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172 results for “global scale”
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></p>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
Ocean Basin Evolution and Global-Scale Plate Reorganization Events Since Pangea Breakup
<p><strong>Abstract </strong></p> <p>We present a revised global plate motion model with continuously closing plate boundaries ranging from the Triassic at 230 Ma to the present day, assess differences between alternative absolute plate motion models, and review global tectonic events. Relatively high mean absolute plate motion rates around 9–10 cm yr-1 between 140 and 120 Ma may be related to transient plate motion accelerations driven by the successive emplacement of a sequence of large igneous provinces during that time. A ~100 Ma event is most clearly expressed in the Indian Ocean and may reflect the initiation of Andean-style subduction along southern continental Eurasia, while an ~80 Ma acceleration of mean rates from 6 to 8 cm yr-1 reflects the initial northward acceleration of India and simultaneous speedups of plates in the Pacific. An event at ~50 Ma expressed in relative, and some absolute plate motion changes around the globe and in a reduction of global mean velocities from about 6 to 4–5 cm yr-1, indicates that an increase in collisional forces (such as the India-Eurasia collision) and ridge subduction events in the Pacific (such as the Izanagi-Pacific Ridge) play a significant role in modulating plate velocities.</p> <p><strong>Muller et al. (2016) AREPS model file versions</strong></p> <p>This model has been maintained for some time after initial publication. There are six versions of the model that we provide, including:</p> <ul> <li>v1.10 – Some minor fixes were made to plate topologies, and so conforms to the originally-published model.</li> <li>v1.11 – A back-arc basin north of Arabia was introduced in the Cretaceous (see note below), and hence slightly diverges from the original model in plate topologies, velocities, and seafloor age-grids for this region.</li> <li>v1.14 – The latest version of the model that has duplicated topology segments cleaned from the evolving polygons, which helps with quantifying plate boundary lengths in the resolved topology output.</li> <li>v1.15 – The correction to the pre-83 Ma Pacific rotations according to Torsvik et al. (2019) has been applied.</li> <li>v1.16 – Some fixes to topologies</li> <li>v1.17 – Major update to the seafloor age-grids and topologies. Age-grids are consistent with v1.15 and 1.16 as well. We strongly recommend you use this version of the model.</li> </ul> <p>Note about the evolution of the western Tethys in this model: The Western Tethys, north of Arabia, is punctuated by ophiolite formation and obduction in Cretaceous times. The first end-member involves applying the central and eastern Tethys analogues of back-arc opening and closure following ophiolite obduction, much like is usually implied in the Kohistan-Ladakh and Greater India collision zone. This scenario makes the Western Tethys north of Arabia consistent with the model of the eastern Tethys. However, a second end-member interpretation for the formation of many of the ophiolites in the region is that they develop when a mid-oceanic ridge inverts to become a subduction zone. Both options are plausible, but we implemented a change in this plate model after it was published to reflect the first end-member scenario in order to link the region to the eastern Tethys in a plausible way. This scenario is based on back-arc opening from ~125 Ma (Jolivet et al., 2016), with subduction of back-arc initiating in Albian times from ~110 Ma (Ghazi et at., 2003; Aygul et al., 2015). Obduction and Arabia collision with an arc occurs at 85 Ma (Jolivet et al., 2016; Jagoutz et al., 2016). The scenario is also consistent with the recent work of Morris et al. (2016) on the Oman Ophiolite.</p> <p> </p> <p>The agegrids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/</a></p>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Amplitude of seasonal cycles of vegetation at global scale AVHRR
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the amplitude of the cycles.</p>
Number of seasonal cycles of vegetation at global scale AVHRR
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the number of seasonal cycles of vegetation.</p> <p>Value 1: one cycle</p> <p>Value 2: two cycles</p> <p>Value 3: three cycles</p>
Stability of seasonal cycles of vegetation at global scale
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the Stability of seasonal cycles of vegetation.</p>
Final models for "Global-Scale Full-Waveform Ambient Noise Inversion" by Sager et al. (2020)
<p>The exodus model contains the inverted structure model and the source distribution can be found in the HDF5 file. Both can be visualized in ParaView. For the source model, we recommend opening it with the correspoding XDMF file (select "XDMF Reader" in the dialogue box).</p>
Where does subduction initiate and cease? A global scale perspective (Supplementary material)
<p>Supplementary material for Where does subduction initiate and cease? A global scale perspective</p>
LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011
<p>Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.</p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Supplementary Data: Global fits of GUT-scale SUSY models with GAMBIT (arXiv:1705.07935)
<p>Supplementary Data</p> <p><em>Global fits of GUT-scale SUSY models with GAMBIT</em><br> <em>arXiv:1705.07935</em></p> <p>The files in this record contain data for the CMSSM, NUHM1 and NUHM2 models considered in the GAMBIT "Round 1" GUT-scale SUSY paper.</p> <p>For each model, there are</p> <ul> <li>A number of YAML files, each corresponding to a different set of sampling parameters and/or priors</li> <li>A set of YAML files used for postprocessing: CMSSM_intermediate.yaml, CMSSM.yaml, NUHM1.yaml and NUHM2.yaml</li> <li>A final hdf5 file, containing the combined results of all sampling runs</li> <li>An example pip file, for producing plots from the hdf5 file using pippi</li> <li>SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit. These can be found inside the tarball best_fits_SLHA.tar.gz.</li> </ul> <p>The record also contains</p> <ul> <li>StandardModel_SLHA2_scan.yaml and StandardModel_SLHA2_postprocessing.yaml, two universal YAML fragments included from other yaml files</li> <li>gambit_preamble.py, a collection of python functions used for in-line data processing in the pip files</li> </ul> <p>The different YAML files corresponding to different samplers and/or priors follow the naming scheme [model]_[scanner]_[prior]_[slice]_[special].yaml, where</p> <ul> <li>model = CMSSM, NUHM1, NUHM2</li> <li>scanner = Diver, MN</li> <li>prior = log, flat</li> <li>slice = pmu, nmu (positive or negative mu)</li> <li>special = sqcoann, slcoann, [blank] (squark co-annihilation, slepton co-annihilation, or bulk)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>For each model, the final hdf5 results file included here was generated in the following way:</p> <ul> <li>carry out initial runs using YAML files following the naming scheme above</li> <li>combine the resulting hdf5 output files into a single file, using gambit/Printers/scripts/combine_hdf5.py</li> <li>postprocess the samples to remove all points more than 5 sigma from the current best fit, using [model]_strip.yaml</li> <li>postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT). For the CMSSM, this happened in two steps, due to persistent flavour bugs, using CMSSM_intermediate.yaml and CMSSM.yaml. For the NUHM1 and NUHM2, this was done in a single step each, using NUHM1.yaml and NUHM2.yaml.</li> </ul> </li> <li> <p>It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.</p> </li> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip files are tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file for each model is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Earthquakes unveil the global-scale fractality of the lithosphere
<p>The repository contains the post-processed data required to produce the figures of the article entitled <i>"Earthquakes unveil the global-scale fractality of the lithosphere"</i> under consideration in Communications Earth & Environment.</p><p>Data are divided in directories, each corresponding to an article figure and numbered accordingly.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all input and output files that were used in the study reported in:</p> <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary</strong></p> <p>The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes.</p> <p>We aim to answer two questions:</p> <p>1. What is the intrinsic spatial resolution of global river dynamics?</p> <p>2. How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes?</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> <p><strong>Software</strong></p> <p>The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling.</p> <p><strong>Data Products</strong></p> <p>The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii).</p> <p><strong>Riv_coast.zip</strong> contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean.</p> <p><strong>· </strong><strong>riv_coast.zip</strong></p> <p><strong> o </strong><strong>cor:</strong> riv_coast_pfaf_ii_COR.shp</p> <p><strong> o </strong><strong>uncor: </strong>riv_coast_pfaf_ii_UNCOR.shp</p> <p><strong> </strong></p> <p><strong>Qout_rivwidth.zip </strong>contains csv files of the aggregate river discharge to the ocean (km<sup>3</sup>/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>Qout_rivwidth.zip: </strong>Qout_pfaf_ii_rivwidth.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_low.zip</strong> contains csv files of the aggregate river storage (km<sup>3</sup>) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_low.zip:</strong> V_pfaf_ii_rivwidth_low.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_nrm.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_nrm.zip: </strong>V_pfaf_ii_rivwidth_nrm.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_hig.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_hig.zip: </strong>V_pfaf_ii_rivwidth_hig.csv</p> <p><strong> </strong></p> <p><strong>Largest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins.</p> <p><strong>· </strong><strong>largest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge contributed by each basin</p> <p><strong> o </strong><strong>riv:</strong> riv_top10_nxx.shp – river reaches that drain the 10 largest basins</p> <p><strong> </strong></p> <p><strong>Smallest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m.</p> <p><strong>· </strong><strong>smallest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge to the ocean from each narrow river reach</p> <p><strong> o </strong><strong>riv: </strong>riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii</p> <p><strong> </strong></p> <p><strong>Global_summary.zip </strong>contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage.</p> <p><strong>· </strong><strong>global_summary.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth: </strong>global summary files for discharge to the ocean (km<sup>3</sup>/yr) under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_low:</strong> global summary files for total river storage (km<sup>3</sup>) for the low residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_nrm:</strong> global summary files for total river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_hig: </strong>global summary files for total river storage (km<sup>3</sup>) for the hig residence time scenario under river width sampling</p> <p><strong> o </strong><strong>cat_small_gl: </strong>cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean</p> <p><strong> </strong></p> <p><strong>Rivwidth_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations.</p> <p><strong>· </strong><strong>Rivwidth_sens.zip</strong></p> <p><strong> o </strong><strong>riv_coast</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_VIC</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_CLSM</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_NOAH</strong></p> <p><strong> o </strong><strong>global_summary_VIC</strong></p> <p><strong> o </strong><strong>global_summary_CLSM</strong></p> <p><strong> o </strong><strong>global_summary_NOAH</strong></p> <p><strong> </strong></p> <p><strong>Cor_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis.</p> <p><strong>· </strong><strong>Cor_sens.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_ENS</strong></p> <p><strong> o </strong><strong>global_summary_ENS</strong></p> <p><strong> </strong></p> <p><strong>Width_val.zip </strong>contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018).</p> <p><strong>· Width_val.zip: </strong>width_validation_pfaf_ii.csv</p> <p> </p> <p><strong>Known bugs in this dataset or the associated manuscript</strong></p> <p>No bugs have been identified at this time.</p> <p> </p> <p><strong>References</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, <em>361</em>(6402), 585-588. https://doi.org/10.1126/science.aat0636</p> <p>Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. <em>Nature Geoscience</em>, 1–7. https://doi.org/10.1038/s41561-024-01421-5</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. https://doi.org/10.1029/2019WR025287</p> <p>Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). <em>Bulletin of the American Meteorological Society</em>, <em>102</em>(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1</p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Compilation of hydrological datasets at the global scale
<p>Hydrological variables (evapotranspiration, precipitation, runoff, and terrestrial water storage) are distributed through several datasets that do not have the same characteristics (spatial and temporal coverage, spatial and temporal resolution). This dataset aims at providing a coherent gathering of all datasets available as of January 2021, covering at least the period from 2003 to 2014, and from 50°S to 50°N.</p> <p>This file contains:</p> <ul> <li>14 datasets for evapotranspiration (ERA5-Land, FLUXCOM, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GLEAM, JRA55, MERRA2, MOD16, and SEBBop)</li> <li>11 datasets for precipitation (CPC, CRU, ERA5-Land, PGF, GPCC, GPCP, GPM, JRA55, MERRA2, MSWEP, and TRMM)</li> <li>11 datasets for runoff (ERA5-Land, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GRUN, JRA55, and MERRA2)</li> <li>2 datasets for terrestrial water storage (GRACE CSR mascons, and GRACE JPL mascons)</li> </ul> <p>Each dataset is given as the original version (on a regular grid) and also as a post-treated file averaged for 189 river basins (whose borders are also provided).</p>
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
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