the natural disaster risk evaluation mode, a quantitative danger degree evaluation model was developed to evaluate the danger degree of earth dam reservoir staged operation in the flood season. A formula for the overtopping risk rate of the earth dam reservoir staged operation was established, with consideration of the joint effect of flood and wind waves in the flood sub-seasons with the Monte Carlo method, and the integrated overtopping risk rate for the whole flood season was obtained via the total probability approach. A composite normalized function was used to transform the dam overtopping risk rate into the danger degree, on a scale of 0-1. Danger degree gradating criteria were divided by four significant characteristic values of the dam overtopping rate, and corresponding guidelines for danger evaluation are explained in detail in this paper. Examples indicated that the dam overtopping danger degree of the Chengbihe Reservoir in China was 0.33-0.57, within the range of moderate danger level, and the flood-limiting water level （FLWL） can be adjusted to 185.00 m for the early and main flood seasons, and 185.00-187.50 m for the late flood season. The proposed quantitative model offers a theoretical basis for determination of the value of the danger degree of an earth dam reservoir under normal operation as well as the optimal scheduling scheme for the reservoir in each stage of the flood season.

The Operational Forecasting of Total Precipitation in Flood Seasons (April to September) of 5 Years (1983-1987)

Ⅰ.INTRODUCTION We have discovered that there exists a good corresponding relationship between theanomalous axes of soil temperature at a depth of 1.6m in winter (December to February) andprecipitations in following flood season (Tang et al., 1982a). We have also designed a simplethermodynamical model and applied it to the forecasting of precipitations in the flood season(Tang et al., 1982 b,c). The practical forecast started from 1975. Before 1980, however, therewere only 40-50 stations in China for measuring the soil temperature at a 1.6m depth. Since1980, the stations have been increased to a total of about 180, but no available mean valueshad been obtained from newly added stations before 1982. Therefore the analysis and map-ping of anomalies of soil temperature was not performed until 1983, and from then on theprecision of analysis has been greatly improved. The following is the actual situation of forecast in five years from 1983 to 1987.

PREDICTION OF FLOOD SEASON PRECIPITATION IN SOUTHWEST CHINA BASED ON IMPROVED PSO-PLS

In order to achieve the best predictive effect of the Partial Least Squares(PLS) regression model, Particle Swarm Optimization(PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares(PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season.Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function(MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also built up to improve the prediction results.Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model.The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation(PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.

Frequency, Intensity Statistics and Cyclical Analysis of Rainstorm in the Flood Season in Guangzhou

[Objective] The research aimed to analyze the variations of rainstorm frequency, intensity and period in the flood season in Guangzhou. [Method] Based on the daily precipitation data in Guangzhou City during 1951-2010, the interannual and interdecadal variation characteristics of rainstorm in the flood season in recent 60 years were analyzed by using the linear regression analysis, correlation analysis, wavelet analysis and so on. Moreover, the relationship between the rainstorm in the flood season and annual average temperature was analyzed. [Result] In recent 60 years, the rainstorm amount and days in the flood season in Guangzhou respectively increased with 6.23 mm/10 a and 0.27 d/10 a linear trends. The most rainstorm days (rainfall) was in 2001 and was 15 d (1 085.7 mm). There was no rainstorm in the least year (1990). The interannual variations of rainstorm amount and days in the flood season in Guangzhou obviously increased in recent 20 years. The decadal and interannual variations of rainstorm in the prior and latter flood seasons had the difference. The trend in the prior flood season increased and in the latter flood season slightly decreased. The positive correlation between the rainstorm days and the annual average temperature in the flood season in Guangzhou was significant, and the relative coefficient was 0.22, which passed α=0.02 significance level test. The total rainstorm days in the prior flood season in Guangzhou City mainly had 4.2-year interannual and 52.9-year interdecadal periodic variations. The total rainstorm days in the latter flood season mainly had 5.5-year interannual and 18.4-year interdecadal periodic variations. [Conclusion] The research provided the scientific basis for the precipitation forecast in the flood season.

抚顺地区旱涝分析及对粮食产量的影响(英文)

Based on monthly precipitation data during 1961-2008 in 50 stations in Fushun,drought and flood indicators of three counties were calculated with Z index method. The geographical and seasonal distribution characteristics of Fushun were analyzed,and so was the impact of droughts and floods on food production. It shows that,since 1961,there are 7 poor harvest years in Fushun,with quadrennial caused by continuous seasonal floods or droughts,two years by year drought,one year by summer flood.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Runoff characteristics in flood and dry seasons based on wavelet analysis in the source regions of the Yangtze and Yellow rivers

By decomposing and reconstructing the runoff information from 1965 to 2007 of the hydrologic stations of Tuotuo River and Zhimenda in the source region of the Yangtze River, and Jimai and Tangnaihai in the source region of the Yellow River with db3 wavelet, runoff of different hydrologic stations tends to be declining in the seasons of spring flood, summer flood and dry ones except for that in Tuotuo River. The declining flood/dry seasons series was summer 〉 spring 〉 dry; while runoff of Tuotuo River was always increasing in different stages from 1965 to 2007 with a higher increase rate in summer flood seasons than that in spring ones. Complex Morlet wavelet was selected to detect runoff periodicity of the four hydrologic stations mentioned above. Over all seasons the periodicity was 11-12 years in the source region of the Yellow River. For the source region of the Yangtze River the periodicity was 4-6 years in the spring flood seasons and 13-14 years in the summer flood seasons. The differences of variations of flow periodicity between the upper catchment areas of the Yellow River and the Yangtze River and between seasons were considered in relation to glacial melt and annual snowfall and rainfall as providers of water for runoff.

STUDY ON THE POLLUTION OF URBAN SCENIC WATER BODY BY MUNICIPAL DRAINAGE IN FLOOD SEASON AND ITS CONTROL PLANNING

In this article, based on river quality simulation and system optimization, a water quality model was established for scenic river after rainfall discharge in flood season, with the target of making water pollutants meet the standard in priority and saving expenditure on pollution control. With the principle of reducing sewage from combined sewage pumping station and heavily polluted initial rainwater, a mathematical multiobjective planning model was constructed for rain sewage pollution control in flood season, and one scenic river in a northern city was taken for simulation example. The results show that： the optimization result meets the requirements of planning, among which, sewage reduction from the combined pumping station accounts for 17.38% in the total reduction of rain sewage, and the reduction in the heavily polluted rain water accounts for 77.24% in the total reduction of rainwater pumping station. The planning scheme can provide theoretical basis for pollution control of scenic river in flood season, and for rational reconstruction and layout of outfalls along two banks of the river.

Using a Hidden Markov Model to Analyze the Flood-Season Rainfall Pattern and Its Temporal Variation over East China

The homogeneous hidden Markov model(HMM), a statistical pattern recognition method, is introduced in this paper. Based on the HMM, a 53-yr record of daily precipitation during the flood season(April-September) at 389 stations in East China during 1961-2013 is classified into six patterns: the South China(SC) pattern, the southern Yangtze River(SY) pattern, the Yangtze-Huai River(YH) pattern, the North China(NC) pattern, the overall wetter(OW) pattern, and the overall drier(OD) pattern. Features of the transition probability matrix of the first four patterns reveal that 1) the NC pattern is the most persistent, followed by the YH, and the SY is the least one; and 2) there exists a SY-SC-SY-YH-NC propagation process for the rain belt over East China during the flood season. The intraseasonal variability in the occurrence frequency of each pattern determines its start and end time. Furthermore,analysis of interdecadal variability in the occurrence frequency of each pattern in recent six decades has identified three obvious interdecadal variations for the SC, YH, and NC patterns in the mid-late 1970 s, the early 1990 s, and the late 1990 s. After 2000, the patterns concentrated in the southern region play a dominant role, and thus there maintains a "flooding in the south and drought in the north" rainfall distribution in eastern China. In summary, the HMM provides a unique approach for us to obtain both spatial distribution and temporal variation features of flood-season rainfall.