1960-2009年辽宁冬季风寒温度时空变化特征

基于1960-2009年辽宁省52个气象站气象资料,采用风寒温度计算公式和适合辽宁地区的分级标准并分类,得出50 a历年各类别风寒温度日数。使用旋转经验正交分解法（REOF）,分别针对各类别风寒温度日数进行区域划分,并分析了其空间分布和变化趋势。结果表明：辽宁省可按4个风寒温度类别分别分区,凉爽至轻度风寒可分为4个区域,而轻度、中度及重度风寒可分为3个区域;辽宁省辽东半岛五个地市所在地理区域为易患风寒日数最少的地域,辽宁北部、东北部五个地市为易患风寒日数最多的地域;辽宁地区各风寒类别所在划分区域的日数变化相位基本一致;年平均易患风寒日数总体变化趋势是在1986年发生一次突变,在1960-1985年相对偏多,1986-2009年相对偏少。     

遥感信息与作物模型结合在冬小麦区域模拟中的应用

遥感数据与作物模型结合是当前农业信息技术应用研究的重要内容和发展趋势之一,能够解决单独利用遥感或作物模型无法解决的问题。为了开展大范围、区域性作物生长过程的模拟和产量预测,首先对作物模型WOFOST进行了订正和验证,使得调整后的模型适于模拟河南新乡县冬小麦生长;根据实际生产分三类情形模拟新乡县2002—2003年度冬小麦的生长发育状况;利用全生育期内能获取的Landsat-7 ETM+数据反演叶面积指数,结合WOFOST模型的模拟情况,确定每个像元对应的冬小麦的生长状况,从而在像元上实现了WOFOST模型对冬小麦生长的模拟;最后对照比较本研究方法的结果与当年新乡县冬小麦的统计情况,结果相近,验证了本文研究方法的技术可行性。     

A topoclimatic classification to map spring frost risk for six deciduous tree fruit varieties

Abstract

A topoclimatic classification was developed to map the frequency of damaging spring radiation frosts to six deciduous fruit varieties at a scale of 1:25,000. The methodology utilizes night‐time temperatures from climatological stations operated by the Atmospheric Environment Service (aes) ofEnvironment Canada, as well as night‐time temperatures from a temporary network of standard climate stations and mobile temperature surveys. Maps of the deviation of minimum temperature from the local aes climate station, and of spring frost risk for each fruit variety are produced.     

中国东部冬季降水的动力结合统计预测方法研究

针对中国东部6个气候关键区,首先,通过相关分析指出,冬季降水既与前期气候因子有关,又受同期大气环流的影响。因此,有必要采用动力与统计相结合的方法进行气候预测研究。然后,从实时预测的角度出发,综合考虑前期预测因子的观测信息和具有数值可预测性的同期气候因子的数值模式结果,使用多元线性回归分析方法就各区域平均冬季降水逐一建立了短期气候预测模型,并在预测模型中考虑了模型结果中系统误差的订正。交叉检验分析结果表明,所建立的各区域预测模型普遍具有较好的预测效果,预测优势主要表现在对冬季降水的变化趋势、年际变化、以及异常符号的预测准确率上。就6个区域平均而言,1982—2008年交叉检验结果与实况间的相关系数和距平同号率分别为0.69和78%,表明该预测思想具有可行性。     

A sensitivity-based approach to evaluating future changes in Colorado River discharge

Projections of a drier, warmer climate in the U.S. Southwest would complicate management of the Colorado River system—yet these projections, often based on coarse resolution global climate models, are quite uncertain. We present an approach to understanding future Colorado River discharge based on land surface characterizations that map the Colorado River basin’s hydrologic sensitivities (e.g., changes in streamflow magnitude) to annual and seasonal temperature and precipitation changes. The approach uses a process-based macroscale land surface model (LSM; in this case, the Variable Infiltration Capacity hydrologic model, although methods are applicable to any LSM) to develop sensitivity maps (equivalent to a simple empirical model), and uses these maps to evaluate long-term annual streamflow responses to future precipitation and temperature change. We show that global climate model projections combined with estimates of hydrologic sensitivities, estimated for different seasons and at different change increments, can provide a basis for approximating cumulative distribution functions of streamflow changes similar to more common, computationally intensive full-simulation approaches that force the hydrologic model with downscaled future climate scenarios. For purposes of assessing risk, we argue that the sensitivity-based approach produces viable first-order estimates that can be easily applied to newly released climate information to assess underlying drivers of change and bound, at least approximately, the range of future streamflow uncertainties for water resource planners.     

A social-ecological network approach for understanding wildfire risk governance

Large wildfire events (e.g. >100 square km) highlight the importance of governance systems that address wildfire risk at landscape scales and among multiple land owners and institutions. A growing body of empirical work demonstrates that environmental governance outcomes depend upon how well patterns of interaction among actors align with patterns of ecological connectivity, such as wildfire transmission. However, the factors that facilitate or inhibit this alignment remain poorly understood. It is crucial to improve understanding of the conditions under which actors establish or maintain linkages with other actors with whom they are interdependent because of ecological linkages. To this end, we introduce the concept of “risk interdependence archetypes” based on the spatial configurations by which one actor (i.e. a particular organization) is exposed to risk via the actions of another actor. We then develop a set of hypotheses to explore how different sets of conditions associated with each spatial configurations of risk interdependence may shape the likelihood that an actor coordinates with another actor in ways that promote social-ecological alignment. We test these hypotheses using network analysis of a wildfire transmission network developed through simulation of wildfires over several thousand fire seasons and a governance network created from interviews with 154 representatives of 87 organizations involved in efforts to mitigate wildfire risk in the Eastern Cascades Ecoregion, USA. Results indicate that social-ecological alignment is more likely when actors have opportunities to influence forest management practices on ignition-prone lands that they do not manage themselves, and when actors bear greater responsibility for averting losses from wildfires that spread to lands they manage independently. Importantly, not all forms of risk interdependence increase the likelihood of alignment, implying that organizations have limited capacity for interaction and may prioritize certain risk mitigation partnerships over others. While the performance of risk governance systems may hinge on the alignment of social and ecological networks, our results suggest that alignment in turn may depend on actor-level strategies for interaction with other actors.     

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.     

Integrating agriculture and energy to assess GHG emissions reduction: a methodological approach

Agriculture is responsible for approximately 25% of anthropogenic global GHG emissions. This significant share highlights the fundamental importance of the agricultural sector in the global GHG emissions reduction challenge. This article develops and tests a methodology for the integration of agricultural and energy systems modelling. The goal of the research is to extend an energy systems modelling approach to agriculture in order to provide richer insights into the dynamics and interactions between the two (e.g. in competition for land-use). We build Agri-TIMES, an agricultural systems module using the TIMES energy systems modelling framework, to model the effect of livestock emissions and explore emissions reduction options. The research focuses on Ireland, which is an interesting test case for two reasons: first, agriculture currently accounts for about 30% of Ireland's GHG emissions, significantly higher than other industrialized countries yet comparable with global levels (here including emissions associated with other land-use change and forestation); second, Ireland is both a complete and reasonably sized agricultural system to act as a test case for this new approach. This article describes the methodology used, the data requirements, and technical assumptions made to facilitate the modelling. It also presents results to illustrate the approach and provide associated initial insights.

Policy relevance

Most of the policy focus with regard to climate mitigation targets has been on reducing energy-related CO₂ emissions, which is understandable as they represent by far the largest source of emissions. Non-energy-related GHG emissions – largely from agriculture, industrial processes, and waste – have received significantly less attention in policy discourse. Going forward, however, if significant cuts are made in energy-related CO₂ emissions, the role of non-energy-related GHG emissions will grow in importance. It is therefore crucial that climate mitigation analyses and strategies are not limited to the energy system. This article shows the value of using integrated energy and agriculture techno-economic modelling techniques to draw evidence for new comprehensive climate policy strategies able to discern between the full range of technical solutions available. It enables the production of economy-wide least-cost climate mitigation pathways.     

用带周期分量的逐步回归模型预测冬小麦产量的试验

本文首先对粗选因子进行主分量分析,得到立分量组合因子,将其连同预报量和主分量因子的周期分量一起嵌入逐步回归方程,来组建北方主要产麦区冬小麦产量预报模式,收到较好的效果。     

A place-based approach to payments for ecosystem services

Payment for Ecosystem Services (PES) schemes are proliferating but are challenged by insufficient attention to spatial and temporal inter-dependencies, interactions between different ecosystems and their services, and the need for multi-level governance. To address these challenges, this paper develops a place-based approach to the development and implementation of PES schemes that incorporates multi-level governance, bundling or layering of services across multiple scales, and shared values for ecosystem services. The approach is evaluated and illustrated using case study research to develop an explicitly place-based PES scheme, the Peatland Code, owned and managed by the International Union for the Conservation of Nature’s UK Peatland Programme and designed to pay for restoration of peatland habitats. Buyers preferred bundled schemes with premium pricing of a primary service, contrasting with sellers’ preferences for quantifying and marketing services separately in a layered scheme. There was limited awareness among key business sectors of dependencies on ecosystem services, or the risks and opportunities arising from their management. Companies with financial links to peatlands or a strong environmental sustainability focus were interested in the scheme, particularly in relation to climate regulation, water quality, biodiversity and flood risk mitigation benefits. Visitors were most interested in donating to projects that benefited wildlife and were willing to donate around £2 on-site during a visit. Sellers agreed a deliberated fair price per tonne of CO₂ equivalent from £11.18 to £15.65 across four sites in Scotland, with this range primarily driven by spatial variation in habitat degradation. In the Peak District, perceived declines in sheep and grouse productivity arising from ditch blocking led to substantially higher prices, but in other regions ditch blocking was viewed more positively. The Peatland Code was developed in close collaboration with stakeholders at catchment, landscape and national scales, enabling multi-level governance of the management and delivery of ecosystem services across these scales. Place-based PES schemes can mitigate negative trade-offs between ecosystem services, more effectively include cultural ecosystem services and engage with and empower diverse stakeholders in scheme design and governance.     

Changes in future flood risk due to climate and development in a Dutch polder area

Damages from weather related disasters are projected to increase, due to a combination of increasing exposure of people and assets, and expected changes in the global climate. Only few studies have assessed in detail the potential range of losses in the future and the factors contributing to the projected increase. Here we estimate future potential damage from river flooding, and analyse the relative role of land-use, asset value increase and climate change on these losses, for a case study area in The Netherlands. Projections of future socioeconomic change (land-use change and increase in the value of assets) are used in combination with flood scenarios, projections of flooding probabilities, and a simple damage model. It is found that due to socioeconomic change, annual expected losses may increase by between 35 and 172% by the year 2040, compared to the baseline situation in the year 2000. If no additional measures are taken to reduce flood probabilities or consequences, climate change may lead to an increase in expected losses of between 46 and 201%. A combination of climate and socioeconomic change may increase expected losses by between 96 and 719%. Asset value increase has a large role, as it may lead to a doubling of losses. The use of single loss estimates may lead to underestimation of the impact of extremely high losses. We therefore also present loss–probability curves for future risks, in order to assess the increase of the most extreme potential loss events. Our approach thus allows a more detailed and comprehensive assessment than previous studies that could also be applied in other study areas to generate flood risk projections. Adaptation through flood prevention measures according to currently planned strategies would counterbalance the increase in expected annual losses due to climate change under all scenarios.     

A first approach to calculate BIOCLIM variables and climate zones for Antarctica

Theoretical and Applied Climatology - For testing the hypothesis that macroclimatological factors determine the occurrence, biodiversity, and species specificity of both symbiotic partners of...     

Deep uncertainty in long-term hurricane risk: Scenario generation and implications for future climate experiments

Current projections of long-term trends in Atlantic hurricane activity due to climate change are deeply uncertain, both in magnitude and sign. This creates challenges for adaptation planning in exposed coastal communities. We present a framework to support the interpretation of current long-term tropical cyclone projections, which accommodates the nature of the uncertainty and aims to facilitate robust decision making using the information that is available today. The framework is populated with projections taken from the recent literature to develop a set of scenarios of long-term hurricane hazard. Hazard scenarios are then used to generate risk scenarios for Florida using a coupled climate–catastrophe modeling approach. The scenarios represent a broad range of plausible futures; from wind-related hurricane losses in Florida halving by the end of the century to more than a four-fold increase due to climate change alone. We suggest that it is not possible, based on current evidence, to meaningfully quantify the relative confidence of each scenario. The analyses also suggest that natural variability is likely to be the dominant driver of the level and volatility of wind-related risk over the coming decade; however, under the highest scenario, the superposition of this natural variability and anthropogenic climate change could mean notably increased levels of risk within the decade. Finally, we present a series of analyses to better understand the relative adequacy of the different models that underpin the scenarios and draw conclusions for the design of future climate science and modeling experiments to be most informative for adaptation.     

The integrated socio-perceptual approach: Using ecological mental maps and future imaginaries to understand land use decisions

Rural places are important centers of environmental and social transformation. Landholders are not only affected by socio-environmental changes, but they are influencing futures related to climate change, food security, freshwater, biodiversity, and social and economic development. Much environmental land use work understands individual landholders as rational actors, portrayed through the economic lens of “producer.” These approaches generally focus on present capacities and limitations as the principal factors contributing to land use, and the individual farm as the reference unit for decisions. Our research takes steps to expand conceptualizations of rural landholders as active and knowledgeable in envisioning, managing, and shaping environmental futures. We design and test a new approach using ecological mental maps and future imaginaries to understand land use practices through a case study in the cocoa-producing and Atlantic Forest region of Southern Bahia, Brazil. The integrated socio-perceptual (ISP) approach combines qualitative and quantitative methodologies to honor the depth of landholders’ experiences and perspectives and to allow broader regional relationships and insights to emerge. We demonstrate that the ISP approach—through 49 surveys—has the capacity to capture variation and identify patterns in ecological mental maps and future imaginaries in a population, approximate the relevant spatial scales underlying these factors, and identify relationships between these and land uses. In this context, the types of narratives landholders hold about the region’s future are associated with current forest land use on their properties. We discuss potential applications of the ISP approach for land use study and practice.     

Flood insurance arrangements in the European Union for future flood risk under climate and socioeconomic change

Flood risk will increase in many areas around the world due to climate change and increase in economic exposure. This implies that adequate flood insurance schemes are needed to adapt to increasing flood risk and to minimise welfare losses for households in flood-prone areas. Flood insurance markets may need reform to offer sufficient and affordable financial protection and incentives for risk reduction. Here, we present the results of a study that aims to evaluate the ability of flood insurance arrangements in Europe to cope with trends in flood risk, using criteria that encompass common elements of the policy debate on flood insurance reform. We show that the average risk-based flood insurance premium could double between 2015 and 2055 in the absence of more risk reduction by households exposed to flooding. We show that part of the expected future increase in flood risk could be limited by flood insurance mechanisms that better incentivise risk reduction by policyholders, which lowers vulnerability. The affordability of flood insurance can be improved by introducing the key features of public-private partnerships (PPPs), which include public reinsurance, limited premium cross-subsidisation between low- and high-risk households, and incentives for policyholder-level risk reduction. These findings were evaluated in a comprehensive sensitivity analysis and support ongoing reforms in Europe and abroad that move towards risk-based premiums and link insurance with risk reduction, strengthen purchase requirements, and engage in multi-stakeholder partnerships.     

A systematic regional approach for climate change adaptation to protect biodiversity

Developing appropriate climate change adaptations to protect biodiversity requires taking into account the dynamics of agro-ecological and socio-economic change. A framework for approaching this problem was proposed, but not applied in detail, as part of a major biodiversity and climate change report prepared in Australia. This paper describes the first trial application of the method. It was applied across Vietnam as the Government is interested in identifying adaptation options and detailed data are readily available for its 65 provinces. The process involves identifying ecoregions and collating information for each region based on the current conditions and trends in biodiversity, population, income and agricultural production. Climate change scenarios are identified for each region, together with governance options. Educational needs and key adaptation actions are then identified for each region taking into account the agro-ecological and socio-economic input data. It is concluded that the framework could easily be applied in other countries and should assist the development of strategic adaptation options.     

A survey of temporal and spatial reference crop evapotranspiration trends in Iran from 1960 to 2005

Reference crop evapotranspiration (ET₀) is one of the most important climatic parameters which plays a key role in estimating crop water demand and scheduling irrigation. Under global warming and climate change conditions, it is needed to survey the trend of ET₀ in Iran. In this study, ET₀ values were determined based on FAO-56 Penman-Monteith equation over 32 synoptic meteorological stations during 1960–2005; and analyzed spatially and temporally in monthly, seasonal and annual time scales. After removing the significant lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann–Kendall (MK) test was used to detect the trends. The slope of the changes was determined by Sen’s slope estimator. In order to facilitate in trend analysis, the 10 moving average low pass filter were also applied on the normalized annual ET₀ time series. Annual ET₀ time series and filtered ones were then classified by hierarchical clustering in three clusters and then mapped in order to show the patterns of different clusters. Results showed that the significant decreasing trends were more considerable than increasing ones. Among surveyed stations, and on an annual time scale, the highest and lowest annual values of Sen’s slope estimator were observed in Tabas with (+) 72.14 mm per decade and Shahrud with (?) 62.22 mm per decade, respectively. Results also indicated that the clustered map based on normalized and filtered annual ET₀ time series is in accordance with another map which showed spatial distribution of increasing, decreasing and non-significant trends of ET₀ on annually time scale. Exploratory and visual analysis of smoothed time series showed increasing trend in recent years especially after 1980 and 1995. In brief, the upward trend of ET₀ in recent years is a crucial issue with regard to the high cost of dam construction for agricultural aims in arid and semi-arid regions e.g. Iran.     

A first approach to assessing future climate states in the UK over very long timescales: Input to studies of the integrity of radioactive waste repositories

Projected timescales for the transport of radionuclides from an undisturbed undergound nuclear waste repository to the surface are in the range of tens of thousands to millions of years. Over these timescales major natural and potentially major anthropogenic changes in climate can be expected. As part of the UK disposal safety assessment programme, time-dependent models of the repository environment are being developed. The Climatic Research Unit has undertaken a study of relevant climatic change and climatic effects. This has required assessment of the probable range, succession and duration of major climate states likely to be experienced in the UK over very long timescales, up to 10⁶ yr. Two methodologies have been employed. The first uses the Milankovitch theory, which is considered to be the major cause of the Pleistocene glacial/interglacial cycles. The second involves empirical analysis of the long-term reconstructed climate record: no assumptions about the specific causes or mechanisms are made. A period of sub-tropical climate is included in the sequence to represent a period of anthropogenically-induced greenhouse warming. The climate sequence established using these methods will form the basis for studying related processes, such as erosion and groundwater movement and transfer by vegetation, and their implications for radioactive waste disposal. This has involved the construction of instrumental climate analogues.