From Global to Local: Providing Actionable Flood Forecast Information in a Cloud‐Based Computing Environment

Global and continental scale flood forecast provide coarse resolution flood forecast, but from the perspective of emergency management, flood warnings should be detailed and specific to local conditions. The desired refinement can be provided by the use of downscaling global scale models and through the use of distributed hydrologic models to produce a high‐resolution flood forecast. Three major challenges associated with transforming global flood forecasting to a local scale are addressed in this work. The first is using open‐source software tools to provide access to multiple data sources and lowering the barriers for users in management agencies at local level. This can be done through the Tethys Platform that enables web water resources modeling applications. The second is finding a practical solution for the computational requirements associated with running complex models and performing multiple simulations. This is done using Tethys Cluster that manages distributed and cloud computing resources as a companion to the Tethys Platform for web app development. The third challenge is discovering ways to downscale the forecasts from the global extent to the local context. Three modeling strategies have been tested to address this, including downscaling of coarse resolution global runoff models to high‐resolution stream networks and routing with Routing Application for Parallel computatIon of Discharge (RAPID), the use of hierarchical Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) distributed models, and pre‐computed distributed GSSHA models.     

基于灰色新陈代谢马尔可夫模型的飞行事故预测

灰色预测适用于时间短、数据量少和波动不大的预测问题,在长期预测时,数据序列拟合较差,预测精度偏低;而马尔可夫链适用于长期、数据序列随机波动大的预测问题。笔者结合灰色新陈代谢GM(1,1)模型和马尔可夫链理论的优点,建立飞行事故预测模型。模型去掉已失去参考价值的历史老信息,补充新信息,克服了随机波动性数据对飞行事故预测精度的影响,提高了灰色预测的应用水平。实例预测1973—2008世界飞行事故,其结果证明了灰色新陈代谢马尔可夫GM(1,1)模型预测精度较高,可用于飞行事故预测,具有较强的科学性和实用性。     

塔克拉玛干沙漠公路交通事故趋势预测分析

应用塔克拉玛干沙漠干线公路建成通车以来的交通事故统计数据,在分析交通事故主要指标发展变化特征和分布特征的基础上,建立了适用于沙漠公路交通事故特点的GM(1,1)预测模型,对未来几年内交通事故发展趋势进行了预测。预测结果表明,未来几年沙漠公路交通事故次数、死亡人数、受伤人数以及直接经济损失等4项指数将分别以15.9%,10%,4.5%和5%的年平均增长率快速增长。同时针对预测结果提出了相应的对策。     

煤场扬尘影响预测与措施研究

对储煤场扬尘的影响因素作了分析,通过对煤场起尘量及在不同风速不同含水率下对煤场扬尘源强进行估算,并对煤场下风向TSP的落地浓度进行了预测,最后根据预测结果提出了防止煤场扬尘的措施.     

Streamflow Forecasting Using Singular Value Decomposition and Support Vector Machine for the Upper Rio Grande River Basin

The current study improves streamflow forecast lead‐time by coupling climate information in a data‐driven modeling framework. The spatial–temporal correlation between streamflow and oceanic–atmospheric variability represented by sea surface temperature (SST), 500‐mbar geopotential height (Z₅₀₀), 500‐mbar specific humidity (SH₅₀₀), and 500‐mbar east–west wind (U₅₀₀) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). SVD significant regions are weighted using a nonparametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed model for the period of 1965–2014. The April–August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1–13 months. SVD results showed the streamflow variability was better explained by SST and U₅₀₀ as compared to Z₅₀₀ and SH₅₀₀. The SVM model showed satisfactory forecasting ability with best results achieved using a one‐month lead to forecast the following four‐month period. Overall, the SVM results showed excellent predictive ability with average correlation coefficient of 0.89 and Nash–Sutcliffe efficiency of 0.79. This study contributes toward identifying new SVD significant regions and improving streamflow forecast lead‐time of the URGRB.     

南京居民的雾霾风险认知及应对行为调查研究

雾霾灾害严重影响了居民的生活,使得民众不得不对其加以认识和了解.通过问卷的方式对南京居民的雾霾风险认知状况进行调查发现,南京居民对雾霾风险的认知程度并不高.主要表现在:居民无法区分PM2.5与雾霾之间的确切差异;居民的雾霾成因认知存在偏差;但居民对雾霾后果及其主体责任的认知较为清晰.另外,在日常生活中,居民通过佩戴口罩、改变饮食、调整户外活动和进行健康支付等一系列行为方式来防止自雾霾的侵害,居民表现出较高的防霾积极性.     

海湾综合整治与资源环境优化研究进展

依据近年来国内外的研究成果,综述了海湾综合整治专项研究的内容、方法,阐述了基于数值模拟技术的海湾综合整治效果预测的研究方法.在深入分析的基础上,就海湾综合整治目前存在的问题和今后的研究工作进行了讨论,并就此提出了几点建议.     

探析FH-62-C14颗粒物监测仪测量维护要点

本文着重介绍美国赛默飞世尔公司生产的FH-62-C14颗粒物监测仪的测量原理,并针对使用和维护中容易出现的故障,介绍使用及维护方法和要点,并通过经典重量法与FH-62-C14β射线颗粒物监测仪进行测量均值比对,验证了设备维护的效果,为环境监测工作者提供应用及维护方面的参考,以获得更加准确的监测数据。     

利用人工神经网络对空气中O3浓度进行预测

将人工神经网络应用于对空气中O3的浓度预测,提出了完整的预测模型,选取风速、风向、相对湿度、云量、平均气温、最高气温等6项气象因子作为输入量,经过两个月的预测实验,结果表明,实测值与预测值的平均相对误差为21.49%,相关系数为0.837.表明人工神经网络对O3的浓度预测是一种有效的工具.     

T213数值预报产品对云南秋季强降水预报能力检验

利用Micaps系统下的T213数值预报产品从环流背景、散度场、温度场、水汽通量和降水预报对2005年10月29日云南中部及以南地区暴雨天气进行分析,结果表明:T213数值预报UV场对这次强降水的中低层影响系统切变的位置作了准确预报,强降水区的预报范围与实况接近,散度场、温度场、水汽通量对强降水天气有较好的预报指示意义.