近50年三江源地表湿润指数变化特征及其影响因素

基于三江源区1959—2008年月平均气温、最高气温、最低气温、相对湿度、降水量、风速和日照百分率等气候要素资料,应用修订的Penman-Monteith（P-M）模型计算了最大潜在蒸散量和地表湿润指数,分析其空间分布、年际和年代际变化特征及其主要气象因子的影响。结果表明：1959—2008年间,研究区年降水量呈增加趋势,降水量变化曲线线性拟合倾向率为5.316~13.047 mm.（10a）-1,春夏季增幅较大;最大潜在蒸散量呈增加趋势,年最大潜在蒸散量变化曲线线性拟合倾向率在5.073~10.712 mm.（10a）-1,夏季增幅最大;地表湿润指数变化也呈增加趋势,年地表湿润指数变化曲线线性拟合倾向率0.011~0.026（10a）-1,冬季增幅最大,在15年周期附近,出现了3~5个干湿交替期,1984年之后为偏湿期,在中高频区,1998—2006年有偏干振荡;影响三江源区地表湿润指数的主要因子是降水量、相对湿度和平均最高气温。     

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.     

Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model

To study the interaction between species- and ecosystem-level impacts of climate change, we focus on the question of how climate-induced shifts in key species affect the positive feedback loops that lock shallow lakes either in a transparent, macrophyte-dominated state or, alternatively, in a turbid, phytoplankton-dominated state. We hypothesize that climate warming will weaken the resilience of the macrophyte-dominated clear state. For the turbid state, we hypothesize that climate warming and climate-induced eutrophication will increase the dominance of cyanobacteria. Climate change will also affect shallow lakes through a changing hydrology and through climate change-induced eutrophication. We study these phenomena using two models, the full ecosystem model PCLake and a minimal dynamic model of lake phosphorus dynamics. Quantitative predictions with the complex model show that changes in nutrient loading, hydraulic loading and climate warming can all lead to shifts in ecosystem state. The minimal model helped in interpreting the non-linear behaviour of the complex model. The main output parameters of interest for water quality managers are the critical nutrient loading at which the system will switch from clear to turbid and the much lower critical nutrient loading – due to hysteresis – at which the system switches back. Another important output parameter is the chlorophyll-a level in the turbid state. For each of these three output parameters we performed a sensitivity analysis to further understand the dynamics of the complex model PCLake. This analysis showed that our model results are most sensitive to changes in temperature-dependence of cyanobacteria, planktivorous fish and zooplankton. We argue that by combining models at various levels of complexity and looking at multiple aspects of climate changes simultaneously we can develop an integrated view of the potential impact of climate change on freshwater ecosystems.     

Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest,U.S.A.

Although long-lived tree species experience considerable environmental variation over their life spans, their geographical distributions reflect sensitivity mainly to mean monthly climatic conditions. We introduce an approach that incorporates a physiologically based growth model to illustrate how a half-dozen tree species differ in their responses to monthly variation in four climatic-related variables: water availability, deviations from an optimum temperature, atmospheric humidity deficits, and the frequency of frost. Rather than use climatic data directly to correlate with a species’ distribution, we assess the relative constraints of each of the four variables as they affect predicted monthly photosynthesis for Douglas-fir, the most widely distributed species in the region. We apply an automated regression-tree analysis to create a suite of rules, which differentially rank the relative importance of the four climatic modifiers for each species, and provide a basis for predicting a species’ presence or absence on 3737 uniformly distributed U.S. Forest Services’ Forest Inventory and Analysis (FIA) field survey plots. Results of this generalized rule-based approach were encouraging, with weighted accuracy, which combines the correct prediction of both presence and absence on FIA survey plots, averaging 87%. A wider sampling of climatic conditions throughout the full range of a species’ distribution should improve the basis for creating rules and the possibility of predicting future shifts in the geographic distribution of species.     

On the sudden disappearance of Egeria densa from a Ramsar wetland site of Southern Chile: A climatic event trigger model

Contemporary shallow lakes theory proposes that these ecosystems may experience abrupt regime shifts due to small changes in controlling variables or triggers. So far, these triggers have been related mostly to nutrients as the immediate driver. During May 2004 the río Cruces wetland, a Ramsar site located in Southern Chile, underwent a major regime shift, from a clear water state, vastly dominated by the invasive macrophyte Egeria densa, to a turbid water state. In this article we show, through the analysis of long-term meteorological data that late fall 2004 was anomalous due to the presence of a high-pressure cell that persisted most of the month of May over Southern Chile. This climatic event caused an almost complete absence of precipitations and lower temperatures during this period, including several freezing nights. Eco-physiological experiments showed that 6 h exposure to desiccation kill the macrophyte. We developed a simple-biology dynamic model, under Stella Research 9.1, to show that the climatic anomaly of May 2004, plus the increased sedimentation of the wetland's floodplains, and the associated response of E. densa, explains its sudden disappearance from río Cruces wetland.     

Slope,aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland

The slope and aspect of a vegetated surface strongly affects the amount of solar radiation intercepted by that surface. Solar radiation is the dominant component of the surface energy balance and influences ecologically critical factors of microclimate, including near-surface temperatures, evaporative demand and soil moisture content. It also determines the exposure of vegetation to photosynthetically active and ultra-violet wavelengths. Spatial variation in slope and aspect is therefore a key determinant of vegetation pattern, species distribution and ecosystem processes in many environments. Slope and aspect angle may vary considerably over distances of a few metres, and fine-scale species’ distribution patterns frequently follow these topographic patterns. The availability of suitable microclimate at such scales may be critical for the response of species distributions to climatic change at much larger spatial scales. However, quantifying the relevant microclimatic gradients is not straightforward, as the potential variation in solar radiation flux under clear-sky conditions is modified by local and regional variations in cloud cover, and interacts with long-wave radiation exchange, local meteorology and surface characteristics.     

Consequences of changed wetness on riverine nitrogen – human impact on retention vs. natural climatic variability

The HBV-N model was used for a scenario analysis of changes in nitrogen retention and transport caused by alterations of wetness due to land drainage, lowering of lakes, building of dams and climatic variability in a river basin in south-central Sweden (1885–1994). In general, dams were situated in locations more favourable for retention, compared to the lowered lakes. Rather modest conversions of water bodies only changed nitrogen transport by about 3%. The 180-times-larger increase of (mainly) tile-drained agricultural land had, according to simulations, increased the nitrogen transport by 17%, due to reduced retention. However, compared to human-induced alteration of the landscape N retention, the choice of 10-year periods of climatological data had the overriding effect on the calculated nitrogen transport. Weather-induced variations resulted in a 13% difference in nitrogen retention between various 10-year periods. When the model was driven by climatological data from the driest 10-year period (1905–1914), the estimated average annual load was only half of that obtained with climatological data from the wettest 10-year period (1975–1984). Electronic Publication     

环洱海地区气候变化特征研究

环洱海地区是云南省具有高原湖泊生态脆弱区、民族文化多元融合区和乡村经济发展活跃区等多重叠合特征的典型区域,是全球气候变化影响的敏感区和脆弱区。以环洱海地区1951~2014年6个基本站点的逐年平均气温、极端最高气温、极端最低气温、降水量、最大日降水量和日降水量≥0.1 mm日数资料为基础。采用线性倾向估计、Mann-Kendall趋势检验、Morlet小波分析和R/S分析等方法,研究了环洱海地区气候变化规律。结果发现:自1951年以来,环洱海地区年均气温和极端最低气温呈现出升高的趋势,而极端最高气温则呈现降低的趋势,变化速率分别为0.07℃/10 a、0.03℃/10 a和–0.14℃/10 a,对于年降水量、最大日降水量和降水日数而言,三者均为减少趋势,速率分别为–12.85 mm/10 a、–1.09mm/10 a和–1.73 d/10 a;环洱海地区年均气温、极端最高和极端最低气温均没有发生突变,年降水量和降水日数在2010年发生了一次减少突变,而最大日降水量则没有检测到突变的年份;环洱海地区年平均气温和年降水量在长时间尺度上的周期性变化最为显著,分别存在30 a和33 a左右的周期变化,并贯穿整个研究时段,而短时间尺度上的周期变化局域性特征突出;从未来演变趋势来看,年平均气温和极端最低气温将维持升温趋势,而极端最高气温则将持续降低趋势,年降水量继续减少的趋势未来将会逆转,但最大日降水量和降水日数两者将持续减少的概率更大。     

Linking permit markets multilaterally

We formally study the determinants, magnitude and distribution of efficiency gains generated in multilateral linkages between permit markets. We provide two novel decomposition results for these gains, characterize individual preferences over linking groups and show that our results are largely unaltered with strategic domestic emissions cap selection or when banking and borrowing are allowed. Using the Paris Agreement pledges and power sector emissions data of five countries which all use or considered using both emissions trading and linking, we quantify the efficiency gains. We find that the computed gains can be sizable and are split roughly equally between effort and risk sharing.     

Occupational injury rates among Norwegian farmers: A sociotechnical perspective

Introduction: This study addressed relative injury risk among Norwegian farmers, who are mostly self-employed and run small farm enterprises. The aim was to explore the relative importance of individual, enterprise, and work environment risks for occupational injury and to discuss the latent conditions for injuries using sociotechnical system theory. Method: Injury report and risk factors were collected through a survey among Norwegian farm owners in November 2012. The response rate was 40% (n = 2,967). Annual work hours were used to calculate injury rates within groups. Poisson regression using the log of hours worked as the offset variable allowed for the modeling of adjusted rate ratios for variables predictive of injury risk. Finally, safety climate measures were introduced to assess potential moderating effects on risk. Results: Results showed that the most important risk factors for injuries were the design of the workplace, type of production, and off-farm work hours. The main results remained unchanged when adding safety climate measures, but the measures moderated the injury risk for categories of predominant production and increased the risk for farmers working with family members and/or employees. An overall finding is how the risk factors were interrelated. Conclusions: The study identified large structural diversities within and between groups of farmers. The study drew attention to operating conditions rather than individual characteristics. The farmer’s role (managerial responsibility) versus regulation and safety climate is important for discussions of injury risk. Practical Applications: We need to study sub-groups to understand how regulation and structural changes affect work conditions and management within different work systems, conditioned by production. It is important to encourage actors in the political-economic system to become involved in issues that were found to affect the safety of farmers.