Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
Supporting data for Savoy et al. (2019): Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes
Authors: |
|
|
---|---|---|
Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
Type: | Resource | |
Storage: | The size of this resource is 1.0 MB | |
Created: | May 28, 2019 at 7:04 p.m. | |
Last updated: | Aug 19, 2019 at 7:47 p.m. | |
DOI: | 10.4211/hs.eba152073b4046178d1a2ffe9a897ebe | |
Citation: | See how to cite this resource |
Sharing Status: | Published |
---|---|
Views: | 2392 |
Downloads: | 72 |
+1 Votes: | Be the first one to this. |
Comments: | No comments (yet) |
Abstract
This document describes several of the derived datasets used in Savoy et al. (2019) as well as Koenig et al. (2019). The analysis presented in Savoy et al. (2019) describes identifying similar characteristic regimes of gross primary productivity (GPP) across 47 U.S. streams and rivers through the use of clustering analysis. This resource contains basic site information about each of the sites used in this analysis as well as the resulting cluster membership for each site. Additionally, representative time series of GPP are provided for each of the sites. Please refer to the readme.md file for descriptions of the contents of each file and a brief overview of how the data contained within them was created. A full description of the methods and results can be found in Savoy et al. (2019).
Subject Keywords
Coverage
Temporal
Start Date: | |
---|---|
End Date: |
Content
readme.md
Supporting data for Savoy et al. (2019): Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes
This document describes several of the derived datasets used in Savoy et al. (2019) as well as Koenig et al. (2019). The analysis presented in Savoy et al. (2019) describes identifying similar characteristic regimes of gross primary productivity (GPP) across 47 U.S. streams and rivers through the use of clustering analysis. This resource contains the following files:
- site basic.csv: A table containing basic site information and clustering assignments for each site
- avg_gpp.csv: Representative time series of GPP for each of the 47 rivers
- avg_gpp_filled.csv: Representative time series of GPP that has been gap-filled for each of the 47 rivers
- normalized.csv: Representative z-normalized time series of GPP for each of the 47 rivers
Below, each of these datasets is described individually including detailed information about the data contained within each file and the derivation of this data.
1. site_basic.csv
This file contains the following columns:
- Site_ID: The National Water Information System (NWIS) unique identifier for each site. Sites represent USGS gauged sites, and each Site_ID corresponds to a location in the dataset available from Appling et al. (2018b).
- Site_name: The full site name for each location
- Lat: Latitude (decimal degrees, NAD83)
- Lon: Longitude (decimal degrees, NAD83)
- WS_area: Watershed area (Km2)
- Width: Channel width (m) derived from regional hydraulic geometry coefficients (Gomez-Velez et al. 2015)
- two_clus: The cluster each site was assigned to for a two cluster solution (summer peak, spring peak)
- four_clus: The cluster each site was assigned to for a four cluster solution (summer peak, spring peak, summer decline, aseasonal)
The columns two_clus and four_clusare the clustering assignments for each site based on Savoy et al. (2019). Here, a brief overview of this clustering analysis is provided. Clustering identifies groups based on a measure of dissimilarity, and then this dissimilarity is used to identify a classification. Dynamic time warping (DTW) (Sakoe and Chiba 1978; Berndt and Clifford 1994) was used to define the similarity between time series because of its widespread application in time series analysis. The DTW dissimilarity matrix was then used to perform a hierarchical agglomerative clustering. We defined between two and ten clusters because no a priori optimal number of clusters exists. A suite of indices that effectively describes a combination of within cluster cohesion and between cluster separation was used to assess the clusters and determine a final set of clusters. The results presented within Savoy et al. (2019) focus primarily on a two clustering solution as the most conservative and robust result across several different clustering methodologies. These clusters were named Summer Peak Rivers and Spring Peak Rivers based on the temporal patterns of the resulting representative GPP regimes. However, several results indicated the possibility of a less conservative solution of four clusters and these are also presented in the paper. These clusters were named Summer Peak Rivers, Spring Peak Rivers, Summer Decline Rivers, and Aseasonal based on the temporal patterns of the resulting representative GPP regimes. Because of this, both the two cluster solution (two_clus) and four cluster solution (four_clus) are provided for each site. For a full description of the methods used to derive these clusters and the interpretation of these results please refer to Savoy et al. (2019).
2. avg_gpp.csv
This file contains representative time series of GPP for each of the 47 sites used. This data is derived from a subset of daily estimates of stream metabolism described in Appling et al. (2018a) and the original data are freely available to download (Appling et al. 2018b) and full descriptions of the original datasets can be found within these sources. The original set of 356 sites was filtered based on a combination of data quality and coverage to select a subset of 47 rivers that all had data for the time period of 2013-2016. This file consists of the a mean time series of GPP for each site that was calculated by taking the mean GPP for each day of the year across all four years of data. The first column (DOY) is the day of year and each subsequent column corresponds to a specific Site_ID.
3. avg_gpp_filled.csv
This file contains representative time series of GPP for each of the 47 sites used; however, the time series of GPP has been gap-filled. To create these series the original daily GPP estimates were gap-filled using a generalized additive model with both seasonal and trend components. These gap-filled series were then used to calculate the mean time series of GPP for each site that was calculated by taking the mean GPP for each day of the year across all four years of data. The first column (DOY) is the day of year and each subsequent column corresponds to a specific Site_ID.
4. normalized.csv
To calculate similarity with DTW it is necessary to z-normalize each time series. The representative time series of gap-filled GPP as described above were thus z-normalized. The first column (DOY) is the day of year and each subsequent column corresponds to a specific Site_ID.
5. Metadata (LO_letters).pdf
An accompanying set of metadata using the format from Limnology & Oceanography letters. Note, this metadata largely reiterates the information covered in this readme file but is provided as a separate resource to conform with journal data policy guidelines.
References
Appling, A. P., and others. 2018a. The metabolic regimes of 356 rivers in the United States. Sci. Data 5: 180292. https://doi.org/10.1038/sdata.2018.292
Appling, A.P., Read, J.S., Winslow, L.A., Arroita, M., Bernhardt, E.S., Griffiths, N.A., Hall, R.O., Jr., Harvey, J.W., Heffernan, J.B., Stanley, E.H., Stets, E.G., and Yackulic, C.B. 2018b, Metabolism estimates for 356 U.S. rivers (2007-2017): U.S. Geological Survey data release. https://doi.org/10.5066/F70864KX
Berndt, D. J., and J. Clifford. 1994. Using dynamic time warping to find patterns in time series. AAAI technical report WS-94-03. Association for the Advancement of Artificial Intelligence.
Gomez-Velez, J. D., J.W. Harvey, M. B. Cardenas, and B. Kiel. 2015. Denitrification in the Mississippi River network controlled by flow through river bedforms. Nature Geoscience 8:941. 10.1038/ngeo2567
Koenig, L. E., Helton, A.M., Savoy, P., Bertuzzo, E., Heffernan, J.B., Hall, R.O., Jr, and Bernhardt, E. S. 2019. Emergent productivity regimes of river networks. Limnology and Oceanography Letters 0. https://doi.org/10.1002/lol2.10115
Sakoe, H., and S. Chiba. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26:43–49. https://doi.org/10.1109/TASSP.1978.1163055
Savoy, P. , Appling, A. P., Heffernan, J. B., Stets, E. G., Read, J. S., Harvey, J. W. and Bernhardt, E. S. 2019. Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes. Limnol Oceanogr. https://doi.org/10.1002/lno.11154
Related Resources
This resource is referenced by | Savoy, P. , Appling, A. P., Heffernan, J. B., Stets, E. G., Read, J. S., Harvey, J. W. and Bernhardt, E. S. 2019. Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes. Limnol Oceanogr. https://doi.org/10.1002/lno.11154 |
The content of this resource references | Appling, A. P., and others. 2018. The metabolic regimes of 356 rivers in the United States. Sci. Data 5: 180292. https://doi.org/10.1038/sdata.2018.292 |
The content of this resource references | Appling, A. P., R. O. Hall, M. Arroita, and C. B. Yackulic. 2018. streamMetabolizer: Models for estimating aquatic photosynthesis and respiration. R package version 0.10.9. https://github.com/USGS-R/streamMetabolizer |
This resource is referenced by | Koenig, L. E., Helton, A.M., Savoy, P., Bertuzzo, E., Heffernan, J.B., Hall, R.O., Jr, and Bernhardt, E. S. 2019. Emergent productivity regimes of river networks. Limnology and Oceanography Letters 0. https://doi.org/10.1002/lol2.10115 |
The content of this resource references | Appling, A. P., R. O. Hall Jr., C. B. Yackulic, and M. Arroita. 2018. Overcoming equifinality: Leveraging long time series for stream metabolism estimation. J. Geophys. Res. Biogeosci. 123: 624–645. https://doi.org/10.1002/2017JG004140 |
The content of this resource is derived from | Appling, A. P., and others. 2018. Metabolism estimates for 356 U.S. rivers (2007-2017). U.S. Geological Survey. https://doi.org/10.5066/F70864KX. |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
---|---|---|
National Science Foundation | Defining Stream Biomes to Better Understand and Forecast Stream Ecosystem Change | EF 1442439 |
USGS Powell Center | Continental-scale overview of stream primary productivity, its links to water quality, and consequences for aquatic carbon biogeochemistry |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/![CC-BY](https://storage.googleapis.com/hydroshare-prod-static-media/static/img/cc-badges/CC-BY.04d377d96855.png)
Comments
There are currently no comments
New Comment