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171.
The variability of the summer rainfall over China is analyzed using the EOF procedure with a new parameter (namely, mode station variance percentage) based on 1951-2000 summer rainfall data from 160 stations in China. Compared with mode variance friction, the mode station variance percentage not only reveals more localized characteristics of the variability of the summer rainfall, but also helps to distinguish the regions with a high degree of dominant EOF modes representing the analyzed observational variable. The atmospheric circulation diagnostic studies with the NCEP/NCAR reanalysis daily data from 1966 to 2000 show that in summer, abundant (scarce) rainfall in the belt-area from the upper-middle reaches of the Yangtze River northeastward to the Huaihe River basin is linked to strong (weak) heat sources over the eastern Tibetan Plateau, while the abundant (scarce) rainfall in the area to the south of the middle-lower reaches of the Yangtze River is closely linked to the weak (strong) heat sources over the tropical western Pacific. 相似文献
172.
Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment 总被引:1,自引:0,他引:1
Estimation of runoff is a prerequisite for many applications involving conservation and management of water resources. This study is undertaken in the Upper Damodar Valley Catchment (UDVC) having a drainage area of 17513.08 km2 for prediction of monthly runoff. Thirty one microwatersheds and 15 sub-watersheds were selected from a total of 716 microwatersheds in the catchment area for this study. The feasibility of using different soil attributes (particle size distribution, organic matter content and apparent density), topographic attributes (primary, secondary and compound), geomorphologic attributes (basin, relief and network indices) and vegetation attribute as Normalized Difference Vegetation Index (NDVI), on prediction of monthly runoff were explored in this study. Principal Component Analysis (PCA) was applied to minimize the data redundancy of the input variables. Ten significant input variables namely; watershed length (km), elongation ratio, bifurcation ratio, area ratio, coarse sand (%), fine sand (%), elevation (m), slope (°), profile curvature (rad/m) and NDVI were selected. The selected input variables were added in hierarchy with monthly rainfall (mm) as inputs for prediction of monthly runoff (mm) using Bootstrap based artificial neural networks (BANN). The performance of the models was tested using Spearman’s correlation coefficient (r), coefficient of efficiency (COE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Best performance was observed for model with monthly rainfall, slope, coarse sand, bifurcation ratio and Normalized Difference Vegetation Index (NDVI) as inputs (r = 0.925 and COE = 0.839). Increase in number of input variables did not necessarily yield better performances of the BANN models. Selection of relevant inputs and their combinations were found to be key elements in determining the performance of BANN models. Annual runoff map was generated for all the microwatersheds utilizing the weights of the best performing BANN model. This study reveals that the specific combinations of soil, topography, geomorphology and vegetation inputs can be utilized for better prediction of monthly runoff. 相似文献
173.
基于HBV模型的淮河流域洪水致灾临界雨量研究 总被引:4,自引:0,他引:4
根据流域暴雨洪水致灾机制,文章提出了考虑前期基础水位的动态致灾临界雨量指标,并以淮河上游地区为例,基于HBV水文模型建立了降水-流量-水位关系,并根据这种关系确立了临界雨量确定的方法流程.首先基于历史水文数据率定和验证模型,得到适用于研究区的最优化模型参数,然后构建洪水上涨期水位流量关系,最后以是否达到致灾水位为标准,通过模型试算并结合水位流量关系曲线反推出致灾临界雨量值.在淮河上游地区的研究中,利用2002-2009年逐日气象水文数据对HBV模型进行了参数率定和检验,并针对洪水过程进行了参数优化,经过率定后HBV模型对王家坝以上流域具备较好的适用性,对典型洪水过程模拟的确定性系数和NASH效率系数均在0.8以上;根据王家坝站实测流量水位数据,构建了概化的单一关系曲线;结合HBV模型和水位流量关系得到了王家坝以上流域的动态致灾临界雨量指标,临界雨量值随前期基础水位升高而减小,并且随着前期水位的变化,临界雨量值呈现了明显的非线性响应特征. 相似文献
174.
2014年7月大气环流和天气分析 总被引:3,自引:2,他引:3
提2014年7月环流特征如下:北半球高纬地区为单一极涡,中高纬地区呈5波型分布,巴尔喀什湖附近低槽和东亚大槽强度均较常年偏强;西北太平洋副热带高压带呈东西带状分布,强度与常年同期相当。7月全国平均降水量115.0 mm,较常年同期(120.6 mm)偏少4.6%;全国平均气温为22.3℃,较常年同期(21.9℃)偏高0.4℃。月内共出现8次强降水过程,多站出现极端日降水量。7月共有5个热带气旋(风力8级以上)在西北太平洋和南海活动,生成个数较常年偏多,并有“威马逊”、“麦德姆”2个热带气旋登陆。华南、江南等地出现持续高温天气,全国87个气象观测站发生极端高温事件,74站发生极端日降水量事件。 相似文献
175.
176.
177.
This study developed a correction approach to improve the rainfall field estimation using the TRMM rainfall product in a sparsely-gauged mountainous basin. First, Thiessen polygons were generated to define the measurement domain of each raingauge. Second, the rainfall of TRMM pixels in each Thiessen polygon was corrected using a benchmark method based on the difference between the monthly rainfall estimated by a raingauge and the TRMM pixel that possessed a gauge station (referred to as a gauged pixel). Third, the rainfall values in the gauged pixels were adjusted to the weighted average value of the gauge rainfall and corrected pixel rainfall. Finally, the rainfall in the non-gauged TRMM pixels was corrected as the sum of two terms. The first term is the adjusted rainfall in the corresponding gauged pixel in the same Thiessen polygon, and the second term is the rainfall (after benchmark correction) difference between the current pixel and the gauged pixel. Our results indicate that the corrected rainfall data outperforms the original TRMM product in the simulations of moderate and low flows and outperforms the sparse raingauges in the simulations of both peak and low flows.
EDITOR A. Castellarin; ASSOCIATE EDITOR S. Huang 相似文献
178.
考虑径流影响的滑坡降雨入渗二维有限元模拟及应用 总被引:1,自引:0,他引:1
降雨是滑坡失稳的常见诱发因素之一,现有数值模拟方法由于不能考虑来自基岩边坡的径流对滑体渗流的补给而低估了真实的降雨入渗水量,导致在评价降雨对滑坡稳定性特别是深层滑动影响时存在一定不足。以Richards方程和有限元法为基础,忽略降雨对渗透性极低的滑床和滑带的影响,将滑坡渗流计算域缩小为滑体,以避免因滑体与滑床(滑带)渗透性差异巨大引起的数值计算困难;依据基岩边坡水平长度和滑体降雨入渗边界饱和情况,修正降雨入渗边界,实现了考虑径流补给的滑坡降雨入渗简化数值模拟。算例表明,该方法所得渗流场更加符合实际情况。以三峡库区某滑坡为例,模拟了2006年10月~2009年12月间滑坡在库水升降和降雨条件下的渗流场和安全系数的演化过程;计算结果表明,考虑径流补给时滑坡后部的渗流场饱和区域明显较大,稳定系数降低较多,与位移监测资料显示的佐证较为吻合;若不考虑径流补给,则降雨对滑坡稳定性影响不大。 相似文献
179.
本文详细阐述了采用矩形网格法绘制雨量等值线的原理、方法和步骤.提出了利用ASP+IIS直接在Internet上动态生成雨量等值线的功能实现和应用,为同行,特别是基层水文职工了解、掌握绘制雨量等值线的方法,有一定的参考价值. 相似文献
180.
AbstractThe accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time. 相似文献