Application of Wavelet Random Coupling Model in Monthly Rainfall Prediction

A Trous algorithm of wavelet transform was used to decompose wavelet signal, and the cross-correlation analysis was used to analyze the sequence of each wavelet transform, and then the mathematical model correspond with wavelet transform sequence was established, finally wavelet random coupling model was obtained by wavelet reconstruction algorithm. Then, according to the rainfall data in crop growth period of Farm Chahayang from 1956 to 2008, the wavelet random coupling model was established to fit the model prediction test. The results showed that the prediction and fitting accuracy of the model was high, the model could reflect the rainfall variation regulation in the region, and it was a practical prediction model. It was very important for us to determine reasonably irrigation schedule and to use efficiency coefficient of precipitation resource.

A GCM-Based Forecasting Model for the Landfall of Tropical Cyclones in China

A statistical dynamic model for forecasting Chinese landfall of tropical cyclones （CLTCs） was developed based on the empirical relationship between the observed CLTC variability and the hindcast atmospheric circulations from the Pusan National University coupled general circulation model （PNU-CGCM）.In the last 31 years,CLTCs have shown strong year-to-year variability,with a maximum frequency in 1994 and a minimum frequency in 1987.Such features were well forecasted by the model.A cross-validation test showed that the correlation between the observed index and the forecasted CLTC index was high,with a coefficient of 0.71.The relative error percentage （16.3%） and root-mean-square error （1.07） were low.Therefore the coupled model performs well in terms of forecasting CLTCs;the model has potential for dynamic forecasting of landfall of tropical cyclones.

The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times

Leading time length is an important issue for modeling seasonal forecasts. In this study, a comparison of the interannual predictability of the Western North Pacific (WNP) summer monsoon between different leading months was performed by using one-, four-, and sevenmonth lead retrospective forecasts (hindcasts) of four coupled models from Ensembles-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) for the period of 1960 2005. It is found that the WNP summer anomalies, including lower-tropospheric circulation and precipitation anomalies, can be well predicted for all these leading months. The accuracy of the four-month lead prediction is only slightly weaker than that of the one-month lead prediction, although the skill decreases with the increase of leading months.

Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide–Wind–Wave Conditions

Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4.

Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.

A Hybrid Coupled Ocean-Atmosphere Model and ENSO Prediction Study

A hybrid coupled ocean-atmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific. Retroactive experimental predictions initiated in each season from 1979 to 1994 are performed. Analyses indicate that: (1) The overall predictive capability of this model for SSTA over the central-eastern tropical Pacific can reach one year, and the error is not larger than 0.8 degrees C. (2) The prediction skill depends greatly on the season when forecasts start. However, the phenomenon of SPB (spring prediction barrier) is not found in the model. (3) The ensemble forecast method can effectively improve prediction results. A new initialization scheme is discussed.

A Model of Debris Flow Forecast Based on the Water-Soil Coupling Mechanism

Debris flow forecast is an important means of disaster mitigation. However, the accuracy of the statistics-based debris flow forecast is unsatisfied while the mechanism-based forecast is unavailable at the watershed scale because most of existing researches on the initiation mechanism of debris flow took a single slope as the main object. In order to solve this problem, this paper developed a model of debris flow forecast based on the water-soil coupling mechanism at the watershed scale. In this model, the runoff and the instable soil caused by the rainfall in a watershed is estimated by the distrib- uted hydrological model （GBHM） and an instable identification model of the unsaturated soil. Because the debris flow is a special fluid composed of soil and water and has a bigger density, the density esti- mated by the runoff and instable soil mass in a watershed under the action of a rainfall is employed as a key factor to identify the formation probability of debris flow in the forecast model. The Jiangjia Gulley, a typical debris flow valley with a several debris flow events each year, is selected as a case study watershed to test this forecast model of debris flow. According the observation data of Dongchuan Debris Flow Observation and Research Station, CAS located in Jiangjia Gulley, there were 4 debris flow events in 2006. The test results show that the accuracy of the model is satisfied.

Spectral Model for Soybean Yield Estimate Using MODIS/EVI Data

Attaining reliable and timely agricultural estimates is very important everywhere, and in Brazil, due to its characteristics, this is especially true. In this study, estimations of crop production were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) images. The objective was to evaluate the coupled model (CM) performance of crop area and crop yield estimates based solely on MODIS/EVI as input data in Rio Grande do Sul State, which is characterized by high variability in seasonal soybean yields, due to different crop development conditions. The resulting production estimates from CM were compared to official agricultural statistics of Brazilian Institute of Geography and Statistics (IBGE) and the National Company of Food Supply (CONAB) at different levels from 2000/2001 to 2010/2011 crop years. Results obtained with CM indicate that its application is able to generate timely production estimates for soybean both at municipality and local levels. Validation estimates with CM at State level obtained R2 = 0.95. Combining all cropping years at municipality level, estimates were highly correlated to official statistics from IBGE, with R2 = 0.91 and RMSD = 10,840 tons. Spatially interpolated comparisons of yield maps obtained from the CM estimates and IBGE data also showed visual similarity in their spatial distribution. Local level comparisons were performed and presented R2 = 0.95. Implications of this work point out that time-series analysis of production estimates are able to provide anticipated spatial information prior to the soybean harvest.

耦合多元混沌要素的非线性交叉预测误差模式对极端天气延伸期预测研究

本文首先分析随机误差、初始误差和模式参数误差对建立的单变量非线性交叉预测误差模式(Nonlinear Cross Prediction Error,NCPE)的极端天气延伸期预报敏感性,及不同气象要素场对延伸期预报的敏感性,再利用可降水、温度、位势高度资料建立多混沌变量耦合的模式(Multivariate NCPE,MNCPE),深入对比分析两种模式延伸期预报特征。结果表明:随机误差和初始误差的比率量级差异会影响NCPE模式的延伸期预报特征,存在临界值效应;而针对参数误差,合适的相空间维m能较好表征吸引子的局部细节。不同气象要素场对暴雨、台风等灾害天气延伸期预报的敏感性也表现不一。耦合多变量的MNCPE模式,相比单变量NCPE,能更完备表征混沌吸引子局部结构的动力特征,降低10~30 d延伸期预报的不确定性。

基于通用模型平台的秦淮河流域水文预报技术探讨

秦淮河流域位于长江下游,其上游为山区、中游为平原、下游为感潮河网,流域内干支水系叶脉相连,水库、圩区、闸坝等水利工程众多,快速在线滚动洪水预报富有挑战。基于江苏省防汛抗旱预警调度智慧决策系统平台,研究了降雨径流、河段洪水演进、水库调洪等各类通用模型及其算法,考虑流域上游大中型水库及其调度影响,划分了适用于秦淮河水系特点的分布式预报单元,构建了秦淮河流域洪水预报模型,以2024年7月暴雨洪水前垾村控制断面为例进行验证。结果表明,利用在线通用模型算法能够实现快速洪水预报方案构建及其模拟计算,并且具备较好的准确度,可为秦淮河流域洪水预报与调度决策提供有力的技术支撑。

Real-time flood forecast and flood alert map over the Huaihe River Basin in China using a coupled hydro-meteorological modeling system

A coupled hydro-meteorological modeling system is established for real-time flood forecast and flood alert over the Huaihe River Basin in China. The system consists of the mesoscale atmospheric model MC2 (Canadian Mesoscale Compressible Community) that is one-way coupled to the Chinese Xinanjiang distributed hydrological model, a grid-based flow routing model, and a module for acquiring real-time gauge precipitation. The system had been successfully tested in a hindcast mode using 1998 and 2003 flood cases in the basin, and has been running daily in a real-time mode for the summers of 2005 and 2006 over the Wangjiaba sub-basin of the Huaihe River Basin. The MC2 precipitation combined with gauge values is used to drive the Xinanjiang model for hydrograph prediction and production of flood alert map. The performance of the system is illustrated through an examination of real-time flood forecasts for the severe flood case of July 4―15, 2005 over the sub-basin, which was the first and largest flood event encountered to date. The 96-h forecasts of MC2 precipitation are first evaluated using observations from 41 rain gauges over the sub-basin. The forecast hydrograph is then validated with observations at the Wangjiaba outlet of the sub-basin. MC2 precipitation generally compares well with gauge values. The flood peak was predicted well in both timing and intensity in the 96-hour forecast using the combined gauge-MC2 precipitation. The real-time flood alert map can spatially display the propagation of forecast floods over the sub-basin. Our forecast hydrograph was used as opera-tional guidance by the Bureau of Hydrograph, Ministry of Water Resources. Such guidance has been proven very useful for the Office of State Flood Control and Drought Relief Headquarters in operational decision making for flood management. The encouraging results demonstrate the potential of using mesoscale atmospheric model precipitation for real-time flood forecast, which can result in a longer lead time compared to traditional methods.

基于MIKE耦合模型的开都河中短期径流预报

为提供开都河水库优化调度和水资源科学管理的依据,应用MIKE耦合模型进行流域中短期径流预报。选取欧洲天气预报中心(ECMWF)气象预报模式作为气象数据输入,构建包含6个子模块(蒸散发、非饱和带、饱和带、坡面流、河流与湖泊)和融雪模块的MIKE SHE模型。将MIKE Hydro river模型作为河道汇流计算与MIKE SHE模型耦合对未来10 d径流信息进行预报。选用相关系数(R^(2))、纳什效率系数(NSE)和相对误差(BAIS)评价模型率定与验证结果,添加预报效率(E)评价模型预报精度。研究表明:在开都河日尺度径流模拟中,验证期R 2和NSE均大于0.70,相对误差仅为-15%,可见模拟径流与实测径流之间具有较好的拟合性和相关性;预报期的NSE=0.53、R^(2)=0.61、E=0.51,说明模拟值与实测值的相关性达0.61。MIKE耦合模型的中短期径流预报可为开都河流域水资源优化利用提供参考。

黄河流域水资源-生态-社会系统耦合协调分析

充分认识水资源-生态-社会系统的耦合协调状态,对缓解黄河流域水资源和生态压力、实现流域可持续发展具有重要意义。选取2006—2021年黄河流域九省(区)的面板数据,基于压力-状态-响应理论(PSR)确定各子系统的评价指标,采用主成分分析法、耦合协调度模型分析水资源-生态-社会系统耦合协调状态及其时空变化特征,并利用GM(1,1)灰色预测模型进行协调度预测。结果表明:1)黄河流域水资源-生态-社会系统综合评价指数整体呈上升趋势,各省(区)历年水资源子系统综合评价指数波动较大但整体向好,大多省(区)生态子系统发展情况较好,社会子系统发展在经济和人口大省有较大优势;2)各省(区)的水资源-生态-社会系统耦合协调度在研究期内均逐步上升,但各省(区)发展差距较大,2021年内蒙古和四川已达到良好协调,而宁夏刚迈入中级协调;3)预测显示,2022—2025年黄河流域整体水资源-生态-社会系统耦合协调度会继续上升至0.798,接近良好协调。

“双碳”目标下绿色物流与区域经济耦合协调发展研究——以安徽省为例

以安徽省为例,研究“双碳”目标下绿色物流与区域经济的耦合协调度。通过文献综述,明确绿色物流和区域经济的定义及国内外研究现状。分析安徽省绿色物流与区域经济的发展现状,探讨二者之间的作用机理。运用熵值法、耦合协调度模型、灰色关联度模型和灰色预测模型等进行研究,选取相关指标,对安徽省2011—2022年的数据进行实证分析。结果表明,安徽省绿色物流与区域经济的耦合协调度逐年上升,从轻度失调发展到优质协调,两者存在极强的正相关关系,并在未来5年持续保持优质协调关系。

A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

Coupled hydrological and atmospheric model- ing is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling sys- tem, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

应对极端暴雨洪水与提升防洪安全韧性研究——以太湖流域为例

为应对类似于河南省郑州市“7·20”特大暴雨可能带来的灾害风险,保障极端暴雨条件下的太湖流域防洪安全,提升流域防洪韧性,将郑州“7·20”特大暴雨移植到太湖流域,采用水文水动力学耦合模型推演现状骨干防洪工程体系(太浦河、望虞河、江南运河、沿长江、沿杭州湾等)3种洪水调度条件下,太湖流域、水利分区与城市代表站应对极端暴雨洪水的响应情况。研究表明:将暴雨中心移植至太湖流域武澄锡虞区,3天累计降雨量达398.0 mm;现状调度下北部分区代表站最高水位分别较有记录以来的历史最高水位偏高0.15~0.88 m,外圈全力排水调度下北部分区水位较历史最高水位偏高0.07~0.76 m,内外圈全力排水调度下北部分区水位较历史最高水位偏高0.03~0.28 m,无锡(大)站水位峰值将会降至5.78 m,低于江南运河无锡段现状堤顶高程。研究成果有助于提升流域、区域与城市应对超标准暴雨洪水的应急处置能力,为强化太湖流域洪涝灾害风险感知、预报预警、风险研判、调度决策、响应联动等关键环节提供技术支撑和科学依据。

考虑综合需求响应的Transformer-图神经网络综合能源系统多元负荷短期预测

为提高在需求响应情境下,综合能源系统的多元负荷短期预测精度,基于消费者心理学、响应不确定性原理、耦合响应原理,构建了考虑综合需求响应的Transformer-图神经网络(Trans-GNN)预测模型。通过响应不确定性随电价差产生的变化规律和消费者心理学原理,量化在不同概率条件下的电力需求响应结果。通过耦合响应原理,求解包含冷、热耦合响应的综合需求响应信号,最终利用注意力机制将综合需求响应信号引入Trans-GNN预测模型,提高网络模型在需求响应情境下的多元负荷预测能力。算例分析结果表明,该模型能有效地提高预测精度,为计及综合需求响应的多元负荷预测研究提供了一定的理论基础。

闽江口近海环境耦合模型及其轻量化框架

受制于数值模拟的复杂性,当前对闽江流域的研究大多将流域和海洋视为独立的两个系统各自开展,且缺少能够快速预报河口环境的技术手段。本研究构建了一个模拟闽江流域-河口的生态-动力耦合模型。模型以流域水文模型提供入海径流,利用Regional Ocean Modeling System(ROMS)模型模拟河口水动力,利用Carbon,Silicate,Nitrogen Ecosystem(CoSiNE)生态模块对闽江口海域的低营养级生态系统过程进行模拟,因而模型能够更好地再现闽江河口生态环境的多时空尺度变化。进而基于模型模拟结果作为学习数据,采用自组织神经网络(SOM)方法结合隐马尔科夫链(HMM)方法,建立了能够用于快速预测叶绿素场的轻量化预测框架。

基于态势感知的配电网空间负荷预测系统

针对动态地区配电网空间负荷优化预测问题,设计了一种基于实时自控态势感知的配电网空间负荷预测系统。首先构建配电网空间负荷预测目标决策要素经验池;然后利用深度长短期神经网络对配电网历史运行数据集进行处理,实现配电网实时自控态势感知;最后利用深度确定性策略梯度算法构建空间负荷预测与目标决策要素经验池之间的耦合模型,实现配电网空间负荷高效精准预测。对模型展开了实际算例分析,多类型配网供电区域场景下配电网空间负荷预测精度达到91.03%,多类型配网供电区域场景下配电网空间负荷预测效率提高了23.71%。

基于水文水动力耦合模型的三江流域防洪模拟预报应用研究

为解决下游河网站点防洪模拟预报过程中存在预报精度偏低,以及采用水动力模型方法时所需资料数量过于庞大的问题,基于不同下垫面水文特征的分布式架构模型理论,通过对流域内山丘和平原区各阶段产汇流进行分解模拟的方式,建立了以交界面动量与通量交换实现互馈耦合的水文水动力一体化模型。以三江流域为研究区域,构建耦合了上游山丘区、平原区和下游长江水动力模型,利用代表站点开展了全流域一体化模型的率定与验证工作。对流域内2016一2022年降雨进行分析和场次降雨大于100mm的多场洪水进行验证,结果显示耦合模型的模拟精度较好,在不需要大量资料的前提下有效提高了平原河网区预报精度及整个流域防洪调度能力,具有较强的借鉴意义,可为其他类似流域防洪模拟预报研究提供参考。