Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability

The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EIR) between consecutive years (e.g. HSi= |∆ EIR /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016.  A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years.  For each map, where HSi >1, regions are classified as Sensitive and for HSi ≤1, Resilient. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive.  This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against  latitudinal change. We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees. Contents: <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names. <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>

opencc-by-4.0Nov 2021View details →

River network and hydro-geomorphological parameters at 1/12° resolution for global hydrological and climate studies

Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data. This paper presents a new global scale river network at 1/12° derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2°) and the other with the new spatial resolution (1/12°). It is shown that CTRIP at 1/12° overall outperforms CTRIP at 1/2°, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.

opencc-by-4.0Aug 2022View details →

Compilation of hydrological datasets at the global scale

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. This file contains: <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> 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).

opencc-by-4.0Mar 2022View details →

Global forest cover loss tipping points leading to changing hydrologic responses

This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses. EVENTS_List_45.docx is a table describing each deforestation event used for the study MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one. MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx

opencc-by-4.0Jun 2023View details →

Statistical blending of global-gridded climatological products: an approach to inverse hydrological model

The growing use of global-scale environmental products in hydro-climatic modeling (with different assumptions, resolutions, and precisions) has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending (fusion) of these products to achieve optimal modeling while avoiding difficulties. The proposed statistical blending in this study includes five large-scale and satellite precipitation (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA5-Land of ECMWF (ERA), Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM), and Terra) and evapotranspiration (Global Land Evaporation Amsterdam Model (GLEAM), SSEBop, Moderate Resolution Imaging Spectroradiometer (MODIS), Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on in situ values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well.

opencc-by-4.0May 2022View details →

Caravan - A global community dataset for large-sample hydrology

This is the accompanying dataset to the following paper <a href="https://www.nature.com/articles/s41597-023-01975-w">https://www.nature.com/articles/s41597-023-01975-w</a> Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time. If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place. All current development and additional community extensions can be found at <a href="https://github.com/kratzert/Caravan">https://github.com/kratzert/Caravan</a> IMPORTANT: Due to size limitations for individual repositories, the netCDF version and the CSV version of Caravan (since Version 1.6)  are split into two different repositories. You can find the CSV version at <a href="https://zenodo.org/records/15530021">https://zenodo.org/records/15530021</a> Channel Log: <ul> <li>23 May 2022: Version 0.2 - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.</li> <li>24 May 2022: Version 0.3 - Fixed gaps in forcing data in some "camels" (US) basins.</li> <li>15 June 2022: Version 0.4 - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).</li> <li>1 December 2022: Version 0.4 - Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).</li> <li>16 January 2023: Version 1.0 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan</li> <li>10 May 2023: Version 1.1 - No data change, just update data description.</li> <li>17 May 2023: Version 1.2 - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.</li> <li>16 April 2024: Version 1.4 - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller  than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code</li> <li>16 Jan 2025: Version 1.5 - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). </li> <li>27 May 2025: Version 1.6 <ul> <li>Updated the CAMELS-AUS data to source from CAMELS-AUS v2. This means more basins (561 compared to 222) and more recent streamflow data (2022 compared to 2014). Note that the gauge id for four basins changed between the original CAMELS-AUS version and v2. Those gauges are ['camelsaus_224213A', 'camelsaus_224214A', 'camelsaus_227225A', 'camelsaus_403213A'] that all lost their trailing "A". To stay synced with CAMELS-AUS (v2), we also adapted the new naming.</li> <li>Added VERSION file to the root directory that contains the current version number.</li> <li>Updated the code to the most recent GitHub snapshot (commit 6eab036).</li> <li>Due to the 50GB repository limit, we had to split the netCDF version and the CSV version into two separate repositories. The CSV version can be found under https://zenodo.org/records/15530021</li> </ul> </li> </ul>

opencc-by-4.0Oct 2022View details →

Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution

Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a). Output data includes: <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA). In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>.

opencc-by-4.0Jul 2019View details →

Supporting Information for "Geochemistry constrains global hydrology on Early Mars"

Copy of the Supporting Information for "Geochemistry constrains global hydrology on Early Mars", by Edwin S. Kite and Mohit Melwani Daswani.

opencc-by-4.0Jan 2023View details →

Data for "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover"

Dataset for the "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover". Dataset 01 includes site locations, basin area, dissolved organic carbon (DOC), dissolved lignin concentration and relevant references. Dataset 03 includes mean/discharge-weighted DOC, mean/discharge-weighted dissolved lignin concentrations. Dataset 03 includes geomorphological, climatic, hydrological and land-cover data for the 25 rivers. Dataset 04 includes the reconstructed yield of dissolved lignin and basin area of the 79 rivers.

openmit-licenseJul 2023View details →

The exemplary basin modeling with Global Hydrologic Data Cloud (GHDC) and SHUD model.

GHDC = Global Hydrological Data Cloud SHUD = Simulator of Hydrologic Unstructured Domains   Files in the package:   <ul> <li> Conestoga - The data retrived from GHDC and the modeling result by SHUD model, for Gonestoga, Pennsylvania, USA. </li> <li> Gummara - The data retrived from GHDC and the modeling result by SHUD model, for Gummara, Ethiopia </li> <li> heihe - The data retrived from GHDC and the modeling result by SHUD model, for Heihe Headwater, Gansu, China </li> <li> threeBasin - The R analysis code for the three basin simulations, including loading data and visualization. </li> </ul>   The file structure in each basin folder: <table> <thead> <tr> <th>FOLDER</th> <th>FILE OR SUBFOLDER(BOLD)</th> <th>DESCRIPTION</th> </tr> </thead> <tbody> <tr> <td>ETV</td> <td>-</td> <td>General ETV data.</td> </tr> <tr> <td> </td> <td>dem.tif</td> <td>DEM subset of ASTER Global DEM.</td> </tr> <tr> <td> </td> <td>Soil.csv</td> <td>Soil texture of soil layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>Geol.csv</td> <td>Soil texture of geology layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Geol.csv</td> <td>Soil texture, organic matter and bulk density of geology layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Soil.csv</td> <td>Soil texture, organic matter and bulk density of soil layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.tif</td> <td>HWSD data subset.</td> </tr> <tr> <td> </td> <td>buff.shp, .dfb, .prj, .shx</td> <td>Shapefile of buffered polygon from user-provided watershed.</td> </tr> <tr> <td> </td> <td>outlets.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>stm_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>wbd_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>TSD</td> <td>Time-series forcing files.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Subfolder, terriestial data in GCS</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Subfolder, terriestial data in PCS.</td> </tr> <tr> <td> </td> <td>PCS/landuse.tif</td> <td>Landuse raster subset.</td> </tr> <tr> <td> </td> <td>PCS/soil.tif</td> <td>Soil classification raster subset.</td> </tr> <tr> <td> </td> <td>PCS/geology.tif</td> <td>Geology classification raster subset.</td> </tr> <tr> <td> </td> <td>Figure</td> <td>The figures during pre-processing.</td> </tr> <tr> <td> </td> <td>Figure/ETV_watershed_delineation</td> <td>Watershed delineation, include boundary, river, pourpoint.</td> </tr> <tr> <td> </td> <td>Figure/dem_buf</td> <td>The DEM, and buffer zone.</td> </tr> <tr> <td> </td> <td>Figure/ETV_LDAS</td> <td>The coverage of reanalysis grid.</td> </tr> <tr> <td> </td> <td>Figure/ETV_Landuse</td> <td>The landuse classification of the research area</td> </tr> <tr> <td> </td> <td>Figure/ETV_Soil</td> <td>The soil classification of the research area</td> </tr> <tr> <td> </td> <td>Figure/ETV_Geol</td> <td>The geology classification of the research area</td> </tr> <tr> <td>Modeling</td> <td>-</td> <td> </td> </tr> <tr> <td> </td> <td>Figure</td> <td>The figures during model deployment</td> </tr> <tr> <td> </td> <td>input</td> <td>Model input folder.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Spatial data for model deployment in GCS.</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Spatial data for model deployment in PCS.</td> </tr> <tr> <td> </td> <td>deployConfig.txt</td> <td>The configuration file for model deployment script.</td> </tr> <tr> <td>StaticFiles</td> <td>-</td> <td>Static files, include the model, citation, description and executable model file.</td> </tr> <tr> <td> </td> <td>SHUD_model</td> <td>Source code of SHUD model</td> </tr> <tr> <td> </td> <td>Citation.bib</td> <td>Citation of Data and models.</td> </tr> <tr> <td> </td> <td>ReadMe_cn.html</td> <td>The readme file in Chinese.</td> </tr> <tr> <td> </td> <td>ReadMe_en.html</td> <td>The readme file in English.</td> </tr> <tr> <td> </td> <td>shud.exe</td> <td>The executable files of SHUD model, for Windows platform only.</td> </tr> <tr> <td>UserData</td> <td>-</td> <td>The files uploaded by user</td> </tr> </tbody> </table>

dryad32/100

Data from: A global dataset for economic losses of extreme hydrological events during 1960-2014

A comprehensive dataset of extreme hydrological events (EHEs) – floods and droughts, consisting of 2,171 occurrences worldwide, during 1960‐2014 was compiled, and then their economic losses were normalized using a price index in U.S. dollar. The dataset showed a significant increasing trend of EHEs before 2000, while a slight post‐2000 decline. Correspondingly, the EHEs‐caused economic losses increased obviously before 2000 followed by a slight decrease; the post‐2000 decline could be partially attributed to the decreases in drought and flood‐prone area, or climate adaptation practices. Spatially, Asia experienced most EHEs (969), corresponding to the largest share of economic losses (approximately $868 billion for floods and $50 billion for droughts, respectively), while Oceania had the least EHEs (102) and the least economic losses (approximately $19 billion for floods and $45 billion for droughts). The five countries with the highest EHE‐caused economic losses were China, USA, Canada, Australia, and India. Countries that suffered the highest flood‐caused economic losses were China, USA, and Canada. This dataset provides a quantitative linkage between climate science and economic losses at a global scale; and it is beneficial for the regional climatic impact assessments and strategical development for mitigating climate change impacts.

opencc-zeroJun 2019View details →

dryad32/100

Data from: A global dataset for economic losses of extreme hydrological events during 1960-2014

Open the record for dataset details and reuse information.

publicJun 2019View details →

dryad32/100

Data from: The hydrological legacy of deforestation on global wetlands

Open the record for dataset details and reuse information.

publicOct 2015View details →

Pre SWOT Hydrology Global Lake/Reservoir Storage Time Series V2

The Global Lake/Reservoir Storage Time Series is derived from the Surface Water Height Time Series and Surface Water Extent Mask Time Series products. The purpose of this dataset is to provide surface water storage estimates for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.

Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling

This dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system.

Monthly gridded Global Land Data Assimilation System (GLDAS) from Noah-v3.3 land hydrology model for GRACE and GRACE-FO over nominal months

The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format. Currently, the days included in these monthly anomaly computation are same as GRACE-FO monthly Level-2 RL06.3 JPL solutions.

Pre SWOT Hydrology Global Lake/Reservoir Surface Inland Water Height GREALM V.2

The Global Lake/Reservoir Surface Inland Water Height Time Series is derived from the G-REALM10 lake level product https://ipad.fas.usda.gov/cropexplorer/global_reservoir/ The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. Water level variation is also a key component required for the determination of surface water storages and fluxes. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.

Pre SWOT Hydrology Global Lake/Reservoir Surface Inland Water Area Extent V2

The Global Lake/Reservoir Surface Inland Water Extent Mask Time Series are derived from the MODIS instruments. The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs throughout the globe, with a base temporal resolution of 8 days and a spatial resolution of 500 meters. With the exception of periods of low-quality input data, these time series will extend across the lifespan of the MODIS multispectral reflectance products, from roughly 2000 to present. These time series will allow us to satisfy the project goal to produce ESDRs of suitable quality to support long-term trend analysis and global water dynamics models for the longest length possible (in most cases, about 20 years, the length of the altimetry record) of key measures of surface water storages and fluxes. This product should be accessible and of direct use to both water managers and the scientific community worldwide, and will allow for improved assessment and modeling of human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.