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Refining Estimates of Land for Biomass (RELB) was a project commissioned and funded by the ETI. As part of the RELB project,The ETI commissioned the project team to complete three case studies of farms growing bioenergy crops. This workbook covers the data and calculations for the Brackenthwaite Farm SRC Willow case study

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these ...

Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

An extensive compilation of field data on net primary productivity (NPP) of natural and agricultural ecosystems worldwide was synthesized in the 1970s and early 1980s by Prof. H. Lieth, Dr. G. Esser and others. Much of this work was carried out at the University of Osnabrueck, Germany. More than 700 single point estimates of NPP or biomass were extracted from the scientific literature, each with a geographical reference (latitude/longitude). The literature cited dates from 1869 to 1982, with the majority of references from the 1960s and 1970s. Although this data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. In the early 1970s, a subset of these NPP data was used by Lieth, Esser and co-workers to develop and test a series of statistical-correlative models of NPP as a function of mean annual temperature and precipitation. The later versions of these models included modifications for soil, seasonality, agriculture, and other human influences ("Osnabrück Biosphere Mode,""High Resolution Biosphere Model," etc.). Most of the 720 unique NPP records (632, or 88 percent) have been matched to a bibliography of 356 references from the primary literature. The original form of this bibliography contained many more references than records, including multiple sources for the same author and study, as well as additional references to data on standing biomass, soils, and so forth. Since this is a useful resource in its own right, an edited and corrected compilation of these 858 references is available here with the cross-references to the NPP records highlighted. Of the 720 unique NPP records, about two-thirds have above-ground NPP estimates that range between 1 and 8530 g/m2/year (dry matter) -- or 2923 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Total NPP, for which more than half of the sites have estimates, ranges from 3 to 9320 g/m2/year (dry matter) -- or 3580 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Each record includes a site identifier, latitude, longitude, author, country, NPP estimates, vegetation type, and other variables. The vegetation-type field begins with a generalized biome type (including tundra, forest, Mediterranean, savanna, grassland, desert, wetland, and a number of managed vegetation types) and is followed by more specific vegetation terminology derived from the original data. Caution is advised in using these biome/vegetation types because they were not defined consistently within the original data set and nearly 200 sites lack any vegetation designation. To achieve completeness in a single synthesis file, a single NPP value (NPP_C) is included for each site that represents the sum of above-ground (ANPP) and below-ground (BNPP) components, expressed in grams of carbon per square meter per year (g C/m2/year). Where BNPP was not reported, it was assumed to be equal to ANPP. A ratio of 0.475 was used to convert dry biomass weight to carbon content. Total NPP was estimated as TNPP (where available), or as the sum of ANPP and BNPP (or from ANPP x 2, if BNPP was not estimated), and then converted to g C/m2/year.

The major goal of this EAGER project is to create a Big Data mining toolset for the Landsat Time Series that captures, labels, and maps glacier change for use in climate science, hydrology, and Earth science education. This pilot study demonstrates the potential for interactively mapping, visualizing, and labeling glacier changes. What is truly innovative is that IceTrendr not only maps the changes but also uses expert knowledge to label the changes and such labels can be applied to other glaciers exhibiting statistically similar changes. This is much more than just a simple "then and now" approach to glacier mapping. IceTrendr is a means of integrating the power of computing, remote sensing, and expert knowledge to "tell the story " of glacier changes. Our key findings are that the IceTrendr concept and software can provide important functionality for glaciologists and educators interested in studying glacier changes during the Landsat TM timeframe (1984-present). With additional time and funding, there is the exciting and innovative opportunity to build on the IceTrendr framework, to develop much greater utility for mapping glaciers and characterizing glacier change globally. Although this pilot study focused on just five glaciers, with some future funding and effort, IceTrendr will have the potential to map changing glaciers EVERYWHERE over the full Landsat TM timeframe (1984-present). Specifically, concerns with the Landsat TM imagery are that many images are missing during the period 1984-1995 and the automated cloud mask is not effective requiring the user to manually identify cloud-free images. We found that the visualization of the glacier in the IceTrendr window worked well with high-resolution satellite data from Google Earth and visualization was improved with additional high-resolution images from the Polar Geospatial Center. The automated clustering algorithm was a good first step in glacier mapping and when augmented with glacier outlines from the Randall Glacier Inventory, users could readily see changes in glacier extent, brightness, debris cover, as well as changes in surrounding area including glacial lakes and rivers, vegetation, and moraines.

Aim: We propose that forest trees create a vertical dimension for ecological niche variation that generates different regimes of climatic exposure, which in turn drives species elevation distributions. We test this hypothesis by statistically modelling the vertical and elevation distributions and microclimate exposure of rainforest ants. Location: Wet Tropics Bioregion, Australia Methods: We conducted 60 ground-to-canopy surveys to determine the vertical (tree) and elevation distributions, and microclimate exposure of ants (101 species) at 15 sites along four mountain ranges. We statistically modelled elevation range size as a function of ant species’ vertical niche breadth and exposure to temperature variance for 55 species found at two or more trees. Results: We found a positive association between vertical niche and elevation range of ant species: for every 3 m increase in vertical niche breadth our models predict a ~150% increase in mean elevation range size. Temperature variance increased with vertical height along the arboreal gradient and ant species exposure to temperature variance explained some of the variation in elevation range size. Main Conclusions: We demonstrate that arboreal ants have broader elevation ranges than ground-dwelling ants and are likely to have increased resilience to climatic variance. The capacity of species to expand their niche by climbing trees could influence their ability to persist over broader elevation ranges. We propose that wherever vertical layering exists - from oceans to forest ecosystems - vertical niche breadth is a potential mechanism driving macrogeographic distribution patterns and resilience to climate change. Data_collections.csv Main survey collections data in a site by species matrix showing all data for all sites surveyed. Tuna baited vials were placed every three metres from ground to canopy in trees at elevation sites at four subregion mountain ranges of the Australian Wet Tropics Bioregion. Note data file includes empty vials that lacked ants. Microclimate_AthertonTemp.csv This file contains Atherton Uplands temperature data from ibuttons deployed at one tree per elevation (200, 400, 600, 800, 1000) at every three metres in height in Dec-Jan 2017- 2018 set to record every half hour. See file Metadata for details of column names and data values.

Insect samples were collected at Kelso dunes over a nine-day period between April 24th and May 2nd, 2013. Insects were sampled daily using pan traps (approximately 18 cm in diameter) set at ground-level along an east-west axis parallel to Kelso Dunes. Thirty pairs of pan traps were set along two parallel 45 m transects (transects were 10 m apart) with alternating blue, yellow, and white traps approximately every 3 m using the NSERC-CANPOLIN protocol (http://www.uoguelph.ca/canpolin). Pan traps were paired so that each replicate had one pan trap under the southern portion of a L. tridentata canopy, halfway between the base of the shrub and the drip-line, and within a patch of annual plants. The other pan traps were deployed 2 m south of each paired shrub in an adjacent open microsite, also with annual plants present (see Appendix A; Fig. A2). Open microsites were located two metres from the drip-line of shrubs because this was on average the maximum distance possible without being within a two metre radius of another shrub (Ruttan pers. obs). Pan traps were half-filled with a solution of soapy water prepared by mixing five drops of unscented dish detergent per litre of water (for protocol, see: http://www.uoguelph.ca/canpolin). Pan traps were set out by 9:00 a.m. and collected at 5:00 p.m. daily targeting typical peak insect activity (http://www.uoguelph.ca/canpolin). All samples were collected on sunny days with no precipitation. Samples were collected from each pan trap replicate and stored in vials of 70% ethanol. Insects were then sorted from samples and identified to the family level for ease of identification using Goulet and Huber (1993) and Borror et al. (1989). Following identification, insects were categorized into their primary functional groups, including pollinators (mostly bees), herbivores, granivores, parasites, nec- tarivores (that contribute only marginally to pollination), and others.

Demographic data of Sedum lanceolatum under a climate manipulation experiment (heating and watering). Dataset includes one .csv with demographic data for 232 individuals monitored over 2013-2014 which was used, in part, to draw conclusions in "Elevation effects on vital rate sensitivities generate variation in neighbor effects on population growth rate in Sedum lanceolatum" by Herzog et al. (in review). All data was collected under a watering and warming experiment as part of the Alpine Treeline Warming Experiment at Niwot Ridge, Colorado, USA. There are two main data file formats in this archive: comma-separated values (.csv) which can be read using any simple text editor program, such as TextEdit (Mac) and Notepad (Windows). The .pdf data user’s guide can be read using Adobe Acrobat Reader, or any other compatible software.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.MOHC.HadGEM3-GC31-MM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.