The influence of various factors, mechanisms, and principles affecting runoff are summarized as periodic law, random law, and basin-wide law. Periodic law is restricted by astronomical factors, random law is restricte...The influence of various factors, mechanisms, and principles affecting runoff are summarized as periodic law, random law, and basin-wide law. Periodic law is restricted by astronomical factors, random law is restricted by atmospheric circulation, and basin-wide law is restricted by underlying surface. The commensurability method was used to identify the almost period law, the wave method was applied to deducing the random law, and the precursor method was applied in order to forecast runoff magnitude for the current year. These three methods can be used to assess each other and to forecast runoff. The system can also be applied to forecasting wet years, normal years and dry years for a particular year as well as forecasting years when floods with similar characteristics of previous floods, can be expected. Based on hydrological climate data of Baishan (1933-2009) and Nierji (1886-2009) in the Songhua River Basin, the forecasting results for 2010 show that it was a wet year in the Baishan Reservoir, similar to the year of 1995; it was a secondary dry year in the Nierji Reservoir, similar to the year of 1980. The actual water inflow into the Baishan Reservoir was 1.178 × 10 10 m 3 in 2010, which was markedly higher than average inflows, ranking as the second highest in history since records began. The actual water inflow at the Nierji station in 2010 was 9.96 × 10 9 m 3 , which was lower than the average over a period of many years. These results indicate a preliminary conclusion that the methods proposed in this paper have been proved to be reasonable and reliable, which will encourage the application of the chief reporter release system for each basin. This system was also used to forecast inflows for 2011, indicating a secondary wet year for the Baishan Reservoir in 2011, similar to that experienced in 1991. A secondary wet year was also forecast for the Nierji station in 2011, similar to that experienced during 1983. According to the nature of influencing factors, mechanisms and forecasting methods and the service objects, mid-to long-term hydrological forecasting can be divided into two classes:mid-to long-term runoff forecasting, and severe floods and droughts forecasting. The former can be applied to quantitative forecasting of runoff, which has important applications for water release schedules. The latter, i.e., qualitative disaster forecasting, is important for flood control and drought relief. Practical methods for forecasting severe droughts and floods are discussed in this paper.展开更多
The occurrence of debris flow is affected by many factors. Risk zoning of debris flow plays a vital role in the early-warning and prediction of abrupt geological hazards, and exploration of new method is needed in the...The occurrence of debris flow is affected by many factors. Risk zoning of debris flow plays a vital role in the early-warning and prediction of abrupt geological hazards, and exploration of new method is needed in the early-warning and prediction of geological hazards. The extension theory is a new method to solve contradiction matters. Based on extension theory, AHP and GIS, the risk zoning model of debris flow was established in this paper. The result of this research provides a new way in the risk zoning, early-warning and prediction of debris flow展开更多
Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersectio...Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.展开更多
Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extre...Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extreme EVI （value Type-l）, LN （Log normal） and LPIII （Log Pearson Type III）. The models were used to predict and compare corresponding flood discharge estimates at 2, 5, 10, 25, 50, 100 and 200 years return periods. The results indicated that Extreme Value Type 1 distribution predicted discharge values ranging from 26.6 m3/s for two years to 431.8 m3/s for 200 years return periods; the Log Pearson Type III distribution predicted discharge values ranging from 127.2 m3/s for two years to 399.54 m3/s for 200 years return periods and the Log normal distribution predicted discharge values ranging from 116.2 m3/s for two years to 643.9 m3/s for 200 years return periods. From the results~ it was concluded that for lower return periods （T_〈 50 yrs） Extreme Value Type 1 and Log Pearson Type III could be used to estimate flood quantile values at the station while for higher return periods （T 〉 50 yrs） Log Normal probability distribution model which gives higher estimates could be utilized for safe design in view of the short length of discharge records used for the analysis.展开更多
基金Under the auspices of National Natural Science Foundation(No.50879028)Open Fund of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering of Nanjing Hydraulic Research institute(No.2009491311)+1 种基金Open Research Fund Program of State key Laboratory of Hydroscience and Engineering,Tsinghua University(No.sklhse-2010-A-02)Application Foundation Items of Science and Technology Department of Jilin Province(No.2011-05013)
文摘The influence of various factors, mechanisms, and principles affecting runoff are summarized as periodic law, random law, and basin-wide law. Periodic law is restricted by astronomical factors, random law is restricted by atmospheric circulation, and basin-wide law is restricted by underlying surface. The commensurability method was used to identify the almost period law, the wave method was applied to deducing the random law, and the precursor method was applied in order to forecast runoff magnitude for the current year. These three methods can be used to assess each other and to forecast runoff. The system can also be applied to forecasting wet years, normal years and dry years for a particular year as well as forecasting years when floods with similar characteristics of previous floods, can be expected. Based on hydrological climate data of Baishan (1933-2009) and Nierji (1886-2009) in the Songhua River Basin, the forecasting results for 2010 show that it was a wet year in the Baishan Reservoir, similar to the year of 1995; it was a secondary dry year in the Nierji Reservoir, similar to the year of 1980. The actual water inflow into the Baishan Reservoir was 1.178 × 10 10 m 3 in 2010, which was markedly higher than average inflows, ranking as the second highest in history since records began. The actual water inflow at the Nierji station in 2010 was 9.96 × 10 9 m 3 , which was lower than the average over a period of many years. These results indicate a preliminary conclusion that the methods proposed in this paper have been proved to be reasonable and reliable, which will encourage the application of the chief reporter release system for each basin. This system was also used to forecast inflows for 2011, indicating a secondary wet year for the Baishan Reservoir in 2011, similar to that experienced in 1991. A secondary wet year was also forecast for the Nierji station in 2011, similar to that experienced during 1983. According to the nature of influencing factors, mechanisms and forecasting methods and the service objects, mid-to long-term hydrological forecasting can be divided into two classes:mid-to long-term runoff forecasting, and severe floods and droughts forecasting. The former can be applied to quantitative forecasting of runoff, which has important applications for water release schedules. The latter, i.e., qualitative disaster forecasting, is important for flood control and drought relief. Practical methods for forecasting severe droughts and floods are discussed in this paper.
基金Supported by Project of Special Foundation for Outstanding Scientists of Beijing, China(No.20051D1100205)
文摘The occurrence of debris flow is affected by many factors. Risk zoning of debris flow plays a vital role in the early-warning and prediction of abrupt geological hazards, and exploration of new method is needed in the early-warning and prediction of geological hazards. The extension theory is a new method to solve contradiction matters. Based on extension theory, AHP and GIS, the risk zoning model of debris flow was established in this paper. The result of this research provides a new way in the risk zoning, early-warning and prediction of debris flow
基金Project(71101109) supported by the National Natural Science Foundation of China
文摘Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.
文摘Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extreme EVI （value Type-l）, LN （Log normal） and LPIII （Log Pearson Type III）. The models were used to predict and compare corresponding flood discharge estimates at 2, 5, 10, 25, 50, 100 and 200 years return periods. The results indicated that Extreme Value Type 1 distribution predicted discharge values ranging from 26.6 m3/s for two years to 431.8 m3/s for 200 years return periods; the Log Pearson Type III distribution predicted discharge values ranging from 127.2 m3/s for two years to 399.54 m3/s for 200 years return periods and the Log normal distribution predicted discharge values ranging from 116.2 m3/s for two years to 643.9 m3/s for 200 years return periods. From the results~ it was concluded that for lower return periods （T_〈 50 yrs） Extreme Value Type 1 and Log Pearson Type III could be used to estimate flood quantile values at the station while for higher return periods （T 〉 50 yrs） Log Normal probability distribution model which gives higher estimates could be utilized for safe design in view of the short length of discharge records used for the analysis.
