Lake Biogeochemical Model Output for One Retrospective and 12 Future Climate Runs in Northern Wisconsin & Michigan, USA
July 18, 2022
This dataset contains modeled daily lake area, volume, constituent mass, and biogeochemical rates for 3,692 lakes in the Northern Highlands Lake District (NHLD) for one retrospective model run (1986-2010) and 12 model runs under future climate scenarios. This dataset was created using published tools developed to simulate detailed hydrological and biogeochemical fluxes for thousands of lakes and reservoirs over large spatiotemporal scales. The lake hydrology model utilized a computationally-efficient integrated surface water and groundwater modeling framework that informed a lake water budget model incorporating daily hydrologic inputs and exports from individual lakes within the modeling domain. The lake biogeochemical model was informed by the hydrologic information and was built upon a simple lake energy budget, constituent loading, and lake biogeochemical model to track carbon storage and processing for all lakes within the NHLD modeling domain. Our one retrospective model run was driven by historic meteorological data and the projected model runs were driven by projected future climate scenario periods that are representative through the year 2100. For more details on the historic and projected driver data and model set up, please see Zwart et al. (year and DOI to be entered once MS is published).
Citation Information
Publication Year | 2022 |
---|---|
Title | Lake Biogeochemical Model Output for One Retrospective and 12 Future Climate Runs in Northern Wisconsin & Michigan, USA |
DOI | 10.5066/P9S7EMTB |
Authors | Jacob A Zwart, Jordan S Read, Michael N Fienen, Alan F. Hamlet, Stuart E. Jones, Diogo Bolster |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Water Resources Mission Area - Headquarters |
Rights | This work is marked with CC0 1.0 Universal |
Related
Cross-scale interactions dictate regional lake carbon flux and productivity response to future climate
Lakes support globally important food webs through algal productivity and contribute significantly to the global carbon cycle. However, predictions of how broad-scale lake carbon flux and productivity may respond to future climate are extremely limited. Here, we used an integrated modeling framework to project changes in lake-specific and regional primary productivity and carbon fluxes...
Authors
Jacob Aaron Zwart, Zachary J Hanson, Jordan Read, Michael N. Fienen, Alan F. Hamlet, Diogo Bolster, Stuart E. Jones
Jacob Zwart, PhD
Senior Data Scientist
Senior Data Scientist
Email
Jordan S Read, PhD (Former Employee)
Chief, Data Science Branch
Chief, Data Science Branch
Jacob Zwart, PhD
Senior Data Scientist
Senior Data Scientist
Email
Jordan S Read, PhD (Former Employee)
Chief, Data Science Branch
Chief, Data Science Branch
Related
Cross-scale interactions dictate regional lake carbon flux and productivity response to future climate
Lakes support globally important food webs through algal productivity and contribute significantly to the global carbon cycle. However, predictions of how broad-scale lake carbon flux and productivity may respond to future climate are extremely limited. Here, we used an integrated modeling framework to project changes in lake-specific and regional primary productivity and carbon fluxes...
Authors
Jacob Aaron Zwart, Zachary J Hanson, Jordan Read, Michael N. Fienen, Alan F. Hamlet, Diogo Bolster, Stuart E. Jones
Jacob Zwart, PhD
Senior Data Scientist
Senior Data Scientist
Email
Jordan S Read, PhD (Former Employee)
Chief, Data Science Branch
Chief, Data Science Branch
Jacob Zwart, PhD
Senior Data Scientist
Senior Data Scientist
Email
Jordan S Read, PhD (Former Employee)
Chief, Data Science Branch
Chief, Data Science Branch