LANLoad NEEPP - Lake County: Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) in the Northern Everglades and Estuaries Protection Program (NEEPP) Region
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This raster dataset consists of cells (9.6m x 9.6m) ranked to reflect the likelihood that nutrients applied to a given terrestrial location will reach the nearest downgradient surface waterbody. Possible ranks range from 1 to 9 with values increasing as the likelihood of nutrient transport to downgradient surface waterbodies increases. Ranks are based on six physical landscape parameters selected by Subject Matter Experts (SMEs) who also assigned relative weights to each parameter using the Analytical Hierarchy Process (AHP). During this exercise, the location considered by SMEs was the pilot study area, St. Lucie County, FL, and the focal nutrient source was Onsite Sewage and Treatment Disposal Systems (OSTDS). Despite the original focus on OSTDS, LANLoad NEEPP can be used to gauge the likelihood of nutrient transport to surface waterbodies from other, similar, nutrient sources. The resulting AHP model demonstrated high internal consistency (Consistency Ratio: 0.01) and resulted in the following parameters and weights, in order of importance: Distance to Waterbody, 30.0%; Depth to Water, 21.6%; Hydraulic Conductivity, 20.7%; Potential for Flooding, 10.9%; Slope, 9.8%; and Surficial Karstic Deposits, 7.0%. The weights assigned by SMEs in the St. Lucie County pilot were deemed applicable to the NEEPP region due to highly similar geologic/hydrologic conditions. Relative parameter weights might not be applicable to regions without such similarities. Geospatial datasets representative of these parameters were combined using a weighted overlay to produce LANLoad NEEPP. The performance of LANLoad NEEPP was evaluated across randomly selected, stratified locations within the NEEPP region. LANLoad NEEPP categories were classified into “lower” likelihood (LANLoad NEEPP categories ≤ 4) and “higher” likelihood (LANload NEEPP categories ≥ 6). We subjected these classifications to two independent, blind evaluation methods. First, SMEs evaluated 30 locations using their best professional judgment. This expert review yielded a 92% consistency rate with LANLoad NEEPP classifications. Second, the groundwater numerical model ArcNLET-Py was used to simulate uniform nutrient loading across 10 locations, analyzing a total of 500 model points. This yielded a 100% consistency rate with the LANLoad NEEPP classifications where the locations identified by LANLoad NEEPP as "higher likelihood" directly corresponded with the highest simulated nutrient loads, while "lower likelihood" locations matched the lowest simulated nutrient loads. LANLoad NEEPP was developed in ArcGIS Pro 3.5.2 (ESRI) at a 9.6-meter resolution, using the NAD83 (2011) Datum and the Florida GDL Albers Ellipsoid: GRS 1980 projection. Standard geoprocessing workflows and authoritative reference materials were utilized to cleanse data, fill spatial gaps, and eliminate null values for the hydraulic conductivity, depth to groundwater, the National Flood Hazard Layer, and the Surficial Geology of Florida geospatial datasets. Three geospatial datasets derived from source data, Waterbodies, Distance to Waterbodies and Slope, were developed by the Balmoral Group in collaboration with the University of South Florida - Ecohydrology Research Group. Table 1: The geospatial datasets incorporated in LANLoad NEEPP (2026). Abbreviations: Federal Emergency Management Agency (FEMA), Florida Department of Environmental Protection (DEP), National Resources Conservation Service (NRCS), United States Geological Survey (USGS), University of South Florida - Ecohydrology Research Group (USF-ERG) Parameter Geospatial Dataset Source Date Acquired Waterbodies Florida NHD Waterbody Polygons DEP 3/2026 Florida NHD Area Polygons DEP 3/2026 Florida NHD Flowlines DEP 3/2026 Soil Survey Geographic Dataset (SSURGO) - FL NRCS 3/2026 Statewide Land Use Land Cover (Codes 5000 and 6000) DEP 3/2026 Distance to Waterbodies Waterbodies The Balmoral Group 3/2026 DEM USGS 3/2026 Depth to Groundwater Soil Survey Geographic Dataset (SSURGO) -FL NRCS 3/2026 Hydraulic Conductivity Soil Survey Geographic Dataset (SSURGO) -FL NRCS 3/2026 Potential for Flooding National Flood Hazard Layer FEMA 3/2026 Slope Distance to Waterbodies The Balmoral Group 5/2026 DEM USGS 3/2026 Surficial Geology Surficial Geology of Florida DEP 11/2025 Contact Information Kai C. Rains, PhD, PWS – krains@usf.edu Dataset Citation Rains, Kai; Guerron-Orejuela, Edgar; Davis, Sara; Okonkwo, Moses; Rains, Mark (2026), “LANLoad NEEPP: Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) in the Northern Everglades and Estuaries Protection Program (NEEPP) region”, University of South Florida, v03, doi: 10.17632/7nw285j9bk.3 License CC BY 4.0 Links: USF Ecohydrology Research Group LANLoad Phase III Final Report Florida Geographic Information Office Logos:
该栅格数据集（raster dataset）由9.6米×9.6米的像元组成，其分级结果用于反映施加于某一陆地区域的营养物抵达最近下坡向地表水体的可能性。可能的分级范围为1至9，数值越高，营养物向坡下地表水体输运的可能性就越大。 该分级体系基于主题专家（Subject Matter Experts, SMEs）遴选的6项地表景观参数，专家还通过层次分析法（Analytical Hierarchy Process, AHP）为各参数赋予了相对权重。本次研究的考量区域为试点研究区——佛罗里达州圣露西县（St. Lucie County, FL），核心营养物来源为现场污水与处理处置系统（Onsite Sewage and Treatment Disposal Systems, OSTDS）。尽管最初的研究聚焦于OSTDS，但LANLoad NEEPP可用于评估其他类似营养物来源向地表水体输运营养物的可能性。 所构建的AHP模型内部一致性极高（一致性比率：0.01），按重要性排序的参数及对应权重如下：距水体距离（30.0%）、地下水埋深（21.6%）、导水率（20.7%）、洪水潜势（10.9%）、坡度（9.8%）以及表层喀斯特沉积（7.0%）。由于圣露西县试点中专家赋予的参数权重与NEEPP区域的地质/水文条件高度相似，该权重被认为适用于NEEPP区域；但若区域不具备此类相似条件，则该相对参数权重可能不适用。 研究通过加权叠加法整合上述参数对应的地理空间数据集，生成LANLoad NEEPP。在NEEPP区域内，研究人员随机选取分层样点对LANLoad NEEPP的性能进行了评估。将LANLoad NEEPP的分级划分为“较低”可能性（LANLoad NEEPP分级≤4）与“较高”可能性（LANLoad NEEPP分级≥6），并采用两种独立的盲评方法对该分级结果进行验证。 第一种验证方法为专家评估：专家基于其专业判断对30个样点进行评估，结果显示与LANLoad NEEPP分级的一致性达92%。第二种验证方法为数值模拟：采用地下水数值模型ArcNLET-Py对10个样点开展均匀营养物负荷模拟，共分析500个模型点，结果显示与LANLoad NEEPP分级完全一致——LANLoad NEEPP标记为“较高可能性”的样点对应最高模拟营养物负荷，“较低可能性”样点则对应最低模拟营养物负荷。 LANLoad NEEPP基于ArcGIS Pro 3.5.2（ESRI）开发，分辨率为9.6米，采用NAD83（2011）基准面与Florida GDL Albers椭球体：GRS 1980投影坐标系。研究采用标准地理处理工作流与权威参考资料，对导水率、地下水埋深、国家洪水灾害图层（National Flood Hazard Layer）以及佛罗里达州表层地质地理空间数据集进行数据清洗、空间间隙填充与空值剔除。 由巴尔莫勒尔集团（The Balmoral Group）与南佛罗里达大学生态水文学研究组合作开发了3项源地理空间数据集：水体（Waterbodies）、距水体距离（Distance to Waterbodies）与坡度（Slope）。 表1 纳入LANLoad NEEPP的地理空间数据集（2026） 缩写说明：联邦紧急事务管理局（Federal Emergency Management Agency, FEMA）、佛罗里达州环境保护部（Florida Department of Environmental Protection, DEP）、美国自然资源保护局（National Resources Conservation Service, NRCS）、美国地质调查局（United States Geological Survey, USGS）、南佛罗里达大学生态水文学研究组（University of South Florida - Ecohydrology Research Group, USF-ERG） | 参数 | 地理空间数据集 | 来源 | 获取日期 | | ---- | ---- | ---- | ---- | | 水体 | 佛罗里达州NHD水体多边形 | DEP | 2026年3月 | | | 佛罗里达州NHD区域多边形 | DEP | 2026年3月 | | | 佛罗里达州NHD水流线 | DEP | 2026年3月 | | | 土壤调查地理数据集（SSURGO）- 佛罗里达州 | NRCS | 2026年3月 | | | 全州土地利用/土地覆盖（代码5000与6000） | DEP | 2026年3月 | | 距水体距离 | 水体 | 巴尔莫勒尔集团 | 2026年3月 | | 数字高程模型（Digital Elevation Model, DEM） | USGS | 2026年3月 | | 地下水埋深 | 土壤调查地理数据集（SSURGO）-佛罗里达州 | NRCS | 2026年3月 | | 导水率 | 土壤调查地理数据集（SSURGO）-佛罗里达州 | NRCS | 2026年3月 | | 洪水潜势 | 国家洪水灾害图层 | FEMA | 2026年3月 | | 坡度 | 距水体距离、DEM | 巴尔莫勒尔集团 | 2026年5月 | | 表层地质 | 佛罗里达州表层地质 | DEP | 2025年11月 | 联系方式：Kai C. Rains, PhD, PWS – krains@usf.edu 数据集引用：Rains, Kai; Guerron-Orejuela, Edgar; Davis, Sara; Okonkwo, Moses; Rains, Mark (2026), "LANLoad NEEPP: Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) in the Northern Everglades and Estuaries Protection Program (NEEPP) region", 南佛罗里达大学, 版本v03, doi: 10.17632/7nw285j9bk.3 许可协议：CC BY 4.0 相关链接：南佛罗里达大学生态水文学研究组LANLoad第三阶段最终报告；佛罗里达州地理信息办公室 标识：
