我国旱涝空间型的马尔科夫概型分析

旱涝灾害在我国是影响最大、也最为频繁的气候灾害。本文利用马尔科夫概型分析的原理和方法，在验证了我国旱涝空间型序列具有马尔科夫性质的基础上，计算出各状态的转移概率，进而分析了旱涝空间型序列的静态和动态结构，并揭示出旱涝空间型各状态演化的优势倾向。     

洪水入海对养殖环境影响的数值分析

基于三维流体动力学和热力学原始方程 ,采用σ坐标变换技术 ,建立了三维斜压场数值模型。模型采用ADI格式求解连续方程和动量方程 ,采用FS法和TVD格式求解各标量方程 ,从而使模型的精度提高到二阶 ,并且节省运算时间。利用建立的三维斜压场数值模型 ,对鸭绿江洪水期形成的低盐水体的分布进行了数值模拟 ,进而分析了鸭绿江洪水入海对黄海北部海域的海水养殖业的可能影响。鸭绿江特大泄洪形成的超低盐混浊水体入侵 ,是筏养扇贝致死的主要原因。及时预报大洪水影响下的低盐水体分布 ,可有效地防止洪水污损灾害。     

宁波市水旱灾害孕灾环境因子分析

分析了宁波市水旱灾害形成的主要因子,认为水旱灾害是在天气气候因子、地貌因子、水文因子、土壤植被因子、社会经济因子、防洪抗旱能力因子等自然因素和人为因素的共同作用下产生和发展的。水旱灾害的形成,自然条件固然重要,但形成严重灾害,则与人为因素有密切的关系。     

近百年广西汛期降水的诊断及预测研究

本文利用奇异谱分析及最大熵谱方法对广西近百年前、后汛期降水进行诊断分析，结果表明：广西前汛期降水主要存在20-30年的长期变化、2～3年左右的准两年振荡及12-16年左右的年代际变化；后汛期降水除存在明显下降趋势外，还存在20-30年的长期变化、2-3年左右的准两年振荡及4年左右振荡周期。且各主要振荡模振幅有明显的低频振荡特征，在不同时段里表现有强有弱。结合白回归方法对重建分最预测的基础上进行降水趋势预测，从1998-2005年预测结果来看，效果较好。     

INSTANTANEOUS PEAK FLOW ESTIMATION PROCEDURES FOR NEWFOUNDLAND STREAMS1

ABSTRACT: A procedure of estimating instantaneous flood flows for various return periods on the Island of Newfoundland is presented. The procedure is based on annual maximum instantaneous flows rather than annual maximum daily-mean flows, as the latter requires the conversion of estimated daily-mean flows into instantaneous flows. Regression equations were developed for each of three homogeneous regions for the desired return periods. The flood flow estimation capability of the presented procedure is demonstrated to be better than any other currently available procedure on the Island.     

A NEW LOOK AT FLOOD RISK DETERMINATION1

ABSTRACT: The probability distributions of annual peak flows used in flood risk analysis quantify the risk that a design flood will be exceeded. But the parameters of these distributions are themselves to a degree uncertain and this uncertainty increases the risk that the flood protection provided will in fact prove to be inadequate. The increase in flood risk due to parameter uncertainty is small when a fairly long record of data is available and the annual flood peaks are serially independent, which is the standard assumption in flood frequency analysis. But standard tests for serial independence are insensitive to the type of grouping of high and low values in a time series, which is measured by the Hurst coefficient. This grouping increases the parameter uncertainty considerably. A study of 49 annual peak flow series for Canadian rivers shows that many have a high Hurst coefficient. The corresponding increase in flood risk due to parameter uncertainty is shown to be substantial even for rivers with a long record, and therefore should not be neglected. The paper presents a method of rationally combining parameter uncertainty due to serial correlation, and the stochastic variability of peak flows in a single risk assessment. In addition, a relatively simple time series model that is capable of reproducing the observed serial correlation of flood peaks is presented.     

DEVELOPMENT OF A GIS‐BASED FLOOD INFORMATION SYSTEM FOR FLOODPLAIN MODELING AND DAMAGE CALCULATION1

ABSTRACT: This work presents a flexible system called GIS‐based Flood Information System (GFIS) for floodplain modeling, flood damages calculation, and flood information support. It includes two major components, namely floodplain modeling and custom designed modules. Model parameters and input data are gathered, reviewed, and compiled using custom designed modules. Through these modules, it is possible for GFIS to control the process of flood‐plain modeling, presentation of simulation results, and calculation of flood damages. Empirical stage‐damage curves are used to calculate the flood damages. These curves were generated from stage‐damage surveys of anthropogenic structures, crops, etc., in the coastal region of a frequently flooded area in Chia‐I County, Taiwan. The average annual flood damages are calculated with exceedance probability and flood damages for the designed rainfalls of 2, 5, 10, 25, 50, 100, and 200 year recurrence intervals with a duration of 24 hours. The average annual flood depth in this study area can also be calculated using the same method. The primary advantages of GFIS are its ability to accurately predict the locations of flood area, depth, and duration; calculate flood damages in the floodplain; and compare the reduction of flood damages for flood mitigation plans.     

“We cannot let this happen again”: reversing UK flood policy in response to the Somerset Levels floods, 2014

The Multiple Streams Framework offers a theoretical account of how policy proposals move from latent possibilities to becoming favored for implementation. We apply this framework in the context of the policy response to the 2013–2014 flooding of the Somerset Levels and Moors. Stakeholder interviews and analysis of news media coverage evidence the way in which a specific policy option that had fallen out of favor with the national Environment Agency – dredging – came to the fore and was eventually adopted during the period in which the conjunction of problem, policy, and political pressures came to a head. Local political activists mobilized a wider campaign with the help of social media and capitalized on national political sensitivities to successfully promote dredging. What is less clear is the longevity of the policy reversal, given funding constraints.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.