中国历史时期土地覆被数据集地理空间重建进展评述

重建长时间序列具有空间属性的土地覆被数据集,对研究历史时期土地利用/土地覆被变化及其气候和生态效应具有重要意义。近年来,国内外学者就定量重建中国区域历史土地覆被数据集进行了积极探索。但由于历史时期土地利用数据来源多元、重建方法多样、验证方式各异等原因,不同学者的重建结果迥异,其中重建方法是导致差异形成的重要原因之一。本文从重建思路、假设和方法、结果验证等方面对覆盖中国区域的主要空间数据集进行了综合评述,结果表明：①基于历史记录的还原法和基于地理空间模型的重建法是历史土地覆被空间重建的主要方法,而根据建模过程,后者又可进一步分为“自上而下”的配置模型和“自下而上”的演化模型法。②基于数量重建进行空间重建是当前历史土地覆被数据集重建的主流,在缺少充分、客观历史数据的条件下,对基础数据、分布控制因素和限制因子进行合理假设是取得合理结果的重要条件。③为提高研究成果的解释力,需要对重建结果进行检验,直接验证法虽较为准确,但受时空尺度限制,具有显著的局限性,间接验证法可作为有效的补充。     

基于栅格尺度的沙漠化预警模型研究——以塔里木河下游中段区域为例

以塔里木河下游为研究区,基于沙漠化在气候、地表和人文3方面的成因构建沙漠化预警指标体系,并建立预警模型;基于研究区1990年、2000年和2006年的遥感、气候、地表和人文数据,利用ArcGIS对各因子数据进行量化和栅格化处理,通过预警模型实现了研究区3个时期在栅格尺度上（30 m×30 m）的沙漠化程度分布,分别利用前两期和第3期的结果进行参数修正和验证;在此基础上预测研究区“间歇输水”和“无输水”情况下2015年的沙漠化发展趋势。通过参数修正、预警模型对研究区沙漠化程度分布模拟正确率达90%以上,预警模型适用性较好。间歇输水对塔里木河下游沙漠化逆转起到一定的作用,是防止其完全沙漠化的必要措施,但此逆转作用基本上只局限于沿河道有限范围内,无法彻底改变研究区的沙漠化现状。     

Improved Minimum Miscibility Pressure Prediction for Gas Injection Process in Petroleum Reservoir

Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO₂, H₂S, N₂ and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R²) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.     

A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.     

中国短时洪涝灾害危险性评估与验证

基于自然灾害风险理论、借助GIS空间分析功能、采用归一化和层次分析法,对中国全国范围尺度进行短时洪涝灾害危险性评估。通过对洪涝灾害危险性因子分析,分别提取当天降雨量、前三天降雨量、地形高程、地形标准差、河湖网络等因素作为评估因子,提出了各因子危险性指数计算方法,以及全国洪涝灾害危险性指数计算模型公式,根据统计分析危险指数的最小值、最大值,结合历史灾情,利用阈值分割法确定危险等级分割值为0.3、0.45及0.6,将洪涝灾害危险等级划分为高危险、中危险、低危险与无危险四个等级,从而建立类似于天气预报模式的全国洪涝灾害危险性评估模型,并以2009年9月14日为例进行了洪涝灾害危险等级评估的实际应用。最后,本文提出了基于昨日灾情的危险性评估结果验证方法,通过整理同期昨日灾情资料构建灾情数据库,对2009年汛期的洪涝灾害危险性评估结果分别从数量和面积两个角度,基于县和地市两级行政区划作为验证单元进行验证。验证结果表明,洪涝灾害危险性评估结果无论是从数量上还是从面积上看均具有较高的正确率,与实际灾情基本吻合。     

Validation of landslide hazard models using a semantic engine on online news

The objective of this work is twofold: (i) automatically setting up a landslide inventory using a state-of-the art semantic engine based on data mining on online news and (ii) evaluating if the automatically generated inventory can be used to validate a regional scale landslide warning system based on rainfall-thresholds.The semantic engine scanned internet news in real time in a 50 months test period. At the end of the process, an inventory of approximately 900 landslides was automatically set up for the Tuscany region (23,000 km², Italy). Using a completely automated procedure, the inventory was compared with the outputs of the regional landslide early warning system and a good correspondence was found, e.g. 84% of the events reported in the news is correctly identified by the warning system.On the basis of the obtained results, we conclude that automatic validation of landslide models using geolocalized landslide events feedback is possible. The source of data for validation can be obtained directly from the Internet channel using an appropriate semantic engine dedicated to perform a monitoring of the Google News aggregator.Moreover, validation statistics can be used to evaluate the effectiveness of the predictive model and, if deemed necessary, an update of the rainfall thresholds could be performed to obtain an improvement of the forecasting effectiveness of the warning system.In the near future, the proposed procedure could operate in continuous time and could allow for a periodic update of landslide hazard models and landslide early warning systems with minimum or none human intervention.     

A framework for uncertainty assessment in simulation models

In this article, we introduce a conceptual framework for systematic identification and assessment of sources of uncertainty in simulation models. This concept builds on a novel typology of uncertainty in model validation and extends the GIScience research focus on uncertainty in spatial data to uncertainty in simulation modelling. Such a concept helps a modeller to interpret and handle uncertainty in order to efficiently optimise a model and better understand simulation results.

To illustrate our approach, we apply the proposed framework for uncertainty assessment to the TREE LIne Model (TREELIM), an individual-based model that simulates forest succession at the alpine tree line. Using this example, uncertainty is identified in the modelling workflow during conceptualisation, formalisation, parameterisation, analysis and validation. With help of a set of indicators we quantify the emerging uncertainties and assess the overall model uncertainty as a function of all occurring sources of uncertainty.

An understanding of the sources of uncertainty in an ecological model proves beneficial for: (1) developing a structurally valid model in a systematic way; (2) deciding if further refinement of the conceptual model is beneficial for the modelling purpose; and (3) interpreting the overall model uncertainty by understanding its sources. Our approach results in a guideline for assessing uncertainty in the validation of simulation models in a feasible and defensible way, and thus functions as a toolbox for modellers. We consider this work as a contribution towards a general concept of uncertainty in spatially explicit simulation models.     

区域能源消费与经济和环境绩效——基于14个能源输出和输入大省的实证研究

本文对中国的能源输出大省和能源输入大省进行了界定,运用DEA方法测度了2000-2010年能源输出和输入大省的能源消费经济绩效、环境绩效及节能潜力,对能源输出和输入大省的能源消费经济绩效、环境绩效特征进行系统归纳和比较,分析能源消费绩效形成原因,并利用Malmquist生产率指数方法对2000-2010年能源输出和输入大省的能源消费经济绩效、环境绩效变化趋势进行了深入研究,探讨提高能源输出和输入大省能源消费绩效的对策措施。研究结果表明：① 能源输出大省的能源消费绩效未达最优主要受规模效率制约,规模效率和纯技术效率对能源输入大省能源消费绩效未达最优的共同制约比较明显;② 能源输出大省的整体能源消费经济绩效改善落后于能源输入大省,但在环境绩效改善方面能源输出大省整体上强于能源输入大省;③ 技术进步是决定能源输出和输入大省能源消费绩效变化趋势的关键;④ 半数能源输出大省和大部分能源输入大省存在投入冗余现象,基于环境产出投入冗余远高于基于经济产出投入冗余,尤其是能源输入大省。     

Subduction beneath western South America: evidence from converted phases

Summary. There are two conflicting models for the dip of the subduction zone beneath central Peru and beneath central Chile. One model, based primarily on the distribution of hypocentres thought to be most reliably located, postulates a shallow (∼ 10°) dip for the downgoing plate. The second model, re-examined in this paper and based chiefly on an analysis of ScSp converted phases, postulates a normal (∼ 30°) dip for the subduction.
A detailed examination of ScSp data for central Peru and central Chile shows that ScSp arrivals can be identified on all seismograms on which the predicted signal/noise ratio is greater than 1, and the measured amplitude ratios of ScSp to ScS imply a normal dip for the conversion interfaces. The characteristics of the ScSp arrivals in these regions are virtually identical to those for ScSp arrivals in southern Peru, where a well-defined Benioff zone and the calculated ScSp -conversion region both imply a normal dip for the subduction. Hence ScSp observations in South America support the model of an approximately constant dip of subduction from central Peru to central Chile to a depth of at least 120 km, and impose a constraint that must be satisfied by any satisfactory tectonic model for these regions.     

结构动力机制视角下的城市网络解释框架

中心地理论采用的是规模—等级分布体系。然而,新时期出现的一些新特征显然无法沿用中心地理论进行解释,如信息技术对城市体系的重构、基础设施网络对时空距离的压缩等。城市网络研究强调外部连接对城市发展的重要作用,可很好地解释城市与城市之间的关联特性,因此对城市与城市之间的关系研究已逐渐从城市等级转向于城市网络研究。通过构建社会分工、技术进步、地方背景三者相互作用的概念性解释框架,阐述城市网络的作用机制。在城市网络解释框架的基础上,提出了单个城市的网络化生存策略—界面交互模型,阐述单个城市与城市网络中其他城市的联系方式与互动关系。     

Downscaling climate-model output in mountainous terrain using local topographic lapse rates for hydrologic modeling of climate-change impacts

One of the challenges in using general circulation model (GCM) output is the need to downscale beyond the model’s coarse spatial grid for use in hydrologic modeling of climate-change impacts. In mountainous terrain, using elevation as a primary control on temperature and precipitation at the local scale provides the potential for topographic variables to be used to adjust climate-model output. Here, local topographic lapse rates (LTLR) were estimated from gridded climate data for the Pacific Northwest of the United States and used to downscale GCM output. Skill scores were calculated for the LTLR-downscaled climate-model output relative to an existing set of model output downscaled using the established statistical downscaling technique of localized constructed analogs (LOCA). The results indicate that the LTLR method performs well in the mountainous study region relative to the LOCA method. LTLR downscaling offers a promising method for downscaling climate-model output in regions in which elevation strongly controls climate, particularly for studying impacts of future climate change on water resources.     

基于系统动力学的资源枯竭型矿业城市产业转型仿真分析——以吉林省辽源市为例

伴随着矿产资源的逐步枯竭,资源枯竭型矿业城市的经济发展面临着诸多困境,产业转型已经迫在眉睫。吉林省辽源市的产业转型取得了良好的效果,被誉为"辽源样本"。基于系统动力学研究方法,通过对系统结构的深入分析,构建辽源市经济系统的SD模型,将CD生产函数嵌入到模型当中,根据辽源市2005-2010年数据进行趋势外推仿真,得到2005-2030年经济系统的若干仿真曲线。结果表明:在2027年辽源市煤炭资源完全枯竭,煤炭产业产值降低为0,但这对其整体经济的扰动很小,辽源市GDP、第二产业产值、接续产业产值、第一产业产值、第三产业产值、其它工业产业产值均呈现快速上升的态势,并且包括人均GDP在内的人均指标亦都将呈现上升的趋势,辽源经济将呈现整体繁荣的局面。     

挠力河流域河流径流量对气候变化的敏感性分析

流域径流量对气候变化的敏感性分析是理解气候变化对流域水资源影响的重要手段。本文利用非更新式人工神经网络(ANN)模型,以年平均降雨、年最低气温和最高气温为输入参数,年平均径流量为输出变量,构建了三江平原挠力河流域的径流量预测ANN模型;并根据IPCC第四次报告的气候变化模式,设定了9种不同的气候变化情景,利用构建的ANN模型分析了流域径流量对气候变化的敏感性。结果表明:构建的人工神经网络模型能够较好的模拟径流量,可用于气候变化的敏感性分析;挠力河流域上游径流量对气候变化的敏感性要大于中游区域的,降水变化对径流量的影响大于气温对其产生的影响。     

Sensitivity of DaisyGIS to LAI: Implications for land use based modelling of nitrate leaching

Geografisk Tidsskrift, Danish Journal of Geography 106(2): 75–85, 2006

Scaling is an important issue when measuring, modelling and understanding coupled socio-environmental terrestrial systems. Scaling may be enhanced through the incorporation of remotely sensed data. This paper assesses the sensitivity of DaisyGIS to changes in the Leaf Area Index (LAI) and discusses the implications for land use based modelling of nitrate leaching using remote sensing based LAI estimates. It is shown how DaisyGIS simulates nitrate leaching quite well over the period 2001 to 2004 except for one plot during a season with winter rape. It was found that the model is sensitive to changes in LAI with increases of up to 262% on nitrate leaching rates when reducing LAI by 15%. At the same time, the nitrate leaching rates at the field level vary considerably due to local changes in the water balance determined by soil profile characteristics. DaisyGIS is already an important tool for aiding decision makers and this study has shown how remote sensing based LAI estimates may be a valuable tool for model parameterisation and potentially for model validation.     

江苏省耕地后备资源的空间分异及开发时序模型研究

该文运用数理统计分析耕地后备资源数量和类型的空间分异特征与分布规律,构建以投入产出比为基础,并以耕地转化潜力系数、耕地后备资源密集度系数加以修正的耕地后备资源开发时序模型,得到江苏各地区耕地后备资源的开发时序。研究结果表明,江苏省耕地后备资源具有明显的空间分异特征,根据开发时序模型提出江苏省各地区耕地后备资源的开发利用建议。     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.     

Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

The Soil and Water Assessment Tool(SWAT) was implemented in a small forested watershed of the Soan River Basin in northern Pakistan through application of the sequential uncertainty fitting(SUFI-2) method to investigate the associated uncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period(1991–2000) with an initial 4-year warm-up period(1987–1990), and was validated for the subsequent 10-year period(2001–2010). The model evaluation indices R~2(the coefficient of determination), NS(the Nash-Sutcliffe efficiency), and PBIAS(percent bias) for stream flows simulation indicated that there was a good agreement between the measured and simulated flows. To assess the uncertainty in the model outputs, p-factor(a 95% prediction uncertainty, 95PPU) and r-factors(average wideness width of the 95 PPU band divided by the standard deviation of the observed values) were taken into account. The 95 PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factor was 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients(R~2 and NS) for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78 and 0.74, respectively. Meanwhile, the 95 PPU covered more than 60% of the observed sediment data during calibration and validation. Moreover, improved model prediction and parameter estimation were observed with the increased number of iterations. However, the model performance became worse after the fourth iterations due to an unreasonable parameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertainty during the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessment of water balance components, climate change studies, and land use management practices.     

A method for estimating vegetation change over time and space

Plant diversity is used as an indicator of the well-being of vegetation and ecological systems. Human activities and global change drive vegetation change in composition and competition of species through plant invasions and replacement of existing species on a given scale. However, species diversity indices do not consider the effects of invasions on the diversity value and on the functions of ecosystems. On the other hand, the existing methods for diversity index can not be used directly for cross-scale evaluation of vegetation data. Therefore, we proposed a 3-dimensional model derived from the logistic equation for estimating vegetation change, using native and non-native plant diversity. The two variables, based on the current and the theoretical maximum diversity of native plants on a given scale, and the result of the model are relative values without units, and are therefore scale-independent. Hence, this method developed can be used directly for cross-scale evaluations of vegetation data, and indirectly for estimating ecosystem or environmental change.