LANLoad NEEPP - St. Lucie County: Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) in the Northern Everglades and Estuaries Protection Program (NEEPP) Region V2
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This raster dataset consists of cells (10m x 10m) 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 may not be applicable to regions without such similarities. Geospatial datasets representative of these parameters were acquired in 2024 and combined using a weighted overlay to produce LANLoad NEEPP. The performance of LANLoad NEEPP was evaluated at multiple locations (selected through a random stratified process) within the NEEPP region by classifying LANLoad NEEPP ranks less than or equal to 4 as “lower” likelihood and those more than or equal to 6 as “higher” likelihood. Then, two independent assessment methods were applied, both conducted blind: SME Review: SMEs were provided with input datasets corresponding to 30 locations and asked to assign a classification of lower or higher likelihood to each location using best professional judgement. There was 92 % consistency between classifications assigned by LANLoad NEEPP and those assigned by SMEs. Numerical modeling: Using ArcNLET-Py, nutrient loading to surface waters from uniform inputs was modeled in 10 locations, each containing 50 model points. Classifications assigned by LANLoad NEEPP were 100% consistent with those assigned through ArcNLET-Py model results, i.e., locations classified by LANload NEEPP as “higher” likelihood also had the highest ArcNLET-Py modeled nutrient loads while those classified as “lower” likelihood had the lowest modeled nutrient loads. LANLoad NEEPP was developed in ArcGIS Pro 3.4.2 (ESRI) at a 10-meter resolution, using the NAD83 (2011) Datum and the Florida GDL Albers Ellipsoid: GRS 1980 projection. Table 1: The geospatial datasets incorporated in LANLoad NEEPP (2025). Abbreviations: Florida Department of Environmental Protection (DEP), National Resources Conservation Service (NRCS), South Florida Water Management District (SFWMD), Federal Emergency Management Agency (FEMA), Florida Geological Survey (FGS), University of South Florida (USF) and U.S. Geological Survey (USGS). Parameter Geospatial Dataset Source Date Acquired Waterbodies Florida NHD Waterbody Polygons DEP 11/8/2024 Florida NHD Area Polygons DEP Florida NHD Flowlines DEP Soil Survey Geographic Dataset (SSURGO) NRCS 9/20/2024 Distance to Waterbodies DEM + Waterbodies SFWMD, USGS 9/24/2024 Depth to Groundwater Soil Survey Geographic Dataset (SSURGO) NRCS 9/20/2024 Hydraulic Conductivity Soil Survey Geographic Dataset (SSURGO) NRCS 9/20/2024 Potential for Flooding National Flood Hazard Layer (NFHL) FEMA 9/9/2024 Topography (slope) DEM + Distance to Waterbodies SFWMD, USGS, USF-ERG 9/24/2024 Surficial Karstic Deposits Surficial Geology of Florida FGS 9/16/2024 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, V2, doi: 10.17632/7nw285j9bk.2 Links: USF Ecohydrology Research Group LANLoad Phase III Final Report Florida Geographic Information Office Logos:
本栅格数据集（raster dataset）由10米×10米的栅格单元组成，各单元通过赋值排名，以反映施用于某一陆地区域的营养物质抵达最近下坡方向地表水体的可能性。排名区间为1至9，数值越高，代表营养物质向下坡方向地表水体输运的可能性越大。 该排名基于领域专家（Subject Matter Experts, SMEs）遴选的六项自然景观参数，领域专家同时通过层次分析法（Analytical Hierarchy Process, AHP）为各参数赋予相对权重。本次建模所针对的试点研究区域为佛罗里达州圣露西县（St. Lucie County, FL），核心营养物质来源为现场污水与处理处置系统（Onsite Sewage and Treatment Disposal Systems, OSTDS）。 尽管最初的建模聚焦于现场污水与处理处置系统，但LANLoad NEEPP仍可用于评估其他同类营养物质来源向地表水体输运营养物质的可能性。最终构建的层次分析法模型展现出极高的内部一致性（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%）、洪涝潜在风险（10.9%）、坡度（9.8%）以及表层喀斯特沉积（7.0%）。 由于NEEPP区域与圣露西县试点区域的地质、水文条件高度相似，领域专家在试点中赋予的参数权重被认为可适用于NEEPP区域；对于不具备此类相似条件的区域，该参数相对权重可能并不适用。研究团队于2024年获取了代表上述六项参数的地理空间数据集，并通过加权叠加分析将其整合，最终生成LANLoad NEEPP数据集。 研究团队在NEEPP区域内通过随机分层抽样选取多个点位，对LANLoad NEEPP的性能开展评估：将数据集排名≤4的点位归类为“低”输运可能性，排名≥6的点位归类为“高”输运可能性。随后采用两种独立的盲法评估方法： 1. 领域专家评审：向领域专家提供对应30个点位的输入数据集，要求其凭借专业判断为每个点位赋予“低”或“高”输运可能性的分类。LANLoad NEEPP给出的分类与领域专家的分类一致性达92%。 2. 数值模拟：通过ArcNLET-Py软件，对10个各包含50个模拟点的点位开展均匀输入下的地表水体营养负荷模拟。LANLoad NEEPP给出的分类与ArcNLET-Py模拟结果完全一致：即被LANLoad NEEPP归类为“高”可能性的点位，其ArcNLET-Py模拟的营养负荷也最高；而被归类为“低”可能性的点位，其模拟营养负荷则最低。 LANLoad NEEPP数据集基于ESRI公司的ArcGIS Pro 3.4.2软件开发，空间分辨率为10米，采用NAD83（2011）基准面以及佛罗里达GDL阿尔伯斯椭球体：GRS 1980投影坐标系。 表1：LANLoad NEEPP（2025）所纳入的地理空间数据集 缩写说明：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）、南佛罗里达水资源管理区（South Florida Water Management District, SFWMD）、联邦紧急事务管理局（Federal Emergency Management Agency, FEMA）、佛罗里达地质调查局（Florida Geological Survey, FGS）、南佛罗里达大学（University of South Florida, USF）以及美国地质调查局（U.S. Geological Survey, USGS）。 参数 地理空间数据集 数据来源 获取日期 水体 佛罗里达NHD水体多边形 DEP 2024年11月8日 佛罗里达NHD区域多边形 DEP 未标注日期 佛罗里达NHD水系线 DEP 未标注日期 土壤调查地理数据集（Soil Survey Geographic Dataset, SSURGO） NRCS 2024年9月20日 距水体距离 DEM+水体数据 SFWMD、USGS 2024年9月24日 地下水埋深 土壤调查地理数据集（SSURGO） NRCS 2024年9月20日 导水率 土壤调查地理数据集（SSURGO） NRCS 2024年9月20日 洪涝潜在风险 全国洪水灾害图层（National Flood Hazard Layer, NFHL） FEMA 2024年9月9日 地形（坡度） DEM+距水体距离数据 SFWMD、USGS、USF-ERG 2024年9月24日 表层喀斯特沉积 佛罗里达表层地质图 FGS 2024年9月16日 联系方式：凯·C·雷恩斯博士，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", University of South Florida, V2, doi: 10.17632/7nw285j9bk.2 相关链接：南佛罗里达大学生态水文研究组LANLoad第三阶段最终报告；佛罗里达地理信息办公室标识：
