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opencc-by-4.0Mar 2022View details →

Inputs and Outputs for paper The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations

Contains the DESC and VMEC input and output files used in the paper, as well as the plotting scripts used to create the figures in the paper (Current with the <a href="https://arxiv.org/abs/2203.17173">arxiv Mar 31 2023 version</a>):   3D equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two 3D equilibrium codes, VMEC, which uses a steepest-descent algorithm to reach a minimum-energy plasma state, and DESC, which minimizes the MHD force error in real space directly. Accuracy as measured by final plasma energy and satisfaction of MHD force balance, as well as other metrics, will be presented for each code, along with the computation time. It is shown that DESC is able to achieve more accurate solutions, especially near-axis. DESC's global Fourier-Zernike basis also yields the solution everywhere in the plasma volume, not just on discrete flux surfaces. Further, DESC can compute the same accuracy solution as VMEC in an order of magnitude less time.   Updated dataset for better readability 5-5-23

opencc-by-4.0Mar 2023View details →

Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Files used in initial submission of manuscript to Geoscientific Model Development. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.

Raw output data for "Chemical design of electronic and magnetic energy scales of tetravalent praseodymium materials", DOI: 10.1038/s41467-023-38431-7

This repository contains output data from the first-principles calculations reported in the following article: Authors: Arun Ramanathan, Jensen Kaplan, Dumitru-Claudiu Sergentu, Jacob A. Branson, Mykhaylo Ozerov, Alexander I. Kolesnikov, Stefan G. Minasian, Jochen Autschbach, John W. Freeland, Zhigang Jiang, Martin Mourigal, Henry S. La Pierre Title: Chemical Design of Electronic and Magnetic Energy Scales in Tetravalent Praseodymium Published in Nature Communications at: https://doi.org/10.1038/s41467-023-38431-7 Preprint on arXiv at: https://doi.org/10.48550/arXiv.2212.10401 Description: The archive contains raw output files generated from quantum chemistry calculations conducted using the OpenMolcas program. OpenMolcas is a freely available program that can be accessed at <a href="https://gitlab.com/Molcas/OpenMolcas">https://gitlab.com/Molcas/OpenMolcas</a>. For specific details about the content of the archive, please refer to the '00README' file included in the archive.

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

ICON-Coast model output for a study on increasing CO2 uptake of the coastal ocean

Primary output of the ocean-biogeochemistry model ICON-Coast that has been used to create the figures in the publication Mathis et al. (2024) on the increasing CO2 uptake of the coastal ocean. https://www.nature.com/articles/s41558-024-01956-w

opencc-by-4.0Jun 2023View details →

EAMv1 output from simulations using tag v1_cflx_2021: annual averages

EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). In the official release of EAMv1, the dry removal of aerosols is calculated after the surface emissions have been applied in a timestep. The EAMv1 code posted at <a href="https://doi.org/10.5281/zenodo.7995850">https://doi.org/10.5281/zenodo.7995850</a> includes an option to apply the surface emissions after dry removal and before turbulent mixing. The option can be turned on by setting the namelist variable cflx_cpl_opt to 2. The original sequence of calculations in the official release of EAMv1 corresponds to cflx_cpl_opt = 1. This upload contains annually averaged model output from the following simulations, all of which were conducted for the year 2010 using the F20TRC5-CMIP6 compset, with the winds above 850 hPa nudged to the ERA-Interim reanalysis. <ul> <li>v1_ori_L30 (cflx_cpl_opt = 1, 30 grid layers)</li> <li>v1_ori_L72 (cflx_cpl_opt = 1, 72 grid layers)</li> <li>v1_rev_L30 (cflx_cpl_opt = 2, 30 grid layers)</li> <li>v1_rev_L72 (cflx_cpl_opt = 2, 72 grid layers)</li> </ul>

opencc-by-4.0Jun 2023View details →

EAMv1 output from simulations using tag v1_cflx_2021: instantaneous values

EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). This upload contains 6-hourly instantaneous model output from the following simulations, both conducted for the year 2010 using the F20TRC5-CMIP6 compset and with the winds above 850 hPa nudged to the ERA-Interim reanalysis. <ul> <li>v1_ori_L30 (30 grid layers)</li> <li>v1_ori_L72 (72 grid layers)</li> </ul>

opencc-by-4.0Feb 2023View details →

Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme

The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the  GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions  to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.

opencc-by-4.0Jun 2023View details →

CROVER model output data for paper "Future Warming Decreases the Benefits of Irrigation in Global Food Production"

This data is output data simulated by the CROVER crop-river coupled model for the scientific paper entitled: “Future Warming Decreases the Benefits of Irrigation in Global Food Production”. Simulations are based on 120-year simulations (1981-2100) using 20 climate projections (four Representative Concentration Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) × five General Circulation Models (GCMs) (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M)). The dataset includes crop yield of the modeled five crops (maize, rice, soybean, spring wheat, and winter wheat) each for irrigated and rainfed with a spatial resolution of 1.125 degree. The file name consists of a series of identifiers, separated by underscore, according to the following pattern. crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.grd crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.ctl Format: grads binary (global, 1.125 degree) These files were compressed as a zip file for each GCM and RCP.

Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America

The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021).  We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper.  Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. The Cryosphere, 15(2), 771-791. Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)

Sources of prey availability data alter interpretation of outputs from prey choice null networks

Spider surveys Data collection was described previously by Cuff, Tercel, et al., (2022). This study pertains to a subset of those data, collected between 1st May and 9th July 2018 at 19 separate locations, for which paired sticky trap and vacuum sample data were collected (described below). Briefly, money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from webs and the ground. Transects were randomly distributed across the entire field. Along these transects, separate 4 m2 quadrats, at least 10 m apart, were searched and all observed linyphiids and lycosids were collected. Spiders were placed in 100 % ethanol using an aspirator, regularly changing meshing to limit potential cross-contamination. Linyphiids occupying webs were prioritised for collection, but ground-active spiders were also collected. Spiders were taken to Cardiff University, transferred to fresh ethanol and stored at -80 °C in 100 % ethanol until DNA extraction. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al., (2022) and Drake et al., (2022), and is also detailed below. Extraction and high-throughput sequencing of spider gut DNA Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera Erigone, Tenuiphantes, Bathyphantes and Microlinyphia (Araneae: Linyphiidae), and the Lycosidae genus Pardosa. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key (Roberts, 1993). Abdomens were removed from spiders and again transferred to and washed in fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens (Krehenwinkel et al., 2017). For amplification of DNA, two primer pairs were used. BerenF-LuthienR (Cuff et al., 2021) amplified a broad range of invertebrates including spiders, and TelperionF-LaureR (Cuff et al., 2022), amplified a range of invertebrates but fewer spiders. Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively. Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet et al. (2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed Drake et al. (2022). Bioinformatic analysis The Illumina run generated 11,165,405 and 10,959,010 reads for BerenF-LuthienR and TelperionF-LaureR, respectively, which were quality-checked and paired via FastP (Chen et al., 2018)  to retain only sequences of at least 200 bp with a quality threshold of 33, resulting in 10,561,874 and 9,355,112 paired reads. The paired reads were demultiplexed and assigned to their respective spider sample according to their MID-tags via the “trim.seqs” command in Mothur v1.39.5 (Schloss et al., 2009), leaving 7,854,610 and 7,437,929 reads with exact matches to the primer and MID-tags. Replicates were removed, and denoising and clustering to zero-radius operational taxonomic units (ZOTUs; clustered without % identity to avoid multiple species represented within a single operational taxonomic unit (OTU)) completed via Unoise3 in Usearch11 (Edgar, 2010). The resultant sequences were assigned a taxonomic identity from GenBank via BLASTn v2.7.1 (Camacho et al., 2009) using a 97 % identity threshold (Alberdi et al., 2017). The BLAST output was analysed in MEGAN v6.15.2 (Huson et al., 2016). Where the top BLAST hit, determined by lowest e-value, was resolved at a higher taxonomic level than species-level, the results were checked; where possibly erroneous entries were preventing species-level assignment (e.g., poorly resolved identifications on GenBank), finer resolution was assigned based on the next-closest match. Where ZOTUs were assigned the same taxon, these were aggregated. Data clean-up used the optimal minimum sequence copy thresholds identified by Drake et al. (2022). The maximum value for a ZOTU present in blank or negative controls was identified and subtracted from all read counts for that ZOTU to remove background contaminants. Simultaneously, known lab contaminants (e.g., German cockroach Blattella germanica), artefacts and errors of the sequencing process, unexpected reads in positive controls and positive control taxon reads in dietary samples were identified. These were calculated as a percentage of their respective sample’s read count and any read counts lower than the highest of these percentages for their respective sample were removed to eliminate additional instances of contamination. These thresholds were defined as 0.38 % and 0.39 % for BerenF-LuthienR and TelperionF-LaureR, respectively. The data from the two libraries (i.e., from each primer pair) were then aggregated together by sample and aggregated again by taxon. Non-target taxa (e.g., fungi) and instances in which predator DNA was amplified (i.e., ZOTUs with high read counts matching the individual’s morphological identity) were removed.  The resultant sequencing read counts were converted into relative proportions (all values made to sum to one within each sample) and a mean value across the two primer pairs retained for each taxon within each sample. Relative read abundances were converted to presence-absence data of each detected prey taxon in each individual spider, but relative read abundance data were also retained for separate analyses to compare experimental outcomes between treatments. Invertebrate surveys To estimate prey availability using sticky traps, we placed one white dry 100 mm x 125 mm trap (Oecos) in the 4 m2 quadrat centred at the position where the spider was captured. The trap was suspended with wire approximately 25 mm above the ground to catch falling, crawling and flying invertebrates, and left in place for 72 hours. Invertebrates were identified on the traps under a stereomicroscope. To estimate prey availability using suction sampling, ground and crop stems were sampled using a ‘G-vac’ for approximately 30 seconds at each location. The collected material was emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level to match the resolution of the least resolved of the metabarcoding-derived trophic interaction data, and due to difficulties associated with identification to finer taxonomic resolution for many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level, and wasps of the superfamily Ichneumonoidea which were identified no further due to obscurity of wing venation due to damage following suction sampling. Statistical Analysis All analyses were conducted in R v4.0.3 (R Core Team, 2021) and carried out on invertebrate data at the family or superfamily level. Alongside the dietary data derived from metabarcoding, and prey availability as determined directly by suction sampling (abundance) and sticky trapping (activity density), three additional datasets were generated where two were designed to combine data from the two trapping methods. The first approach simply set all invertebrate taxa detected in the field to have equal abundance, to provide a baseline against which to assess the effects of different prey abundance estimates. When generating the two combined data sets, it was apparent that simply adding them together would underrepresent one of the datasets as abundance and activity density are measured in different units. Therefore, a ‘proportional combined’ dataset was generated by converting counts to relative proportions of each sample (to equally weight the two methods), which were then combined by summing proportions between the two methods for each sample, multiplied by the total count of individuals across both methods for each sample (to create realistic abundance values), and then rounded to the nearest integer (to return count data). In addition, a ‘frequency of occurrence (FOO) combined’ dataset was generated by converting counts to binary presence-absence values of each sample, which were then summed between the two methods for each sample. To assess the diversity represented by the two sampling methods and their combinations, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell et al., 2021) using the ‘iNEXT’ package with families represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016). The remaining analyses were performed using both presence-absence and relative read abundance dietary data separately to show how differences in the treatment of the observed data are reflected in the outcomes of the analyses. Figures and outputs given in the main text relate to the presence-absence data, while relative read abundance figures and outputs are presented in the Supplementary Information. Prey preferences of spiders were analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ function. Econullnetr generates null models based on prey availability to predict how consumers would forage if based on the availability of resources alone. These null models are then compared against the observed interactions of consumers (e.g., interactions of spiders with their prey based on dietary metabarcoding) to ascertain the extent to which resource consumption deviated from random. In five separate null models, prey availability was represented separately by the datasets described above: abundance (suction sampling), activity density (sticky trapping), proportional combined, FOO combined and equal prey abundance. To compare effect sizes between null models for each resource taxon, mean prey preference standardised effect size (SES) values were calculated from the individual spiders per model. The SES values were plotted and joined between taxa to visualise paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via an econullnetr null model with 999 simulations with outputs extended to allow the comparison of the null interactions for individual consumers (generate_null_net_indiv; Cuff, Kitson, et al., 2023). A visualisation of the per-individual differences in null model and observed data was generated via non-metric multi-dimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 9999 simulations, with Euclidean distance. Centroid coordinates for each null model and the observed data were extracted and pairwise distances calculated between model centroids: The ‘observed’ network (i.e., the network determined solely by dietary data, not necessarily the objectively ‘true’ network) and each null network were visualised with the associated prey choice effect sizes as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016) via an ‘igraph’ object (Csardi & Nepusz, 2006). The degree of each prey node, weighted nestedness and linkage density were generated using the ‘bipartite’ package (Dormann et al., 2008) for each network and compared visually via ggplot2.

Deep-SDMs in the open oceans - OUTPUTS - World +2°C

This repository contains global distribution maps with a +2°C increase in sea surface temperature, on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. A 00-predictions.csv file containing the raw outputs of the model. 2. Distribution maps as png files (named after taxon and date). 3. Distribution maps as GeoTIFF rasters (zipped in 01-geotiff-rasters.zip). Each of these elements can be downloaded separately by scrolling to the Files section.

Deep-SDMs in the open oceans - OUTPUTS - Western Indian Ocean

This repository contains distribution maps for the Western Indian Ocean on 53 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. A 00-predictions.csv file containing the raw outputs of the model. 2. Distribution maps as animated gifs. 3. Distribution maps as GeoTIFF rasters (zipped in 01-geotiff-rasters.zip). Each of these elements can be downloaded separately by scrolling to the Files section.

Deep-SDMs in the open oceans - OUTPUTS - World

This repository contains global distribution maps on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. A 00-predictions.csv file containing the raw outputs of the model. 2. Distribution maps as png files (named after taxon and date). 3. Distribution maps as GeoTIFF rasters (zipped in 01-geotiff-rasters.zip). Each of these elements can be downloaded separately by scrolling to the Files section.

Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified

# Description of the data file This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*. This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components. # Details of the variables and attribute of the file This study was led over 2196 river basins over Europe.  For each catchment used in the study, it gathers: - name of the station at the outlet *name* - name of the river associated *rivers* - lat/lon of the station *Localisation* - upstream area of the catchment *upstream* - Observed discharge (data not used in the article) *DisObs* These data come from three different sources: * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ; * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ; * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020 Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article. The results for four different run of the LSM are included in this dataset. This dataset gathers for each run: * Precipitation *P* * Evapotranspiration *E* * Potential evapotranspiration *PET* * Discharge *DisMod* The different synthetic forcings are: - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501 - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant. - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000). - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing. \end{itemize} ## ncdump -h Filename.nc ``` dimensions:     basins = 2196 ;     years = UNLIMITED ; // (110 currently)     loc = 2 ;     lenstr = 57 ; variables:     short years(years) ;         years:long_name = "Years" ;         years:units = "year" ;     char names(basins, lenstr) ;         names:long_name = "Name of the station at the catchment output" ;     char rivers(basins, lenstr) ;         rivers:long_name = "Name of the river where the station is positioned" ;     float upstream(basins) ;         upstream:long_name = "Upstream area of the catchment" ;         upstream:units = "km^2" ;     float Localisation(basins, loc) ;         Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;         Localisation:units = "degrees_east, degrees_north" ;     float DisObs(years, basins) ;         DisObs:long_name = "Discharge observation at the outlet of the catchment" ;         DisObs:units = "m3/s" ;     float E_ref(years, basins) ;         E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;         E_ref:units = "m3/s" ;     float P_ref(years, basins) ;         P_ref:long_name = "Average annual precipitation for forcing ref" ;         P_ref:units = "m3/s" ;     float PET_ref(years, basins) ;         PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;         PET_ref:units = "m3/s" ;     float DisMod_ref(years, basins) ;         DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;         DisMod_ref:units = "m3/s" ;     float E_f2000(years, basins) ;         E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;         E_f2000:units = "m3/s" ;     float P_f2000(years, basins) ;         P_f2000:long_name = "Average annual precipitation for forcing f2000" ;         P_f2000:units = "m3/s" ;     float PET_f2000(years, basins) ;         PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;         PET_f2000:units = "m3/s" ;     float DisMod_f2000(years, basins) ;         DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;         DisMod_f2000:units = "m3/s" ;     float E_cstmean(years, basins) ;         E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;         E_cstmean:units = "m3/s" ;     float P_cstmean(years, basins) ;         P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;         P_cstmean:units = "m3/s" ;     float PET_cstmean(years, basins) ;         PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;         PET_cstmean:units = "m3/s" ;     float DisMod_cstmean(years, basins) ;         DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;         DisMod_cstmean:units = "m3/s" ;     float E_cstintravar(years, basins) ;         E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;         E_cstintravar:units = "m3/s" ;     float P_cstintravar(years, basins) ;         P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;         P_cstintravar:units = "m3/s" ;     float PET_cstintravar(years, basins) ;         PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;         PET_cstintravar:units = "m3/s" ;     float DisMod_cstintravar(years, basins) ;         DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;         DisMod_cstintravar:units = "m3/s" ; // global attributes:         :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;         :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;         :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;         :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;         :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;                 :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;         :date = "28/07/2023"; } ```

Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

Output raster datasets from the 2023 Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative (ARRI) area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities. Output raster datasets include ecological condition for canopy, midstory and groundcover/shrub layers as well as overall ecological condition. Each raster contains ranked scores of estimated ecological condition: 1- Excellent, 2- Good, 3-Fair, and 4-Poor.  NOTE- These outputs were created using tools stored in this repository: <a href="https://doi.org/10.5281/zenodo.8236853">https://doi.org/10.5281/zenodo.8236853</a> as well as several raster input layers stored in this repository: https://doi.org/10.5281/zenodo.8234220.

opencc-by-4.0Dec 2021View details →

Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle

These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution.  The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index.  Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.   These ten outputs are listed here: "wy_current_mean" Based on 2018 BA conditions; Mean ARID "wy_18_mean" BA reduced to 18m2ha-1; Mean ARID "wy_11_mean" BA reduced to 11m2ha-1; Mean ARID "wy_7_mean" BA reduced to 7m2ha-1; Mean ARID "wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID "wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID "wy_7_max" BA reduced to 7m2ha-1; Maximum ARID "wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID "wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID "wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID   Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool: <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> For detailed information on how the above inputs were developed, please see the associated publication: Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a>

Model_Input_Files_and_Simulation_Output

This provides the BioRT-HBV model input and output for the paper submission "As above, so below: the growing importance of water and carbon processes beneath soils in a warmer and drier climate". Coal_Creek_Original: Input and Output files associated with the model calibrated to average DOC years; used for making Figures 3, 4, 5, 6, 7, and 8. Coal_Creek_High: Input and Output files associated with the model calibrated to high DOC years; used for making Figure 4. Rxn_Implementation_Results: Output files associated with the model simulations that have Resp-SZ only, and Resp-SZ & Resp-DZ; used for making Figure 5. Input files and output for the various numerical experiments, used in figure 9: Coal_Creek_DryandWet, Coal_Creek_DryYears, Num_Exp_HBVResults_and_precipchem_files, Num_Exp_output HBV-BioRT-new_ver(1).zip : files for the BioRT-HBV model

Standard Error Estimates from ARRI ensemble GAM model outputs

Summary (Purpose) Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds. Description The GAM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus Pinus), and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches. Ensemble generalized additive models (GAM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM models for each cell were averaged (mean) to produce the GAM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell.     The 246 Restore field plots used to train the GAM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems.  Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.   The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel.     Reference: Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a> St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114 Credits This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project. Use Limitations This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.

Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods

Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.   Data from two studies <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol>   Folders and Files valve-2D.zip is the folder with data from the idealized models of vertical and curved 2D valves: <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul>   inflow-outflow-3D.zip is the folder with data from the inflow-outflow model in MPS: <ul> <li>Fluid physical properties of water <ul> <li>$\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}$</li> </ul> </li> <li>Pipes of length $L=0.15m$: <ul> <li>circular section of diameter $D=0.1m$.</li> <li>square section of sides $S=0.1m$.</li> </ul> </li> <li>Constante pressure variation ($\Delta P = 30 \,\, or \,\, 50 \,\, Pa$) between inflow and outflow: <ul> <li>$\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}$</li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation ($\Delta P =700Pa \,\, , \,\, T = 2.0s$) between inflow and outflow <ul> <li>$\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}$</li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul>   References <a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a> <a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a> <a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a> <a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a> <a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a>