Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.     

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.     

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.     

基于智能电网大数据的工业企业大气污染排放特征研究

本文尝试构建基于智能电网大数据的工业企业污染排放预测方法。通过分析上海大中型工业企业用电量与工业总产出、工业总产出与主要污染物直接排放量之间的关联关系,本文建立了工业企业基于用电量的直接污染排放清单估算方法。利用此估算方法,可在实时的智能电网大数据基础上估算工业企业直接污染排放量,服务于大气污染的实时预警和预测。本文研究表明,这种清单估算方法可直接应用于工业企业污染的实时防控,既可服务于政府大气污染监测、应急机制启动时防控对象的选择,也可服务于未来的污染物排放权实时交易市场的供需分析等,是大数据在污染防治领域应用的可行路径。     

北京与津冀之间隐性土地资源流动研究

京津冀区域面临着严重的资源紧缺、生态环境恶化和发展失衡的问题,亟需制定科学合理的区域合作新机制。京津冀之间紧密的产品贸易背后隐藏的土地资源流动情况可以为新机制的建立提供科学支撑。这种隐性土地资源的流通情况可以用生态足迹来表征。因此,本研究主要运用京津冀三省市之间的投入产出矩阵来计算北京与津冀之间的生态足迹转入和转出量,并进行分析。研究结果表明,北京的社会经济发展需要周边津冀生态生产性土地资源的支撑,尤其是河北的农用地资源,包括耕地、林地和水产养殖用地。从生态贸易类型来看,2002年和2007年,就北京和河北之间生态足迹流通情况而言,北京是净生态输入型,属于"贸易逆差";河北是净生态输出型,属于"贸易顺差"。2007年北京对津冀生态资源的使用量比2002年有所下降。总体而言,北京对河北生态生产性土地资源的消耗对河北的生态压力不大,其中,水产养殖用地的生态压力最大,2007年水产养殖净流量占河北水产养殖土地的54.27%。2002年北京对天津生态生产性土地资源的依赖加大了天津的生态压力,林业用地和水产养殖用地的净流出量分别是天津相应承载土地面积的4.96倍和3.57倍;2007年,北京对天津生态土地资源利用程度降低,随之,北京对天津生态生产性土地资源的压力变小。尽管2007年与2002年相比,北京对河北生态生产性土地的消耗量降低了,但是,由于河北土地利用结构的变化影响着北京和区域的可利用的土地资源质量和生态环境质量,国家和北京应该帮助河北保护其土地资源,尤其是河北东南部的农田资源在区域中承担着重要的绿色隔离作用,应该受到严格的保护。此外,京津冀区域应该制定整体空间开发战略,加强土地资源保护力度,合理布局产业结构,构建生态安全格局。     

城镇化与工业化对能源强度影响的实证研究——基于截面相关和异质性回归系数的非平衡面板数据模型

城镇化、工业化对中国能源强度的影响如何?如何在快速推进城镇化、工业化进程的同时确保节能减排目标的实现?论文以能源强度指标代替传统的能源消费指标来反映能源综合利用效率,并应用考虑截面相关性和异质性回归系数的非平衡面板数据模型,使用共同相关效应组均值(CCEMG)估计方法对中国1978-2014年城镇化、工业化与能源强度之间的关系进行分析。研究结果表明:人均实际GDP增长1%,能源强度将会降低0.412%,工业化水平增长1%,能源强度将会上升0.630%,而由于生产消费等经济活动的增加、高度集中化以及规模经济的综合作用,使得城镇化对能源强度的影响并不确定。联系研究结论,本文提出政策建议:我国应加快产业结构升级,转变经济增长方式;构建绿色制造体系,推进"五化"协同发展;推进绿色、循环、低碳发展的新型城镇化建设,提高城镇化质量,提升我国整体的能源效率,确保节能减排目标的实现,推动经济全面、协调、可持续发展。     

双重外部性约束下中国资源型企业绩效研究

中国资源型产业的可持续发展受到代际外部性与环境外部性的双重制约,却长期存在过度进入导致的过度开采,加剧了资源型企业的产能过剩与配置扭曲,但与此相关的"结构-行为-绩效"关系研究尚未将外部性纳入考量。本文引入双重外部性,构建了"SCP-2E"模型,并基于80家资源开采上市公司2011-2014年的面板数据,首先运用使用者成本法和条件估值法分别估算了代际外部性和环境外部性,然后将它们作为企业行为的因变量和影响绩效的自变量分别纳入资源型企业的SCP-2E模型中,同时考虑双重外部性下市场结构、所有权性质等因素对绩效的影响。结果发现,中国资源型产业有着明显的周期性特征,而2011年以来资源型企业的兼并重组只是单纯扩大了企业规模,而没有切实提高企业效率;进一步结合企业规模和所有权性质的相关性来看,大型企业多以国有企业为主,由此说明企业规模扩大、绩效却显著降低的原因在很大程度是由于国有企业的低效率造成的;另外,目前企业绩效的提高,主要还是依赖于地区经济增长的拉动,但在用电量增速较快的地区,也伴随着较大的环境外部性损失,暂时没有受到资源枯竭的约束。基于以上结论,本文认为,监管部门应加强资源开采权的审查,提高资源型企业的进入门槛和技术标准,适当鼓励企业在不增加生产投资和规模的前提下,通过技术进步和劳动效率的提高提升绩效,而国有企业改革力度重点应放在解决大型国企的低效率问题上。政府应该权衡经济增长与代际和环境负外部性,在保证能源、资源安全的前提下,鼓励和扩大可耗竭资源的进口,逐步建立国家和企业的战略资源储备制度。     

基于面板数据的土地投入对经济增长的影响-以浙江省为例

采用生产函数与面板数据回归模型相结合的研究手段,以地处经济发达地区的浙江省为实证研究区域对土地要素投入对经济增长的影响进行了定量的研究。研究结果表明：(1)就规模效应而言,浙江省的经济增长处于规模报酬不变的发展阶段;(2)浙江省经济增长对劳动力要素的投入最为敏感,其中土地要素、劳动力要素和资本要素增加1个单位的投入量对经济增长的推动为 0.247 3、0.538 5 和 0.321 6;（3）固定资产投入在研究期间是浙江省经济增长的主要推动力,其贡献率达到6823%,而劳动力要素与土地要素则分别为2346%和625%;(4)浙江省土地利用不够集约,在研究期间土地要素对经济增长的影响能被资本要素与劳动力要素有效替代,而劳动力要素则难以被资本投入与土地投入有效替代,劳动集约型产业应该成为浙江省未来发展方向之一。     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

复杂社会技术系统安全事故的成因结构敏感性及预防对策研究

简要回顾现有复杂社会技术系统安全事故的成因理论存在的局限性,根据大量统计资料和经验总结构建事故成因理论的缺陷。该研究试图从分析和推理入手,根据复杂社会技术系统运行机制及事故成因结构敏感性特征,探索由于新技术快速普及应用而不断涌现的复杂社会技术系统的失效机制及事故的成因理论;解释复杂社会技术系统安全事故的严重程度差异性、时间方向性及情境依赖性;为任何因新技术普及应用而产生的人造系统的安全分析及事故预防提供理论和方法支撑。