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113 results for “Hydrological Models”
Model simulated hydrological estimates for the North Slope drainage basin, Alaska, 1980-2010
Estimates of runoff, river discharge, snow water equivalent (SWE), subsurface runoff, and soil temperatures are drawn from the Permafrost Water Balance Model (PWBM). The simulation and derived data span the period 1980-2010. The model was forced with daily gridded meteorological data obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis (version 5.2.0). The estimates of total runoff (daily), soil temperature (daily), subsurface runoff (monthly), and SWE (monthly) are expressed on a spatial grid (N=312; 25x25 km EASE-Grid version 1, Northern Hemisphere) over the North Slope drainage basin, with the coastline extending from Utqiagvik (formerly Barrow) to just west of the Mackenzie River delta. River discharge, calculated as a volume flux of runoff at each grid cell, was routed through the river network defined on a simulated topological network (STN). Archived files contain discharge flux through the grid cell representing the outlet of each of forty-two basins defined across the region on the 25 km resolution EASE-Grid. Details of the PWBM, forcing variables, model validation and results of analysis are described in Rawlins et al. (2019).
Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. Pr
Hydrological regime in a model High Arctic catchment (Bratteggdalen, Svalbard) under warming and precipitation rise
<p><span>Climate change is impacting water flow worldwide and is particularly important for High Arctic basins. Thawing permafrost and melting of glaciers, as well as higher air temperatures and precipitation, affect hydrological regimes and retention in polar basins. However, knowledge is limited as regards long-term changes in discharge from catchments in the High Arctic. Our aim was to evaluate the impact of local conditions on hydrological regime in glacial-fluvio-lacustrine model system in the High Arctic. We used mainly hydrological and meteorological data from 9 summer seasons (June-September) between 2005 and 2019 extracted from the entire database (16 seasons in 1972-2019). Wide range of statistical methods was applied including bootstrapping, random forest and multiple regression, to determine the coupling between hydrometeorological parameters (air and water temperature, discharge, sunshine duration, precipitation). The hydrological regime exhibits a distinct seasonal pattern with a pronounced, snowmelt-derived peak (maximum discharge) in the early part of the season (June-July) affected by precipitation. In the late part of the season (August-September), low-intermediate discharge is primarily governed by air temperatures and, only secondarily by precipitation. The hydrometeorological coupling in August-September is stronger that in June-July. The statistically significant increase in air temperature (0.45°C per decade) in August-September during 1979-2018 makes this part of the season important in terms of long-term changes in the permafrost-underlain catchment. Thawing of the permafrost active layer thaw is clearly reflected by air–temperature-dependent low-to-intermediate discharge.</span></p> <p><span>Database consists of following data obtained from long-term discharge analyses: daily discharge data at the gauging station from 1983-2019 (1983-2019</span><span>_Brattegg_River_Discharge_v1.csv</span><span>), daily water stage data from 1972-1983 (1972-1983 </span><span>_ Brattegg_River_Water_Stage_v1.csv</span><span>), daily water level at gauging station and outflow from Bratteggbreen from 2017 (</span><span>2017_Brattegg_River_water_stage_gauging_station_Bratteggbreen_v1.csv</span><span>).</span></p> <p><span>This study is a contribution to the National Science Centre projects: 2021/43/D/ST10/00687 (SONATA17 funding scheme, ŁS), 2020/39/I/ST10/02129 (OPUS-LAP funding scheme, MB), 2017/27/B/ST10/01269 (OPUS funding scheme, KM), and SONATA 2015/19/D/ST10/02869 (SONATA funding scheme, MK). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ŁS was also supported from the Bekker Programme (award no. BPN/BEK/2021/1/00431) at the Polish National Agency for Scientific Exchange. The study was carried out by DI, EL as part of scientific activity of the Centre for Polar Studies (University of Silesia in Katowice) with the use of research and logistic equipment (monitoring and measuring equipment, sensors, multiple AWS, GNSS receivers, snowmobiles and other supporting equipment) of the Polar Laboratory of the University of Silesia in Katowice. MW and HM acknowledge the </span><span>statutory fund of University of Wrocław for suport during fieldwork in 2005-2010.</span></p> <p> </p> <p> </p>
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Towards Parameter Estimation in Global Hydrological Models
<p>The provided elementary effects are used in the publication J. Kupzig, R. Reinecke, F. Pianosi, M.Flörke and T. Wagener: Towards Parameter Estimation in Global Hydrological Models (submitted to Environmental Research Letters in Feb 2023).</p> <p>In a large sample study, the Morris Method (Morris 1991) application produces the provided elementary effects using a new lightweight version of the global hydrological model WaterGAP3: WaterGAPLite.</p> <ul> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/elementary_effects.zip?versionId=e980e961-2334-41db-900a-637b2dcec119">elementary_effects.zip </a>: elementary effects for all 50 trajectories and all basins (each trajectory is the result of 18 model runs; used bounds of parameters can be found in the Supplement of the manuscript)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/results_overview.xlsx?versionId=6f38c60f-9084-4376-907d-579414285506">results_overview.xlsx</a>: parameter ranks for each basin and different evaluation criteria based on the elementary effects.</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_Sample.csv">MC_Sample.csv</a>: normalized parameter samples of the additional Monte-Carlo Simulation (used bounds of parameters are the same as for the Morris method)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_NSE.csv">MC_NSE.csv</a>: resulting NSE values of the Monte-Carlo simulation</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/better_performing_basins.csv">better_performing_basins.csv</a>: list of basins (using GRDC no.) where minimal NSE is greater than -1 within all Monte-Carlo runs</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib.csv">standard_calib.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using the standard calibration for WaterGAP3 (fit to mean discharge)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib_mod.csv">standard_calib_mod.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using a modified version of the standard calibration for WaterGAP3 (maximizing the NSE)<br> </li> </ul>
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Future hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Saddle Catchment, 2001 - 2100.
The Saddle Catchment of the Niwot Ridge LTER is subject to warming in a future climate and thus changes in precipitation phase, precipitation redistribution, and timing and distribution of surface water inputs (the summation of rainfall and snowmelt) as well as changes in atmospheric demand (potential evapotranspiration, PET) and the amount of evapotranspiration (ET). The input warming data were developed to first force a future climate across the Saddle Catchment and evaluate resultant hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM). Future forcing data were generated by calculating and implementing delta values between daily average historical data and those generated from end-of-current-century Weather Research Forecasting model data. The variables perturbed in the warming DHSVM simulation were: precipitation, air temperature, relative humidity and longwave radiation. Target outputs included: daily spatially distributed precipitation (historical and future), daily spatially distributed surface water inputs (historical and future), total spatially distributed PET (historical and future), and total spatially distributed ET (historical and future). The precipitation and surface water inputs products are orthorectified (UTM projection) raster products, and the forcing data and PET and ET are CSV files. The forcing data represent catchment averages, which are distributed within DHSVM, and all other files are at the 2 m resolution.
Hydrological modeling on Costa Rica using HYPE
<p>This study aims to explore the use of the large-scale process-based HYPE (Hydrological Predictions for the Environment) model (Lindström et al., 2010) for Costa Rica. Due to the lack of ground meteorological data, precipitation and temperature from global products were used as model forcings. Moreover, PET and ET from MODIS additionally to streamflow timeseries were used to calibrate and validate the model. To deal with the lack of a common period between streamflow and PET-ET, different step-wise calibration procedures were tested to evaluate the most effective strategy to constraint the parameters space and reduce the model uncertainty.</p> <p>Our specific objectives were to:</p> <p>1. Adjust the open-source conceptual rainfall-runoff model HYPE to simulate catchments at the national scale of Costa Rica.</p> <p>2. Use remotely-sensed data and global products to drive and evaluate the model using four different step-wise calibration strategies.</p> <p>3. Analyze the effect of remotely-sensed PET and ET data on model calibration and its capabilities to improve the water balance and the hydrological signatures.</p>
Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories
<p>These files provide useful data and supplementary material associated with the publication 'Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories' (MNT information, atmospheric forcings, R scripts used to process and draw the graphs from the EcH2O-iso simulations, and observed water discharges).</p>
Parameter fields for the Hydrological Discharge (HD) model at 0.5° and 5 Min. horizontal resolution
<p><strong>HD model parameter files</strong></p> <p>This dataset comprises global parameter data that are necessary to run the Hydrological Discharge (HD) model Vs. 5.1, which has been published on <a href="http://doi.org/10.5281/zenodo.5707587">Zenodo</a>. The HD model calculates the lateral transport of water over the land surface to simulate discharge into the oceans. The HD model parameter dataset comprises parameter fields at 0.5° global resolution and at 5 Min. resolution (global, Europe). Details for both resolutions are provided below. </p> <p><strong>Authors</strong>: Stefan Hagemann, Tobias Stacke <br> <strong>Copyright 2021</strong>: Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon<br> <strong>License</strong>: under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0; https://creativecommons.org/licenses/)</p> <p><br> <strong>HD model parameter file at 5 Min resolution: hdpara_vs5_1.nc</strong></p> <p>River directions and digital elevation data were provided by Bernhard Lehner (pers. comm., 2014) and were derived from the HydroSHEDS (Lehner et al., 2006) database and from the Hydro1K dataset for areas north of 60°N (https://lta.cr.usgs.gov/HYDRO1K).<br> For a number of rivers (most of them north of 60°N), flow directions and model orography were manually corrected based on available GIS data, such as from DIVA (https://www.diva-gis.org/gdata), CCM River and Catchment Database<br> (Vogt et al. 2007), SMHI (Swedish Meteorological and Hydrological Institute), NVE (Norges vassdrags- og energidirektorats), SYKE (Finnish Environment Institute).</p> <p>This corrected dataset is referred to as HDvs5 in the following.<br> The HD model parameters for overland flow, base flow and river flow are generated as described in Hagemann and Dümenil (1998) and Hagemann et al. (2020). However, different to the HD model vs. 4 described in Hagemann et al. (2020), Vs5 utilizes inland water fractions from the ESA CCI Water Bodies Map v4.0 (Lamarche et al. 2017) and wetland fractions from the Global Lakes and Wetlands Database (Lehner and Döll 2004) instead of the previously used lake and wetlands fractions. Changes from Vs. 5.0 to Vs. 5.1 are provided in the file history_data.md that should be previewed below.</p> <p>The HD parameter dataset contains 14 variables which are shortly described in the following table.</p> <ul> <li> FLAG | Land sea mask | -</li> <li> FDIR | Flow direction | - | defined as written below</li> <li> ALF_K | HD model parameter Overland flow k | d-1</li> <li> ALF_N | HD model parameter Overland flow n | -</li> <li> ARF_K | HD model parameter Riverflow k | d-1</li> <li> ARF_N | HD model parameter Riverflow n | -</li> <li> AGF_K | HD model parameter Baseflow flow k | d-1</li> <li> AREA | Grid cell area | m-2 | based on own computation</li> <li> FILNEW | River flow target indices for longitudes | -</li> <li> FIBNEW | River flow target indices for latitudes | -</li> <li> DISTANCE | Distance between gridboxes in flow direction | m</li> <li> RIVERLENGTH | Distance between gridbox and the river mouth (or final sink) | km</li> <li> CAT_AREA | Upstream catchment area of gridbox | km²</li> <li> CAT_ID | Catchment ID of gridbox | -</li> </ul> <p> <em> Flow directions in variable FDIR are defined as on the Num Pad of a PC keyboard: </em> </p> <ul> <li> 7 8 9</li> <li> \ | /</li> <li> \|/</li> <li> 4--5--6</li> <li> /|\</li> <li> / | \</li> <li> 1 2 3</li> </ul> <p> Special directions: 5 = Sink point, i.e. no outflow<br> 0 = River mouth point in the ocean <br> -1 = Ocean point, but no river mouth</p> <p>Forcing data masks file: masks_5min.nc</p> <p>In the offline HD model version, this file is usually only used to obtain the grid information of the forcing data, i.e. of surface runoff and drainage (subsurface runoff). However, it contains four variables that are read in by the model, and that are actually used un coupled applications within the MPI-ESM. Even though these variables are not used in the HD model offline version, it was decided to keep them in order to allow future developments regarding the usage of these data and to keep some consistency with the HD model code implemented in MPI-ESM.</p> <ul> <li> ALAKE | Lake fraction within a grid box | Lamarche et al. 2017</li> <li> GLAC | Glacier fraction within a grid box | Hagemann 2002</li> <li> SLF | Land fraction within a grid box | Lamarche et al. 2017</li> <li> SLM | Land Sea Mask | Lamarche et al. 2017</li> </ul> <p>For simplicity, the data provided at the HD model resolution. Hence, these masks can be used when the forcing data are interpolated to the HD model resolution before they are read during the model run.</p> <p>This tar archive also include a subset of this global dataset for the European domain, hdpara_vs5_0_euro5min.nc.</p> <p> </p> <p><strong>HD model parameter file at 0.5° resolution: hdpara_vs1_11.nc</strong></p> <p>In addition, a global 0.5° HD parameter file hdpara_vs1_11 included. This is an update of the previous version 1.10 that was consistent to the parameter files used in previous offline and coupled applications of the HD model at 0.5° resolution (see, e.g. studies cited in Sect. 2.1 of Hagemann et al., 2020). Compared to the previous version 1.10, Vs. 1.11 now also utilzes the ESA water bodies and GLWD wetlands database such as in the 5 Min vs. 5.1 (see above). In additon, some flow directions have been updated. Except for DISTANCE and RIVERLENGTH, it comprises the same variables as for the 5 Min. version, but flow directions and parameters are generated as described in Hagemann and Dümenil (1998) and Hagemann and Dümenil Gates (2001). Here, the 0.5 degree mask file mask_05.nc comprises those masks that were utilized in the HD parameter generation. Only the land fraction is taken from Hagemann (2002) where the HD land sea mask indicates land.</p> <p><br> <strong>References</strong></p> <ul> <li>Hagemann, S., L. Dümenil (1998) A parameterization of the lateral waterflow for the global scale. Clim. Dyn. 14 (1), 17-31</li> <li>Hagemann, S., L. Dümenil Gates (2001) Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model, J. Geophys. Res. 106, 1503-1510</li> <li>Hagemann, S., 2002: An improved land surface parameter dataset for global and regional climate models, MPI Report No. 336, Max Planck Institute for Meteorology, Hamburg, Germany</li> <li>Hagemann, S., T. Stacke and H. Ho-Hagemann (2020) High resolution discharge simulations over Europe and the Baltic Sea catchment. Front. Earth Sci., 8:12. doi: 10.3389/feart.2020.00012.</li> <li>Lamarche, C., Santoro, M., Bontemps, S., d’Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P. and Arino, O. (2017) Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing, 9(1), p.36.</li> <li>Lehner, B., P. Döll (2004) Development and validation of a global database of lakes, reservoirs and wetlands.</li> <li>J. Hydrol., 296: 1-22, doi:10.1016/j.jhydrol.2004.03.028.</li> <li>Vogt, J.V. et al. (2007): A pan-European River and Catchment Database. European Commission - JRC, Luxembourg, (EUR 22920 EN) 120 pp.</li> </ul> <p> </p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
OMS Project for the hydrological modelling of the Posina River
<p>The OMS project contains the simulations, jar files of the components, the inputs and the ouputs used in the thesis " A flexible approach to the estimation of water budgets and its connection to the travel time theory ", Bancheri (2017). The project can be run using the OMS console available within the project.</p>
Coupled Hydrological and Thermal Models of Rockwall Permafrost
<p>This dataset contains forcing data and selected output of coupled thermal and hydrological simulations applied to a high-elevated rockwall site (the Aiguille du Midi, 3842 m asl, Mont Blanc massif, France).</p> <p>All data are provided as .shp and .shx for display, as well as a .dbf file for quick reading. They are made of 5 columns, whose:</p> <ul> <li>« Node » is the Node ID,</li> <li>« X » is the position (in m) on the x axis,</li> <li>« Y » is the position (in m) on the y axis,</li> <li>« xINIT » is the calculated value for the parameter indicated in the file name(head, temperature, etc.)</li> <li>« Time » is the time step at which the value is calculated.</li> </ul> <p>The model output are gathered according to various cases studies of saturation and water flows. The model settings of the various cases studies are outlined in this file but more details about the mathematical approach and modeling settings and strategy are provided in the study to which the dataset belongs and which was submited for the first time to Journal of Geophysical Research: Earth Surface in July 2020.</p> <ul> <li><strong><em>SaFl </em></strong>corresponds to a saturated with forced water flows case study by assuming a constant recharge and discharge.</li> <li><strong><em>SaNF</em></strong> is a saturated case study with no water flows.</li> <li><strong><em>uSFl</em></strong> corresponds to an unsaturated case study with forced water flows in selected fractures only.</li> <li><strong><em>uSLF</em></strong> is unsaturated with limited water flows.</li> </ul> <p>For <strong><em>SaFl</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at various time step after of transient simulations (1550 AD, 2000 AD, 2015 AD, 2030 AD) such as displayed in Figures S2 and S3.</li> <li>Hydraulic heads (m) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 6 and S3.</li> <li>The ice bulk volumetric fraction at various time step after of transient simulations (2000 AD, 2015 AD, 2030 AD) such as in Figure S3.</li> <li>Temperature (°C) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 4 and S3.</li> </ul> <p>For <strong><em>SaNF </em></strong>the following output are provided:</p> <ul> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> <li>Hydraulic heads (m) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 6.</li> </ul> <p>For <strong><em>uSFl </em></strong>the following output are provided:</p> <ul> <li>Hydraulic heads (m) and temperature (°C) at 2100 AD such as in Figure 4 and 6.</li> <li>Saturation in 852 AD, 853 AD and 854 AD such as in Figure 5.</li> </ul> <p>For <strong><em>uSLF</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at 1550 AD and 2015 AD such as in Figure S2</li> <li>Hydraulic heads (m) after initialization (0 AD) and at 1850 AD and 2100 AD) such as in Figure 6.</li> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> </ul> <p>In addition, the temperature and hydrological (hydraulic head changes for the unsaturated cases studies only) forcing data (« Input_data_boundary_conditions.csv ») are provided. This last file contains:</p> <ul> <li><em>A to D</em>: the surface points extracted along the 4-m resolution DEM transect (Horizontal (X) position in m, Vertical (Y) position (elevation in m)) and MARST map (for the period 1961-1990: MARST<sub>init</sub>) as illustrated on Fig. S1, together with the adjusted MARST to run the model initialization simulations.</li> <li><em>F to I</em>: the surface points taken on for forcingthe model at its upper boundary and in between which the forcing data were interpolated.</li> <li><em>L to BH</em>: Data used for transient simulations with the time in year (<em>L</em>), the MARST anomaly applied to the adjusted MARST (<em>M</em>), the time in days (<em>N</em>), the MARST value applied at each surface node from 1 to 23 (<em>O </em>to <em>AK</em>), as well as the changes in head values applied at at each surface node from 1 to 23 (<em>AL </em>to <em>BH</em>) for the concerned simulations.</li> </ul> <p> </p> <p>More information can be made available by contacting Florence Magnin or Jean-Yves Josnin at <a href="mailto:florence.magnin@univ-smb.fr"><em>florence.magnin@univ-smb.fr</em></a><em> </em>or <a href="mailto:jean-yves.josnin@univ-smb.fr"><em>jean-yves.josnin@univ-smb.fr</em></a></p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Implementation of GR hydrological models in 95 near-natural catchments across Chile
<p>All the files included here contain the data and calibration results produced for the paper "Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall-runoff models" accepted for publication in Hydrological Sciences Journal (HSJ). This database summarizes the calibrated parameter sets for the GR4J, GR5J and GR6J conceptual rainfall-runoff models, all coupled to the CemaNeige snow module (i.e., GRXJ + CemaNeige = GRXJCN), using 12 objective functions. The models are configured for 95 near-natural catchments located in Continental Chile. Each basin is identified by a unique code registered in the National Water Bank (BNA by its acronym in Spanish) by the Chilean Water Bureau (DGA; https://dga.mop.gob.cl/). Meteorological forcings and hypsometric curves for each basin studied are also included.</p> <p>The information is organized as follows:</p> <p>- "01 Forcings" : It includes a "Comma-separated value" file (".csv") per basin (according to the notation "BNA code.csv") which contains daily time series of precipitation (P; mm/d), temperature (°C) and potential evapotranspiration (E; mm/d) for the period 1980-01-01 to 2017-12-31. The daily runoff observations (Q; mm/d) and snow water equivalent (SWE; mm) from Cortés et al. (2017) are included. P and T are estimated from the basin-scale average of the CR2Met v2.0 gridded product, while E was calculated using Oudin's formula.<br> - "02 Hypsometry" : It includes a "Comma-separated value" file (".csv") per basin with elevation (in m a.s.l.) vs. area below elevation (in percentage) in the format required by GR models ("BNA code.csv" notation), and the full hypsometric curve ("BNA code_original.csv" notation) retrieved from the SRTM DEM clipped to the basin of interest.<br> - "03 Calibrated parameters": It includes one sub-directory per basin, containing a summary of the calibrated parameters for each combination of model structure (GR4JCN, GR5JCN and GR6JCN) and objective function.</p> <p>Additionally, we include the following "Comma-separated value" (i.e., ".csv") files:</p> <p>- "BNA_select.csv": list of case study basins (BNA code and name).<br> - "Catchment_attributes_CAMELS-CL.csv": catchments attributes directly obtained from CAMELS-CL.<br> - "Catchment_attributes.csv": catchments attributes used for this study. Note that this file contains re-calculated values for climatic attributes. </p>
Statistical blending of global-gridded climatological products: an approach to inverse hydrological model
<p>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 <em>in situ</em> 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<strong>.</strong></p>
Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp)
<p>MERV-Jp is the dataset of meteorological forcing and multi-model runoff simulation in 135 (ver1.1) / 87 (ver2.0) Japanese basins, and contributes to carrying out a large sample rainfall-runoff simulation in Japan. In addition, MERV-Jp can be used as a benchmark to evaluate user's hydrological modeling. <br> The detailed description of MERV-Jp can be found at "Y. Sawada, S. Okugawa and T. Kimizuka (2022): Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins, Hydrological Research Letters, 16, 73-79" (https://doi.org/10.3178/hrl.16.73).</p>
Hydrologic response units (base units for PRMS streamflow model), Andrews Experimental Forest, 1993
Hydrologic Response Units are used as base units for the Precipitation-Runoff Modeling System (PRMS) streamflow model. Created by Alok Sikka as part of landscape runoff modeling.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.