DOI: 10.17632/7nw285j9bk.1
title: LANLoad NEEPP: Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) in the Northern Everglades and Estuaries Protection Program (NEEPP) region

description: The Landscape Assessment of Nutrient Loading to Waterbodies (LANLoad) is a geospatial screening tool designed to facilitate water quality management decisions. It provides an estimate of the relative likelihood that nutrient inputs applied to locations on land will impact surface water quality. LANLoad is based solely on physical landscape characteristics and may be used independently or with other relevant datasets such as those that reflect nutrient application rates. This dataset includes LANLoad for the Northern Everglades and Estuaries Protection Program (NEEPP) region. 

The dataset is available as a single comprehensive file "LANLoad_NEEPP_Overall.tif.zip" and as subsets corresponding to intersections between NEEPP and 15 FL counties. 

These raster dataset consist 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 are from 1 to 9 with values increasing as the likelihood increases that mobile nutrients would reach a downgradient surface waterbody. Ranks are based on 6 physical landscape parameters selected by Subject Matter Experts (SMEs) who also assigned relative weights of each parameter using the Analytical Hierarchy Process (AHP). During this exercise, the focal location for SMEs was the pilot study area, St Lucie County, FL, and the focal nutrient source was Onsite Sewage and Treatment Disposal Systems. The resulting AHP model demonstrated high internal consistency (Consistency Ratio: 0.01) and resulted in the following parameter 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%. 

Geospatial datasets representative of these parameters were acquired in 2024 and combined using a weighted overlay to produce LANLoad NEEPP. 

LANLoad NEEPP performance was evaluated at multiple locations (selected through a random stratified process) within the NEEPP region by classifying LANLoad ranks less than or equal to 4 as “lower” and those more than or equal to 6 as “higher”. Then, two assessment methods were applied, both conducted blind: 
1) SME Review: SMEs were provided with input datasets corresponding to 30 locations and asked to assign a classification of lower or higher. There was 92 % consistency between classifications assigned by LANLoad NEEPP and those assigned by SMEs. 2) 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 were 100% consistent with those assigned through ArcNLET-Py model results, i.e., locations classified by LANload as “higher” also had the highest ArcNLET-Py modeled nutrient loads while those classified as “lower” had the lowest modeled nutrient loads. 

Contact: Kai Rains – krains@usf.edu