徐州农业生产面临气象灾害风险的评估

根据徐州市历年来各种气象灾害对农业造成影响的资料, 应用信息扩散的模糊数学理论模型, 对徐州地区洪涝、干旱、风雹和霜冻等气象灾害进行风险评估, 并对客观风险评估值进行分析, 得到与历史上实际灾害发生的概率较吻合的结论, 使用这种方法为我们开展气象灾害风险评估提供了客观的数据。     

辽宁暴雨诱发山洪灾害风险区划研究

基于自然灾害风险评估理论,利用2005—2019年辽宁省1639个自动站逐时降水观测资料、2017年辽宁省30 m分辨率的基础地理信息和山洪沟资料以及风险普查数据,对辽宁暴雨诱发山洪灾害风险区划进行研究,并将风险区划结果与历史山洪灾情进行对比分析。结果表明：通过山洪灾害与降水相关性统计发现,6 h暴雨作为辽宁省山洪致灾因子更为合适,因此构建了6 h综合利用分级暴雨强度及暴雨频次精细评估暴雨致灾危险性;山洪沟沟口高程、沟床比降及河网密度等资料可有效评估山洪孕灾环境敏感性;人口密度、耕地比例两个风险暴露度指标以及灾损敏感系数可大体评估承灾体的易损性;与历史山洪灾害空间和频率分布对比,山洪灾害的高发区与在本次风险区划高风险区基本吻合;精确到每个山洪沟风险区划的结果,提高了山洪灾害的风险区划精度,为辽宁暴雨诱发山洪灾害精准防御提供参考。     

Risk degree of debris flow applying neural networks

A number of methods for prediction of debris flows have been studied. However, the successful prediction ratios of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for risk degree predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 171 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 99.12% demonstrates that the presented ANN model with seven significant factors can provide a highly stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

气候模式对长江流域气温和降水的模拟性能评估及未来情景预估

利用政府间气候变化专门委员会第四次评估报告的22个新一代全球气候模式基准期(1961～1990年)模拟结果,从时空尺度分别讨论了与观测过程的差异,评估了模式对长江流域气温和降水的模拟性能。结果表明22个气候模式对长江流域具有一定的模拟能力,地面气温的模拟值都偏低,部分降水的模拟值局部偏高。不同的气候模式的模拟能力差异显著,大部分模式对长江流域的模拟精度有待进一步改进,只有少数几个模式（降水有6个模式,气温有5个模式）的年变化趋势与实况基本一致。综合比较,UKMO_HadCM3和NCAR_PCM两个模式基本能再现长江流域降水和气温的年变化特征。长江流域降水和气温未来情景预估表明各个模式和情景结果虽然存在差异,但对未来90年气候变化的模拟趋势基本一致,将持续增温、降水出现区域性增加,并着重讨论了UKMO_HadCM3模式在2020s（2010～2039年）、2050s（2040～2069年）和2080s（2070～2099年）3个时段的降水和气温时空变化特征,研究结果表明3个时段气温和降水在不同情景下都是逐渐增加的,A2情景下未来降水增幅最显著,B1情景增幅最小。     

气象灾害风险因素分析与风险评估思路

加强对气象灾害风险评估的研究,系统地分析气象灾害风险因素,是制定防灾减灾对策的需要。通过从致灾因子、孕灾环境和承灾体的易损性三方面进行分析,得到了3类10多种有代表性的气象灾害风险因素。由于不同类型的气象灾害有着不同的孕灾环境,因此不同类型气象灾害的风险因素不完全相同,由此提出了风险评估的思路和分析重点。     

地铁施工中的监测技术与安全风险管理

作为地下工程,城市地铁建设安全隐患大,风险高。在地铁施工过程中,采取何种措施和手段,保障地铁项目自身及周边建筑环境的安全,是地铁建设的一项重要内容。本文通过介绍目前地铁施工中的监测技术,结合风险管理的有关理论,指出了目前地铁施工中的监测技术及安全风险管理的不足,并提出了相应的改进措施,为类似工程建设提供参考。     

The Shared Socioeconomic Pathways and their energy,land use,and greenhouse gas emissions implications: An overview

This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO₂ emissions of the baseline scenarios range from about 25 GtCO₂ to more than 120 GtCO₂ per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m² that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).     

A Coupled Model Approach to Reduce Nonpoint-Source Pollution Resulting from Predicted Urban Growth: A Case Study in the Ambos Nogales Watershed

The development of new approaches for understanding processes of urban development and their environmental effects, as well as strategies for sustainable management, is essential in expanding metropolitan areas. This study illustrates the potential of linking urban growth and watershed models to identify problem areas and support long-term watershed planning. Sediment is a primary source of nonpoint-source pollution in surface waters. In urban areas, sediment is intermingled with other surface debris in transport. In an effort to forecast the effects of development on surface-water quality, changes predicted in urban areas by the SLEUTH urban growth model were applied in the context of erosion-sedimentation models (Universal Soil Loss Equation and Spatially Explicit Delivery Models). The models are used to simulate the effect of excluding hot-spot areas of erosion and sedimentation from future urban growth and to predict the impacts of alternative erosion-control scenarios. Ambos Nogales, meaning "both Nogaleses," is a name commonly used for the twin border cities of Nogales, Arizona and Nogales, Sonora, Mexico. The Ambos Nogales watershed has experienced a decrease in water quality as a result of urban development in the twin-city area. Population growth rates in Ambos Nogales are high and the resources set in place to accommodate the rapid population influx will soon become overburdened. Because of its remote location and binational governance, monitoring and planning across the border is compromised. One scenario described in this research portrays an improvement in water quality through the identification of high-risk areas using models that simulate their protection from development and replanting with native grasses, while permitting the predicted and inevitable growth elsewhere. This is meant to add to the body of knowledge about forecasting the impact potential of urbanization on sediment delivery to streams for sustainable development, which can be accomplished in a virtual environment.     

Seismic hazard model for loss estimation and risk management in Taiwan

We developed a seismic hazard model for Taiwan that integrates all available tectonic, seismicity, and seismic hazard information in the region to provide risk managers and engineers with a model they can use to estimate earthquake losses and manage seismic risk in Taiwan. The seismic hazard model is composed of two major components: a seismotectonic model and a ground-shaking model. The seismotectonic model incorporates earthquakes that are expected to occur on the Ryukyu and Manila subduction zones, on the intermediate-depth Wadati-Benioff seismicity zones, on the active crustal faults, and within seismotectonic provinces. The active crustal faults include the Chelungpu fault zone, the source of the damaging M_W 7.6 Chi-Chi earthquake, and the Huangchi-Hsiaoyukeng fault zone that forms the western boundary of the Taipei Basin. The ground-shaking model uses both US, worldwide, and Taiwanese attenuation relations to provide robust estimates of peak ground acceleration and response spectral acceleration on a reference site condition for shallow crustal and subduction zone earthquakes. The ground shaking for other site conditions is obtained by applying a nonlinear soil-amplification factor defined in terms of the average shear-wave velocity in the top 30 m of the soil profile, consistent with the methodology used in the current US and proposed Taiwan building codes.     

Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique

A landslide-hazard map is intended to show the location of future slope instability. Most spatial models of the hazard lack reliability tests of the procedures and predictions for estimating the probabilities of future landslides, thus precluding use of the maps for probabilistic risk analysis. To correct this deficiency we propose a systematic procedure comprising two analytical steps: “relative-hazard mapping” and “empirical probability estimation”. A mathematical model first generates a prediction map by dividing an area into “prediction” classes according to the relative likelihood of occurrence of future landslides, conditional by local geomorphic and topographic characteristics. The second stage estimates empirically the probability of landslide occurrence in each prediction class, by applying a cross-validation technique. Cross-validation, a “blind test” here using non-overlapping spatial or temporal subsets of mapped landslides, evaluates accuracy of the prediction and from the resulting statistics estimates occurrence probabilities of future landslides. This quantitative approach, exemplified by several experiments in an area near Lisbon, Portugal, can accommodate any subsequent analysis of landslide risk.