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.     

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.     

排污申报数据审核的一般方法

排污申报登记工作具有数据量大、逻辑性强、专业要求高的特点,环保部门必须力求通过强化数据的审核来获得完整、准确的排污申报数据。     

外军皮肤防护器材化学防护性能测试与评价用标准物质研究进展

本文简迷了皮肤防护器材--化学防护服(衣)化学防护性能试验与评价用标准物质的重要性,系统地概述了外军化学防护服(衣)化学防护性能试验与评价用标准物质的研究现状,并提出了我国化学防护服(衣)防护性能试验与评价用标准物质研制过程中应注意的一些问题.     

基于本质安全的供应链风险识别方法研究

由于集成供应链的构成较为复杂,来自供应链内部、外部的风险因素众多,单凭建立附加的防护系统并不能从根本上解决集成供应链的风险损失问题。因而需要从本质安全的视角出发,从根源上辨识风险源,及时发现供应链中的薄弱环节,消除因风险隐患及控制风险而造成的损失。SCOR模型可以清晰地将整个供应链的组成结构、操作流程描述出来,以此为基础运用模型的顶层、配置层、元素层和实施层这4个层级对集成供应链风险进行了研究。根据集成供应链主体构成的特点将供应链整体风险划分为5类,分别为需求风险、供应风险、运作过程风险、环境风险以及制度风险。并通过建立供应链风险识别指标体系以有针对性地识别某一企业的供应链风险。     

张北6.2级地震邯郸地区前兆异常特征研究

1998年1月10日张北发生MS6.2级地震。在震前一、二年,远在400km外的邯郸地区的多种前兆手段准同步地出现了中期异常变化,这些异常的变化幅度显著,在时间上与张北地震有较好的对应关系,信度较高。分析邯郸地区的前兆异常与张北地震的关系,有利于该区强震监测预报水平的提高,也可为地震前兆特征和机理的深入研究提供实际依据。     

浅议提高废水污染源监测数据代表性

对废水污染源监测中影响监测数据代表性的主要因素进行了分析,并对提高废水污染源监测数据代表性提出了可行性建议。     

中国耕地资源数量变化的趋势分析与数据重建:1949～2003

由于统计方面的原因,1949年以来中国耕地资源数量变化至今尚没有一个客观的描述。针对该问题,论文对1949～2003年不同时期不同来源的耕地数据系列进行分析,认为统计数据存在问题较多的时期为1960～1985年,并采用粮食产量对耕地面积进行了分时期的反演,对于1986～1996年间的耕地数量则按照全国土地利用详查统一到1996年的结果再进行反推,由此重新刻画建国以来中国耕地资源数量变化的趋势和特征,并结合不同时段耕地相关政策对耕地数量变化的驱动作用进行分析。结果表明,1949年以来中国耕地资源数量呈现波动性变化,但在1979年之前总体上是增加的,自20世纪80年代起呈现缓慢下滑,1999年后由于生态退耕等原因引起耕地数量迅速减少,由此导致的中国粮食安全问题与耕地安全问题值得关注。在经过一段快速的大规模生态退耕时期后,中国生态退耕速度将逐步趋缓,耕地安全与粮食安全将会成为影响中国耕地数量变化的主要问题,严格的耕地保护政策势必将贯彻下去,预计2010年后中国的耕地资源数量将趋于稳定。