ABSTRACT:

The objective of this project is to develop a canopy-cover raster from point data. The study watershed is the South Fork Flathead River, Montana. Tree canopy cover, as measured at permanent forest monitoring plots, will be modeled as a function of NLCD cover class, PRISM mean annual precipitation, elevation, slope, and aspect. Alternative models will be compared using k-fold cross-validation. The final model will be calibrated with all data points and then applied to the entire study watershed.

ABSTRACT:

This dataset includes the NED 30 m tif for the South Fork Flathead River above Twin Creek.

ABSTRACT:

Data for the South Fork Flathead River above Hungry Horse Reservoir.

ABSTRACT:

This resource contains shapefiles and text/csv files for the Teton River basin, Montana, above USGS gage with STAID 06102500. The point shapefile represents the gage location, as identified in the USGS StreamStats online application, and the polygon shapefile is the basin boundary as delineated by StreamStats given the gage location as the outflow. The Teton_River_basin.csv contains descriptive information about the watershed, such as percent forest, min/max/mean elevation, precipitation, etc.

ABSTRACT:

This resource contains shapefiles and text/csv files for the North Fork Sun River basin, Montana, above USGS gage with STAID 06078500. The point shapefile represents the gage location, as identified in the USGS StreamStats online application, and the polygon shapefile is the basin boundary as delineated by StreamStats given the gage location as the outflow. The N-Fk_Sun_River_basin.csv contains descriptive information about the watershed, such as percent forest, min/max/mean elevation, precipitation, etc.

ABSTRACT:

This resource contains shapefiles and text/csv files for the Mission Creek basin, Montana, above USGS gage with STAID 12377150. The point shapefile represents the gage location, as identified in the USGS StreamStats online application, and the polygon shapefile is the basin boundary as delineated by StreamStats given the gage location as the outflow. The Mission_Creek_basin.csv contains descriptive information about the watershed, such as percent forest, min/max/mean elevation, precipitation, etc.

ABSTRACT:

The purpose of this resource is to present all reports and supporting documentation for a term project for CEE 6490. The project objective was to identify a target in-stream flow rate for restoring and maintaining riparian forests in the lower Bear River, Utah, and assess the ability of meeting in-stream flow requirements on other water users.

ABSTRACT:

This resource contains a notebook and dataset for testing RHESSys workflows and RHESSys in the CUAHSI Jupyter Hub environment, given custom GIS data inputs (downloaded from HydroTerre) and a USGS gage ID.

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)

ABSTRACT:

This resource contains the data and scripts used for:
Goeking, S. A. and D. G. Tarboton, (2022). Spatially distributed overstory and understory leaf area index estimated from forest inventory data. Water. https://doi.org/10.3390/w1415241.

Abstract from the paper:
Abstract: Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and re-mote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of under-story LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how for-est change influences hydrologic processes.

This resource contains one CSV file, two shapefiles (each within a zip file), two R scripts, and multiple raster datasets. The two shapefiles represent the boundaries of the Middle Fork Flathead river and South Fork Flathead River watersheds. The raster datasets represent annual leaf area index (LAI) at 30 m resolution for the entire modeling domain used in this study. LAI was estimated using method LAI4, which produced separate overstory and understory LAI datasets. Filenames contain years, e.g., "LAI4_2019" is overstory LAI for 2019; "LAI4under_2019" is understory LAI for 2019.

The CSV files in this Resource contain annual time series of LAI and ET ratio (annual evapotranspiration divided by annual precipitation) for the South Fork Flathead River and Middle Fork Flathead River watersheds, 1984-2019. LAI methods represented in this time series are LAI1 and LAI4 from the paper. LAI1 consists of only overstory LAI, and LAI4 consists of overstory (LAI4), understory (LAI4_under), and total (LAI4_total) LAI. For each LAI estimation method, summary statistics of the entire watershed are included (min, first quartile, median, third quartile, and max).

The two R scripts (R language and environment for statistical computing) summarize Forest Inventory & Analysis (FIA) data from the FIA database (FIADB) to estimate LAI at FIA plots.
1) FIADB_queries_public.r: Script for compiling FIA plot measurements prior to estimating LAI
2) LAI_estimation_public: Script for estimating LAI at FIA plots using the four methods described in this paper

Before running the R scripts, users must obtain several FIADB tables (PLOT, COND, TREE, and P2VEG_SUBP_STRUCTURE; all four tables must be renamed with lower-case names, e.g., "plot"). These tables can be obtained using one of two methods:
1) By downloading CSV files for the appropriate U.S. state(s) from the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html). If this method is used, the CSV files must be imported (read) into R before proceeding.
2) By using r package 'rFIA' to download the tables from FIADB for the U.S. state(s) of interest.

Note that publicly available plot coordinates are accurate within 1 km and are not true plot locations, which are legally confidential to protect the integrity of the sample locations and the privacy of landowners. Access to true plot location data requires review by FIA's Spatial Data Services unit, who can be contacted at SM.FS.RMRSFIA_Help@usda.gov.