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Data for Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach
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Type: | Resource | |
Storage: | The size of this resource is 1.6 MB | |
Created: | Nov 05, 2021 at 6:45 p.m. | |
Last updated: | Jun 10, 2022 at 2:48 p.m. (Metadata update) | |
Published date: | Jun 06, 2022 at 4:28 p.m. | |
DOI: | 10.4211/hs.2a674715887a4604ad951d87bdb3c847 | |
Citation: | See how to cite this resource |
Sharing Status: | Published |
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Views: | 1330 |
Downloads: | 128 |
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Abstract
This resource contains the data and scripts used for:
Goeking, S. A. and D. G. Tarboton, (2022). Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach. Water Resources Research, 58, e2021WR031575. https://doi.org/10.1029/2021WR031575.
Abstract from the paper:
Forest cover and streamflow are generally expected to vary inversely because reduced forest cover typically leads to less transpiration and interception. However, recent studies in the western US have found no change or even decreased streamflow following forest disturbance due to drought and insect epidemics. We investigated streamflow response to forest cover change using hydrologic, climatic, and forest data for 159 watersheds in the western US from the CAMELS dataset for the period 2000-2019. Forest change and disturbance were quantified in terms of net tree growth (total growth volume minus mortality volume) and mean annual mortality rates, respectively, from the US Forest Service’s Forest Inventory and Analysis database. Annual streamflow was analyzed using multiple methods: Mann-Kendall trend analysis, time trend analysis to quantify change not attributable to annual precipitation and temperature, and multiple regression to quantify contributions of climate, mortality, and aridity. Many watersheds exhibited decreased annual streamflow even as forest cover decreased. Time trend analysis identified decreased streamflow not attributable to precipitation and temperature changes in many disturbed watersheds, yet streamflow change was not consistently related to disturbance, suggesting drivers other than disturbance, precipitation, and temperature. Multiple regression analysis indicated that although change in streamflow is significantly related to tree mortality, the direction of this effect depends on aridity. Specifically, forest disturbances in wet, energy-limited watersheds (i.e., where annual potential evapotranspiration is less than annual precipitation) tended to increase streamflow, while post-disturbance streamflow more frequently decreased in dry water-limited watersheds (where the potential evapotranspiration to precipitation ratio exceeds 2.35).
The following scripts (R language and environment for statistical computing) produce the results, figures, and tables in this paper (in the order in which they appear in the paper; requires either running data compilation/aggregation scripts first OR using provided data files watersheds.csv and wb_filtered.csv):
1. Map_watersheds.r
2. Analysis_M-K_trend_test.r
3. analysis_M-K_quadrant_figure.r
4. analysis_timetrend_linear.r
5. analysis_regressn_w-veg.r
The following scripts (R) compile the data, aggregated from other sources prior to the analyses in the scripts listed above:
1. compilation_CAMELS.r
2. compilation_Daymet.r
3. compilation_USGS.r
4. compilation_FIA.r
5. compilation_CAMELS_Daymet_USGS.r (must run scripts #1-3 first)
6. watershed_compilation.r (must run scripts #1-5 first)
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.md
List of scripts and datasets in this HydroShare resource
Datasets:
Scripts:
Scripts are listed below in three sequential categories:
1. acquisition and/or compilation of individual data sources
2. aggregation of multiple data sources into single dataset for analysis
3. analyses used to produce results, tables, and figures
All scripts have a .r extension and need to be run using the R language and environment for statistical computing available from: http://r-project.org.
Reproducibility notes:
To reproduce only analysis of compiled datasets, run scripts only the ANALYSIS block below, which require two csv files included in this HydroShare resource (wb_filtered.csv and watersheds.csv).
To reproduce all work, beginning with data acquisition, scripts are intended to be run in the order in which they appear in this Readme file. Some manual downloads are required (as described in each script).
Scripts
ACQUISITION AND/OR COMPILATION OF INDIVIDUAL DATA SOURCES
compilation_CAMELS.r
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inputs:
- CAMELS watershed attributes (long-term or time invariant) from several text files included in the CAMELS data download:
- camels_hydro.txt
- camels_clim.txt
- camels_geol.txt
- camels_name.txt
- camels_soil.txt
- camels_vege.txt
- CAMELS daily streamflow data, from the downloaded CAMELS dataset, in the filepath /basin_timeseries_v1p2_metForcing_obsFlow/basin_dataset_public_v1p2/usgs_streamflow/XX/*_streamflow_qc.txt, where XX=USGS region (2-digit number) and * is an 8-digit gauge ID
- CAMELS forcing data (Daymet), from the downloaded CAMELS dataset, in the filepath basin_timeseries_v1p2_metForcing_obsFlow/basin_dataset_public_v1p2/basin_mean_forcing/daymet/XX/*_lump_cida_forcing_leap.txt, where XX=USGS region (2-digit number) and * is an 8-digit gauge ID
- CAMELS watershed attributes (long-term or time invariant) from several text files included in the CAMELS data download:
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outputs:
- camels_chars.csv - watershed attributes (long-term or time invariant) from the CAMELS dataset
- wb_1980_2014.csv - annual and seasonal water budget components and forcing data for the CAMELS period of record (1980-2014); note that seasonal values were not analyzed for this paper
compilation_Daymet.r
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inputs:
- HCDN_nhru_final_671.shp - shapefile included with CAMELS data download in the folder /basin_set_full_res
- monthly Daymet gridded prcp, tmin, tmax, srad, swe, and dayl - respectively, precipitation, min temperature, max temperature, solar radiation, snow water equivalent (which was not used to do missing data in the CAMELS dataset), and day length (used to estimate PET); all downloaded via R package daymetr.
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outputs:
- camels_prcp_2014_2019.csv - precipitation for 2014-2019 from Daymet prcp
- camels_tave_2014_2019.csv - mean temperature for 2014-2019 based on Daymet tmin and tmax
- camels_pet_2014_2019.csv - estimated PET for 2014-2019 using the Hamon method, based on Daymet inputs
compilation_USGS.r
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inputs:
- daily streamflow for each watershed's gauge, obtained and summarized using R package DataRetrieval
- wb_1980_2014.csv - annual water budget components and forcing data for the CAMELS period of record (1980-2014); created by compilation_CAMELS.r
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outputs:
- flow_2015_2019.csv - annual streamflow for each watershed for 2014-2019, summarized from USGS daily data
compilation_FIA.r
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inputs:
- data_FIA.csv - compiled and formatted data based on outputs of tree live volume, net growth, growth per acre, and mortality, all based on queries of the online EVALIDator tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp) from 10 April 2021
- data_FIA.csv - compiled and formatted data based on outputs of tree live volume, net growth, growth per acre, and mortality, all based on queries of the online EVALIDator tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp) from 10 April 2021
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outputs:
- forest_data.csv - a csv file including tree growth and mortality output from FIA's EVALIDator online tool
- forest_data.csv - a csv file including tree growth and mortality output from FIA's EVALIDator online tool
COMPILATION OF MULTIPLE DATA SOURCES
compilation_CAMELS_Daymet_USGS.r
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inputs:
- wb_1980_2014.csv - annual and seasonal water budget components and forcing data for the CAMELS period of record (1980-2014); note that seasonal values were not analyzed for this paper
- camels_prcp_2014_2019.csv - precipitation for 2014-2019 from Daymet prcp
- camels_tave_2014_2019.csv - mean temperature for 2014-2019 based on Daymet tmin and tmax
- camels_pet_2014_2019.csv - estimated PET for 2014-2019 using the Hamon method, based on Daymet inputs
- flow_2015_2019.csv - annual streamflow for each watershed for 2014-2019, summarized from USGS daily data
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outputs:
- wb_1980_2019.csv - annual water budget components (Q, P) and drivers (PET, T) for 1980-2019
watershed_compilation.r
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inputs:
- wb_1980_2019.csv - annual water budget components and climatic drivers
- camels_chars.csv - watershed attributes (long-term or time invariant) from the CAMELS dataset
- camels_ann.csv - an intermediate dataset used to summarize long-term mean incoming solar radiation
- forest_data.csv - a csv file including tree growth and mortality output from FIA's EVALIDator online tool
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outputs:
- watersheds.csv - one of two main data files used for analysis; this one contains watershed characteristics compiled from CAMELS, FIA, and the extended record of Daymet + USGS gage data
- wb_filtered - two of two main data files used for analysis; this one is a time series of water budget components and forcing data, and it has been filtered to exclude watersheds where runoff ratio > 1 in any year.
ANALYSIS - scripts that produce results, figures, and tables in the associated the manuscript (can be run in any order; each is independent)
Map_watersheds.r
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inputs:
- wb_filtered.csv - annual water budget components and climatic drivers, filtered to exclude watersheds with runoff ratio > 1
- watersheds.csv - compiled watershed characteristics from FIA data and CAMELS dataset + extended record
- HCDN_nhru_final_671.shp - shapefile included with CAMELS data download in the folder /basin_set_full_res/
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outputs:
- FIGURE: map of CAMELS watersheds used in this study
- TABLE: summarizing a few topographic and climatic characteristics of all watersheds
Analysis_M-K_trend_test.r
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inputs:
- wb_filtered.csv - annual water budget components and climatic drivers, filtered to exclude watersheds with runoff ratio > 1
- watersheds.csv - compiled watershed characteristics from FIA data and CAMELS dataset + extended record
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outputs:
- FIGURE:
- boxplot of Kendall's tau for Q, Q/P, P, PET, and T for 2000-2019
- maps of watersheds, symbolized as significant increasing trend, significant decreasing trend, or no significant trend
- FIGURE:
analysis_M-K_quadrant_figure.r
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inputs:
- wb_filtered.csv - annual water budget components and climatic drivers, filtered to exclude watersheds with runoff ratio > 1
- watersheds.csv - compiled watershed characteristics from FIA data and CAMELS dataset + extended record
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outputs:
- FIGURE:
- scatter plot of trend in Q/P (Kendall's tau) vs. net tree growth
- Budyko plot of evaporative index (ET/P) vs. aridity index (PET/P)
- boxplot of aridity index (PET/P) for each quadrant of the scatter plot
- boxplot of incoming solar radiation for each quadrant of the scatter plot
- map showing locations of watersheds in each quadrant of the scatter plot
- FIGURE:
analysis_timetrend_linear.r
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inputs:
- wb_filtered.csv - annual water budget components and climatic drivers, filtered to exclude watersheds with runoff ratio > 1
- watersheds.csv - compiled watershed characteristics from FIA data and CAMELS dataset + extended record
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outputs:
- FIGURE: map showing watersheds where mean annual Q for 2010-2019 deviated significantly from mean annual Q for 2000-2010, as well as magnitude of deviation and disturbance status (disturbed vs. undisturbed)
- TABLE: tabular summary of the number of watersheds where mean annual Q for 2010-2019 deviated from mean annual Q for 2000-2010, by disturbance status (disturbed vs. undisturbed) and signifance (yes/no)
analysis_regressn_w-veg.r
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inputs:
- wb_filtered.csv - annual water budget components and climatic drivers, filtered to exclude watersheds with runoff ratio > 1
- watersheds.csv - compiled watershed characteristics from FIA data and CAMELS dataset + extended record
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outputs:
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FIGURES:
- forest plot of standardized coefficients (how many SDs the response will change per SD change in the predictor)
- partial regression plots of the five terms in the regression model
- boxplot of delta-Q/Q1 by aridity (PET/P) and mortality level, based on observations
- fixed effects plot of mortality and aridity on delta-Q
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TABLES:
- multiple regression coefficients, statistics, and p-values
- predicted percent change in Q due to multiple values of mortality and aridity, with and without 1 degree C of warming
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Related Resources
This resource is referenced by | Goeking, S. A. and D. G. Tarboton, 2022. Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach. Water Resources Research, 58, https://doi.org/10.1029/2021WR031575. |
The content of this resource is derived from | A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D |
The content of this resource is derived from | N. Addor, A. Newman, M. Mizukami, and M. P. Clark, 2017. Catchment attributes for large-sample studies. Boulder, CO: UCAR/NCAR. https://doi.org/10.5065/D6G73C3Q |
The content of this resource is derived from | USDA, 2020. Forest Service, Forest Inventory EVALIDator web-application Version 1.8.0.01, St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern Research Station. |
The content of this resource is derived from | Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P., 2017. The CAMELS data set: catchment attributes and meteorology for large-sample studies, HESS, 21, 5293–5313, http://doi.org/10.5194/hess-21-5293-2017 |
The content of this resource is derived from | Newman, A.J., et al., 2015. Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. HESS, 19, 209-223, http://doi.org/10.5194/hess-19-209-2015 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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