人工神经网络方法预报长江上游流域面雨量的探讨

利用1998～2000年3～11月长江上游六大流域逐日降水实况和MAPS、T106(T213)、欧洲中心等数值产品资料，采用人工神经网络方法，建立了六大流域强降水面雨量等级预报模型。并于2002年6～9月进行了业务试验。     

基于生态经济理论的生态需水计算方法研究

在简述生态需水研究现状的基础上,结合以可持续发展为基础的生态经济理论,提出了基于生态经济思想的生态需水概念,并进行了理论分析.根据生态经济领域中的生态价值理论,确定不同生态需水量条件下的生态价值,进而通过水资源的生态价值与经济价值之间的相互关系确定合适的生态需水量,并通过实例对该方法的具体步骤进行了说明,认为由该法所确定的生态需水量符合客观实际,可以作为生态建设过程中生态需水研究的方法之一.该法通过水资源将生态系统和经济系统联系在一起,对于研究可持续发展条件下的水资源利用有一定的现实意义.     

生态需水的理论内涵探讨

从生态系统水分循环的角度,较系统地探讨了生态需水的理论内涵,对生态系统水资源配置中相关且容易混淆的概念加以辨析,并分析了它们之间的相互关系.研究认为:生态缺水是生态系统水资源配置最直接的依据,而生态需水、生态储水的计算评价则是确定生态缺水的基础.通常,生态系统的水资源配置,必须在区域生态调查的基础上,结合社会经济发展状况,提出生态目标,然后进行生态需水估算、生态储水评价、生态缺水评估,并通过生态用水的可行性分析,最终确定生态保护和建设的水资源配置方案.     

第二松花江流域水电厂综合中长期水文预报的分析研究

基于对第二松花江流域上游小山、松山、两江水电站中长期水文预报研究成果，论述了综合中长期水文预报的研究思路和定性预报、定量预报的分析研究方法。该研究通过2002年实践检验，具有较高的预报精度。     

通用洪水预报模型库设计建设研究

在介绍洪水预报模型库的基本概念和性质的基础上，分析了各类洪水预报模型的输入、输出数据类型，规定了各类数据文件格式，设计了洪水预报模型库的通用数据接口，并在中国洪水预报系统中实现了通用洪水预报模型库的建设。     

矿产品供需关系经济分析

矿产品供需关系决定矿产品价格的市场走向，运用供需二维图解可以预测矿产品供需动态，给出精确的供需二维图要注意资源增加与减少、代用材料和新产品开发等因素，指出精心积累矿产品供需资料，仔细观察供需关系，可以预测矿产品价格和市场走向。     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.     

The impact of epistemic uncertainty on an earthquake loss model

Models capable of estimating losses in future earthquakes are of fundamental importance for emergency planners, for the insurance and reinsurance industries, and for code drafters. Constructing a loss model for a city, region or country involves compiling databases of earthquake activity, ground conditions, attenuation equations, building stock and infrastructure exposure, and vulnerability characteristics of the exposed inventory, all of which have large associated uncertainties. Many of these uncertainties can be classified as epistemic, implying—at least in theory—that they can be reduced by acquiring additional data or improved understanding of the physical processes. The effort and cost involved in refining the definition of each component of a loss model can be very large, for which reason it is useful to identify the relative impact on the calculated losses due to variations in these components. A mechanically sound displacement‐based approach to loss estimation is applied to a test case of buildings along the northern side of the Sea of Marmara in Turkey. Systematic variations of the parameters defining the demand (ground motion) and the capacity (vulnerability) are used to identify the relative impacts on the resulting losses, from which it is found that the influence of the epistemic uncertainty in the capacity is larger than that of the demand for a single earthquake scenario. Thus, the importance of earthquake loss models which allow the capacity parameters to be customized to the study area under consideration is highlighted. Copyright © 2005 John Wiley & Sons, Ltd.     

Stochastic Modelling of the Impact of Flood Protection Measures Along the River Waal in the Netherlands

River flooding is a problem of international interest. In the past few years many countries suffered from severe floods. A large part of the Netherlands is below sea level and river levels. The Dutch flood defences along the river Rhine are designed for water levels with a probability of exceedance of 1/1250 per year. These water levels are computed with a hydrodynamic model using a deterministic bed level and a deterministic design discharge. Traditionally, the safety against flooding in the Netherlands is obtained by building and reinforcing dikes. Recently, a new policy was proposed to cope with increasing design discharges in the Rhine and Meuse rivers. This policy is known as the Room for the River (RfR) policy, in which a reduction of flood levels is achieved by measures creating space for the river, such as dike replacement, side channels and floodplain lowering. As compared with dike reinforcement, these measures may have a stronger impact on flow and sediment transport fields, probably leading to stronger morphological effects. As a result of the latter the flood conveyance capacity may decrease over time. An a priori judgement of safety against flooding on the basis of an increased conveyance capacity of the river can be quite misleading. Therefore, the determination of design water levels using a fixed-bed hydrodynamic model may not be justified and the use of a mobile-bed approach may be more appropriate. This problem is addressed in this paper, using a case study of the river Waal (one of the Rhine branches in the Netherlands). The morphological response of the river Waal to a flood protection measure (floodplain lowering in combination with summer levee removal) is analysed. The effect of this measure is subject to various sources of uncertainty. Monte Carlo simulations are applied to calculate the impact of uncertainties in the river discharge on the bed levels. The impact of the “uncertain” morphological response on design flood level predictions is analysed for three phenomena, viz. the impact of the spatial morphological variation over years, the impact of the seasonal morphological variation and the impact of the morphological variability around bifurcation points. The impact of seasonal morphological variations turns out to be negligible, but the other two phenomena appear to have each an appreciable impact (order of magnitude 0.05–0.1 m) on the computed design water levels. We have to note however, that other sources of uncertainty (e.g. uncertainty in hydraulic roughness predictor), which may be of influence, are not taken into consideration. In fact, the present investigation is limited to the sensitivity of the design water levels to uncertainties in the predicted bed level.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.