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。     

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。     

Methods for Setting up a Three-Dimensional Industrial Surveying System of “Building Blocks Type”

1　Methodsfororientatinginitialdirec tioninindustrialsurveyDifferentfromconventionalengineeringsurvey,thedistancebetweenthetwoangle measurementdevicesinindustrialsurveyisnomorethantenmeters.Theaccu racyforthemeasurementofobjectpointsisrequiredtobebetween± ( 0 .0 2～ 0 .0 5～ 0 .2 0 )mm ,sotheaccu racyfortheorientationoftheinitialdirectionoughttobehigh .Theexistingmethodsfororientatingtheinitialdirectionbetweenthetwoangle measurementdevicesincludemethodofusinginnertarget,collimatormethod,metho…     

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.     

Stability and accuracy analysis of the central difference method for real‐time substructure testing

The central difference method (CDM) that is explicit for pseudo‐dynamic testing is also believed to be explicit for real‐time substructure testing (RST). However, to obtain the correct velocity dependent restoring force of the physical substructure being tested, the target velocity is required to be calculated as well as the displacement. The standard CDM provides only explicit target displacement but not explicit target velocity. This paper investigates the required modification of the standard central difference method when applied to RST and analyzes the stability and accuracy of the modified CDM for RST. Copyright © 2005 John Wiley & Sons, Ltd.     

Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods*

Predictive vegetation modeling can be used statistically to relate the distribution of vegetation across a landscape as a function of important environmental variables. Often these models are developed without considering the spatial pattern that is inherent in biogeographical data, resulting from either biotic processes or missing or misspecified environmental variables. Including spatial dependence explicitly in a predictive model can be an efficient way to improve model accuracy with the available data. In this study, model residuals were interpolated and added to model predictions, and the resulting prediction accuracies were assessed. Adding kriged residuals improved model accuracy more often than adding simulated residuals, although some alliances showed no improvement or worse accuracy when residuals were added. In general, the prediction accuracies that were not increased by adding kriged residuals were either rare in the sample or had high nonspatial model accuracy. Regression interpolation methods can be an important addition to current tools used in predictive vegetation models as they allow observations that are predicted well by environmental variables to be left alone, while adjusting over‐ and underpredicted observations based on local factors.     

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.