Bed changes at toe of inclined seawalls

This study was carried out to investigate the scour phenomenon at the toe of seawalls and the different parameters that affected it. Experiments were achieved using different wave steepnesses, bed material grain sizes, wall positions and inclinations. Based on experimental results, the parametric plots of toe scour for smooth impermeable inclined seawalls were prepared. Also, this paper presents the bed changes prediction at seawalls toe using artificial neural networks on the basis of experimental data to widen the range of application. Suitability of using a neural network model was developed, and a model was validated. It is proposed that this model can be used in coastal engineering applications.     

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24‐h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40‐year reanalysis data for the periods of the eight events. A two‐dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first‐level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two‐dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models. Copyright © 2011 John Wiley & Sons, Ltd.     

基于WebGIS的江西省山洪灾害预报系统设计与应用

针对南方丘陵多雨地区山洪灾害防御需求,采用B/S三层体系结构设计了基于WebGIS技术和水文预报模型的江西省山洪灾害预报系统。系统提供山洪灾害实时预报、临近预报和短期预报山洪灾害风险产品,发布山洪灾害风险等级、基础地理信息等数据,实现了地图浏览、GIS基本操作、图层管理、数据查询等功能。预报时效检验表明系统预报结果较山洪灾害实际发生时间提前3 h以上,并在2009年7月2—3日赣州地区一次山洪过程进行模拟,能够准确预报出山洪风险等级,应用效果良好。     

Regression modeling method of space weather prediction

A regression modeling method of space weather prediction is proposed. It allows forecasting Dst index up to 6 hours ahead with about 90% correlation. It can also be used for constructing phenomenological models of interaction between the solar wind and the magnetosphere. With its help two new geoeffective parameters were found: latitudinal and longitudinal flow angles of the solar wind. It was shown that Dst index remembers its previous values for 2000 hours.     

Prévision des crues éclair

Flash-flood events resulting from paroxystic meteorological events concentrated in time and space are insufficiently documented as they produce destructive effects. They are hardly measurable and present single features that are not transposable to another event. In the South of France, the flash flood of November 1999 gives a perfect illustration of these characteristics. The physical complexity of the process and consequently the volume and the variety of the data to take into account are incompatible with the real time constraint allocated to the forecasters confronted to the occurrence of such phenomena. So, we have to make choices to afford acceptable simplifications to the complete mechanical model. MARINE (‘Modélisation de l'Anticipation du Ruissellement et des Inondations pour des évéNements Extrêmes’) is the operational and robust tool we developed for flash-flood forecasting. This model complies with the criterions of real-time simulation. It is a physically based distributed model composed of two parts: first the flood runoff process simulation in the upstream part of the basin modelled from a rainfall–runoff approach, then the flood propagation in the main rivers described by the Saint-Venant equations. It integrates remote sensed data – Digital Elevation Model, land-use map, hydrographic network for the observations from satellites and the rainfall evolution from meteorological radar. The main goal of MARINE is to supply real time pertinent information to the forecasters. Results obtained on the Orbieu River (Aude, France) show that this model is able to supply pertinent flood hydrograph with a sufficient precision for the forecasting service to take the appropriate safety decisions. Furthermore, MARINE has already been tested in the French National Flood Forecasting Service of Haute-Garonne in real conditions. To cite this article: V. Estupina Borrell et al., C. R. Geoscience 337 (2005).     

气象要素对精神疾病的影响与预测

利用1997～1999年天津市安定医院5000余例精神疾病(本文指精神分裂症和燥狂症)入院资料和同期气象资料进行对比分析。对各种气象要素与精神疾病发病的关系作了详细的讨论，揭示了发病情况与气象要素的变化规律。用多元回归方法找出诱发精神疾病的主要气象因子，组建精神疾病预报方程，拟合和业务使用效果良好，有较好的推广意义。     

Drought forecasting using stochastic models

Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed data. The predicted results show reasonably good agreement with the actual data, 1–2 months ahead. The predicted value decreases with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy.     

基于单多普勒天气雷达产品的强对流天气预报预警方法

本文对发生在常德新一代天气雷达探测区内的强对流天气(雷雨大风、冰雹、龙卷)在雷达回波强度场及速度场上的表现特征进行了归类分析,既用实例证实了美国强对流天气在多普勒天气雷达产品上的表现特征同样适用于中国,又对其表现特征进行了完善。在此基础上建立了适用于当地的基于单多普勒天气雷达产品的强对流天气预报方法,并根据雷达实时体扫资料对强对流天气进行语音、文字自动报警以及对风暴移向、移速进行预报作了实用性的探讨。在多普勒天气雷达产品应用还处在探索、起步阶段的我国,该方法为制作强对流天气预报提供了较系统的预报思路。     

聊城市汛期降水气候特征及旱涝预测模型

利用1961~2000年聊城市8个测站汛期降水距平百分率资料,分析了聊城市汛期旱涝气候特征,并利用模糊均生函数的概念建立了相应的旱涝预测模型。将建立好的预测模型用历史资料进行回代检验,并对2001~2003年进行预报试用,拟合值曲线与实况值曲线十分接近,预报结果也和实况基本一致。     

The water-bearing numerical model and its operational forecasting experiments part I: the water-bearing numerical model

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