Engineering topics

Bouskill, Nick

Publications and source records attributed to Bouskill, Nick.

4 records

DOE OSTI2022

Data used in Wainwright, H.M. et al. 2021, “Watershed zonation through hillslope clustering for tractably quantifying above- and belowground watershed heterogeneity and functions”

This data package contains spatial data layers and processing scripts used in Wainwright, H.M. et al. 2021, “Watershed zonation approach for tractably quantifying above-and- belowground watershed heterogeneity and functions”. The purpose of the data and paper is to develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by applying clustering methods to multiple spatial data layers. The data package contains the geotiff files of spatial data layers, and the processed data values corresponding to the figures in the paper. The Data_description file describe each file in details. The spatial data sets (geotiff) are included in the zip files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Enhanced prediction of terrestrial feedbacks to the coastal carbon cycle:using machine learning to improve sub-grid biogeochemical processes

Focal Areas: The paper aims to improve predictability of the coastal carbon cycle through improved model representation and quantification of the terrestrial feedbacks to aquatic ecosystems. The paper covers two focus areas, 1. Improved predictive modeling of terrestrial and aquatic biogeochemistry through ML-enabled surrogate models, and 2. Employing machine learning to integrate multimodal data sets collected across terrestrial and aquatic ecosystems at high spatial and temporal resolution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Alaskan carbon-climate feedbacks will be weaker than inferred from short-term manipulations: Alaskan Benchmark Data and Model runs

This submission aimed to assess differences in short-term step warming manipulations and long-term chronic response to climate change in Alaskan ecosystems. Briefly, climate warming is occurring fastest at high latitudes. Based on short-term field experiments, this warming is projected to stimulate soil organic matter decomposition, and promote a positive feedback to climate change. We show here that the tightly coupled, nonlinear nature of high-latitude ecosystems implies that short-term (< 10 year) warming experiments produce emergent ecosystem carbon stock temperature sensitivities inconsistent with emergent multi-decadal responses. We first demonstrate that a well-tested mechanistic ecosystem model accurately represents observed carbon cycle and active layer depth responses to short-term summer warming in four diverse Alaskan sites. We then show that short-term warming manipulations do not capture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur in response to thermal, hydrological, and nutrient transformations belowground. Our results demonstrate significant spatial heterogeneity in multi-decadal Arctic carbon cycle trajectories and argue for more mechanistic models to improve predictive capabilities.The model used in the current study is available publicly (https://github.com/jinyun1tang/ECOSYS), and the current submission contains the python/ matlab codes for analyzing output from the model (includng a readme file to explain the codes). The benchmark data, also enclosed, was collected from a range of published and publicly available sources (extracted using GRABIT: https://www.mathworks.com/matlabcentral/fileexchange/7173-grabit). These sources describe warming induced changes in tundra/ boreal ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Gap-filled water quality, Normalized Differenced Vegetation Index, total nitrogen (nitrate and ammonia) deposition, and land cover data trends for the Continental United States

The purpose of this work was to quantify trends in water quality, total nitrogen deposition, vegetation, and land-use trends for the Continental United States (CONUS). This dataset contains gap-filled daily and yearly in-stream carbon (C) and nitrogen (N) concentrations, discharge, and calculated exports from United States Geological Survey (USGS) National Water Information System (NWIS) stations across the United States of six different nitrogen parameters (Supplementary Table A1), one carbon parameter, and temperature (USGS, 2016, 2018) (https://waterdata.usgs.gov/nwis). The final water quality dataset is merged with the normalized differenced vegetation index (NDVI), total deposition (TDEP), landcover, and elevation datasets. All data are available at the CONUS scale as well as at the scale of hydrologic unit codes: (HUC) HUC8, HUC6, HUC4, and HUC2 watersheds. There are two zip files associated with this data package. I split the data into two equal sized zip files to comply with the data size limits, however the datasets are the same within each zip file. All data files can be read in directly into the R software.

54 ENVIRONMENTAL SCIENCES↗