1) low flow forecast
枯水径流预报
2) dry season prediction
枯季径流预报
1.
Then the dry season prediction of Tangnaihai Hydrology Station of Yellow River is given by Neural Network Toolbox of Matlab7.
采用反馈Elman网络,对黄河唐乃亥水文站及上游玛曲站近45年(1959~2003年)年降雨及径流流量进行分析,建立了基于反馈神经网络的黄河源区枯季径流预报模型。
4) low flow
枯水径流
1.
Daily runoffs during low flow were simulated with the model for the four study basins in the Dongjiang watershed.
用该模型对东江流域中的四个流域进行枯季日径流过程模拟,在对模拟结果进行初步分析的基础上,着重以连续多日最小平均流量、基流分割结果等描述枯水径流特点的枯水特征值为研究对象,分析用TOPMODEL模型所模拟出来的枯水流量的模拟精度。
2.
The 7-day low flow was regarded as statistical quantity,on the base of analysis of the discordancy measure and heterogeneity measure,the regional low flow frequency for Donjiang basin was carried out using L-moments based five frequency distributions with three parameters viz.
以年最小七日平均流量作为枯水径流的统计特征量,在水文站点一致性分析、水文相似区鉴别的基础上,以常见的5种三参数分布函数:Generalized Pareto分布(GPA)、Generalized Extreme-Value分布(GEV)、Generalized Logistic分布(GLO)、LogNormal分布(LN3)和Pearson typeⅢ(PⅢ)为研究对象进行东江流域区域枯水频率分析。
3.
The importance of low flow forecast was briefly described in this paper.
建立了６种数据处理方式下的泾河、北洛河、渭河及大通河主要水文站的候、旬、月枯水径流预报模型。
5) Low water runoff
枯水径流
1.
This paper mainly expounds the hydrographical features of the river low water runoff and its extremum, including discharge variation, occurring time, water quality and water temperature of river low water runoff In the paper, discussion is emphsized on the mechanism of river low water runoff and its influencing factors such as: ① zonal elements, including climate, vegetation, etc.
主要论述了河川枯水径流与极值的基本特征——枯水流量、枯水发生时间等，重点分析研究了枯水径流与极值形成的机理，即影响枯水径流极值形成机理的地带性因素——气候、植被等，以及枯水径流产生机理的非地带性因素——地质（包括水文地质）、地形地貌和人为因素。
6) low flow runoff
枯水径流
1.
Using the systemic and comparative methods, this paper reviews the low flow runoff from natural and anthropogenic factors, its forecast methods.
采用系统和比较的方法对影响枯水径流的自然与人为因素、枯水径流的预测方法进行总结。
补充资料：短期天气预报(见天气预报和天气图预报)
短期天气预报(见天气预报和天气图预报)
u。。，，，:‘，anq，yubao短期天气预报见关匀布绍升，二，二一一
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条