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676 results for “hydrology”
Generative deep learning for hydrological forecasting: CVAE-75 basins from CANOPEX_v1
<p>Data associated with https://doi.org/10.1016/j.jhydrol.2023.130498</p>
What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica (Interpolated Data)
<p>This dataset accompanies the paper 'What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica' in Journal of Glaciology, and can be used alongside the code found on Github (https://github.com/rohaizharis/inversion_radar2022) to reproduce the figures. The dataset consists of ice-penetrating radar data (specularity and relative reflectivity) and basal shear stress inversions that have been linearly interpolated onto radar flight tracks.</p>
Data for: Assessing hydrology, biogeochemistry and organic micropollutants in an urban stream-aquifer system: an interdisciplinary dataset
<p>Accompanying data for data article "Assessing hydrology, biogeochemistry and organic micropollutants in an urban stream-aquifer system: a comprehensive dataset" of Popp et al., JGR:Biogeosciences.</p> <p><br>In this repository, all data described in Table 1 of the manuscript can be found, except for the data already published by Popp et al., 2020, ES&T, doi: 10.1021/acs.est.9b05393. These data can be freely accessed in ERIC (Eawag Research Data Institutional Collection): doi.org/10.25678/0001JD. </p> <p>Data are structured the following way:<br>1_logger-data: time series of logger data (water temperature, water levels, electrical conductivity and pH [the latter only for the stream]) obtained at the stream Chriesbach and piezometers P1 and P4;<br>2_tracer-data: time series of nutrients, ions, and other tracer data obtained at the stream Chriesbach, the piezometers (P1, P4, P5) and the regional groundwater well (reg-gw); <br>3_micropollutant_data: time series of organic micropolluntants obtained at the stream Chriesbach, P1, P4, P5 and the regional groundwater well (reg-gw);<br>4_R-script: R script used for statistical analysis and to create the plots shown in the manuscript.</p> <p>Units, estimated uncertainties or other measures of uncertainty such as limits of quantification are provided in the respective files. Each subfolder contains its own readme file with relevant metadata. </p> <p>Coordinates (WGS84): <br>Location Latitude Longitude<br>Stream logger monitoring 47.404613 8.6113<br>Stream sampling 47.404459 8.607777<br>Piezometer 1 (P1) 47.4044 8.6080<br>Piezometer 4 (P4) 47.4044 8.6078<br>Piezometer 5 (P5) 47.404392 8.607619<br>Regional groundwater 47.40501 8.60822</p>
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the Max Forest scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the base scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 land use data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land use/ land cover input data for CESM2 which was used in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2000-2100. The files correspond to experiments described in the study as follows:</p> <p> </p> <p>Base: landuse.timeseries_0.9x1.25_SSP1-2.6_78pfts_CMIP6_simyr2000-2100_c220715.nc</p> <p>Max Forest: landuse.timeseries_0.9x1.25_hist_78pfts_SSPRFAFRS_SSP1_edit_xarray_4_simyr2000-2100_c221024.nc</p> <p>No LULCC: landuse.timeseries_0.9x1.25_hist_78pfts_SSPNOLULCC_3_simyr2000-2100_c221025.nc</p> <p> </p> <p>Files were created in collaboration by James A. King, James Weber, Peter Lawrence, and Stephanie Roe.</p> <p> </p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the No LULCC scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Episodes of critical salinity values in the estuary of the river Ulla (Galicia, Spain) in the hydrological years 1961 to 2022
<p>The dataset includes the number of critical salinity episodes, as a function of salinity thresholds and their duration, that could have caused mortality events of different intensity of bivalves of commercial interest in the Ulla river estuary (Galicia, Spain) for hydrological years 1962 to 2022. </p> <p>A hydrological year is defined as the one starting in October and ending in September of the following year. The series includes four of the possible critical episodes defined by Parada <em>et al., </em>2012 (DOI: 10.1007/s12237-011-9426-2): Salinity (S) below 10 for 1 day (S10-1d); S<30 for 18 consecutive days (S30-18d); S<5 for 1 day or more (S5-1d) and S<30 for 19 consecutive days or more (S30-19d). The first two types of events can cause moderate mortalities of commercial bivalve molluscs and the second severe mortalities (Parada <em>et al., </em>2012; DOI: 10.1007/s12237-011-9426-2). Moderate mortalities are defined as events where mortalities of less than 50% of the cockle (<em>Cerastodema edule</em>, L. 1758) and up to 15% of the clams <em>Ruditapes decussatus</em> (L., 1758) and <em>R. philippinarum</em> (A. Adams & Reeve, 1850) are recorded in the estuary. Severe mortalities are defined as events in which mortalities of 50% or more of the population of <em>C. edule</em> and the clam <em>Venerupis corrugata</em> (Gmelin, 1791), and 15% or more of <em>R. decussatus</em> and <em>R. phillipinarum</em> are recorded (Parada <em>et al., </em>2012; DOI: 10.1007/s12237-011-9426-2). The file is composed of 5 columns: the first column contains the hydrological year and the following columns contain the number of episodes recorded for each of the four categories (S10-1d, S30-18d, S5-1d and S30-19d).</p>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>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).</p> <p>Output data includes:</p> <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> <p>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).</p> <p>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>. </p>
Caravan-DE: Caravan extension Germany - German dataset for large-sample hydrology
<p><em>Caravan</em> is an open community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world:<a href="../record/6578598" target="_blank" rel="noopener"> https://zenodo.org/record/6578598</a>. <br>We have employed the published code to derive meteorological forcing data and catchment attributes from global data sources to extend Caravan with data for <strong>1887 catchments</strong> in Germany. The time series data are in <strong>daily resolution and span up to 70 years, from January 1951 to December 2020</strong>.</p> <p>Most of the catchments in Caravan-DE are also part of the CAMELS-DE dataset (<a href="https://doi.org/10.5281/zenodo.13837553" target="_blank" rel="noopener">10.5281/zenodo.13837553</a>, 1582 catchments). As CAMELS-DE relies on meteorological forcing data that is only available within the borders of Germany, catchments going beyond the German national borders had to be discarded. Caravan uses global data products for the meteorological forcing data and catchment attributes, which is why Caravan-DE includes these catchments that are partly located outside of Germany. As catchments in Caravan-DE and CAMELS-DE are identified by the same ID, the datasets can be used together.<br>Please refer to the CAMELS-DE paper (<a href="https://doi.org/10.5194/essd-2024-318">https://doi.org/10.5194/essd-2024-318</a>) for information about discharge data and the catchment geometries used for both Caravan-DE and CAMELS-DE.</p> <p>For the processing of the data the following guide was followed step by step: <a href="https://github.com/kratzert/Caravan/wiki/Extending-Caravan-with-new-basins">https://github.com/kratzert/Caravan/wiki/Extending-Caravan-with-new-basins</a></p> <h3> </h3> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <p> </p> <h3>Changelog</h3> <ul> <li>v1.0.1 <ul> <li>Small changes to be consistent with other Caravan extensions (see <a href="https://github.com/kratzert/Caravan/issues/35" target="_blank" rel="noopener">https://github.com/kratzert/Caravan/issues/35</a>): <ul> <li>camelsde_basin_shapes.shp: removed column gauge_name</li> <li> <div>attributes_other_camelsde.csv: added river name to gauge_name column</div> </li> </ul> </li> </ul> </li> <li>v1.1.0 <ul> <li>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 climate indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). <br>This was conducted to be consistent with <a href="https://doi.org/10.5281/zenodo.14673536" target="_blank" rel="noopener">Caravan version 1.5</a>.</li> </ul> </li> <li>v1.1.1 <ul> <li>By mistake, the new variables for v1.1.0 were missing in the climatic indices, these have now been added.</li> </ul> </li> </ul>
Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH)
<p>CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland) is a large-sample hydro-meteorological data set for hydrological Switzerland in Central Europe that covers 331 basins within Switzerland and neighboring countries (Austria, France, Germany and Italy). CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes.</p> <p>The data set covers 40 years of data between 1st January 1981 and 31st December 2020 for each catchment: daily time series of stream flow and water levels, of meteorological data such as precipitation and air temperature and of daily snow water equivalent data. Additionally, CAMELS-CH encompasses annual time series of land cover change and glacier evolution per catchment. The static catchment attributes comprise the following categories: location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers.</p> <p>The corresponding manuscript is published at the journal "Earth System Science Data" (ESSD) and available <a href="https://essd.copernicus.org/articles/15/5755/2023/">here</a>. The code used to generate the dataset is available on <a href="https://github.com/camels-ch">Github</a>.</p> <p>The data description file below contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources. Further, this repository contains the "Caravan extension CH" for the "Caravan - A global community dataset for large-sample hydrology" <a href="../records/7944025">Caravan dataset</a> (see the <a href="https://github.com/kratzert/Caravan/discussions/10">list of extensions</a>). This extension has the same format like other Caravan parts and is based on the same data sources. Note that some features like the annual glacier time series, etc. are therefore only available in the original CAMELS-CH dataset.</p> <p> </p> <h2>Updates:</h2> <p>- Update version 0.9: affects "Caravan_extension_CH" - In version 1.5 of the Caravan dataset, Penman-Monteith PET was added as an additional time series feature. Additional to the new time series feature, also all pet-related climate indices were recomputed using the new Penman-Monteith PET. For consistency, the old ERA5-Land potential_evaporation time series and climate indices were kept, but renamed for a better identification of the differences. </p> <p>- Update version 0.8: resolving projection issue for shapefiles in "Caravan_extension_CH" using EPSG:4326 (WGS84); updating readme file of "camels_ch" regarding the <a href="../communities/dischma/">Dischma</a> catchment</p> <p>- Update version 0.7: update corresponding to the revision of the manuscript at "Earth System Science Data" (ESSD)</p> <ul> <li>dataset file delimiters have been changed to commas from semicolons</li> <li>the "time_series" folder was renamed to "timeseries"</li> <li>in the simulation-based data, there was an error in the previous aggregation of precipitation and evapotranspiration. The corresponding time series, affected hydrologic signatures and climatic indices were corrected</li> <li>the order of simulation-based variables in the timeseries files was changed to resemble the order shown in the tables of the corresponding publication in ESSD</li> <li>blank values that were masked by "NA" are now consistently indicated by "NaN"</li> <li>the readme file has been extended</li> </ul> <p>- Update version 0.6: updating links to related material (all links and references are available in the preprint/manuscript) and abstract</p> <p>- Update version 0.5: adding the "camels_ch_data_description.pdf" file</p> <p>- Update version 0.4: update of several static attributes in "Caravan_extension_CH" following a general update in Caravan and all its extensions + adopting the geographic coordinate system to Caravan-standard EPSG:4326</p> <p>- Update version 0.3: renaming single files/entries in "Caravan_extension_CH" to start with "camelsch" as unique Caravan extension identifier</p> <p>- Update version 0.2: CH extension to <a href="../records/7944025">Caravan</a> added</p>
Data for: Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian
<p><span>Global forest cover affects the Earth system by altering surface mass and energy exchange. Physiology determines plant environmental limits and influences geographical vegetation distribution. Ancient plant physiology, therefore, likely affected vegetation-climate feedbacks. We combine climate modeling and ecosystem-process modeling to simulate arboreal vegetation in the late Paleozoic ice age. Using GENESIS V3 GCM simulations, varying <i><span>p</span></i>CO<sub><span>2</span></sub>, <i><span>p</span></i>O<sub><span>2</span></sub>, and ice extent for the Pennsylvanian, and fossil-derived leaf C:N, maximum stomatal conductance, and specific conductivity for several major Carboniferous plant groups, we simulated global ecosystem processes at a 2-degree (longitude, latitude)</span><span> resolution with </span><i>Paleo</i>-BGC<span>. Based on leaf water constraints, Pangaea could have supported widespread arboreal plant growth and forest cover. However, these models do not account for the impacts of freezing on plants. According to our interpretation, freezing would have affected plants in 89% of unglaciated land during peak glacial periods, and 65% during the warmer interglacials. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology Database supports restriction of global forest extent due to freezing. Many genera were limited to </span>25% <span>of unglaciated land where temperatures remained above −</span>4°C<span>. Freeze-intolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO<sub><span>2</span></sub><span> levels, and</span> climate forcing. As a bounding case, we assume total plant mortality at </span>−4°C <span>and estimate that contracting forest cover increased net global surface runoff by up to 6.1%. Repeated freezing likely influenced freeze- and drought-tolerance evolution in lineages like the coniferophytes, which became increasingly dominant in the Permian and early Mesozoic.</span></p>
Hydrogeological Survey in the Mincio River and Goito aquifer for the hydrological year 2020-2021
<p>Data collected from 2020 to 2021 in surface- and groundwater in the Goito aquifer and Mincio River (Po Plain, northen Italy). These data were published in <a href="https://doi.org/10.3390/hydrology9030044">https://doi.org/10.3390/hydrology9030044</a>.</p>
Data archive for 'Opportunities to curb hydrological alterations via dam re-operation in the Mekong'
<p>This repository contains the data used in the paper '<a href="https://www.nature.com/articles/s41893-022-00971-z">Opportunities to curb hydrological alterations via dam re-operation in the Mekong</a>'.</p> <p>We first use VIC-Res to simulate daily river discharge and available hydropower generation of the Mekong basin from 1996 to 2016 under 32 scenarios (NAT (natural flow conditions), BAU (business as usual), MAX_MB (dams kept at full storage in Mekong), MAX_LMB (dams kept at full storage in Lower Mekong), and 28 OPT (optimized re-operation strategies) scenarios). The 'VIC-Res' folder contains the daily discharge at Stung Treng and hydropower production in Cambodia, Laos, and Thailand. The hydropower outputs are then used in PowNet, a unit commitment/economic dispatch model for the Cambodian, Laotian, and Thai power systems. 'PowNet' folder contains the relevant input and output files for the three scenarios that are elaborated on in the paper (BAU, MAX_LMB, and OPT).</p> <p>For more information on the PowNet models, refer to the following GitHub repositories: <a href="https://github.com/kamal0013/PowNet">PowNet-Cambodia</a>, <a href="https://github.com/kamal0013/PowNet-Laos">PowNet-Laos</a>, <a href="https://github.com/kamal0013/PowNet-Thailand">PowNet-Thailand</a>.</p>
HYDRO-CSI, Project 1.2: In-stream hydrology. Part 2: instantaneous injections
<p>The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes.</p> <p>In this project we investigated solute transport in a headwater stream reach via instantaneous (slug) solute injections. The study site is a 55 m long corridor downstream of the Weierbach experimental catchment (see <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.14140">Hissler et al., 2021)</a>. The stream channel is unvegetated and consists of deposited colluvial material and fragmented schists (up to 50 cm depth) with underlying fractured slate bedrock that sporadically forms the streambed. The average channel slope is 6% and a 50 cm step riffle sits between wells 7W1 and 7W2 (see <a href="https://onlinelibrary.wiley.com/doi/10.1002/hyp.14310">Bonanno et al., 2021</a>). The stream reach is divided in 11 sections that define the nomenclature of the groundwater observation well network (eg. section one is indicated by wells 1W1 and 1W2). The complete list of groundwater measurements has been published in a previous Zenodo dataset and can be found <a href="https://zenodo.org/record/6245818#.YlacmehBxD9">HERE</a>.</p> <p>The tracer chosen for the experiments is chloride. For each experiment, we prepared an NaCl solution using 2 liters of stream water and a fixed mass of reagent-grade NaCl. We injected the solution in a turbulent pool at the beginning of the stream reach (right before section 1) to assure complete mixing in the stream water. Electrical conductivity was measured via portable conductivity meter (Multisonde WTW). Conversion between EC and chloride concentration has been deduced via EC-chloride concentration plots in laboratory where a fixed amount of NaCl solution with known concentration has been progressively added to a sample the stream water collected before the experiment. Every regression equation between EC and chloride concentration plot had a R<sup>2</sup>>0.998.</p> <p>The dataset includes 30 files of chronologically-numbered instantaneous injections. The instantaneous injections have been conducted from 6-Dec-2018 to 11-June-2021. Every file includes:</p> <p>> A map of the investigated stream reach;<br> > WTW sensor location along the stream reach and their distance from injection point;<br> > The amount of NaCl mass injected in the stream;<br> > Pictures of the stream channel and streamflow;<br> > Notes about presence of leaf packs;<br> > Time and net chloride concentration [mg/l] for each sensor. </p> <p>All the experiments, data cleaning, sensor calibration, and conversion from EC to Cl- concentration have been conducted by Bonanno Enrico between 2018 and 2021 as part of the Ph.D. project HYDRO-CSI (PRIDE15/10623093).<br> François Barnic, Laurent Gourdol, Jean François Iffly and Jérôme Juilleret calibrated the Multisonde WTW and provided the necessary training.<br> Laurent Pfister and Julian Klaus managed the project and were responsible for the founding acquisition.</p>
Time to Update the Split-Sample Approach in Hydrological Model Calibration v1.1
<p><strong>Time to Update the Split-Sample Approach in Hydrological Model Calibration</strong></p> <p>Hongren Shen<sup>1</sup>, Bryan A. Tolson<sup>1</sup>, Juliane Mai<sup>1</sup></p> <p><sup>1</sup>Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada</p> <p>Corresponding author: Hongren Shen (hongren.shen@uwaterloo.ca)</p> <p><strong>Abstract</strong></p> <p>Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly-used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of the continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling.</p> <p><strong>Version updates</strong></p> <p><strong>v1.1 Updated on May 19, 2022.</strong> We added hydrographs for each catchment.</p> <p><strong>There are <em>8 parts</em> of the zipped file attached in v1.1. You should download all of them and <em>unzip all those eight parts together</em>.</strong></p> <p>In this update, we added two zipped files in each gauge subfolder:</p> <p> (1) GR4J_Hydrographs.zip and</p> <p> (2) HMETS_Hydrographs.zip</p> <p>Each of the zip files contains 50 CSV files. These CSV files are named with keywords of model name, gauge ID, and the calibration sub-period (CSP) identifier.</p> <p>Each hydrograph CSV file contains four key columns:</p> <p> (1) Date time (note that the hour column is less significant since this is daily data);</p> <p> (2) Precipitation in mm that is the aggregated basin mean precipitation;</p> <p> (3) Simulated streamflow in m3/s and the column is named as "subXXX", where XXX is the ID of the catchment, specified in the CAMELS_463_gauge_info.txt file; and</p> <p> (4) Observed streamflow in m3/s and the column is named as "subXXX(observed)".</p> <p>Note that these hydrograph CSV files reported period-ending time-averaged flows. They were directly produced by the Raven hydrological modeling framework. More information about the format of the hydrograph CSV files can be redirected to the <a href="http://raven.uwaterloo.ca/">Raven webpage</a>.</p> <p><strong>v1.0 First version published on Jan 29, 2022.</strong></p> <p><strong>Data description</strong></p> <p>This data was used in the paper entitled "Time to Update the Split-Sample Approach in Hydrological Model Calibration" by Shen et al. (2022).</p> <p>Catchment, meteorological forcing and streamflow data are provided for hydrological modeling use. Specifically, the forcing and streamflow data are archived in the Raven hydrological modeling required format. The GR4J and HMETS model building results in the paper, i.e., reference KGE and KGE metrics in calibration, validation and testing periods, are provided for replication of the split-sample assessment performed in the paper.</p> <p><strong>Data content</strong></p> <p>The data folder contains a <strong>gauge info file (<em>CAMELS_463_gauge_info.txt</em>)</strong>, which<em> </em>reports basic information of each catchment, and <strong>463 subfolders</strong>, each having four files for a catchment, including:</p> <p> (1) <strong>Raven_Daymet_forcing.rvt</strong>, which contains Daymet meteorological forcing (i.e., daily precipitation in mm/d, minimum and maximum air temperature in deg_C, shortwave in MJ/m2/day, and day length in day) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.</p> <p> (2) <strong>Raven_USGS_streamflow.rvt</strong>, which contains daily discharge data (in m3/s) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.</p> <p> (3) <strong>GR4J_metrics.txt</strong>, which contains reference KGE and GR4J-based KGE metrics in calibration, validation and testing periods.</p> <p> (4) <strong>HMETS_metrics.txt</strong>, which contains reference KGE and HMETS-based KGE metrics in calibration, validation and testing periods.</p> <p><strong>Data collection and processing methods</strong></p> <p> <strong>Data source</strong></p> <ul> <li> Catchment information and the Daymet meteorological forcing are retrieved from the CAMELS data set, which can be found <a href="https://ral.ucar.edu/solutions/products/camels">here</a>.</li> <li> The USGS streamflow data are collected from the U.S. Geological Survey's (USGS) National Water Information System (NWIS), which can be found <a href="https://waterdata.usgs.gov/nwis/sw">here</a>.</li> <li>The GR4J and HMETS performance metrics (i.e., reference KGE and KGE) are produced in the study by Shen et al. (2022).</li> </ul> <p><strong> Forcing data processing</strong></p> <ul> <li>A quality assessment procedure was performed. For example, daily maximum air temperature should be larger than the daily minimum air temperature; otherwise, these two values will be swapped.</li> <li>Units are converted to Raven-required ones. Precipitation: mm/day, unchanged; daily minimum/maximum air temperature: deg_C, unchanged; shortwave: W/m2 to MJ/m2/day; day length: seconds to days.</li> <li>Data for a catchment is archived in a RVT (ASCII-based) file, in which the second line specifies the start time of the forcing series, the time step (= 1 day), and the total time steps in the series (= 12784), respectively; the third and the fourth lines specify the forcing variables and their corresponding units, respectively.</li> <li>More details of Raven formatted forcing files can be found in the Raven manual (<a href="http://raven.uwaterloo.ca/">here</a>).</li> </ul> <p><strong> Streamflow data processing</strong></p> <ul> <li>Units are converted to Raven-required ones. Daily discharge originally in cfs is converted to m3/s.</li> <li>Missing data are replaced with -1.2345 as Raven requires. Those missing time steps will not be counted in performance metrics calculation.</li> <li>Streamflow series is archived in a RVT (ASCII-based) file, which is open with eight commented lines specifying relevant gauge and streamflow data information, such as gauge name, gauge ID, USGS reported catchment area, calculated catchment area (based on the catchment shapefiles in CAMELS dataset), streamflow data range, data time step, and missing data periods. The first line after the commented lines in the streamflow RVT files specifies data type (default is HYDROGRAPH), subbasin ID (i.e., SubID), and discharge unit (m3/s), respectively. And the next line specifies the start of the streamflow data, time step (=1 day), and the total time steps in the series(= 12784), respectively.</li> </ul> <p><strong>GR4J and HMETS metrics </strong></p> <p>The GR4J and HMETS metrics files consists of reference KGE and KGE in model calibration, validation, and testing periods, which are derived in the massive split-sample test experiment performed in the paper.</p> <ul> <li>Columns in these metrics files are gauge ID, calibration sub-period (CSP) identifier, KGE in calibration, validation, testing1, testing2, and testing3, respectively.</li> <li>We proposed 50 different CSPs in the experiment. "CSP_identifier" is a unique name of each CSP. e.g., CSP identifier "CSP-3A_1990" stands for the model is built in Jan 1st 1990, calibrated in the first 3-year sample (1981-1983), calibrated in the rest years during the period of 1980 to 1989. Note that 1980 is always used for spin-up.</li> <li>We defined three testing periods (independent to calibration and validation periods) for each CSP, which are the first 3 years from model build year inclusive, the first 5 years from model build year inclusive, and the full years from model build year inclusive. e.g., "testing1", "testing2", and "testing3" for CSP-3A_1990 are 1990-1992, 1990-1994, and 1990-2014, respectively.</li> <li>Reference flow is the interannual mean daily flow based on a specific period, which is derived for a one-year period and then repeated in each year in the calculation period. <ul> <li>For calibration, its reference flow is based on spin-up + calibration periods.</li> <li>For validation, its reference flow is based on spin-up + calibration periods.</li> <li>For testing, its reference flow is based on spin-up +calibration + validation periods.</li> </ul> </li> <li>Reference KGE is calculated based on the reference flow and observed streamflow in a specific calculation period (e.g., calibration).<strong> Reference KGE is computed using the KGE equation with substituting the "simulated" flow for "reference" flow in the period for calculation</strong>. Note that the reference KGEs for the three different testing periods corresponds to the same historical period, but are different, because each testing period spans in a different time period and covers different series of observed flow.</li> </ul> <p><strong>More details of the split-sample test experiment and modeling results analysis can be referred to the paper by Shen et al. (2022).</strong></p> <p><strong>Citation</strong></p> <p><strong>Journal Publication</strong></p> <p>This study:</p> <p>Shen, H., Tolson, B. A., & Mai, J.(2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. <a href="https://doi.org/10.1029/2021WR031523">https://doi.org/10.1029/2021WR031523</a></p> <p>Original CAMELS dataset:</p> <p>A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan (2015). Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, <a href="http://doi.org/10.5194/hess-19-209-2015">http://doi.org/10.5194/hess-19-209-2015</a></p> <p><strong>Data Publication</strong></p> <p>This study:</p> <p>H. Shen, B. A. Tolson, and J. Mai (2022). Time to Update the Split-Sample Approach in Hydrological Model Calibration. Zenodo. <a href="http://doi.org/10.5281/zenodo.5915374">http://doi.org/10.5281/zenodo.5915374</a></p> <p>Original CAMELS dataset:</p> <p>A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR.<a href="http:// https://dx.doi.org/10.5065/D6MW2F4D"> https://dx.doi.org/10.5065/D6MW2F4D</a></p>
Neutron and LIBS data behind figures in Gabriel et al. (2022). On an extensive late hydrologic event in Gale crater as indicated by water-rich fracture halos. JGR-Planets.
<p>This repository contains datasets that allow for the reproduction of certain figures and analysis by Gabriel et al. (2022), a peer-reviewed journal article accepted in the Journal of Geophysical Research: Planets. Below are brief descriptions of the datasets.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol350-400_FigureS10.txt</p> <p>Description: These are raw neutron counts from the thermal and epithermal neutron detectors as part of the Dynamic Albedo of Neutrons instrument. Only data from rover stops for sols 350 to 400 are included. Data from rover stops allows them to be readily colocated rover localization data, which includes 'site' and 'drive' numbers that are specific to each stop.</p> <p><br> File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol900-1500_Figure5.txt</p> <p>Description: This is similar data to the product above, however for the sol range 900 to 1500.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_withMobility_Raw_Data_sol350-420_FigureS13.txt</p> <p>Description: This is similar data to the products above, however the dataset includes passive neutron count rates acquired while the rover was traversing, smoothed over 3 meters of lateral distance traveled. This dataset allows for the analysis of environments that may be present between rover stops, and thus not detected in 'no mobility' datasets.</p> <p> </p> <p>TGabriel_JGR-P_Kukri_CCAM_MajorOxideComposition_FigureS19TableS1.xlsx</p> <p>Description: This is the result of the Major Oxide Quantification pipeline developed by the ChemCam instrument team (sPDL Tool v2.0, 25 July 2015) as run by William Rapin. Additional H quantification in Figure S19 of Gabriel et al. (2022) is not included in this dataset, but is provided in the manuscript.</p>
Present-day and future changes in the hydrology of the Bhagirathi Basin
<p>This repository contains the daily outputs (Jan 1, 1991 to Dec 31 2020) produced in the project SDC project. The folder 'Final_full_30yrs_baseline.rar' contains all the historical outputs generated from the SPHY model. The folder contains data in the different formats (spatial and non spatial) '.map','.csv' and '.tss'</p> <p>The folder 'Climate_change.rar' contains climate runs from (Jan 1, 2021 to Dec 31 2100) for 4 GCM-RCM and ssp combinations.</p>
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