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Gronewold, Andrew D.
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- Creator:
- Do, Hong X., Smith, Joeseph P., Fry, Lauren M., and Gronewold, Andrew D.
- Description:
- This data set contains a new monthly estimate of the water balance of the Laurentian Great Lakes, the largest freshwater system on Earth, from 1950 to 2019. The source codes and inputs to derive the new estimates are also included in this dataset. and ***ADDED 2024-02-27: The component net basins supply data "*NBSC_GLWBData.csv" in "output_ts_posterior.zip" need to be revised for further applications***
- Keyword:
- Laurentian Great Lakes, Bayesian inference, water levels, data assimilation, and water balance
- Citation to related publication:
- Do, H. X., Smith, J. P., Fry, L. M., & Gronewold, A. D. (2020). Seventy-year long record of monthly water balance estimates for Earth’s largest lake system. Scientific Data, 7(1), 276. https://doi.org/10.1038/s41597-020-00613-z, Gronewold, A. D., Smith, J. P., Read, L., & Crooks, J. L. (2020). Reconciling the water balance of large lake systems. Advances in Water Resources, 103505. https://doi.org/10.1016/j.advwatres.2020.103505 , and 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
- Discipline:
- Science
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- Creator:
- Hille, Madeline M., Clark, Marin K., Gronewold, Andrew D., West, A. Joshua, Zekkos, Dimitrios , and Chamlagain, Deepak
- Description:
- This dataset supports the findings of Hille et al. (2021, in review) in Geophysical Research Letters. In this article, we present a multivariate analysis of extreme storm events that occur during the Indian summer monsoon over the Himalayan Range in central Nepal. We resolve storm events at sub daily durations by merging NASA’s Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) 30-minute, gridded 0.1x0.1-degree precipitation product with local rain gauges operated by the Nepal Department of Hydrology and Meteorology (DHM) and the International Centre for Integrated Mountain Development (ICIMOD). We quantify spatial variability in extreme rainfall by isolating storms over a specific intensity threshold and pairing a principal components analysis with a K-means clustering approach to group storms of similar characteristics. and We find that frequent and intense storms occur over the forefront of the central Himalayan range and coincide with a locus of monsoon-driven landslide density. This pattern agrees with observations of elevated annual precipitation volumes near the Himalayan physiographic transition from low to high relief (Bookhagen and Burbank, 2010), and is consistent with orographically-influenced rainfall over other mountain ranges (Marra et al., 2021). In addition to presenting novel methodology to quantifying storm variability, our results highlight the strong orographic effect on precipitation intensity and duration, as well as an association of shallow bedrock landsliding frequency with intense precipitation.
- Keyword:
- orographic rainfall, multivariate analysis, extreme rainfall events, and rainfall-triggered landslides
- Citation to related publication:
- Hille et al. (2021, in review). The orographic influence on storm variability, extreme rainfall characteristics and rainfall-triggered landsliding. Geophysical Research Letters. Forthcoming
- Discipline:
- Science
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/ , and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 (v1.0) [Deprecated]
- Creator:
- Do, Hong X., Smith, Joeseph P., Fry, Lauren M., and Gronewold, Andrew D.
- Description:
- This data set contains a new estimate of monthly water balance components from 1950 to 2019 for the Laurentian Great Lakes, the largest freshwater system on Earth. The source code and inputs to derive the new estimates are also included in this dataset.
- Keyword:
- Great Lakes water levels, statistical inference, water balance, and data assimilation
- Citation to related publication:
- Do, H.X., Smith, J., Fry, L.M., and Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earth’s largest lake system (pending for submission) and Version Note: This dataset is deprecated and has been replaced by version 1.1, found at https://deepblue.lib.umich.edu/data/concern/data_sets/sb3978457
- Discipline:
- Science
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research, University of Michigan
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine Learning, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)
User Collection
- Creator:
- Smith, Joeseph P., Fry, Lauren M., Do, Hong X., and Gronewold, Andrew D.
- Description:
- This collection contains estimates of the water balance of the Laurentian Great Lakes that were produced by the Large Lakes Statistical Water Balance Model (L2SWBM). Each data set has a different configuration and was used as the supplementary for a published peer-reviewed article (see "Citations to related material" section in the metadata of individual data sets). The key variables that were estimated by the L2SWBM are (1) over-lake precipitation, (2) over-lake evaporation, (3) lateral runoff, (4) connecting-channel outflows, (5) diversions, and (6) predictive changes in lake storage. and Contact: Andrew Gronewold Office: 4040 Dana Phone: (734) 764-6286 Email: drewgron@umich.edu
- Keyword:
- Great Lakes water levels, statistical inference, water balance, data assimilation, Great Lakes, Laurentian, Machine learning, Bayesian, and Network
- Citation to related publication:
- Smith, J. P., & Gronewold, A. D. (2017). Development and analysis of a Bayesian water balance model for large lake systems. arXiv preprint arXiv:1710.10161., Gronewold, A. D., Smith, J. P., Read, L., & Crooks, J. L. (2020). Reconciling the water balance of large lake systems. Advances in Water Resources, 103505., and Do, H.X., Smith, J., Fry, L.M., and Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earth’s largest lake system (under revision)
- Discipline:
- Science and Engineering
5Works