利用分裂窗算法提取晴空导风示踪物的数值模拟与应用

气象卫星热红外通道能够探测到晴空区域内由大气水汽、沙尘气溶胶等物质含量分布不均所导致的微弱的纹理信息,通过追踪水汽和沙尘气溶胶纹理的移动可以反演出风场。然而这些信号很容易受地表温度变化的影响,需使用合适的算法来减弱地表温度干扰、提取微弱的示踪物信号。本文结合分裂窗差值理论,对使用分裂窗差值算法提取晴空区微弱示踪物信号进行数值模拟分析,并应用于晴空区风场的实例反演。数值模拟图像的结果显示,分裂窗差值算法可以削弱地表对示踪物信号的影响,突出水汽和沙尘气溶胶的分布特征,验证了分裂窗差值法的可行性与优越性。实例分析也表明,使用分裂窗差值图能够反演出单通道云图难以得到的晴空区低层风场,且反演结果与NCEP风场资料比较一致。     

Robust irrigation system institutions: A global comparison

In many places irrigation systems rely on robust governance for continued existence. Elinor Ostrom listed design principles that should achieve robust governance, but doubted that any list could be both necessary and sufficient to result in robust governance. To date, this assumption has never been formally tested. We conduct a meta-analysis and ultimately evaluate 62 case studies via fuzzy-set qualitative comparative analysis to identify necessary/sufficient conditions for robust irrigation system governance. We identify four necessary conditions and seven configurations sufficient for robust governance. Further, we identify a union of conditions that, when absent, are likely to result in system failure.     

动态数据库模型的研究与应用

现有的时空数据库模型不能完全满足国家基础地理信息动态数据库的要求。本文分析总结了常用的空间数据库模型的结构和功能,提出了动态“版本-差量”模型,并利用此模型对国家基础地理信息动态数据库进行了建库试验。试验表明该模型可以很好的描述和管理复杂的基础地理信息数据,方便的查询任意时刻的空间信息及其空间关系。     

Spatio-temporal Patterns of Vegetation Change in Kazakhstan from 1982 to 2015

The Normalized Difference Vegetation Index (NDVI), as a key indicator of vegetation growth, effectively provides information regarding vegetation growth status. Based on the Global Inventory Monitoring and Modeling System (GIMMS) NDVI time series data for Kazakhstan from 1982 to 2015, we analyzed the spatial pattern and changes in the vegetation growth trend. Results indicated that the three main types of vegetation in Kazakhstan are cropland, grassland and shrubland, and these are distributed from north to south. While the regional distribution pattern is obvious, the vegetation index decreased from north to south. The average NDVI values of the three main vegetation types are in the order of cropland > grassland > shrubland. During the period from 1982 to 2015, the NDVI initially increased (1982-1992), then decreased (1993-2007), and then increased again (2008-2015). The areas where NDVI decreased significantly accounted for 24.0% of the total land area. These areas with vegetation degradation are mainly distributed in the northwest junction between cropland and grassland, and in the cropland along the southern border. The proportions of total grassland, cropland and shrubland areas that were degraded are 23.5%, 48.4% and 13.7%, respectively. Areas with improved vegetation, accounting for 11.8% of the total land area, were mainly distributed in the mid-east cropland area, and the junction between cropland and grassland in the mid-east region.     

利用卫星立体影像生产DEM的关键技术研究

在利用国产资源三号卫星立体影像制作DEM产品过程中,通过遥感手段利用NDVI和ND-WI对植被、水域等要素范围边界进行快速提取,实现了数据制作过程中滤波环节准确性和适应性的增强,进而达到了对DEM产品精度提升和制作效率提高的目的.由此形成的技术方案可以为全球地理信息资源建设与应用提供参考.     

北京气温日变化特征的城郊差异及其季节变化分析

本文利用北京地区近4年67个自动气象站的逐小时气温观测资料,基于北京地区气温的日变化特征,通过分析日最高、最低气温出现时间的概率分布,研究了城区、郊区气温的日变化差异及季节特征.此外,进一步分析研究了不同单位时间间隔变温的日变化特征,及最大变温出现时间的概率分布情况.研究结果表明:平均而言,城区最高温度出现的时间偏晚,而最低温度出现的时间城区偏早于郊区,与郊区相比,北京城区站点温度的日变化特征更为一致,最高(低)温度出现的时间更加集中;温度日变化的特征随季节有明显的变化,最高温度出现时间在秋、冬两季最为集中,在春季和夏季较为分散;而最低温度出现时间在春、夏两季最为集中,在秋季和冬季最为分散.一天中正、负变温过程具有非对称特征,正变温是比较急剧的过程,负变温相对比较缓慢,北京城区站点的变温幅度小于郊区,春、秋和冬季变温幅度较大,夏季变温幅度最小.不同单位时间内变温速率的分析表明,最强的变温过程一般在3小时以内;最大变温出现时间的概率分布分析表明,最大正变温出现时间在冬季最为集中,夏季最为分散;而最大负变温在秋季最为集中,在春季最为分散.最高(低)温度、变温的城、郊特征差异主要是由于城市热容量比郊区大,且具有更多变化的复杂性而形成的.温度日变化的特征和其区域、季节差异性的揭示,不仅有助于更好地认识和理解区域气候特征和城市化对气温的影响,也可以为做好精细化的天气预报提供气候背景参考.     

大同市一次暴雪天气过程多普勒雷达速度特征

利用高空、地面环流形势图、欧洲数值预报产品和大同地区C波段单多普勒雷达产品中的反射率因子、径向速度和VWP产品,对2009年 11月9-10日发生在大同地区的大到暴雪天气过程进行综合分析。结果表明：地面回流和高低空急流是产生强降雪的主要原因;较大降雪回波与连续性降雨回波存在相同特点;利用大面积降水的多普勒雷达PPI径向速度零线的朝向、正负速度面积和径向速度值的大小定性判断辐合辐散图像特征与根据零速度线的弯曲程度、一定距离圈上零速度点与雷达中心连线夹角计算出的辐散值相比较,表明两种方法具有很好的一致性,可以在实际工作中对辐合、辐散快速判断。由重新处理雷达原始资料求得每30 min的雷达风廓线资料,可以清楚地展示强降雪的风场的垂直结构及其变化特点,直观地反映出降水过程中的风场变化特征。     

扩散系数反演及其差分格式研究

空气污染预报属于正问题，而从污染物浓度来求解扩散系数则属于反问题。正问题和反问题有着本质的不同，在解的定义和求解方法上也有很大的区别。从最优控制的角度定义了大气边界层中垂直扩散系数反演问题的解，用伴随模式方法得到目标函数的梯度并求解反问题。研究中发现，反演的结果与模式差分格式的选取有关，与测试源的设置也有直接的关系。在经过多次数值试验后，对于误差的来源进行了理论分析，发现了反演结果与差分格式及测试源之间的联系，得到了满意的反演结果，并为实验测定扩散系数提供了依据。     

黄土高原不同生态类型NDVI时空变化及其对气候变化响应

了解植被的时空变化及其气候主控因子可为植被保护和恢复提供重要的理论依据。基于MOD13A1和气象数据,分析了黄土高原Normalized Difference Vegetation Index （NDVI）时空变化特征,探讨了NDVI对水热条件在不同时间尺度的响应特征。结果表明：黄土高原植被覆盖状态正在不断的改善,气候呈暖湿的发展趋势;83.77%的植被退化区（退化区面积占研究区总面积的5.79%）海拔<2000 m且退化类型以不显著减少为主,不同覆被类型的退化区海拔分布及退化比例差异明显,湿地的退化面积比最高（23.91%）、其次耕地（11.88%）。年尺度上,NDVI与降水呈正相关的面积高于气温,约75.06%的区域受水分条件控制;灌木地（海拔分布<2200 m）、耕地（<3000 m）、草地（<3000 m）和裸地（600~3700 m）等植被生长受水分条件影响;森林（<1000 m、1700~3700 m）和湿地（>2500 m）的植被生长受热量影响。月尺度上,黄土高原植被NDVI对热量响应以滞后1个月为主,不同植被对水热响应的滞后性差异明显,草地、湿地、耕地和裸地对热量响应以滞后1个月为主;森林和灌木地则表现水热同期的特征。伴随滞后时间的推移,水分主控面积逐渐降低,热量成为影响植被生长的主要因素,水热主控及响应滞后性分布受海拔影响明显。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.