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.     

大数据时代政府环境治理路径创新

本文针对政府环境治理面临政府失灵、市场失灵和社会低效三个困境,提出运用大数据方法创新治理路径。政府应用大数据提高环境治理过程中的事前、事中和事后监管效能,创新监管模式提升环境治理能力,改善政府失灵困境;依托大数据完善市场化调节机制,激励企业环境行为市场化,构建企业环境信用评价体系,改变企业环境信息披露模式,弥补市场失灵;利用大数据增进公众环境意识,建设公开、透明、平权化的环境信息交互体系,重视环境信息的舆情引导,促进环保价值理念传播,改变公众参与低效现状。进而提出完善大数据治理平台、技术和体系,构建政府、企业、公众共同参与的立体化环境治理模式,提高环境治理的精准化、精细化和智能化水平。     

人工增雨（雪）效果评估方法探索

人工增雨（雪）常用的评估方法为统计检验和物理检验,统计检验需要大量试验样本,且随机化试验在我国难以实现,大量样本无从获得。物理检验目前主要是雷达观测演变状况和飞机直接观测云内微物理参数,由于技术和能力有限,这些方法都难以实现。为了在人工影响天气作业后尽快得出评估结果供政府及环境保护等部门参考,利用雨滴谱记录数据对一次人工增雪作业后的效果进行了评估,发现这些数据在没有常规降雪记录情况下具有一定的参考价值。从评估结果看,人工增雪作业后,降水强度有明显增大的趋势。     

A Multi‐Scale Analysis of Single‐Family Residential Water Use in the Phoenix Metropolitan Area

Studies that evaluate determinants of residential water demand typically use data from a single spatial scale. Although household‐scale data are preferred, especially when econometric models are used, researchers may be limited to aggregate data. There is little, if any, empirical analysis to assess whether spatial scale may lead to ecological fallacy problems in residential water use research. Using linear mixed‐effects models, we compare the results for the relationship of single‐family water use with its determinants using data from the household and census tract scales in the city of Phoenix. Model results between the household and census tract scale are similar suggesting the ecological fallacy may not be significant. Common significant determinants on these two spatial scales include household size, household income, house age, pool size, irrigable lot size, precipitation, and temperature. We also use city/town scale data from the Phoenix metropolitan area to parameterize the linear mixed‐effects model. The difference in the parameter estimates of those common variables compared to the first two scales indicates there is spatial heterogeneity in the relationship between single‐family water use and its determinants among cities and towns. The negative relationship between single‐family house density and residential water use suggests that residential water consumption could be reduced through coordination of land use planning and water demand management.     

Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations

This article is an assessment of the current state of the art and relative utility of satellite precipitation products (SPPs) for hydrologic applications to support water management decisions. We present a review of SPPs, their accuracy in diverse settings including the influence of geography, topography, and weather systems, as well as the pros and cons of their use for different water management applications. At the end of this broad synthesizing effort, recommendations are proposed for: (1) SPP developers to improve the quality, usability, and relevance of precipitation products; and (2) SPP users to improve the reliability of their predictions and hydrologic applications to better support water management.     

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.     

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.     

NITRATE CONCENTRATIONS IN ILLINOIS RIVERS 1967 TO 1974: COMPARISON AMONG REPORTING AGENCIES1

ABSTRACT: Long term data on surface water quality can sometimes be assembled by combining data collected by different agencies at different times and assuming that between agency differences in data quality are insignificant. The objective of this paper was to assess the quality of riverine nitrate (NO₃) concentrations in Illinois measured and reported by four agencies from 1967 to 1974 by comparing median values for similar sampling locations and periods. A total of 17 river reaches were identified for which two agencies reported NO₃ concentrations during similar periods. Nonparametric comparison of median values and analysis of covariance with discharge as a covariant produced similar results. Nitrate concentrations reported by the U.S. Geological Survey (USGS) from 1967 to 1971 were not statistically (P > 0.05) different from values reported by the Illinois State Water Survey (ISWS) for two of three river reaches. Additionally, NO₃ concentrations reported by USGS from 1972 to 1974 were not statistically different than concentrations reported by the Illinois Environmental Protection Agency (IEPA) for four of five river reaches. From 1969 to 1971, NO₃ concentrations reported by the Illinois Department of Public Heath and the Illinois Environmental Protection Agency (IDPH/IEPA) were less than one‐fourth the magnitude of values reported by ISWS. The median NO₃ concentrations measured by the Central Illinois Public Service (CIPS) were significantly greater than those measured by USGS and IDPH/IEPA in the three comparable sampling locations. The use of NO₃ concentrations measured by CIPS and IDPH/IEPA prior to 1972 is not recommended.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.