基于多年遥感数据分析长江河口海岸带湿地变化及其驱动因子

研究河口海岸带湿地长时间演变对湿地保护管理和海岸带资源评估具有重要意义.本文获取长江口1979—2015年10景Landsat-MSS/TM/OLI影像和2015年13景GF1-PMS高空间分辨率数据,对比两个典型实验区分类算法,选用最优的决策树算法应用到长江口Landsat影像中,得到沿岸湿地要素近40年的面积变化情况.研究表明,2015年长江河口海岸带湿地总面积为4725 km2,自然湿地占63.5%,人工湿地占21.2%,湿地总面积相比1979年增加了662 km2,自然湿地面积减少了163 km2,而人工湿地面积增加了766 km2.长江口自然湿地面积在1979—2000年减少幅度较大,2000年后由于保护管理加强而减少幅度变小;人工湿地和建筑面积增加较为明显,主要是由于大型水库的修建和人工鱼塘开发及港口建设.湿地总的变化趋势为河口区不断淤积,自然湿地转变为人工湿地,人工湿地转变为建筑用地等非湿地;其中,滩涂面积减少283 km2,水库、养殖鱼塘和水稻田面积分别增加了92、355和319 km2,主要发生在崇明东滩和启东沿岸;非湿地中建筑用地面积增加154 km2,灌木草场面积减少147 km2,主要发生在上海和启东沿岸.同时比较分析长江口3个区域湿地驱动因子发现,北岸启东沿岸和南岸南汇东滩湿地因经济快速发展和港口水利工程修建,以及过度开垦滩涂等自然湿地使人工湿地增加明显;而长江上游径流量、区域降水和海平面上涨等自然因素控制着中支河道区(如崇明东滩)自然湿地的变化.     

瑞士土壤环境监测网络构建与运行对中国的启示

“十三五”期间,中国初步构建了国家土壤环境监测网并开展了监测工作。当前,国家层面土壤环境监测网络的构建和运行还处于起步阶段,笔者选取土壤环境监测开展较为成熟的瑞士土壤环境监测网(NABO)作为研究对象,综述了NABO的技术体系及多年来的土壤环境监测结果,并结合中国土壤环境监测工作现状进行讨论,认为NABO在监测网络建立、指标选取、数据分析等方面建立了较为成熟的技术体系,未来中国国家土壤环境监测网可在此方面借鉴瑞士经验。     

地震台站体应变观测资料处理系统简介

介绍了新近研制的地震台站体应变观测资料的日常处理系统 ;简要地介绍了该系统及其数据库的结构以及各功能模块的作用。     

天津市环境监测元数据模式

元数据技术是目前环境监测信息管理的发展趋势 ,文章对基于元数据的天津市环境监测信息共享模式进行了探索与研究 ,提出了元数据系统功能及内容框架的解决方案。     

环境试验数据管理系统的应用

介绍了环境试验数据管理系统的功能及组成,阐述了系统建设的目的和达成的目标,并对其在企业内部的应用效果进行了分析。     

土压平衡矩形顶管施工引起的地表沉降规律研究

为了分析与掌握矩形顶管施工引起的地表沉降变形规律,提出基于MIDAS-GTS有限元软件的分析方法,首先,通过工程实例验证MIDAS-GTS有限元软件在分析顶管施工引起地表沉降变形的有效性;其次,通过影响因素的敏感性分析确定不同影响因素作用下的地表沉降变形特性,这些影响因素主要包括开挖面支护压力、侧摩阻力、地层结构特性等;最后,通过地表沉降变形特性分析得出矩形顶管的适用范围。研究结果表明:基于MIDAS-GTS有限元软件的地表沉降规律分析方法和结果,可为矩形顶管施工过程提供相关参考和借鉴。     

Applying Statistical Causal Analyses to Agricultural Conservation: A Case Study Examining P Loss Impacts

Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by factors such as differing crop types and management practices. As we may not have the full knowledge of these confounding factors, conventional statistical meta‐analysis methods can be misleading. We discuss the use of two statistical causal analysis methods for quantifying the effects of water and soil conservation practices in reducing P loss from agricultural fields. With the propensity score method, a subset of data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling method, data were stratified based on important confounding factors, and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect (total P load reduction avg. ~70%). In addition, both methods show evidence of conservation practices reducing the incremental increase in total P export per unit increase in fertilizer application. These results are presented as examples of the types of outcomes provided by statistical causal analyses, not to provide definitive estimates of P loss reduction. The enhanced meta‐analysis methods presented within are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed‐scale modeling.     

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

A long‐standing “Digital Divide” in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time‐varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long‐time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed “data rods,” are pre‐generated or generated on‐the‐fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on “data curtains.” The on‐the‐fly generation of data rods uses “data cubes,” NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.     

Supporting Diverse Data Providers in the Open Water Data Initiative: Communicating Water Data Quality and Fitness of Use

Shared, trusted, timely data are essential elements for the cooperation needed to optimize economic, ecologic, and public safety concerns related to water. The Open Water Data Initiative (OWDI) will provide a fully scalable platform that can support a wide variety of data from many diverse providers. Many of these will be larger, well‐established, and trusted agencies with a history of providing well‐documented, standardized, and archive‐ready products. However, some potential partners may be smaller, distributed, and relatively unknown or untested as data providers. The data these partners will provide are valuable and can be used to fill in many data gaps, but can also be variable in quality or supplied in nonstandardized formats. They may also reflect the smaller partners' variable budgets and missions, be intermittent, or of unknown provenance. A challenge for the OWDI will be to convey the quality and the contextual “fitness” of data from providers other than the most trusted brands. This article reviews past and current methods for documenting data quality. Three case studies are provided that describe processes and pathways for effective data‐sharing and publication initiatives. They also illustrate how partners may work together to find a metadata reporting threshold that encourages participation while maintaining high data integrity. And lastly, potential governance is proposed that may assist smaller partners with short‐ and long‐term participation in the OWDI.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.