ABSTRACT:

We surveyed more than 2,800 individual trees over an approximately 10 hectare area May 2-5, 2016 at the Eel River Critical Zone Observatory Sagehorn site in Mendocino County, California, USA. Trees species, size (height for juvenile and diameter for mature individuals), canopy position (for mature individuals), and location (meter-scale resolution GPS) were recorded. The survey area is a small portion of a 5,000 acre private cattle ranch that experienced a large fire in 1950 and some logging of Douglas fir in the first half of the twentieth century. The hilly landscape is part of the Central Belt Melange of the Franciscan Formation complex, and surveyed areas were underlain by sheared clay-rich melange matrix (dominant vegetation: annual herbaceous groundcover and Quercus garryana (Oregon White Oak)), as well as poorly sorted, immature sandstone blocks (greywacke; dominant vegetation: Arctostaphylos spp (Manzanita - not surveyed), Pseudotsuga menziesii (Douglas Fir) and Arbutus menziesii (Pacific Madrone)). Trees with diameters at breast height (1.4 m; DBH) greater than 5 cm (n = 313) were tagged for future surveys.

ABSTRACT:

Bedrock weathering regulates nutrient mobilization, water storage, and soil production. Relative to the mobile soil layer, little is known about the relationship between topography and bedrock weathering. Here, we identify a common pattern of weathering and water storage across a sequence of three ridges and valleys in the sedimentary Great Valley Sequence in Northern California that share a tectonic and climate history. Deep drilling, downhole logging, and characterization of chemistry and porosity reveal two weathering fronts. At ridgetops, the elevation of each front relative to the channel increases with hillslope length. The shallower front is approximately 7 m deep at the ridge of all three hillslopes and marks the onset of pervasive fracturing and oxidation of pyrite and organic carbon. A deeper weathering front marks the onset extent of open fractures and discoloration. This front is 11 m deep under two ridges of similar ridge-valley spacing, but 17.5 m deep under a ridge with nearly twice the ridge-valley spacing. In all three hillslopes, closed fractures in otherwise unweathered bedrock are found under ridges to at least the elevation of the adjacent channels. Neutron probe surveys reveal that seasonally dynamic moisture is stored to approximately the same depth as the shallow weathering front. Under the channels that bound our study hillslopes, the two weathering fronts coincide and occur within centimeters of the ground surface. Our findings provide evidence for feedbacks between erosion and weathering in mountainous landscapes that result in systematic subsurface structuring and water routing.

ABSTRACT:

This is the raster dataset associated with "Accounting for snow in the estimation of root-zone water storage capacity from precipitation and evapotranspiration fluxes" (Dralle & Hahm). It includes estimates of root-zone plant-available water storage capacity (S_R) across the continental U.S. Please see the accompanying manuscript for more details on how the dataset was created.

Dataset details:
CRS: EPSG:4326 - WGS 84 - Geographic
File type: GeoTIFF

Band names:
Band 1: S_R
Band 2: S_R_snow_corr
Band 3: mean_snowcover_jan_apr

Band 1 is S_R, in mm, as estimated via the original procedure described by Wang-Erlandsson et al. (https://doi.org/10.5194/hess-20-1459-2016).
Band 2 is S_R, in mm, as estimated via the modified procedure (accounting for snow) described in the accompanying manuscript.
Band 3 is mean January-April percentage snow cover.

ABSTRACT:

This directory includes channel length survey data (outlet discharge and surveyed wetted channel extent for each survey). These data were used, in conjunction with discharge data, to find the scaling factor (α) and scaling exponent (β) for the power function that relates wetted channel extent and discharge (L = αQ^β) reported in the metadata table. Resources associated with channel length include survey data, data ‘thieved’ plots, and studies that reported channel length survey data.

ABSTRACT:

Analysis of the USGS’s blue line network from 7.5’ topographic maps and how these persistent and intermittent stream network length extents compared to the length-duration curves that appear in Lapides et al. (Figure 2). Resources associated with blue line analysis include calculated values for average channel length, shapefiles derived from USGS TopoView maps, a composite image of all watersheds where blue line analysis has been applied, as well as network validation images. Only watersheds located in the U.S. are included in this analysis.

Blueline networks were extracted from the TopoView maps with Safe Software's FME program.

ABSTRACT:

Wetted channel networks expand and contract throughout the year. Direct observation of this process can be made by multiple intensive surveys of a catchment throughout the year. Godsey et al. (2014) suggest that the extent of the wetted channel network scales with discharge at the outlet by a power law (L = αQ^β). Using this relationship, we developed a framework to assess variability in the extent of wetted channels as a function of β and the variability in streamflow Q (Lapides et al., In Review, https://eartharxiv.org/mc6np/). This resource constitutes the empirical basis for that study, a comprehensive dataset compiled from literature including:

1 - Channel length survey data (csv files)
2 - Discharge time series data (csv files)
3 - Watershed metadata (csv files)
4 - Blueline network files (pdf, png, and shp files)

This collection is comprehensive in that it includes all watersheds where at least three channel length surveys have been conducted and where a corresponding discharge time series dataset is available. The requirement of a minimum of three channel length surveys stems from the data requirements to find α and β for the power law relationship between discharge and stream network length for headwater catchments (Godsey et al., 2014). At present, data for 14 watersheds worldwide are included in the collection along with reference maps, watershed metadata, shapefiles and a composite of USGS blueline stream network imagery with terrain for watersheds of interest in the United States. Notably, this collection brings data from a variety of earth science agencies worldwide into a common, clearly labelled format.

Methods used to process the datasets or create other assets in this collection are included in the abstracts or additional metadata for each of the four resources listed above. Python code used to process data, compute variables, and create graphics is available at: https://zenodo.org/record/4057320

ABSTRACT:

Watershed metadata was collected for 14 watersheds from studies where channel length survey data was presented. For variables not found in the publications associated with the channel length surveys, additional sources are referenced. These sources are included in the notes column. Variables without sources were calculated, as described in the Additional Metadata section below. Examples of calculated values include, q_avg_mm_per_day, beta, and l_avg_km.

For Python packages, modules, and functions used to find calculated values, please see the associated GitHub repository: https://zenodo.org/record/4057320

ABSTRACT:

The accompanying files provide the data and processing code for the analyses and figure/table generation for the manuscript "Bedrock vadose zone storage dynamics under extreme drought: consequences for plant water availability, recharge, and runoff" by Hahm et al.

The processing code is in the form of python notebooks, which were originally excuted via Google's colab environment.

To run the code as-is, the entire folder should be placed into the appropriate folder path structure on a user's google drive folder, a Google Earth Engine account must exist, and the code should be run from Colab. This folder structure is: 'My Drive/Colab Notebooks/Rancho - Rock Moisture/'

If this is not possible, the code can be executed by re-arranging the file paths to load in the static .CSV saved data files in the CSVs folder as pandas data frames at the appropriate locations.

ABSTRACT:

Data/code supplement for Age of ET manuscript.

This includes Python notebooks for querying, processing, and plotting age of ET and related contextual figures, as well as QGIS map. The notebooks were run in the free Colab environment.

The .tif file has the following bands:

0: Flux-weighted average minimum ET age (days)
1: MODIS Landcover
2: Koeppen-Geiger climate type
3: Asynchronicity index
4: Longest dry period (days)
5: Mean annual ET (mm)
6: Mean annual precip (mm)

ABSTRACT:

Data files and code (python notebooks, originally executed in the Google Colab environment) to reproduce the analyses and figures for the original submission of the manuscript "Geologic controls on apparent root-zone storage capacity". Note that for the initial data processing a Google Earth Engine account is required; however, you can skip this step and start by ingesting the resulting geotiff directly from a mounted google drive folder (just change the folder paths in the appropriate locations).