降水临近预报及其在水文预报中的应用研究进展

随着全球气候变化、自然变迁及陆表生境改变,极端天气频发且呈现出多尺度时空变异特征,对其进行预报和预警一直是气象水文领域关注的焦点。临近预报可较准确地预报未来短时间天气显著变化,是当前预报强降水等极端事件的主要手段。从基于天气雷达0~3 h外推临近预报、融合数值模式0~6 h临近预报的发展历程梳理了临近预报的研究进展,阐述了雷达外推算法的发展进程、雷达外推预报与数值模式预报融合技术进展,指出"取长补短"的0~6 h融合预报在提高降水预报精度、延长降水预见期等多方面有较大的发展潜力,进一步探寻及提升融合技术是未来融合预报发展的核心。将临近预报以气象水文耦合的方式引入水文预报是从源头提高水文预报精度、保障水文预报效果的主要途径,总结了现阶段主流耦合方式、空间尺度匹配技术、水文模型不确定等陆气耦合中的关键问题,阐述了外推临近预报、融合临近预报作为水文预报输入的研究进展,明确了融合临近预报在延长洪水预见期、提高洪水预报精度中存在优势,并讨论了未来的研究重点及发展方向。     

The applicability of Standardized Precipitation Index: drought characterization for early warning system and weather index insurance in West Africa

The Niger River basin is drought-prone, and farmers are often exposed to the vagaries of severe weather and extreme climate events of the region. Spatiotemporal characteristics of drought are important for its mitigation. With 52 years of gauged-based monthly rainfall, the study investigates the potentials of Standardized Precipitation Index (SPI) as standard measure for meteorological drought, its characterization, early warning systems and use in weather index-based insurance. Gamma probability distribution type 2, which best fits the rainfall frequency distribution of the region, was used for the transformation of the skewed rainfall data to derive the SPI. Results showed 9, 5, 5 and 6 drought events of severe to extreme intensities occurred in the headwaters of the basin, inner delta, middle Niger, and lower Niger sub-watersheds, respectively. Their magnitudes were in the range 1–5, 2–6, 2–8 and 2–7, respectively. Spatially, results further showed that the 1970s and 1980s drought events were dominantly of moderate (SPI values ?1 to ?1.49) and severe (SPI values ?1.5 to ?1.99) intensities, respectively, with sporadic cases of severe to extreme drought intensities occurring in 1970s and extreme to exceptional intensities in the 1980s. Further investigations show that 3-month SPI indicated 85% of variance in the standardized cereal crop yield, which suites well as weather index insurance variable. The study therefore proposes SPI weather index-based insurance as a pathway forward to ameliorate the negative impacts on insured farmers in this region in terms of indemnity payouts whenever drought disaster occurs.     

A Conceptual Framework for Enhancing the Utility of Rainfall Hazard Forecasts for Agriculture in Marginal Environments

Semi-arid and dry sub-humid areas (especially in the tropics) are characterized by high inter-annual and intra-seasonal rainfall variability. Agriculture, which employs the bulk of the rapidly increasing populations, is largely rain-fed, low-input based and highly resource dependent. Recent spates of drought have, therefore, exacerbated the now-too-familiar specter of famine and starvation in these areas with glaring examples being the recurring episodes in sub-Saharan Africa since the great Sahel drought of 1969–1973. A great need for accurate and timely hazard forecast products in aid of agriculture thus exists.Several schemes are currently employed by various agencies around the globe in this direction. There does remain, however, a gap between product provision and user expectations. This paper examines this gap suggesting a five-point framework within which it can be addressed as an action agenda for the climate science community. The paper posits that changes are possible to existing methodologies (related to three of these points), which, within the context of current science, can greatly enhance the utility of forecast products for agriculture in marginal areas. The remaining two points have, however, been identified as requiring additional applied research and necessary pointers for addressing these issues are provided. First is the need for appropriate impact-related indicators for intra-seasonal and interannual rainfall variability that are easy to compute, amenable to forecasting and follow closely the experiences of farmers in marginal areas. The second is a consideration of appropriate forecast information formatting and communication medium that guarantee effective feedback between forecast producers and users. Specific examples of the status quo and of work currently underway are cited from southern Africa – a region currently attracting international attention as a result of recent droughts and the threat of famine.     

岩爆预测的灰色关联系统分析与评价

岩爆由于其发生的突然性和猛烈性,常给地下工程建设带来灾害性破坏,因此,岩爆预测研究具有重大的工程意义.岩爆由于影响因素众多且关系复杂,采用传统的指标和评价办法很难系统地考虑诸多因素,而采用灰色关联分析方法可建立起各种影响因素的相关性.灰色关联系统通过分析洞室围岩应力、岩性条件和能量条件等影响岩爆的主要因素,选取岩石抗压...     

Evaluation of drought and flood risks in a multipurpose dam under climate change: a case study of Chungju Dam in Korea

The purpose of this study was to evaluate the effects of climate change on the drought and flood risks of a multipurpose dam. To achieve this, A2 climate change scenarios of RegCM3 were collected about Chungju Dam in Korea. To analyze drought risks, weather data obtained by the statistical downscaling method were entered to produce runoff series by runoff modeling and water balance was analyzed based on water use scenarios to review changes in the storage volume under climate change. To analyze flood risks, changes in water levels of the dam in future flood seasons were reviewed based on the current dam operation method. The results of the review indicated that both the drought and the flood risks of the dam would increase in the future. The reason was considered to be the movement of the flood season’s runoff characteristics from July and August to August and September because of climate change. Therefore, for climate change adaptation planning, not only quantitative changes in hydrologic values but also changes in temporal characteristics should be considered and given importance.     

Micro-level perception to climate change and adaptation issues: A prelude to mainstreaming climate adaptation into developmental landscape in India

Climate change adds another dimension of challenges to the growth and sustainability of Indian agriculture. The growing exposure to livelihood shocks from climate variability/change and limited resource base of the rural community to adapt has reinforced the need to mainstream climate adaptation planning into developmental landscape. However, a better understanding of micro-level perceptions is imperative for effective and informed planning at the macro-level. In this paper, the grass-root level perspectives on climate change impacts and adaptation decisions were elicited at farm level in the Moga district of Punjab and Mahbubnagar district of Telangana, India. The farmers opined that the climatic variability impacts more than the long-term climate change. They observed change in the quantum, onset and distribution of rainfall, rise in minimum as well as maximum temperature levels, decline in crop yield and ground water depletion. The key socio-economic effects of climate change included decline in farm income, farm unemployment, rural migration and increased indebtedness among farmers. In order to cope with climate variability and change thereon, farmers resorted to adaptation strategies such as use of crop varieties of suitable duration, water conservation techniques, crop insurance and participation in non-farm activities and employment guarantee schemes. Farmers’ adaptation to changing climate was constrained by several technological, socio-economic and institutional barriers. These include limited knowledge on the costs–benefits of adaptation, lack of access to and knowledge of adaptation technologies, lack of financial resources and limited information on weather. Besides, lack of access to input markets, inadequate farm labour and smaller farm size were the other constraints. Further, on the basis of the grass-root elicitation a ‘Need-Based Adaptation’ planning incorporating farmers’ perceptions on climate change impacts, constraints in the adoption of adaptation strategies and plausible adaptation options were linked with the most suitable ongoing programmatic interventions of the Government of India. The study concluded that micro-level needs and constraints for various adaptation strategies and interventions should be an integral part of the programme development, implementation and evaluation in the entire developmental paradigm.     

Partition dynamic threshold monitoring technology of wildfires near overhead transmission lines by satellite

Wildfires are a major natural disaster that can threaten the safe and stable operation of overhead transmission lines. Compared with large-area forest fires, transmission-line wildfires usually cover a small area and spread rapidly, making monitoring accuracy and real-time requirements of high priority. Wildfire monitoring based on satellite remote sensing has advantages in terms of monitoring-range width and the capacity for real-time monitoring; however, the detection threshold changes dynamically due to the influences of climate, geography, weather, and other factors that affect monitoring accuracy. To focus on small-area wildfires near overhead transmission lines, we developed a partition dynamic threshold calculation method based on time-series prediction. Basic thresholds are obtained based on a large number of historical values, followed by partitioning one of these values according to digital elevation model data and subsequent correction. Compared with conventional constant-threshold monitoring methods, our proposed method significantly reduced missed and false detection rates. Additionally, to improve fire-spot localization to the overhead transmission-line towers, we developed a tower-location algorithm based on block searching. Compared with the traditional traversal algorithm, our algorithm enabled a 15,000-fold increase in operation speed. These improvements will significantly enhance the monitoring of transmission-line wildfires, which are highly reliant upon alarm speed.     

Research frontiers in climate change: Effects of extreme meteorological events on ecosystems

Climate change will increase the recurrence of extreme weather events such as drought and heavy rainfall. Evidence suggests that modifications in extreme weather events pose stronger threats to ecosystem functioning than global trends and shifts in average conditions. As ecosystem functioning is connected with ecological services, this has far-reaching effects on societies in the 21st century. Here, we: (i) present the rationale for the increasing frequency and magnitude of extreme weather events in the near future; (ii) discuss recent findings on meteorological extremes and summarize their effects on ecosystems and (iii) identify gaps in current ecological climate change research.     

非典型周期和气候突变的识别与判定

通过两种新方法分析了无锡站降水年际变化的突变性、周期性等特征,揭示了旱涝演化的自然规律。研究表明,1934、1978、2013年是我国近现代气候突变的临界点,这是旱涝演化的自然选择;无锡站年际降水的观测历史可分成四个特征不同的时段,每个时段节点对应的都是一次气候突变;气候突变会改变年际降水序列的一致性,分段法研究可以大幅度降低模拟难度、提高预测效率。发现并确认了3个气候突变点,实现了超长缺测中断期(13a)旱涝趋势的精确插补,完成了无锡站近百年降雨序列的周期性分析与旱涝趋势预测模拟,研究成果改变了近现代气候历史无法细分的现状。     

大气动力学方程组全局分析的研究进展

把天气预报问题提成初值问题的反问题,可以使用多时刻的历史资料去补充和完善数值预报模式。借助于几何直观可以对大气动力学方程组的极限解集进行全局定性分析,运用胞 概念和方法可以对大气的数值模式的整体特征进行了全局分析和借助概率论的语言进行统计描述。还给出“气候”这个模糊概念的严格的数学定义及定量研究大气可预测性问题的途径。全局分析是研究气候不同于简单初值问题的另一条道路。综述了这方面研究的最新进展。     

利用MODIS fAPAR傅立叶时间序列分析研究植被光合作用活动对净辐射和降雨的响应：青藏高原个例研究

气候变化对植被动力学有非常大的影响。为了定量描述气候变化对植被的影响，文章利用MODIS fAPAR 数据和NCEP 的净辐射和降雨再分析数据对青藏高原地区气候变化对植被的影响进行了时间序列分析。研究所用的数据时间跨度为2000年至2005年。首先利用NCEP 再分析数据建立了干旱度因子的时间序列，为了与MODIS fAPAR 具有相同的时间采样间隔，由NCEP的日净辐射和日降雨量得到每8天的平均净辐射和8日降雨的和。根据一定时间间隔的净辐射与降雨量的比可以用来衡量相对于可利用水分的剩余能量，因此该比值也是干旱灾害的度量。其次，对MODIS fAPAR 的傅立叶时间序列分析提供了两个植被光合作用对干旱相应的因子，即fAPAR的年平均值及其年振幅值。在时间和空间尺度上对植被光合作用活动与干旱指数之间的关系进行了定量分析。对湿年和干年之间的响应差异进行了比较。研究表明较干地区对气候变化的响应最为显著。分析应该扩展到更长的时间跨度以便更加有效地在时间和空间尺度上评估气候变化对植被动力学的影响。     

Climate variability/change and attitude to adaptation technologies: a pilot study among selected rural farmers’ communities in Nigeria

Understanding climate variability and change is essential for designing adaptation policies and strategies to deal with the impacts of climate change on the agricultural sector. This Paper aims at assessing climate variability/change, the perception of rural farmers on climate change and preferred  adaptation strategies among the farmers in some selected farming communities in Nigeria. The study thus used both meteorology data and social survey, to examine variability/change in climate and factors determining the adaptation techniques adopts by rural farmers. The results show a relatively uniform temperatures and some seasonal variations in recent years (diurnal range of temperature is about 10 °C) but the rainfall shows much more seasonal variations. The rainfall has relatively undeviating trend from 1981 to 1996 but the trend appears to be upwards from the year 1997 to 2010. About 72.8% participants responded in the affirmative that climate is changing but there appears to be a significant relationship between the length of farming experiences and farmers’ perceptions of climate change adaptation techniques. Water-related (about 53%) and nutrient related (about 52%) technologies appear to have a high preference among the farmers. The major driver that determines farmers’ preference for climate change adaptation techniques is their incomes and experiences.     

Dynamic risk prediction based on discriminant analysis for maize drought disaster

This study presents a discriminant analysis-based method for prediction of agriculture drought disaster risk. We selected the Chaoyang city in the Northeast China as the study area. We employed multi-scale standard precipitation index (SPI) to reflect drought hazard. We used the yield losses to indicate the drought disaster risk, which was divided into no, low, or high drought risk. We used the multi-scale SPI and drought disaster risk as the input factors for the discriminant analysis-based risk prediction model. The results showed that the model’s prediction accuracy varied between 40 and 82.4 %. The accuracy of high drought disaster risk category was higher than low and no drought disaster risk category. The prediction accuracy of the milky maturity stage was highest. We use leave-one-out cross-validation method to validate the model’s accuracy. And the results showed that the model validation accuracy of high drought group could reach 70.6 % in milky maturity stage. This study showed discriminant analysis is an effective and operable method for disaster risk prediction. This model can provide timely information for decision makers to make effective measures for drought disaster management and to reduce the drought effects to yields at the minimum level.     

Paleoclimate studies and natural-resource management in the Murray-Darling Basin II: unravelling human impacts and climate variability

The management of the water resources of the Murray-Darling Basin (MDB) has long been contested, and the effects of the recent Millennium drought and subsequent flooding events have generated acute contests over the appropriate allocation of water supplies to agricultural, domestic and environmental uses. This water-availability crisis has driven demand for improved knowledge of climate change trends, cycles of variability, the range of historical climates experienced by natural systems and the ecological health of the system relative to a past benchmark. A considerable volume of research on the past climates of southeastern Australia has been produced over recent decades, but much of this work has focused on longer geological time-scales, and is of low temporal resolution. Less evidence has been generated of recent climate change at the level of resolution that accesses the cycles of change relevant to management. Intra-decadal and near-annual resolution (high-resolution) records do exist and provide evidence of climate change and variability, and of human impact on systems, relevant to natural-resource management. There exist now many research groups using a range of proxy indicators of climate that will rapidly escalate our knowledge of management-relevant, climate change and variability. This review assembles available climate and catchment change research within, and in the vicinity of, the MDB and portrays the research activities that are responding to the knowledge need. It also discusses how paleoclimate scientists may better integrate their pursuits into the resource-management realm to enhance the utility of the science, the effectiveness of the management measures and the outcomes for the end users.     

Characterizing uncertainty in geotechnical design of energy piles based on Bayesian theorem

It is known that a lot of uncertainties are involved in geotechnical design of energy piles. In this paper, a Bayesian updating framework is presented to characterize those uncertainties. The load-transfer model is developed to predict the thermomechanical response of energy piles. Considering the cross-case variability of the uncertainty in the axial strains of pile, the global model bias is firstly calibrated by establishing a comprehensive database consisting of 12 energy pile cases. Furthermore, the uncertainty in input parameters is considered in the Bayesian updating of model bias in a specific case. The variability of the uncertain parameters is effectively reduced after updating. The coefficient of variation of prediction is decreased from 0.34 to 0.13. The present framework can well quantify uncertain factors and improve the accuracy and reliability of the prediction model.

     

Determing the effects of El Niño-Southern Oscillation events on coastal water quality

The importance of the El Niño-Southern Oscillation (ENSO) on regional-scale climate variability is well recognized although the associated effects on local weather patterns are poorly understood. Little work has addressed the ancillary impacts of climate variability at the community level, which require analysis at a local scale. In coastal communities water quality and public health effects are of particular interest. Here we describe the historical influence of ENSO events on coastal water quality in Tampa Bay, Florida (USA) as a test case. Using approximate randomized statistics, we show significant ENSO influences on water quality particularly during winter months, with significantly greater fecal pollution levels during strong El Niño winters and significantly lower levels during strong La Niña winters as compared to neutral conditions. Similar significant patterns were also noted for El Niño and La Niña fall periods. The success of the analysis demonstrates the feasibility of assessing local effects associated with large-scale climate variability. It also highlights the possibility of using ENSO forecasts to predict periods of poor coastal water quality in urban region which local agencies may use to make appropriate prepations.     

云分析预报方法研究进展

云作为地球大气系统的重要组成部分,不仅影响着气候变化和天气系统的发展演变,还与航空活动密切相关,一直以来是空军和民航部门非常关注的气象要素之一。在云探测、资料同化和反演方法发展的基础上,从实际业务保障和数值模式发展需求出发,综述国内外云分析、预报方法和云分析预报系统开发的研究成果,分析各类方法的优势和不足,明确国内外研究的主要差距,并探讨国内未来研究的方向。云分析方法中,探空对云廓线识别较好,卫星可见光和红外资料在云顶信息反演方面优势明显,多普勒雷达能够获取对流层中层和底层的云信息,而毫米波雷达能够很好地反映云三维结构信息,发展潜力巨大。云预报方法中,传统的统计和诊断方法发展较为成熟,而考虑了大气温湿和云微物理状况的大气辐射传输模式正演模拟云顶亮温的方法是未来的发展趋势。加强云探测技术,综合利用云分析预报方法,借鉴国外先进云分析预报系统的设计理念,积极开发我国自主的云分析预报系统,推动天气预报、航空气象保障和数值预报模式的发展将会是我国云研究的重要方面。     

Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE?>?0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.     

Climate (in)justice,vulnerability and livelihoods in the Caribbean: The case of the indigenous Caribs in northeastern St. Vincent

This paper examines climate justice from the perspective of three remote indigenous Carib communities located in northeastern St. Vincent, amidst their vulnerability to climatic hazards. The study contributes to the growing body of literature that explores the impacts climate-induced changes are having on Indigenous peoples through its explicit focus on this distinctive social group. The paper entails a detail case study of the particular ways the recent onset of two consecutive extreme weather events have impacted livelihood activities in these traditional farming villages. Primary data were collected in the aftermath of a severe drought that was followed by Hurricane Tomas in 2010, using a mixed method approach involving a questionnaire survey of 311 households, 70 unstructured interviews and 2 focus group sessions held in each of the three communities. The combined impact of these extreme weather events not only brought to light how exposed and sensitive these communities are to climatic hazards, but also illustrated some of the underlying issues driving vulnerability at the local scale that must be dealt with if climate justice is to be achieved. We argue that the factors driving vulnerability within these communities are partly a function of centuries of economic neglect and political marginalization and are also strongly related to the communities’ characteristically lower-socio economic status, geographic location, heavy reliance on land-based resources, coupled with a range of cognitive barriers that affect residents’ capacity to adapt to a changing and variable regional climate regime.