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Hydrodynamic Model Output Used to Evaluate Chinook Salmon Movements and Distribution in the South Delta
This data release includes the output variables extracted from the UnTRIM Bay-Delta hydrodynamic model (hydrodynamic model) for use in evaluating the effects of hydrodynamics on the behavior of acoustically-tagged juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin Delta. Work was funded by State Water Contractors (SWC) and completed by Anchor QEA; FlowWest, LLC; and University of Washington under a SWC 2023 Science Plan grant (study name Evaluation of the Influence of State Water Project and Central Valley Project on Chinook Salmon Movements and Distribution in the South Delta), contracted by SWC. Not all the hydrodynamic model output variables in the output provided with this memorandum were used in the final fish models used to analyze Chinook Salmon responses. Model output for additional variables and locations were included for completeness and to make these output files more broadly useful to researchers interested in other locations or variables in the Sacramento-San Joaquin Delta. Hydrodynamic model simulations were conducted for 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017, with hydrodynamic model output variables provided at mostly the same locations for each period simulated. The years 2011 through 2016 were simulated previously for a prior project and model output provided through the Environmental Data Initiative (edi.1124.1). Files for these years were recreated from the prior simulations for this project to add an output location. Additional locations were added to the 2010 and 2017 simulations for the 2010 and 2017 hydrophone arrays, and thus 2010 and 2017 include additional model output, relative to 2011 through 2016. The model simulation for each year spanned the full period of Chinook Salmon detections in the telemetry data collected during that year.
Minneapolis-St. Paul Air Quality Sensor output 2024-2025
In the summers since field research at the MSP LTER began, wildfire smoke has led to record highs in Minnesota's Air Quality Index and there has been increasing public concern over the acute and long-term impacts of exposure to toxins in the air. In 2024, the MSP LTER acquired a small network of PurpleAir sensors to place in key areas alongside transplanted lichens that are also used in air quality research. In addition to Particulate Matter, the sensor models used here also are equipped with experimental VOC detection (Volatile Organic Compounds). PurpleAir sensors were collocated with lichens at two locations in St. Paul MN, one in Roseville MN, and one at Cedar Creek Ecosystem Reserve in Northern Anoka County, MN. After lichen material was collected, the sensors remained at the site to keep reporting to community air quality tracking maps. Data from the sensors is being regularly appended to a file on the MSP LTER GitHub page and is planned to be archived long-term.
Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. Pr
ICARIA: climate projections from statistical downscaling outputs
<p><strong>ICARIA </strong>project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes.</p> <p>For further details, find here a brief of the <strong>methodology </strong>followed:<strong> </strong></p> <p><strong>----- </strong></p> <p><em>The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required.</em></p> <p><em>For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach.</em></p> <p><em>For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”.</em></p> <p><em>For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs.</em></p> <p><em>This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future.</em></p> <p>-----</p> <p>The dataset shared here includes information for the three case studies tackled in ICARIA: <strong>Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR)</strong>. The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table:</p> <p><strong>Table 1</strong>.<em> Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI).</em></p> <div> <div> <table> <tbody> <tr> <td> <p><strong>CMIP6 MODELS</strong></p> </td> <td> <p><strong>Resolution</strong></p> </td> <td> <p><strong>Responsible Centre</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>1,875º x 1,250º</p> </td> <td> <p>Australian Community Climate and Earth System Simulator (ACCESS), Australia</p> </td> <td> <p>Bi, D. et al (2020)</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>1,125º x 1,121º</p> </td> <td> <p>Beijing Climate Center (BCC), China Meteorological Administration, China.</p> </td> <td> <p>Wu T. et al. (2019)</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>2,812º x 2,790º</p> </td> <td> <p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá.</p> </td> <td> <p>Swart, N.C. et al. (2019)</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>1,000º x 1,000º</p> </td> <td> <p>Centro Mediterraneo sui Cambiamenti Climatici (CMCC).</p> </td> <td> <p>Cherchi et al, 2018</p> </td> </tr> <tr> <td> <p>CNRM-ESM2-1</p> </td> <td> <p>1,406º x 1,401º</p> </td> <td> <p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia.</p> </td> <td> <p>Seferian, R. (2019)</p> </td> </tr> <tr> <td> <p>EC-EARTH3</p> </td> <td> <p>0,703º x 0,702º</p> </td> <td> <p>EC-EARTH Consortium</p> </td> <td> <p>EC-Earth Consortium. (2019)</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>0,938º x 0,935º</p> </td> <td> <p>Max-Planck Institute for Meteorology (MPI-M), Germany.</p> </td> <td> <p>Müller et al., (2018)</p> </td> </tr> <tr> <td> <p>MRI-ESM2-0</p> </td> <td> <p>1,125º x 1,121º</p> </td> <td> <p>Meteorological Research Institute (MRI), Japan.</p> </td> <td> <p>Yukimoto, S. et al. (2019)</p> </td> </tr> <tr> <td> <p>NorESM2-MM</p> </td> <td> <p>1,250º x 0,942º</p> </td> <td> <p>Norwegian Climate Centre (NCC), Norway.</p> </td> <td> <p>Bentsen, M. et al. (2019)</p> </td> </tr> <tr> <td> <p>UKESM1-0-LL</p> </td> <td> <p>1,875º x 1,250º</p> </td> <td> <p>UK Met Office, Hadley Centre, United Kingdom</p> </td> <td> <p>Good, P. et al. (2019)</p> </td> </tr> </tbody> </table> <p> </p> <p>The results shared here are developed over each of the observational locations that were retrieved to run the statistical downscaling. Both the observational datasets and the future climate change projections can be found here in a TXT format for each of the locations where they were developed. Observations include the main variables retrieved after a quality and homogeneity control, and climate projections together with extreme indicators include each of the 10 models, the 4 Tier 1 SSPs and data until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections.</p> <strong>Table 2.</strong> <em>Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Thermal indicators</strong></p> </td> </tr> <tr> <td> <p>TX90 / TX10</p> </td> <td> <p>Warm/cold days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>90 / 10%</p> </td> </tr> <tr> <td> <p>HD</p> </td> <td> <p>Heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>EHD</p> </td> <td> <p>Extreme heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 35 °C</p> </td> </tr> <tr> <td> <p>TR</p> </td> <td> <p>Tropical nights</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 20 °C</p> </td> </tr> <tr> <td> <p>EQ</p> </td> <td> <p>Equatorial nights</p> </td> <td> <p>AEMet 2020, ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 25 °C</p> </td> </tr> <tr> <td> <p>IN</p> </td> <td> <p>Infernal nights</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>FD</p> </td> <td> <p>Frost days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>< 0 °C</p> </td> </tr> <tr> <td> <p>Max consec</p> </td> <td> <p>Max spell length for above thermal indicators</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>nd</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Nº events</p> </td> <td> <p>Number of above thermal indicators events</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>ne</p> </td> <td> <p>> 3 days</p> </td> </tr> <tr> <td> <p>TXm</p> </td> <td> <p>Mean maximum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TNm</p> </td> <td> <p>Mean minimum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TM</p> </td> <td> <p>Mean temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TA</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>HWle</p> </td> <td> <p>Heatwave length</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWim/HWix</p> </td> <td> <p>Mean and maximum heatwave intensity</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWf</p> </td> <td> <p>Heatwave frequency</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>ne</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWd</p> </td> <td> <p>Heatwave days</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HI - P90</p> </td> <td> <p>Heat Index (percentile 90)</p> </td> <td> <p>NWS (1994)</p> </td> <td> <p>TX, RH</p> </td> <td> <p>°C</p> </td> <td> <p>TX>27 °C, HR> 40%</p> </td> </tr> <tr> <td> <p>UTCI</p> </td> <td> <p>Universal Thermal Climate Index</p> </td> <td> <p>Bröde et al. (2012)</p> </td> <td> <p>TA<br>RH, W</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>UHI</p> </td> <td> <p>Isla de calor (BCN) anual y estacional</p> </td> <td> <p>AMB, Metrobs 2015</p> </td> <td> <p>T</p> </td> <td> <p>°C</p> </td> <td> <p>TM1-TM2 > 0 °C</p> </td> </tr> <tr> <td> <p><strong>Precipitation indicators</strong></p> </td> </tr> <tr> <td> <p>R20</p> </td> <td> <p>Number of heavy precipitation days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>20 mm</p> </td> </tr> <tr> <td> <p>R50, R100</p> </td> <td> <p>Days with extreme heavy rain</p> </td> <td> <p>AMB et al. (2017)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>50mm</p> <p>>100mm</p> </td> </tr> <tr> <td> <p>Ra</p> </td> <td> <p>Yearly and seasonal rainfall relative change</p> </td> <td> <p>ICARIA</p> </td> <td> <p>P</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>IDF - CCF</p> </td> <td> <p>IDF Curves - Climate Change Factor</p> </td> <td> <p>Arnbjerg-Nielsen (2012)</p> </td> <td> <p>P</p> </td> <td> <p>-</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Forest fire indicators</strong></p> </td> </tr> <tr> <td> <p>Mean FWI</p> </td> <td> <p>Mean Canadian FWI in fire season</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>.</p> </td> <td> <p>June-<br>September</p> </td> </tr> <tr> <td> <p>Very High FWI</p> </td> <td> <p>Very High Canadian FWI</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>nd</p> </td> <td> <p>FWI > 38</p> </td> </tr> </tbody> </table> </div> <p> </p> <p><strong>Table 3</strong>. <em>Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em></p> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Drought indicators</strong></p> </td> </tr> <tr> <td> <p>CDDx</p> </td> <td> <p>Maximum dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>CDDm</p> </td> <td> <p>Mean dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>SPI</p> </td> <td> <p>SPI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>McKee et al. (1993) </p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>SPEI</p> </td> <td> <p>SPEI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>Vicente-Serrano et al. (2010)</p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Oceanic indicators</strong></p> </td> </tr> <tr> <td> <p>SS</p> </td> <td> <p>Storm surge</p> </td> <td> <p>Bryant et al. (2016)</p> </td> <td> <p>MT</p> </td> <td> <p>cm</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>OW</p> </td> <td> <p>Significant/maximum wave height</p> </td> <td> <p>ICARIA</p> </td> <td> <p>WH</p> </td> <td> <p>m</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Wind indicators</p> </td> </tr> <tr> <td> <p>EWG</p> </td> <td> <p>Extreme wind gusts</p> </td> <td> <p>ICARIA</p> </td> <td> <p>W</p> </td> <td> <p>km/h</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> </div> <p> </p> </div> </div>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16_nosnow</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16_nosnow.nc</em>.</p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output)
<p>The datasets archived here include simulation results shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide netcdf files (9-km resolution EASEv2 grid, period Jan 2010 – Nov 2019, and between 45°N and 70°N, NE Asia excluded) of the four experiments of the manuscript: model-only (open-loop, OL) and data assimilation (DA) for each land model version, that is CLSM without and with the use of the PEATCLSM modules. The highest accuracy is provided by the DA product using PEATCLSM and Tb observations. When referring to the latter product use the name ‘PEATCLSM(Tb)’. We provide three types of netcdf files:<br> • daily_images_*.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack<br> • ObsFcstAna_images_*.nc: Brightness temperature observations, forecasts and analysis (Table 2), provided as netCDF image-chunked image stack<br> • incr_timeseries_*.nc: Data assimilation increments (Table 3), provided as netCDF timeseries-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20200505.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output
<p>Model output used in the publication:</p> <p>M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. <em>Proc. Natl. Acad. Sci. </em>(2021) https://doi.org/10.1073/pnas.207541118</p> <p>See README.md for a description of the data.</p>
Relations in the Biographical Dictionary of Republican China - Standardized output
<p>This dataset contains the data on relations in the BDRC. It is based on the raw data output to be found in this collection. This file retained only the person-to-person relations. It served as a reference file to create the edge and node lists used for SNA under Cytoscape. All the corresponding networks are available as interactive networks in the <a href="http://public.ndexbio.org/#/group/4ea8024e-094c-11eb-948d-0ac135e8bacf?searchType=All&searchString=bdrc&searchTermExpansion=false">ENP-China Group</a> on the NDEx platform.</p>
RAPID input and output files corresponding to "River Network Routing on the NHDPlus Dataset"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, Cédric H., David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets and Victor Eijkhout (2011), River Network Routing on the NHDPlus Dataset, Journal of Hydrometeorology, 12(5), 913-934. DOI: 10.1175/2011JHM1345.1. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Time format</strong></p> <p>The times reported in this description all follow the ISO 8601 format. For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1<sup>st</sup> 2000 (2000-01-01), Central Standard Time (-06:00). Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step. For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps. The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus. </li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis. </li> <li>Outputs from a simulation using the community Noah land surface model with multiparameterization options (Noah-MP, Niu et al. 2011, http://www.jsg.utexas.edu/noah-mp). The simulation was run by Guo-Yue Niu, and produced 3-hourly time steps from 2004-01-01T00:00+00:00 to 2008-01-01T00:00+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p> </p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> <p> </p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to two study domains:</p> <ul> <li>The combination of the San Antonio and Guadalupe River Basins, TX. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 5,175). The temporal range corresponding to this domain is from 2004-01-01T00:00-06:00 to 2007-12-31 T00:00-06:00.</li> <li>The Upper Mississippi River Basin. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 182,240). The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul> <p> </p> <p><strong>Description of files for the San Antonio and Guadalupe River Basins</strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_San_Guad.csv.</em> This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel.</li> <li><em>m3_riv_San_Guad_2004_2007_cst.nc. </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00. The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP. This file was prepared using ArcGIS and a Fortran program.</li> <li><em>kfac_San_Guad_1km_hour.csv. </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.</li> <li><em>kfac_San_Guad_celerity.csv. </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using the wave celerity numbers of Table 2 in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_1.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_2.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_3.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_4.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_1.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (17) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_2.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (18) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_3.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (19) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_4.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (21) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>basin_id_San_Guad_hydroseq.csv. </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.</li> <li><em>Qout_San_Guad_1460days_p1_dtR=900s.nc.</em> This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p2_dtR=900s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p3_dtR=900s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p4_dtR=900s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p1_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p2_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p3_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p4_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_San_Guad_2004_2007_full.csv. </em>This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record. The river reaches are sorted in increasing value of COMID. The time range used for determining a full record is daily from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00. The values were computed using the following NHDPlus field: COMID. This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li><em>Qobs_San_Guad_2004_2007_full.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same COMIDs and are sorted similarly to <em>gage_id_San_Guad_2004_2007_full.csv</em>. The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00. The values were computed using the following NHDPlus field: COMID, and the observations from NWIS. This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul> <p> </p> <p><strong>Description of files for the Upper Mississippi River Basin</strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_Reg07.csv. </em>This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel. </li> <li><em>m3_riv_Reg07_100days_dummy.nc. </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The time range for this file is for 100 fictitious days. The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps. This file was prepared using a Fortran program.</li> <li><em>kfac_Reg07_2.5ms.csv. </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011). This file was prepared using a Fortran program. </li> <li><em>xfac_Reg07_0.3.csv. </em>This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The values were computed based on Equation (22) in David et al. (2011). This file was prepared using a Fortran program. </li> <li><em>basin_id_Reg07_hydroseq.csv. </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.</li> <li><em>Qout_Reg07_100days_pfac_dtR900s.nc.</em> This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_Reg07_hydroseq.csv</em>. The time range for this file spans 100 fictitous days. The values were computed using the Muskingum method with parameters of Equation (22) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>The confluence of the Missouri River and the Upper Mississippi River upstream of Saint Louis, MO was overlooked. The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin. This has no effect on the conclusions of David et al. (2011) since the Upper Mississippi River Basin was studied with synthetic data and solely to evaluate parallel performance of RAPID.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was partially supported by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G; by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems; by Ecole des Mines de Paris, France; and by the American Geophysical Union under a Horton (Hydrology) Research Grant.</p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
LAVESI-FIRE simulation output at Lake Satagay, Central Yakutia, Siberia
<p>Simulation output data for long-term fire-vegetation simulations with the individual-based, spatially explicit model LAVESI-FIRE. The individual, numbered simulation folders are described in the included .docx file, as well as in the related research paper. In each folder, following files can be found (_18224XX refers to the climate input used for the simulation):</p> <ul> <li><strong>datatrees_currencies_18224</strong>XX<strong>.csv</strong>: Timeseries data. Simulation area-wide summarized data on tree abundance, climate, environment and fire occurrence. Each row of the table represents an individual annual simulation timestep.</li> <li><strong>databiomassgrid_1_18224</strong>XX<strong>_</strong>XX<strong>00_1_</strong>X<strong>.csv</strong>: Spatial data. Each file includes tree abundance for one species (_1 = <em>Larix gmelinii</em>; _2 = <em>Larix sibirica</em>; _3 = <em>Larix cajanderi</em>; _4 = <em>Picea obovata</em>; _5 = <em>Pinus sylvestris</em>; _6 = <em>Pinus sibirica</em>), summarized in grid cells with x- and y-coordinates.</li> <li><strong>datatrees_Treedensity</strong>XX<strong>00_18224</strong>XX<strong>.csv</strong>: Spatial data. Each file includes tree density, environment, and fire occurrence, summarized in grid cells with x- and y-coordinates.</li> </ul> <p>For more detail, please refer to the linked research paper:</p> <p>Glückler, R., Gloy, J., Dietze, E., Herzschuh, U., & Kruse, S. (2024). Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum. Fire Ecology, 20(1), 1. https://doi.org/10.1186/s42408-023-00238-8</p>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Dynamic Traces from CEA Curie
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the dynamic trace utilizing <a href="https://www.cs.huji.ac.il/labs/parallel/workload/l_cea_curie/index.html">Curie's workload</a> carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per target fair share value that the user that we track its usage. Within, we have a file with the name indicating if the workflow is wrapped or not, the type, vertical or horizontal, and the instant of submission. This file is in <a href="https://www.cs.huji.ac.il/labs/parallel/workload/swf.html">SWF</a> format. Under the results folder, we have the same organizaion: each .trace is the raw file produced by the simulator. </p> <p>The workload log from the CEA Curie system was graciously provided by Joseph Emeras (<a href="mailto:Joseph.Emeras@imag.fr">Joseph.Emeras@imag.fr</a>).</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
Seafloor organic carbon flux output from the NEMO-MEDUSA model
<p>This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990.</p> <p>This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA.</p> <p>The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK).</p> <p>In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation.</p> <p>References:</p> <p>Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104.</p> <p>Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646.</p> <p>Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355.</p> <p>Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460.</p> <p>Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619.</p> <p>Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201.</p> <p>Yool, A., Popova, E.E. and Anderson, T.R. (2013). MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies. Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.</p>
Food and Agriculture Biomass Input–Output (FABIO) database
<p>This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry. </p> <p>The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers <strong>191 countries</strong> + RoW, <strong>118 processes</strong> and <strong>125 commodities</strong> (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on <a href="https://github.com/fineprint-global/fabio">GitHub</a>. For more information please refer to <a href="https://fabio.fineprint.global">https://fabio.fineprint.global</a>.</p> <p>The database consists of the following main components, in compressed .rds format:</p> <ul> <li>Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.</li> <li>Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.</li> <li>X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.</li> <li>L: the Leontief inverse, computed as (I – A)<sup>-1</sup>, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).</li> <li>E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).</li> <li>mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).</li> <li>mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).</li> </ul> <p>A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS</p> <p>Except of <em>X.rds</em>, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes <em>value </em>and <em>mass </em>only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see <a href="http://doi.org/10.1021/acs.est.9b03554">Bruckner et al. 2019</a>). Product, process and country classifications can be found in the file <em>fabio_classifications.xlsx</em>.</p> <p>Footprint results are not contained in the database but can be calculated, e.g. by using this script: <a href="https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R">https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R</a></p> <p> </p> <p><strong><em>How to cite: </em></strong></p> <p>To cite FABIO work please refer to this paper:</p> <p>Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. <em>Environmental Science & Technology</em> 53(19), 11302–11312. DOI: <a href="https://doi.org/10.1021/acs.est.9b03554">10.1021/acs.est.9b03554</a></p> <p> </p> <p><em><strong>License:</strong></em></p> <p>This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at <a href="mailto:martin.bruckner@wu.ac.at">martin.bruckner@wu.ac.at</a>.</p> <p> </p> <p><strong><em>Known issues:</em></strong></p> <p>The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.</p>
Southern African Power Pool GridPath Model Output Data - Chowdhury et al 2022 Joule
<p>This data repository holds GridPath model output data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details. </p>
sami2py sample output files
<p>Example output datasets generated by the sami2py model. These files are in support of the manuscript "The sami2py model -- overview and applications", submitted to Frontiers in Astronomy and Space Science in October 2022. The sami2py model can be accessed via github.</p> <p>sami2py: </p> <p>https://github.com/sami2py/sami2py</p> <p>https://doi.org/10.5281/zenodo.2875799</p>
ParaTAXIS X-ray Scattering Input & Output
<p>This dataset describes the science case of the SIMEX platform tool chain for EUCALL WP4 Milestone M4.3.</p> <p>The file <em>opt_thick_1.6x1.6x3_micron_10fs_around_laser_max.h5</em> contains the ParaTAXIS density input data in openPMD format for the optically thick case (milestone 4.2.2.10). The data was obtained in a 2D PICLS simulation (milestone 4.2.2.7) which modeled the temporal evolution of a silicon grating irradiated by a <span class="math-tex">\(\tau_\mathrm{FWHM} = 83\,\mathrm{fs},\ \lambda = 800\,\mathrm{nm}\)</span> laser pulse of normalized amplitude <span class="math-tex">\(a_0 = 0.25\)</span>. Here 959 slices which correspond to subsequent PIC time steps of length <span class="math-tex">\(\Delta t_\mathrm{PIC} = 1.042 \cdot 10^{-17}\,\mathrm{s}\)</span> were stacked in propagation direction of the XFEL probe pulse thus taking time evolution of the target during X-ray pulse propagation into account. ParaTAXIS reads the density into a simulation volume of 1024 x 512 x 512 cells. The cell sizes of the PIC and the ParaTAXIS simulations are equally <span class="math-tex">\(3.125\,\mathrm{nm}\)</span> in every spatial direction. Density data is given in units of critical densities with respect to the <span class="math-tex">\(800\,\mathrm{nm}\)</span> laser. One critical density corresponds to <span class="math-tex">\(n_\mathrm{c} = 1.7422 \cdot 10^{27}\,\mathrm{m}^{-3}\)</span>. The time window chosen is situated from <span class="math-tex">\(5\,\mathrm{fs}\)</span> before until <span class="math-tex">\(5\,\mathrm{fs}\)</span> after the optical laser main pulse maximum hits the foil indicating a delay of <span class="math-tex">\(\Delta t = 0\)</span>. The optical laser incidence is in z-direction (ParaTAXIS coordinates) under 0°.</p> <p>The total illuminated area for both the optically thick and thin cases was <span class="math-tex">\(1.6 \times 1.6\, \mathrm{\mu m}\)</span>. We assume a target thickness of <span class="math-tex">\(3\,\mathrm{\mu m}\)</span>. The detector distance was <span class="math-tex">\(d = 1.4\,\mathrm{m}\)</span> and the detector pixel size was <span class="math-tex">\(a_\mathrm{D} = 13.5\,\mathrm{\mu m}\)</span>. For Thomson scattering most photons are scattered in forward direction. We therefore assumed a maximum polar scattering angle of <span class="math-tex">\(0.01\,\mathrm{rad}\)</span> in order to increase statistics on the detector.</p> <p>Via 16 simulations we obtained the detector outputs in the optically thick case which can be found in opt_thick_run_<run-number>_detector_<number-of-simulated-photons>_photons.h5 in openPMD format.</p> <p>The file <em>opt_thin_integrated_1.6x1.6x3_micron_10fs_around_laser_max</em> contains the total electron density data for the optically thin case (milestone 4.2.2.9) integrated over 959 slices in the propagation direction of the probe laser beam. The density is only non-zero in the 6th cell of the simulation volume thus enforcing single-scattering in the ParaTAXIS simulation as can be assumed for an optically thin medium. This data was read by a ParaTAXIS into a simulation volume of 12 x 512 x 512 cells.</p> <p>We launched 10 parallel simulations, each arriving at detector images for <span class="math-tex">\(10^{12}\)</span> simulated photons. The detector output of these simulations can be found in <em>opt_thin_detector_1e12_photons_run<run-number>.h5</em> also in openPMD format.</p> <p> </p>
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.