This simulated ecosystem carbon dataset is used to report terrestrial carbon balance of the Nisqually River basin in the Ecological Modelling paper "Modeling watershed carbon dynamics as affected by land cover change and soil erosion" The data is derived from simulations of the LUCAS model. Annual carbon variables of 2017 at 30m spatial resolution with 2426 rows and 2459 columns. Carbon stock and flux units are in kgC/m2 and kgC/m2/yr, respectively. Data are in tif format and Albers equal area projection. Overall data creation steps: 1. The pIBIS model was used to generate annual carbon parameters of typical ecosystems. 2. The USPED model was used to generate annual soil erosion and deposition maps as affected by land cover change. 3. The LUCAS model was used to run simulations using outputs of USPED, pIBIS, and LCMAP data; 4. Model outputs were summarized as annual numbers, spatial data were saved in GeoTiff format; Variable List: aynpptot – net primary productivity of 2017 (flog_127.it1.ts2017_aynpptot.tif, 0.02-1.13 kgC/m2/yr); cbiotot - total biomass carbon of 2017 (stkg_101.it1.ts2017_cbiotot.tif, 0.0-22.72 kgC/m2); totcsoi – total soil carbon of 2017 (stkg_106.it1.ts2017_totcsoi.tif, 2.48-99.43 kgC/m2); ED – total ecosystem soil carbon erosion and deposition of 2017 (stkg_103.it1.ts2017_ED.tif, -1.20-1.20 kgC/m2/yr).

This simulated ecosystem carbon dataset is used to report terrestrial carbon balance of the Nisqually River basin in the Ecological Modelling paper "Modeling watershed carbon dynamics as affected by land cover change and soil erosion" The data is derived from simulations of the LUCAS model. Annual carbon variables of 2017 at 30m spatial resolution with 2426 rows and 2459 columns. Carbon stock and flux units are in kgC/m2 and kgC/m2/yr, respectively. Data are in tif format and Albers equal area projection. Overall data creation steps: 1. The pIBIS model was used to generate annual carbon parameters of typical ecosystems. 2. The USPED model was used to generate annual soil erosion and deposition maps as affected by land cover change. 3. The LUCAS model was used to run simulations using outputs of USPED, pIBIS, and LCMAP data; 4. Model outputs were summarized as annual numbers, spatial data were saved in GeoTiff format; Variable List: aynpptot – net primary productivity of 2017 (flog_127.it1.ts2017_aynpptot.tif, 0.02-1.13 kgC/m2/yr); cbiotot - total biomass carbon of 2017 (stkg_101.it1.ts2017_cbiotot.tif, 0.0-22.72 kgC/m2); totcsoi – total soil carbon of 2017 (stkg_106.it1.ts2017_totcsoi.tif, 2.48-99.43 kgC/m2); ED – total ecosystem soil carbon erosion and deposition of 2017 (stkg_103.it1.ts2017_ED.tif, -1.20-1.20 kgC/m2/yr).

Please review Zhang et al. (2021) for details on study design and datasets (https://doi.org/10.1016/j.watres.2022.118443). In summary, predictor and response variable data was acquired from the Chesapeake Bay Program and USGS. This data was subjected to a trend analysis to estimate the MK linear slope change for both predictor and response variables. After running a cluster analysis on the scaled TN loading time series (the response variable), the cluster assignment was paired with the slope estimates from the suite of predictor variables tied to the nutrient inventory and static geologic and land use variables. From there, an RF analysis was executed to link trends in anthropogenic driver and other contextual environmental factors to the identified trend cluster types. After calibrating the RF model, likelihood of improving, relatively static, or degrading catchments across the Chesapeake Bay were identified for the 2007 to 2018 period. Tabular data is available on the journal website and PUBMED, and the predictor/response variable data can be downloaded individually in the USGS and Chesapeake Bay Program links listed in the data access section. Portions of this dataset are inaccessible because: This data was generate by other federal entities and are housed in their respective data warehouse domains (e.g., USGS and Chesapeake Bay Program). Furthermore, the data can be accessed on the journal website as well as NCBI PUBMED (https://pubmed.ncbi.nlm.nih.gov/35461100/). They can be accessed through the following means: Combined dataset can be accessed on the journal website (https://www.sciencedirect.com/science/article/pii/S0043135422003979?via%3Dihub#ack0001) and will soon be available on NCBI (https://pubmed.ncbi.nlm.nih.gov/35461100/). The predictor variable data can be accessed from the Chesapeake Bay Program (https://cast.chesapeakebay.net/) and USGS (https://pubs.er.usgs.gov/publication/ds948 and https://www.sciencebase.gov/catalog/item/5669a79ee4b08895842a1d47). Format: Please review Zhang et al. (2021) for details on study design and datasets (https://doi.org/10.1016/j.watres.2022.118443). In summary, predictor and response variable data was acquired from the Chesapeake Bay Program and USGS. This data was subjected to a trend analysis to estimate the MK linear slope change for both predictor and response variables. After running a cluster analysis on the scaled TN loading time series (the response variable), the cluster assignment was paired with the slope estimates from the suite of predictor variables tied to the nutrient inventory and static geologic and land use variables. From there, an RF analysis was executed to link trends in anthropogenic driver and other contextual environmental factors to the identified trend cluster types. After calibrating the RF model, likelihood of improving, relatively static, or degrading catchments across the Chesapeake Bay were identified for the 2007 to 2018 period. Tabular data is available on the journal website and PUBMED, and the predictor/response variable data can be downloaded individually in the USGS and Chesapeake Bay Program links listed in the data access section. This dataset is associated with the following publication: Zhang, Q., J. Bostic, and R. Sabo. Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 218: 1-15, (2022).

This data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux parameters that were used in the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides five observation data files (MODIS GPP, MODIS NPP, FIA forest biomass/forest area, NASS crop NPP, and NASS agricultural land fraction) and simulation results from 18 terrestrial biosphere models (TBM) (28 variables; 114 files) and seven inverse models (IM) (two variables; 8 files). To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) resampled original model simulation results and observation measurement data to 1-degree spatial resolution for North American region, interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format.

This dataset contains the result of simulated daily emissions of methane (CH4) and nitrous oxide (N2O) from the soils in Tidal Freshwater Forested Wetlands (TFFW) along the Waccamaw River (SC, USA) and the Savannah River (GA and SC, USA) under drought-induced saltwater intrusion using a process-driven biogeochemistry model.

This dataset contains the result of simulated daily emissions of methane (CH4) and nitrous oxide (N2O) from the soils in Tidal Freshwater Forested Wetlands (TFFW) along the Waccamaw River (SC, USA) and the Savannah River (GA and SC, USA) under drought-induced saltwater intrusion using a process-driven biogeochemistry model.

This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order). The estimates were derived from a water transport model, the Model for Scale Adaptive River Transport (MOSART), coupled with the Dynamic Land Ecosystem Model (DLEM) to link hydrology and ecosystem processes pertaining to N2O flux and transport. Factors driving the model included climate, land use and land cover, and nitrogen inputs (i.e., fertilizer, deposition, manure, and sewage). Nitrogen discharges from streams and rivers to the ocean were calibrated from observations from 50 river basins across the globe.

Supporting information for "River basin simulations reveal wide-ranging wetland-mediated nitrate load reductions". Supporting information includes the calibrated baseline model and the modified Soil and Water Assessment Tool (SWAT) source code and executable file. The supporting information also provides metadata -- and download links -- for the model input and output files for all model runs described in the manuscript. This dataset is associated with the following publication: Evenson, G., H. Golden, J. Christensen, C. Lane, M. Kalcic, A. Rajib, Q. Wu, D.T. Mahoney, E. White, and E. D'Amico. River Basin Simulations Reveal Wide-Ranging Wetland-Mediated Nitrate Reductions. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 27(26): 9822-9831, (2023).