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Open Source Tools to Calculate and Predict Lake Morphometry Metrics
Citation:
Hollister, Jeff. Open Source Tools to Calculate and Predict Lake Morphometry Metrics. Virtual Summit: Incorporating Data Science and Open Science Techniques in Aquatic Research, NA, July 23 - 24, 2020.
Impact/Purpose:
The tools scientists use are often not made available. This limits transparency and creates challenges in reproducing important scientific findings. The move towards open science in many scientific fields is an attempt to correct this. One component of open science is the use of open source software. This allows others to reuse software, see how that software was written, and speed up scientific discovery. In this talk, I highlight two pieces of open source software that were developed to aid in the calculation of shape metrics for lakes. This is important because lake shape is needed information for understanding the ecology of lakes and helps us predict when lake water quality may degrade and potentially impact human health. Open source tools for doing this have a greater impact by allowing other researchers to 1) use the same tools and 2) more readily grow our body of knowledge that can ultimately reduce the risks to the public.
Description:
Lake shape is a key driver of the ecology of lakes and quantifying shape via morphometry metrics is often the first step in limnology studies. Approaches for calculating lake morphometry metrics span the technical spectrum with traditional approaches relying on analog tools, such as planimeters, to higher-tech methods that use 3D modelling and modern GIS tools. Missing from the limnologists toolkit though, is an open, reproducible, and consistent toolset for lake morphomtery metrics. Two R packages, lakemorpho and elevatr begin to address this vision with open source tools for calculating lake morphometry metrics. The lakemorpho package provides tools to calculate standard lake morphometry metrics such as surface area, fetch, maximum lake length, and shoreline development. Working together, lakemorpho and elevatr, which provides access to elevation data, may be used to estimate metrics, such as maximum lake depth and lake volume, in the absence of detailed bathymetry data. These estimated metrics provide maximum lake depth predictions with an average error of 5-6 meters. This error is large, but using these types of estimated depths are often useful. For instance, they improve predictions of nutrient concentrations in lakes over methods that don’t included estimates of lake depth and volume. In this talk I introduce and demonstrate these packages with local examples. Additionally, I discuss plans for future improvements to the tools that will address usability, application to new datasets (e.g. NHDPlus High Resolution), calculation of new metrics.
URLs/Downloads:
https://freshwaterecology.wordpress.com/2020/07/08/conference-workshop-virtual-summit-incorporating-data-science