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。     

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.     

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.     

Application of Distributed Temperature Sensing for coupled mapping of sedimentation processes and spatio‐temporal variability of groundwater discharge in soft‐bedded streams

The delineation of groundwater discharge areas based on Distributed Temperature Sensing (DTS) data of the streambed can be difficult in soft‐bedded streams where sedimentation and scouring processes constantly change the position of the fibre optic cable relative to the streambed. Deposition‐induced temperature anomalies resemble the signal of groundwater discharge while scouring will cause the cable to float in the water column and measure stream water temperatures. DTS applied in a looped layout with nine fibre optic cable rows in a 70 × 5 m section of a soft‐bedded stream made it possible to detect variability in streambed temperatures between October 2011 and January 2012. Detailed monthly streambed elevation surveys were carried out to monitor the position of the fibre optic cable relative to the streambed and to quantify the effect of sedimentation processes on streambed temperatures. Based on the simultaneous interpretation of streambed temperature and elevation data, a method is proposed to delineate potential high‐groundwater discharge areas and identify deposition‐induced temperature anomalies in soft‐bedded streams. Potential high‐discharge sites were detected using as metrics the daily minimum, maximum and mean streambed temperatures as well as the daily amplitude and standard deviation of temperatures. The identified potential high‐discharge areas were mostly located near the channel banks, also showing temporal variability because of the scouring and redistribution of streambed sediments, leading to the relocation of pool‐riffle sequences. This study also shows that sediment deposits of 0.1 m thickness already resulted in an increase in daily minimum streambed temperatures and decrease in daily amplitude and standard deviation. Scouring sites showed lower daily minimum streambed temperatures and higher daily amplitude and standard deviation compared with areas without sedimentation and scouring. As a limitation of the approach, groundwater discharge occurring at depositional and scouring areas cannot be identified by the metrics applied. Copyright © 2015 John Wiley & Sons, Ltd.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

基于多模式集成技术的地面气温精细化预报

基于ECMWF、JMA、T639、WRF四个数值模式2012年6月1日—9月30日地面气温3—60 h预报资料和郑州加密自动站资料,利用多模式集合平均(EMN)、消除偏差集合平均(BREM)、加权消除偏差集合(WBREM)及多模式超级集合(SUP)4种方法,对2012年8月29日—9月27日郑州城区11个站点地面逐3 h气温进行多模式集成预报试验,采用绝对误差对预报结果进行检验评估,结果表明:在30天的预报期内,BREM、WBREM及SUP对于大多数站气温预报效果有明显改善,而EMN方案对11个站预报效果改善则不太明显;4种方案中,BREM和WBREM预报效果相对较好且稳定,各个站上3—60 h预报的绝对误差均在2℃附近或以下;SUP方案虽然对个别站预报误差较低,但是其预报效果并不稳定,一些站点的个别预报时效误差大于2℃。对于郑州观测站的气温预报而言,4种集成方案20时起报的气温误差明显小于08时起报的误差,并且20时起报的SUP集成方案绝对误差明显小于其他方案的绝对误差。总体而言,BREM、WBREM及SUP三种集成方案能够给郑州精细化预报业务提供较好的参考。     

中国东部夏季暴雨的年代际跃变及其大尺度环流背景

本文利用1960~2011年中国东部地面测站的逐日降水资料和JRA-55再分析资料探讨了夏季暴雨分布的年代际跃变及其相关联的大尺度环流异常特征。基于暴雨频数和占比(夏季暴雨占比是指5~8月暴雨降水量对总降水量的贡献百分比)的分析结果表明:中国东部夏季暴雨分布在20世纪70年代末和90年代初经历两次反相的经向"三极子"跃变。中国东部夏季暴雨的年代际演变过程可分为三个时段:1960~1979年为华南和华北暴雨偏多、江淮流域暴雨偏少的经向"三极子"分布;1980~1991年为南方和华北暴雨偏少、江淮流域暴雨偏多的经向三极子"分布;1992~2011年为南方暴雨显著偏多、华北暴雨持续偏少,逐渐形成经向"偶极子"分布,并导致近十多年我国夏季"南涝北旱"的整体格局。1970年代末(1990年代初)跃变相关联的大尺度环流异常配置:东亚夏季风的减弱(增强),西太平洋副高的增强西伸但南撤(北抬),南亚高压的减弱南缩(增强东扩),以及蒙古高原中低层的气旋式(反气旋式)环流异常。与此同时,低层局地环流也发生调整:华北和黄淮地区以及华南和江南地区均为反气旋式(气旋式)环流异常,而江淮流域和四川盆地受控于风场切变式辐合(辐散)异常;涡度场发生相应变化,南北方大部分地区的负(正)涡度异常不(有)利于低涡的发展,而江淮流域和四川盆地的正(负)涡度异常有(不)利于低涡的发展,进而引发江南和华南暴雨减少(增加)、江淮流域和四川盆地暴雨增加(减少)、黄淮和华北暴雨减少(增加)的经向"三极子"跃变。