RCP8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟

基于跨部门影响模型比较计划（ISI-MIP）中20种气候模式与作物模型组合的模拟结果,预估了RCP 8.5排放情景下21世纪印度小麦和水稻单产变化。研究发现：① 多模式集合模拟结果基本再现了印度小麦和水稻单产的空间差异;同时,再现了小麦和水稻单产对温度和降水变化的响应特征：与温度呈负相关,与降水呈正相关。② RCP 8.5情景下,水稻和小麦生长季温度和降水均呈增加趋势,小麦生长季的温度、降水增加幅度大于水稻。空间上,温度增加幅度自北向南逐渐减小,降水增幅则逐渐增加,并且小麦种植区升温幅度大于非种植区,降水增幅则少于非种植区,水稻种植区升温幅度小于非种植区,降水增幅则多于非种植区。③ RCP 8.5情景下,小麦和水稻单产均呈下降趋势,21世纪后半叶尤为明显。小麦单产的下降速度明显大于水稻,其中21世纪前半叶小麦和水稻单产下降速度约分别为1.3%/10a （P < 0.001）和0.7%/10a （P < 0.05）,后半叶分别增至4.9%/10a （P < 0.001）和4.4%/10a （P < 0.001）。小麦和水稻单产变化存在明显的空间异质性,小麦单产的最大下降幅度出现在德干高原西南部,降幅约60%,水稻单产最大下降幅度出现在印度河平原北部,降幅约50%。这意味着未来气候变化情景下印度粮食供给将面临较大的挑战。     

梅江河流域年均降雨量空间插值方法研究

分析了降雨空间分布的影响要素及降雨空间插值的主要方法,并在此基础上,利用地理信息系统(GIS)的空间分析功能,综合考虑高程、坡度、坡向等影响降雨空间分布的多种要素,应用协克里金方法,探讨流域降雨量的空间插值问题及其应用,并与反距离权重法、克里金方法等单要素方法进行了降雨空间插值的效果比较.结果表明:应用协克里金方法,在综合考虑高程、坡度、坡向等影响要素的条件下,对降雨进行空间插值,其结果具有比较好的精度.     

质量滴定分析方法及其应用

介绍了质量滴定分析法,通过质量滴定法对Hg(NO3)2和EDTA滴定液浓度的标定及对CaCl2、MgCl2和LiCl样品溶液浓度的测定,以及测定结果与基准称重法和重量分析法浓度测定结果相比较,验证了质量滴定法测定的准确性和精度,获得了相对偏差小于0.11%的一致结果。结果表明,溶液离子浓度的测定可用质量滴定法替代容量法和重量法。     

梅江河流域年均降雨量空间插值方法研究

分析了降雨空间分布的影响要素及降雨空间插值的主要方法,并在此基础上,利用地理信息系统(GIS)的空间分析功能,综合考虑高程、坡度、坡向等影响降雨空间分布的多种要素,应用协克里金方法,探讨流域降雨量的空间插值问题及其应用,并与反距离权重法、克里金方法等单要素方法进行了降雨空间插值的效果比较.结果表明:应用协克里金方法,在综合考虑高程、坡度、坡向等影响要素的条件下,对降雨进行空间插值,其结果具有比较好的精度.     

“两型社会”背景下长株潭城市群土地利用效率评价

在分析“两型社会”建设与土地利用效率提高之间关系的基础上,构建了“两型社会”背景下的长株潭城市群土地利用效率评价指标体系,并运用熵权法、多目标线性加权函数法和协调度分析等方法对长株潭城市群的土地利用效率进行评价,结果表明：影响城市群土地利用效率提高的主要因素与“两型社会”要求相符,但城市群目前土地利用的整体效率较低,与“两型社会”要求仍有差距;城市群土地利用效率存在一定的区域差异,其中经济效率的差异最大,生态环境效率次之,社会效率和资源集约效率的差异较小,且综合效率在空间上呈以长株潭3市向周边地区递减的格局．     

Visualisation and evaluation of flood uncertainties based on ensemble modelling

This study evaluates how users incorporate visualisation of flood uncertainty information in decision-making. An experiment was conducted where participants were given the task to decide building locations, taking into account homeowners’ preferences as well as dilemmas imposed by flood risks at the site. Two general types of visualisations for presenting uncertainties from ensemble modelling were evaluated: (1) uncertainty maps, which used aggregated ensemble results; and (2) performance bars showing all individual simulation outputs from the ensemble. Both were supplemented with either two-dimensional (2D) or three-dimensional (3D) contextual information, to give an overview of the area.

The results showed that the type of uncertainty visualisation was highly influential on users’ decisions, whereas the representation of the contextual information (2D or 3D) was not. Visualisation with performance bars was more intuitive and effective for the task performed than the uncertainty map. It clearly affected users’ decisions in avoiding certain-to-be-flooded areas. Patterns to which the distances were decided from the homeowners’ preferred positions and the uncertainties were similar, when the 2D and 3D map models were used side by side with the uncertainty map. On the other hand, contextual information affected the time to solve the task. With the 3D map, it took the participants longer time to decide the locations, compared with the other combinations using the 2D model.

Designing the visualisation so as to provide more detailed information made respondents avoid dangerous decisions. This has also led to less variation in their overall responses.     

Study of perturbing method in regional BGM ensemble prediction system

Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation (SSP, including Initial Random Perturbation and Perturbation Restriction) and Dynamic State Perturbation (DSP) are designed. The impacts of both perturbations on precipitation prediction are studied by analyzing a strong precipitation process occurring during July 20–21, 2008. The results show that both SSP and DSP play a positive role in prediction of mesoscale precipitation, such as lowering the (missing) rate of precipitation prediction. SSP is mainly helpful for the 24-hour prediction, while DSP can improve both 24-hour and 48-hour prediction. DSP is better than the two SSPs in the hit rate of regional precipitation prediction. However, the former also has a little higher false alarm rate than the latter. DSP enlarges in some extent the dispersion of EPS, which is good for EPS.     

基于EEMD的明代北京地区干旱灾害特征分析

集合经验模态分解（EEMD）是一种适用于非线性、非平稳序列的信号分析方法,将EEMD应用于气候要素时间序列,可提取真实可靠的气候变化信号。根据北京地区历史时期干旱灾害资料,采用EEMD分解方法对明代（1368—1644年）北京地区干旱灾害等级序列进行多时间尺度的分析,获得简洁且平稳性较好的固有模态函数分量,并与所统计的明代北京地区干旱灾害频次多项式拟合曲线进行对比。结果表明：将EEMD应用于干旱灾害等级序列,可以提取干旱灾害中各个尺度的变化,对明代北京地区干旱灾害进行多尺度分析。明代北京地区干旱灾害存在着2.8年、6.3年的年际周期,11.5年、26.6年、53.6年的年代际周期和118.7年、299.5年的世纪周期。北京地区干旱灾害在明代整个时间跨度上呈现着先增加后微减的变化趋势,总体而言明代中期以后旱灾明显增多。     

基于CMIP5多模式集合资料的中国气温和降水预估及概率分析

选用国际耦合模式比较计划第五阶段（CMIP5）提供的30个全球大气-海洋耦合模式（AOGCMs）在典型浓度路径（RCPs）情景下气温和降水量的预估结果,采用扰动法,用站点观测资料作为气候背景场替代AOGCM模拟的气候平均,尝试校正气候预估结果的系统性偏差。通过集合方法,用概率的形式给出中国平均气温升高1 ℃,2 ℃和3 ℃以及降水量增加10%,20%和30%概率的空间分布,讨论了中国未来平均气温和降水量可能的变化。结果表明：经过扰动法处理后的气温和降水量预估集合保留了当前气候的局地信息。预估平均气温在中国均有上升,北方地区尤其是青藏高原地区变暖的程度大于南方地区,北方大部分地区平均气温升高的趋势为0.28 ℃/10a。在21世纪初,中国北方地区年平均气温升高1 ℃的可能性超过50%。到了21世纪末期,中国大部分地区平均气温升高2 ℃的可能性超过60%,新疆北部以及青藏高原南部地区气温升高3 ℃的可能性超过50%。预估中国降水量普遍增多,中国北方地区降水量增多的程度要明显大于江淮流域及其以南地区,尤其是西北地区降水量增多非常显著,降水量增多30%的可能性超过70%以上。     

论中国古代地缘战略制定中的“权衡”——以《盐铁论》为例

《盐铁论》是记录汉朝时期关于盐铁酒榷均输及对匈奴关系的辩论的一部重要著作,能够较典型地反映中国古代地缘战略制定中的“权衡”过程。构建中国古代地缘战略制定中的“权衡”理论分析框架,继而基于该分析框架对《盐铁论》中的地缘战略辩论进行详细解读。《盐铁论》中辩论双方“权衡”的要点包含匈奴对汉朝产生的威胁程度、领土扩张所带来的收益大小和相应地缘战略的合法性3个方面。从思想、方法、目的等比较中西方地缘政治研究差异,指出“权衡”思想为西方主导的地缘政治理论提供新视角。     

黄河流域降水格局及影响因素北大核心CSCD

黄河流域位于干旱、半干旱与半湿润过渡地带,是中国重要的经济地带和生态屏障,研究流域降水时空格局及其对多驱动因素的响应具有重要意义。本文分析了近70年降水的时空格局规律、多尺度特征以及降水对不同气象要素与环流因子的响应。结果表明:黄河流域降水量呈下降趋势,降水变率为-0.88 mm/10a,而上游地区呈增加趋势。流域降水存在显著的年周期尺度;年际周期尺度为主导模态,集合经验模态分解(EEMD)的累积方差贡献率为94.85%。偏小波相干性(PWC)分析表明蒸散量为降水多尺度特征的主导气象因素,气象因素主要调制降水的季节性与年周期,环流因子主导降水的年际和年代际周期;不同类型因素的耦合可以增强对降水在所有周期尺度的解释能力。     

Storm-scale ensemble forecast based on breeding of growth modes

How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast (EF). The method, Breeding of Growth Modes (BGM), which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model (WRF), occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System (EPS). We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo’s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.     

A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules

Cellular automata (CA) have emerged as a primary tool for urban growth modeling due to its simplicity, transparency, and ease of implementation. Sensitivity analysis is an important component in CA modeling for a better understanding of errors or uncertainties and their propagation. Most studies on sensitivity analyses in urban CA modeling focus on specific component such as neighborhood configuration or stochastic perturbation. However, sensitivity analysis of transition rules, which is one of the core components in CA models, has not been systematically done. This article proposes a systematic sensitivity analysis of major operational components in urban CA modeling using a stepwise comparison approach. After obtaining transition rules, three stages (i.e. static calibration of transition rules, dynamic evolution with varied time steps, and incorporation with stochastic perturbation) are designed to facilitate a comprehensive analysis. This scheme implemented with a case study in Guangzhou City (China) reveals that gaps in performance from static calibration with different transition rules can be reduced when dynamic evolution is considered. Moreover, the degree of stochastic perturbation is closely related to obtain urban morphology. However, a more realistic (i.e. fragmented) urban landscape is achieved at the cost of decreasing pixel-based accuracy in this study. Thus, a trade-off between pixel-based and pattern-based comparisons should be balanced in practical urban modeling. Finally, experimental results illustrate that models for transition rules extraction with good quality can do an assistance for urban modeling through reducing errors and uncertainty range. Additionally, ensemble methods can feasibly improve the performance of CA models when coupled with nonparametric models (i.e. classification and regression tree).     

Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning

The reliability of raster cellular automaton (CA) models for fine-scale land change simulations has been increasingly questioned, because regular pixels/grids cannot precisely represent irregular geographical entities and their interactions. Vector CA models can address these deficiencies due to the ability of the vector data structure to represent realistic urban entities. This study presents a new land parcel cellular automaton (LP-CA) model for simulating urban land changes. The innovation of this model is the use of ensemble learning method for automatic calibration. The proposed model is applied in Shenzhen, China. The experimental results indicate that bagging-Naïve Bayes yields the highest calibration accuracy among a set of selected classifiers. The assessment of neighborhood sensitivity suggests that the LP-CA model achieves the highest simulation accuracy with neighbor radius r = 2. The calibrated LP-CA is used to project future urban land use changes in Shenzhen, and the results are found to be consistent with those specified in the official city plan.     

西北干旱区阿克苏河径流对气候波动的多尺度响应

基于阿克苏河流域1960~2010年的气象、水文观测数据,利用集合经验模态分解（EEMD）方法,对研究期内阿克苏河径流时间序列进行多尺度的分析,并探讨其在不同时间尺度上的振荡模态结构特征及其对气候因子的多尺度响应。结果表明：① 近50年来,阿克苏河年径流整体上呈现出显著的非线性增加趋势,且其变化在年际尺度上表现出准3 a和准6~7 a的周期性波动,在年代际尺度上表现出准13 a和准25 a的周期性变化;② 各周期分量的方差贡献率表明,年际振荡在径流长期变化中占据主导地位,年代际尺度在径流变化过程中也起着重要作用。重构的径流年际变化能够较为详细地描述原始径流序列在研究时期内的波动趋势,重构的径流年代际变化则有效揭示了阿克苏河径流在不同年代丰、枯水期交替出现的状态。③ 在年际尺度上径流与气温、降水和潜在蒸发都表现为不显著的正相关关系,而在年代际尺度上,径流量与气温和降水均表现为显著的正相关关系,与潜在蒸发表现为显著的负相关关系,且在年代际尺度上相关性和显著性明显强于年际尺度,表明年代际尺度更适于评价径流对气候波动的响应。结果表明EEMD是一种甄别非线性趋势和尺度循环的有效方法。     

中国气温未来情景的降尺度模拟

由于GCM模拟的气温数据分辨率不高,很难用于区域尺度上各种生态系统的模拟。本文基于长时间序列（1964～2007年）的全国气温观测数据,结合经纬度数据、以及DEM、坡向、坡度等系列地形特征数据,利用空间统计方法,在构建年平均气温降尺度模型的基础上,运用高精度曲面建模（HASM）方法对HadCM3的A1Fi、A2a和B2...     

不同气候变化情景下亚洲北山羊（Capra sibirica）的响应（英文）

目前很少见到关于气候变化影响亚洲北山羊物种栖息地的研究。通过调查气候变化对塔吉克斯坦东部亚洲北山羊(Capra sibirica)分布的影响,并采用生态位建模比较了亚洲北山羊的适宜栖息地的当前与未来分布情况。预计到2070年,现有适宜栖息地的18%(2689 km^2)将变得不适宜亚洲北山羊的生存,损失的区域主要位于研究区域的东南部和西北部地区。新的适宜栖息地可能会扩展到当前亚洲北山羊范围之外:到2070年将扩展30%(4595 km^2)的范围,这些区域与亚洲北山羊现有的分布有很强的相关性。东南部的损失与该地区当前大多数的亚洲北山羊栖息地重叠,主要出现在比研究区域海拔低得多的区域(3500–4000 m)。当同时考虑损失和收益时,亚洲北山羊可能会净扩展到新的适宜栖息地。到2070年,亚洲北山羊的平均栖息地增加量约为30%(1379 km^2),表明适宜栖息地已向北部低温栖息地转移。研究结果有助于规划气候变化情景下塔吉克斯坦东部山区对生物多样性保护的潜在影响。应该特别注意东南地区的高地山羊种群,那里的栖息地可能由于气候对山区生态系统的影响而变得不适合该物种继续生存。     

A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.     

Dislocation-mediated melting of iron and the temperature of the Earth's core

Summary . Dislocation theories of melting provide a possibility of calculating the melting temperature, from first principles, as the temperature at which the free energy of a crystal saturated with dislocations becomes equal to that of the dislocation-free crystal. After a brief review of the physical bases of the dislocation melting theories, Ninomiya's theory is used to determine the melting temperature as well as the volume and entropy of melting and the slope of the melting curve for iron at atmospheric pressure and under conditions prevailing at the Earth's inner core boundary. The necessary parameters (elastic moduli, Grüneisen parameter) are drawn from seismological earth models. We find a melting temperature of the material of the inner core of about 6150 K, independent of shock-wave experiments but in good agreement with them and with extrapolations using Lindemann's law. With usually accepted values of the melting point depression due to light elements in solution, the temperature at the inner core boundary is found to be T _ICB≅ 5000 K. This temperature is compatible with a temperature of the outer core at the core-mantle boundary T _CMB≅ 3800 K. Dislocation melting theories can thus help constrain the temperature profile in the Earth's core.