融合多源时空数据的城市火灾危险性评估

城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究,在大数据思维的指导下,以相关关系代替因果关系,采用多源数据对评估指标权重、分值进行率定,得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析,将近万条火灾出警地址空间落点,获得福州市历史火灾空间分布;然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据,探索其与历史火灾空间分布的相关性;最后以福州城区为例,初步实现具有充分数理支撑的火灾危险性评估方法,形成火灾危险性动态评估成果,为城市消防规划等提供支撑和依据。     

多重加密格网内插法坐标转换

我国CGCS2000坐标系启用后,各地均面临着大批量、多类型、多坐标系成果数据坐标转换的需求,而国家大地坐标系、地心坐标系及独立坐标系之间的转换参数属于绝密级别,保密要求高,因此给实际应用带来了困难。顾及与参数法坐标转换成果的一致性及数据的安全要求,本文提出了多重加密格网内插坐标转换方法,研制了适用于多种数据格式的二/三维格网坐标转换软件,并将该算法应用于CORS服务,通过实例验证了该方法的可靠性与实用性。     

利用多元回归分析反演西南印度洋区域海底地形

针对海底地形与重力异常和重力异常垂直梯度在相应频段呈现强线性相关的特点,引入多元回归分析技术,提出并详细推导了联合多元重力数据的海底地形建模方法。然后,在西南印度洋SWIR(Southwest India Ridge)所在部分海域开展了海底地形反演试验及地形地貌分析研究。试验结果表明:6种海深模型中,基于多元回归分析技术构建的海深模型(BDVG模型)检核精度最高,相较于S&S V18.1模型和ETOPO1模型精度分别提高了11.51%和57.81%左右;2000 m以上水深海域,各个海深模型的检核精度较高,相对误差波动较小,反映了深海海域具有良好的反演效果;地形起伏剧烈海域或者浅海海域,BDVG海深模型,相较于以重力异常和重力异常垂直梯度作为单一输入源建立的BDG模型和BVGG模型相对误差及相对误差波动变化较小,反映了BDVG模型拥有更好的稳定性,从而体现了联合反演的必要性和优势。Indomed FZ—Gallieni FZ上唯一轴部缺失裂谷洋脊段(27洋脊段)目前属于岩浆供应充足阶段,构造作用的海底扩张对其影响较小;同时由于对称裂离方式影响,27洋脊段沿轴南北对称分布有地形隆起。     

多重压力对重点生态功能区农户生计资产可得性的影响——以甘南黄河水源补给区为例

重点生态功能区主要承担着提供持续、稳定生态服务的功能,农户作为维护其主体功能的最基础单元和该区最主要的经济活动主体,面临着自然、社会、政策等多重压力,其交互作用不仅加剧了农户的生计脆弱性,更影响到该区主体功能的发挥,当前急需辨明多重压力对农户生计的影响,以便寻求有效的生计脆弱性减缓对策。本文以甘南黄河水源补给区为例,运用入户调查数据与有序Logistic回归模型等方法在识别农户遭受关键压力的基础上,进一步探讨生计压力的交互作用对生计资产可得性的影响。结果显示：① 甘南黄河水源补给区大多数农户遭受自然+经济型多重压力,其中,重点保护区、恢复治理区及经济示范区农户遭受的多重压力分别以自然+经济型、自然+经济型、经济+社会型为主。② 在多重压力的冲击下,重点保护区与恢复治理区农户除了社会资产可得性变化处于稳定状态,其余各类资产可得性变化几乎处于降低态势,而经济示范区农户的金融资产与社会资产的可得性变化处于稳定状态,其余各类资产可得性变化均处于降低态势。③ 婚嫁支出高与农牧产品价格下降、牲畜患病与人畜饮用水困难、子女学费支出高+婚嫁支出高+农牧产品价格下降+养老无保障、建房开支高与生态政策、子女就业困难与农业病虫害等压力的交互作用分别对自然资产、物质资产、金融资产、社会资产、人力资产等的可得性变化影响最强烈。     

Dominant climate factors influencing the Arctic runoff and association between the Arctic runoff and sea ice

By using the Arctic runoff data from R-ArcticNET V4.0 and ArcticRIMS, trends of four major rivers flowing into the Arctic Ocean, whose climate factor plays an important role in determining the variability of the Arctic runoff, are investigated. The results show that for the past 30 years, the trend of the Arctic runoff is seasonally dependent. There is a significant trend in spring and winter and a significant decreasing trend in summer, leading to the reduced seasonal cycle. In spring, surface air temperature is the dominant factor influencing the four rivers. In summer, precipitation is the most important factor for Lena and Mackenzie, while snow cover is the most important factor for Yenisei and Ob. For Mackenzie, atmospheric circulation does play an important role for all the seasons, which is not the case for the Eurasian rivers. The authors further discuss the relationships between the Arctic runoff and sea ice. Significant negative correlation is found at the mouth of the rivers into the Arctic Ocean in spring, while significant positive correlation is observed just at the north of the mouths of the rivers into the Arctic in summer. In addition, each river has different relationship with sea ice in the eastern Greenland Sea.     

地理信息技术支持的“多规合一”——以常州市新北区为例

开展"多规合一"是推进生态文明建设、新型城镇化建设,落实供给侧改革的必然要求。本文以地理信息技术为基础,以土地利用规划为底盘,统一城乡规划、环保规划、农业发展规划等的空间基础;以经济社会发展规划为目标协调各规划,划定永久基本农田保护红线、生态保护红线、城市开发边界,实施严格的空间管控。最后,以常州市新北区为试验区,分析各规划的差异并落实重点项目,引导国土空间有序开发,优化资源空间配置,形成国土空间开发规划"一张图"。     

论秦岭造山带的成矿作用

对秦岭造山带区域成矿背景、矿床的时空分布、典型矿床矿田赋存部位、区域成矿元素地球化学背景、多重岩片控矿理论,构造成矿体系、成矿物质来源、矿床成因类型、矿床区域成矿模式进行了讨论,在对大量资料研究的基础上提出了新认识。最后指出秦巴地区进一步寻找大型矿床的地区。     

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

Tropical cyclone(TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature(SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied.During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late ...     

多种模式降水预报的稳定性特征研究

预报的稳定性是指对同一时段在不同时间发布的多时效预报结论的一致性,是模式预报质量的一个重要方面,较大的不稳定性会给使用者造成困扰。为深入了解业务常用模式的稳定性,使用相对标准偏差指标计算不同时效预报的降水量波动大小,并改进了Flip-Flop指数（改进后简称FFnorm）,计算多时效降水量预报变化趋势的翻转程度,衡量预报变化趋势的稳定性,对2种全球模式（ECMWF、NCEP-GFS）、3种区域模式（CMA-MESO、CMA-SH9、HHUPS-ST）,在中国6个气候分区中降水预报的稳定性进行对比分析,分为实况有降水和暴雨及以上降水2种情况进行了讨论。结果表明：实况有降水时,相对区域模式来说,全球模式的多时效降水预报的相对标准偏差较小,即模式降水量预报的波动较小;各模式对西南区的西部、东北区的东部以及华南区的南部预报的波动性相对较小,西北区的西部波动性较大。就多时效降水量预报变化趋势而言,2种情况下均为CMA-MESO、NCEP-GFS和 ECMWF的稳定性较好,其FFnorm指数小于HHUPS-ST和CMA-SH9模式,其中CMA-MESO对西南区、华南部分地区降水量预报变化趋势的稳定性较为突出;CMA-SH9的指数最大,多时效降水量预报变化趋势稳定性较差;各模式对长江中下游地区的FFnorm指数相对较大,多时效预报趋势的稳定性较差。有降水时,CMA-MESO随时效临近的降水量预报变化趋势稳定（单调递增、单调递减或不变）的频次最多,其次是NCEP-GFS,2种降水情况下,该2种模式的降水量预报均为随时效临近单调递增次数大于递减次数,且CMA-MESO单调递增特征尤其显著。以上特征能够为模式调试和预报决策提供参考。     

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO ₂ are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO ₂ ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols c _i Scalar factor that minimizes the discrepancy between andU _i - D Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients - d Distance for which transition matrixU is estimated - d _ij Distance between observed valuesi andj - E Expected value - E _i Row vector of residuals in the standardized model - F(d _ij) Quadratic function of distanced _ij F(d _ij)=a+bd _ij+cd _ij ² - L Diagonal matrix of the eigenvalues ofU - _i Eigenvalue of the matrixU;ith diagonal element ofL - N Number of observations - p Number of variables - Q Total predictive power ofU - R Correlation matrix of the variables - R _0j Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m) - R _1j Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood - R _m ² Multiple correlation coefficient squared for themth variable - S _i Column vectori of the signal values - s _k ² Residual variance for variablek - T _i Amplitude vector corresponding toV _i;ith row ofT=V ^–1 - T Total variation in the system - U Nonsymmetric transition matrix formed by post-multiplyingR ₀₁ ^–1 byR _ij - U _i Componenti of the matrixU, corresponding to theith eigenvectorV _i;U _i= _iV_iT_i - U* _i ComponentU _i multiplied byc _i - U _ij Sum of componentsU _i+U _j - V _i Eigenvector of the matrixU;ith column ofV withUV=VL - w Weighting factor; equal to the ratio of two eigenvalues - X _i Random variable at pointi - x _i Value of random variable at pointi - y _i Residual ofx _i - Z _i Row vectori for the standardized variables - z _i Standardized value of variable