时间序列分析在电离层短期预报中的应用

根据IGS提供的2012年TEC数据,在得到TEC值残差序列的基础上,利用谱分析去掉周期项和Matlab工具箱去掉趋势项并采用平滑算法去噪后,对随机项进行时间序列分析。目前,电离层格网点的预报均建立在原始TEC上,阐述了采用差分后的TEC值进行预报的方法,以减少日变化周期项的影响。根据AR（p）模型预报的结果加上周期项和趋势项后,再加上前一天对应时段的TEC值与IGS发布的数据比较,结果表明,该方法利用短期IGS发布的TEC值进行电离层预报能取得较高的精度。     

太阳辐射短时临近预报R模型的构建及验证

提高太阳辐射短时临近预报（<6 h）的准确率是确保电网调度的重要举措,也是极具挑战性的技术瓶颈之一。基于云-辐射关系,利用地面观测的太阳辐照度反演的云相对辐射强迫比值,构建了太阳辐射短时临近预报模型（R模型）,并用美国南部大平原中心站16 a的辐照度观测数据,对R模型的预报性能进行了评估。结果表明：（1）有云存在的个例中,R模型较传统的简单持续性模型（Simple模型）的预报性能有很大提升,相比于预报性能较高的智能持续性模型（Smart模型或RCRF模型）仍有2%～25%的改进。（2）在16 a包含2.9×105个8类云状个例的总体检验中,当预报时效超过1 h时,R模型的预报性能显著优于Simple模型和RCRF模型。相对于RCRF模型,R模型在6 h预报时效下,对总辐射和直接辐射的预报性能可分别提高25%和19%,预报时效分别延长了1.5 h和1 h。（3）R模型为太阳辐射短时临近预报提供了准确率更高的基准模型。同时,该模型可仅依靠地面短期的辐照度观测资料即可预报,为缺少同期气象要素观测的光伏电厂的辐射预报提供了新的途径或新的可能。     

Potential fish habitat mapping using MODIS-derived sea surface salinity,temperature and chlorophyll-a data: South China Sea Coastal areas,Malaysia

This paper demonstrates the use of moderate resolution imaging spectro-radiometer (MODIS) data for fish forecasting mapping of seasonal spatial distribution of sea surface salinity (SSS), temperature (SST) and chlorophyll-a in the ocean waters off the coast of Semporna, Malaysia. Multi-linear regression analysis was performed to estimate SSS and the Brown and Minnet algorithm was used for the SST. The extracted parameters were validated using in situ measurement taken with Hydro-Lab equipment. The extracted parameters from MODIS images reveal the signature values which establish the relationships between these parameters, and thus delineating the potential fish zonation (PFZ) map. These developed models will help for accurate monitoring of large coverage areas at low cost and within short period of time. Furthermore, such models will allow the prediction of the total fish catch in different seasons, thus contributing to fish industry management and marketing. This research recommends the use of PFZ map for mass scale fish harvesting in short time for larger areas. Finally, the research has developed a potential fish zone model amalgamating all the above parameters. The PFZ mapping was carried out off the coast of Semporna, Sabah as there were sufficient fish catch data for accuracy assessment. The R was computed as 0.93 and the higher fish catch areas have coincided very well with the higher PFZ values, meaning the tool is ready for use for operational near real-time fish forecasting.     

长白山天池火山溃湖洪水最大流量的初步估算及影响分析

长白山天池火山是目前最具潜在喷发危险的活火山。依据长白山天池火山的最新监测研究成果,结合地形地貌、水文流域特点及天池火山历史喷发类型,重点分析了长白山天池火山未来喷发时发生溃湖洪水的危险性。利用相关的水动力学公式,建立了溃口规模和洪水湿周、流量和流速的内在关系。详细分析溃湖洪峰在下游二道白河镇、白山水电站、红石水电站等关键位置的最大流量及流速。结果表明,若天池火山湖水溃泄一半即10亿m3时,距火山口50km处的二道白河镇瞬时洪水流速达84 904m3/s,该镇将全部被淹没。下游的白山水库、丰满水库将分别受到流量23 560m3/s和1 505m3/s洪水的冲击,水库安全受到严重威胁。     

Risk analysis for flood control operation of seasonal flood-limited water level incorporating inflow forecasting error

Abstract

The seasonal flood-limited water level (FLWL), which reflects the seasonal flood information, plays an important role in governing the trade-off between reservoir flood control and conservation. A risk analysis model for flood control operation of seasonal FLWL incorporating the inflow forecasting error was proposed and developed. The variable kernel estimation is implemented for deriving the inflow forecasting error density. The synthetic inflow incorporating forecasting error is simulated by Monte Carlo simulation (MCS) according to the inflow forecasting error density. The risk analysis for seasonal FLWL control was estimated by MCS based on a combination of the forecasting inflow lead-time, seasonal design flood hydrographs and seasonal operation rules. The Three Gorges reservoir is selected as a case study. The application results indicate that the seasonal FLWL control can effectively enhance flood water utilization rate without lowering the annual flood control standard.

Editor D. Koutsoyiannis; Associate editor A. Viglione

Citation Zhou, Y.-L. and Guo, S.-L., 2014. Risk analysis for flood control operation of seasonal flood-limited water level incorporating inflow forecasting error. Hydrological Sciences Journal, 59 (5), 1006–1019.     

Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models

ABSTRACT

In this study, a data-driven streamflow forecasting model is developed, in which appropriate model inputs are selected using a binary genetic algorithm (GA). The process involves using a combination of a GA input selection method and two adaptive neuro-fuzzy inference systems (ANFIS): subtractive (Sub)-ANFIS and fuzzy C-means (FCM)-ANFIS. Moreover, the application of wavelet transforms coupled with these models is tested. Long-term data for the Lighvan and Ajichai basins in Iran are used to develop the models. The results indicate considerable improvements when GA selection and wavelet methods are used in both models. For example, the Nash-Sutcliffe efficiency (NSE) coefficient for Lighvan using FCM-ANFIS is 0.74. However, when GA selection is applied, the NSE is improved to 0.85. Moreover, when the wavelet method is added, the performance of new hybrid models shows noticeable enhancements. The NSE value of wavelet-FCM-ANFIS is improved to 0.97 for Lighvan basin.

Editor D. Koutsoyiannis Associate editor E. Toth     

A SYNOPTIC CLIMATOLOGICAL ANALYSIS OF SUMMERTIME PRECIPITATION INTENSITY IN THE EASTERN UNITED STATES

The spatial patterns of precipitation frequency and intensity over the eastern United States for summer from 1961 to 1990 are analyzed using a recently developed continental-scale air mass-based synoptic classification. This procedure, the spatial synoptic classification (SSC), is based on “seed” day identification of synoptic events and discriminant analysis to group together days that are within the same air-mass type. Results show differences in the types of precipitation associated with different air masses. Two air masses in particular-Moist Tropical and Moist Temperate-appear to be highly correlated with a majority of the precipitation, particularly in the southeast. The synoptic characteristics, daily intensity of rainfall, and radiosonde soundings during prevalence of these two air masses suggest that convective rainfall is common during Moist Tropical, and stratiform-type rainfall occurs during Moist Temperate. A simple stratification scheme based upon a synoptic-based air-mass delineation may be useful for studies that need to divide days into stratiform or convective regimes.     

Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam

ABSTRACT

The potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models – CEEMDAN-ANN and CEEMDAN-M5-MT – with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error.     

A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change

ABSTRACT

A forecasting model is developed using a hybrid approach of artificial neural network (ANN) and multiple regression analysis (MRA) to predict the total typhoon rainfall and groundwater-level change in the Zhuoshui River basin. We used information from the raingauge stations in eastern Taiwan and open source typhoon data to build the ANN model for forecasting the total rainfall and the groundwater level during a typhoon event; then we revised the predictive values using MRA. As a result, the average accuracy improved up to 80% when the hybrid model of ANN and MRA was applied, even where insufficient data were available for model training. The outcome of this research can be applied to forecasts of total rainfall and groundwater-level change before a typhoon event reaches the Zhuoshui River basin once the typhoon has made landfall on the east coast of Taiwan.     

Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach

ABSTRACT

There is an increasing need for accurate groundwater-level (GWL) prediction to support effective seasonal water management. It is desirable for forecasting tools to be not only accurate but also accessible for decision-makers. We test the Prophet forecasting procedure, an open-source code released by Facebook, to address these challenges. It is based on an additive model considering non-periodic changes and periodic components in a Bayesian framework with easily-interpretable parameters. Predictions of daily GWL data in an area affected by pumping near a tourist complex in the Ramsar wetland area of Doñana (Spain) are compared to other forecasting methods. Prophet outperforms most methods in predicting GWL making it a fast and flexible forecasting tool for hydrologists and water managers. Furthermore, it allows gaining insight into the influence of each component of the forecast separately, helping to assess the hydrodynamic response to external drivers such as groundwater pumping.