Title: Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 (v1.1) Open Access Deposited
|Citations to related material|
|Related items in Deep Blue Documents|
(2020). Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 (v1.1) [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/tx97-nn12
Files (Count: 7; Size: 4.41 MB)
|Thumbnail||Title||Original Upload||Last Modified||File Size||Access||Actions|
|GreatLakesWaterBalanceData_19502...e.txt||2020-06-05||2020-06-05||7.46 KB||Open Access||
|L2SWBM_input.zip||2020-06-05||2020-06-05||574 KB||Open Access||
|L2SWBM_Model.zip||2020-06-05||2020-06-05||43.8 KB||Open Access||
|output_plot_posterior.zip||2020-06-05||2020-06-05||834 KB||Open Access||
|output_plot_preview.zip||2020-06-05||2020-06-05||691 KB||Open Access||
|output_plot_prior.zip||2020-06-05||2020-06-05||2.04 MB||Open Access||
|output_ts_posterior.zip||2020-06-05||2020-06-05||275 KB||Open Access||
Date: 06 June, 2020
DATASET TITLE: Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 (version 1.1)
DATASET CREATORS: H.X. Do, J.P. Smith, L. M. Fry, & A.D. Gronewold
DATASET CONTACT: Hong X. Do (firstname.lastname@example.org)
TO CITE DATA
Do, H.X., Smith, J.P., Fry, L.M., & Gronewold, A.D. (2020). Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 (version 1.1) [Data set]. University of Michigan - Deep Blue.
This version replaces the following deprecated dataset:
Do, H.X., Smith, J.P., Fry, L.M., Gronewold, A.D. (2020). Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/0rsp-v195
- We present a new estimate of the monthly water balance from 1950 to 2019 for the Laurentian Great Lakes, the largest surface freshwater system on Earth.
- Lake storage changes and water balance components for each lake were estimated using a Bayesian framework that assimilated multiple independent data sources.
- The source code as well as inputs of the statistical model are also made available.
The water balance of the Laurentian Great Lakes, the largest surface freshwater system on Earth, is of great importance to the sustainable development of both the United States and Canada. In recent years, the water levels across all of the Laurentian Great Lakes have exhibited unprecedented dynamics, indicating potential implications of climate changes. To understand the factors driving changes in water levels, it is compulsory to have a reliable estimate of the components of the water balance (i.e. over-lake precipitation, over-lake evaporation, lateral tributary runoff, connecting channel flows, and diversions) that collectively contribute to changes in the water budget of each lake. However, existing data sources that represent (each of) these variables are often developed independently with limited consideration to keeping one water budget term consistent with the others. As a result, the water balance of the Great Lakes cannot be solved over multiple time periods using existing data alone. Here we present a new estimate of the monthly water balance from 1950 to 2019 for the Laurentian Great Lakes. This dataset was developed using a Bayesian framework, encoded in the Large Lakes Statistical Water Balance Model (L2SWBM). The L2SWBM assimilated multiple independent data sources to infer feasible values of each water balance component. A conventional water balance model was also used to restrain new estimates, ensuring that the water balance can be reconciled over multiple time periods. The new estimates are useful to investigate changes in water availability, or benchmark new hydrological models and data products developed for the Laurentian Great Lakes Region. The source code as well as inputs of the L2SWBM model are also made available, and can be adapted to include new data sources for the Great Lakes, or to address the water balance problems on other large lake systems.
The data are model output from the Large Lakes Statistical Water Balance Model (L2SWBM v.3).
- The L2SWBM was developed in the R programming environment (ver. 3.6.1). To execute the R-scripts available in this dataset, prospective users must install the following dependant libraries: `rjags` (ver. 4.9), `doParallel` (ver. 1.0.15), and `rlecuyer` (ver. 0.3.4). It is important to note that, `rjags` requires the JAGS package (Just Another Gibbs Sampler; ver. 4.3.0) to be installed. This software is open-source and is available at `https://sourceforge.net/projects/mcmc-jags/files/JAGS`.
- The `rootDir` variable in `0_main.r` is set to an absolute path, which causes an error if that path does not exist. Prospective users should change the absolute path to the working directory prior to executing the simulations. In addition, the input zip-archive (item 2 in the FILES CONTAINED HERE section) should also be uncompressed to the same working directory.
FILES CONTAINED HERE:
This data set contains new estimates of the Great Lakes water balance together with the L2SWBM source code and inputs synthesized for this project (monthly data available up to December 2019 depending on variables). All of these files are organized into six zip archives. Each zip file has a size of less than 2 MB.
The files contained in each of the six zip archives are described as follows:
The model is organized in ten individual R-script files, which are accompanied by (i) the R-script of the BUGS (Bayesian inference Using Gibbs Sampling) model of the Bayesian inference framework, and (ii) a model configuration file (csv format; accompanied with a text file explaining the variables of this configuration file). The configuration file can be adjusted to include more data sources or focus on a different analysis period.
Inputs of the L2SWBM were synthesized from a range of independent data sources. These independent data records were used to derive the prior distribution and likelihood functions for each of the variable. Data for each variable of a specific lake is stored in a separate csv file. All input files were contained in the input folder and accompanied with a readme file (pdf format).
A folder of PDF files containing plots of the prior probability distributions of the water balance components. Naming convention is PriorCompare_.pdf (e.g., "evapPriorCompare_19501984.pdf").
A folder of PDF files containing plots of inputs over the analysis period. Each pdf file represents all data for a specific lake over one decade (from decade no. 0 to decade no. n-1, with n = no. of years/10). Naming convention is TS_Preview__.pdf (e.g., "superiorTS_Preview_d0_GLWBData.pdf")
A folder of PDF files containing plots of inputs over the analysis period. Each pdf file represents all data for a specific lake over one decade (from decade no. 0 to decade no. n-1, with n = no. of years/10). Naming convention is TS_ALL__.pdf (e.g., "miHuronTS_ALL_d5_GLWBData.pdf")
A folder of csv files containing monthly inference (50, 2.5 and 97.5 percentiles of the MCMC iterations) of each water balance component across each lake over the analysis period.
Naming convention is _.csv (e.g., "erieRunoff_GLWBData.csv")
Possible values and the meaning of LAKE and VAR variables:
-- superior: Lake Superior
-- miHuron: Lake Michigan-Huron
-- clair: Lake St. Clair
-- erie: Lake Erie
-- ontario: Lake Ontario
-- VAR (Water balance components):
-- Precip: Precipitation
-- Evap: Evaporation
-- Runoff: Runoff
-- Outflow: Connecting Channel Outflow
-- Diversion: Diversion
-- DStorePP: Predictive Change in Storage
-- NBS (for clair only): Net Basin Supply inferred directly (inside each Bayesian inferrence) by the Bayesian framework
-- NBSC (for superior, miHuron, erie and ontario): Net Basin Supply calculated (outside the Bayesian inferrence framework) from the inferred Precip, Evap and Runoff
Do, H.X., Smith, J., Fry, L.M., & Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earths largest lake system (under revision)
Use and Access:
This data set is made available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).