Dam-break flood simulation under various likely scenarios and mapping using GIS: Case of a proposed dam on River Yamuna, India

The precision modeling of dam break floods can lead to formulation of proper emergency action plan to minimize flood impacts within the economic lifetime of the assets.Application of GIS techniques in integration with hydrological modeling for mapping of the flood inundated areas can play a momentous role in further minimizing the risk and likely damages.In the present study,dam break analysis using DAMBRK model was performed under various likely scenarios.Probable Maximum Flood (PMF)calculated for a return period of 1000 years using deterministic approach was adopted for dam break analysis of the proposed dam under various combinations of breach dimensions.The available downstream river cross-sections data sets were used as input in the model to generate the downstream flood profile.Dam break flow depths generated by the DAMBRK model under various combinations of structural failure are subsequently plotted on Digital Elevation Model(DEM)of the downstream of dam site to map the likely affected area.The simulation results reveals that in one particular case the flood without dam may be more intense if a rainfall of significant intensity takes place.     

四川凉山平川镇“7.14”泥石流灾害的气象成因

以盐源县的泥石流发生为例,研究了凉山地区出现局地强降雨引发的中型泥石流灾害的气象成因。运用天气学方法,使用NCEP再分析资料以及卫星云图和雷达回波等遥感资料,从环流形势、强对流不稳定、卫星云图和雷达回波图像演变特征,研究了台风登陆时凉山地区强降雨引起泥石流灾害形成的气象成因,并讨论了应用多普勒雷达回波对局地强降水及泥石流灾害短时临近监测和预报预警的思路。     

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异常冷冬的短期气候预测方法探讨

本文通过对四川省广安市近50年冬季气候资料的聚类分析,研究冷冬气候的演变规律;结合太阳黑子活动资料、夏季极端异常气候事件分析,发现广安市异常冷冬年的发生发展和一月太阳黑子活动存在着密切的相关关系,与本市盛夏期间极端气候事件有明显的周期性对应关系;从而探索得出对异常冷冬年的短期气候预测方法,为准确预测冷冬,特别是预测异常冷冬将发挥重要的作用。     

黄河三角洲风暴潮灾害及其防御对策

古黄河三角洲和现代黄河三角洲风暴潮灾害平均发生周期分别为11.3 a和6.4 a。现代黄河三角洲风暴潮灾害平均发生周期由1949年前的5.5~6.5 a延长至1949年以来的7.9 a,发生频率虽有所降低,但大型风暴潮灾害发生频率呈上升趋势,需修筑高等级的防潮堤坝。黄河三角洲风暴潮灾害取决于气象、地形和水文要素的综合效应:在气象因素制约下具有多发于初冬、春4月和台风期的季节特征;在地形因素制约下具有易发于SE风转NE风的天气特征;在水文因素制约下显示以近无潮点岸段为界,三角洲西、南两地成灾异时的特征。据此,开展风暴潮灾害预报和堤坝建设,可抵御潮灾和减轻潮灾危害。     

灰色系统理论在绵阳市作物病虫害预测中的应用

作物病虫害的综合管理是一个多因素的复杂系统,针对这一特点,本文全面阐述了灰色系统模型的建立与预测方法,建立了作物病虫害测报中的GM (1,1)模型,测报准确性较高,模型模拟检验精度达到I级水平,发展系数<0.3,可用于该地区作物病虫害作中长期预测。     

Spatial and temporal variations in the grain-size characteristics of historical flood plain deposits, Blue River, Wisconsin, USA

This study examined vertical, lateral, and downstream variations in the grain-size characteristics of historical (post-1830) overbank deposits in a watershed that has experienced high rates of accelerated flood plain sedimentation. More than 800 samples were collected from 53 cores along nine flood plain transects. Overbank deposits exhibit a coarsening-upward sequence attributed to historical changes in the sand content of source materials. The erosion of loess-capped soils increased the exposure, erosion, and transport of sandy parent materials. The average sand content of near-channel cores increases moderately downstream along two of the reaches because sandy source materials are increasingly exposed in larger main valleys in the northern part of the watershed. The two northernmost reaches are coarser overall, but do not display significant downstream trends. The sand content of surface and early historical overbank deposits generally decreases laterally as an exponential function of distance from the channel, suggesting transport by turbulent diffusion. The presence of sand throughout the transects and lateral coarsening at two of the transects, however, suggests that sediment transport by convection is also important.     

Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts

Abstract

Data-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.

Editor Z.W. Kundzewicz; Guest editor R.J. Moore     

Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

The evaluation of surface water resources is a necessary input to solving water management problems. Neural network models have been trained to predict monthly runoff for the Tirso basin, located in Sardinia (Italy) at the S. Chiara section. Monthly time series data were available for 69 years and are characterized by non-stationarity and seasonal irregularity, which is typical of a Mediterranean weather regime. This paper investigates the effects of data preprocessing on model performance using continuous and discrete wavelet transforms and data partitioning. The results showed that networks trained with pre-processed data performed better than networks trained on undecomposed, noisy raw signals. In particular, the best results were obtained using the data partitioning technique.