Raster dataset showing the probability of elevated concentrations of nitrate in ground water in Colorado, hydrogeomorphic regions and fertilizer use estimates included.

This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and(or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., http://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting elevated concentrations of nitrate in ground water in Colorado with hydrogeomorphic regions and fertilizer use included. The following text was extracted from Rupert (2003). Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.本数据集为本研究生成的8套数据集之一。其中4套数据集用于预测美国科罗拉多州地下水中检出莠去津（atrazine）及其降解产物脱乙基莠去津（desethyl-atrazine，以下简称DEA），或二者同时检出的概率；剩余4套则用于预测该州地下水中硝酸盐浓度超标的概率。 用于预测莠去津/DEA检出概率的4套数据集，依据是否纳入莠去津使用量数据以及是否纳入水文地貌区数据进行区分。用于预测硝酸盐浓度超标概率的4套数据集，则依据是否纳入化肥使用量数据以及是否纳入水文地貌区数据进行区分。 8套数据集各有其独特的优势与局限。使用者需参阅Rupert（2003年，《科罗拉多州地下水中莠去津/脱乙基莠去津及硝酸盐浓度超标检出概率》，美国地质调查局（U.S. Geological Survey）水资源调查报告02-4269，共35页，网址：http://water.usgs.gov/pubs/wri/wri02-4269/），以判断当前使用的数据集是否最贴合自身的特定需求。 本数据集特指纳入了水文地貌区数据与化肥使用量数据、用于预测科罗拉多州地下水中硝酸盐浓度超标检出概率的数据集。以下内容节选自Rupert（2003年）的研究。 美国联邦法规草案要求各州针对莠去津、甲草胺（alachlor）、异丙甲草胺（metolachlor）及西玛津（simazine）这几种除草剂制定州级农药管理计划。科罗拉多州据此开发了可用于预测该州地下水中莠去津/DEA检出概率的相关地图。此类地图可纳入州农药管理计划，为科罗拉多州的莠去津管理提供可靠的水文地质依据。 由于硝酸盐是科罗拉多州多个区域的重点关注污染物，研究团队同时开发了可预测该州地下水中亚硝酸盐+硝酸盐（以氮计，即硝酸盐）浓度超标的概率地图。 针对莠去津/DEA浓度≥0.1微克/升、地下水中硝酸盐浓度＞5毫克/升的检出概率地图，其开发流程如下： (1) 借助地理信息系统（GIS）将地下水水质数据与人为活动数据、水文地质数据进行叠加，生成包含各监测井对应数据的数据集，涵盖莠去津使用量、化肥使用量、地质条件、水文地貌区、土地覆被、降水量、土壤类型及井施工参数等信息。随后将此类数据导入统计软件包，通过逻辑回归（logistic regression）方法开展分析。 (2) 分析地下水水质与监测井周边圆形缓冲区（buffers）内各类土地覆被占比之间的关联。评估了多种缓冲区尺寸，最终选取与地下水水质关联度最强的缓冲区尺寸用于逻辑回归模型。 (3) 分析地下水中莠去津/DEA与硝酸盐浓度，与莠去津使用量、化肥使用量、地质条件、水文地貌区、土地覆被、降水量、土壤类型及井施工参数之间的关联，并基于自变量的不同组合构建了多组初步多元回归模型。 (4) 筛选出对地下水中莠去津/DEA检出情况及硝酸盐浓度超标情况预测效果最优的多元回归模型。 (5) 采用独立的地下水水质数据集对筛选出的多元回归模型进行验证，确认其预测精度。 (6) 将筛选出的多元回归模型导入地理信息系统，生成概率栅格数据集。