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113 results for “Hydrological Model”
EstSoil-EH: A high-resolution eco-hydrological modelling parameters dataset for Estonia (dataset)
<p>For the EstSoil-EH dataset, we synthesized more than 20 extended eco-hydrological variables for Estonia as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5m Lidar DEM, Estonian Topographic Database and EU-HydroSoilGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75% of mapped units smaller than 4.0 ha, based on Soviet era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only of the actual texture class, but also of classifiers for rock content, peat soils, distinct compositional layers and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil profiles (e.g., layers, depths), texture (clay, silt, sand components), coarse fragments and rock content. In addition, we aggregated and predicted physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content, available water capacity).<br> The developed methodology and dataset will be an important resource for the Baltic region, but possibly also all other regions where detailed field-based soil mapping data is available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia.</p> <p> </p> <p>We created an extended eco-hydrological dataset for Estonia, the EstSoil-EH, containing derived numerical values for the following data in all of the mapped soil units in the 1:10 000 soil map: soil profiles (e.g., layers, depths), texture (clay, silt, and sand components), rockiness, and physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content). Ultimately, our objective was to develop a reproducible method for deriving numerical values to support modelling and prediction of eco-hydrological processes in Estonia using the popular Soil and Water Assessment Tool.</p> <p>For more information on the development of this dataset look for "EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia", Alexander Kmoch, Arno Kanal†, Alar Astover, Ain Kull, Holger Virro, Aveliina Helm, Meelis Pärtel, Ivika Ostonen and Evelyn Uuemaa, 2021, Earth Syst. Sci. Data, 13, 83–97, <a href="https://doi.org/10.5194/essd-13-83-2021">https://doi.org/10.5194/essd-13-83-2021</a> </p>
Outputs of the WiMMed hydrological model for Sierra Nevada (Spain). Sept2015-Aug2022
<p>Ecosystem Services related to flood prevention, aquifer recharge and erosion prevention in SIERRA NEVADA (Spain) were quantified through the WiMMed hydrological model (Watershed Integrated Model in Mediterranean Environments; Herrero et al., 2014). WiMMed is a distributed and physically based model that combines hourly and daily meteorological data with soil hydro-physical properties and land use and land cover information to simulate water balance and flow circulation at basin scale (see Herrero et al. (2014) for details). </p><p>In this study we applied the WiMMed model considering the land use and land cover data for 2020 (according to SIPNA) and the meteorological data from Sept2015 to Aug2022 to evaluate the value of ecosystem services, following the work made by Moreno-Llorca et al. (2020). Specific parameters, expressing the influence of vegetation changes in the hydrological processes of the study area, were considered, namely on evapotranspiration, interception, infiltration, overland flow and soil erodibility. Aquifer recharge (mm/m2/year) was calculated as the total volume of water moving from the soil into the aquifer and becoming groundwater. For that, the model firstly interpolates the precipitation at the cell scale (Herrero et al., 2009), and then calculates rainfall/snowfall partition, reproduces the interception from the vegetation, calculates the snow accumulation and melting (Herrero et al., 2009), and separates surface runoff from infiltration on the ground surface. Vertical and horizontal soil water movement was reproduced by a two-layer soil approach, using Darcy-Buckingham law with Mualem-vanGenuchten parameterization (Muñoz Carpena and Ritter Rodriguez, 2005). Evapotranspiration extract water from soil using a parameterization based on potential evapotranspiration and soil water content (Herrero et al., 2014). Water percolating through the second layer of soil becomes aquifer recharge. Soil erosion prevention (T/ha/year) was calculated by considering the inverse of soil loss by water flow concentration (rill processes) and raindrop impacts (interrill processes). WiMMed uses the variation of different parameters that link soil loss, with changes in vegetation cover and land uses, as described in (Millares et al., 2019). Changes on soil erodibility were estimated from vertical distribution of root biomass, by adapting empirical models (e.g. Gale and Grigal, 1987; Jackson et al., 1996) to Mediterranean environments reported previously (Martinez- Fernandez et al., 1995). From these estimations, distributed soil erodibility was calculated from the empirical model proposed by Flanagan and Livingstone (1995). The calibration and validation of the WiMMed model in Sierra Nevada has been conducted through a series of studies that analysed each hydrological process in the area and designed and corrected each WiMMed module, pertaining to snow (Herrero et al., 2009), soil (Aguilar and Polo, 2011), baseflow (Millares, 2008; Millares et al., 2009), river flow (Pérez-Palazón et al., 2014), or soil loss and sediment transportation (Bergillos et al., 2016; Millares et al., 2020).</p><p><strong>INPUT DATA</strong></p><p>The input data used in the hydrological simulations were:</p><ul><li>Digital elevation model from national remote sensing program PNOA-LIDAR MDT02 and the topographic features calculated by WiMMed from the DEM: surface drainage system, river delineation, slope, aspect, sky view factor and horizon (sky obstruction in 8 directions).</li><li>Meteorological data from more than 50 weather stations in the area: hourly/daily rainfall (mm), hourly and daily temperature (oC), daily solar radiation (MJ/m2), average daily wind speed (m·s−1), average daily relative humidity (%), average daily barometric pressure (hPa).</li><li>Physico-chemical and hydraulic properties of the soil selected from the available spatial database performed by Rodríguez (2008), in which thematic maps were obtained for Andalusia at a 250-m resolution: hydraulic conductivity (mm·h−1), saturation and residual moisture values (mm·mm−1), air-entry matric potential (mm), retention parameter of the van Genuchten (dimensionless) and soil thickness (mm).</li><li>Land cover and land use information from SIPNA 2020.</li><li>Aquifer regions and information from hydrogeological atlas of Andalusia (ITGE-Junta de Andalucía, 1998; Castillo, 2008).</li></ul><p><strong>OUTPUT DATA</strong></p><p>The results contained in this database are raster files in UTM ETRS89 30S, with a spatial resolution of 30x30 meters, for the whole SIerra Nevada. The raster files are Esri-ASCII ArcGIS (.asc) grids with 3846 columns (X) and 2099 rows (Y). There are different time scales for each variable. The prefix of the file indicates this time scale, namely "Ano" for annual maps, "mes" for monthly maps and "Tot" for the whole simulation. The suffix indicates the variable of interest:</p><ul><li>Pre: Accumulated precipitation (solid + liquid) in mm</li><li>T_m: Mean temperature in ºC</li><li>P_n: Accumulated snowfall in mm</li><li>ErT: Accumulated total erosion (rill + interrill) in kg/m2</li><li>ET0: Accumulated potential evapotranspiration in mm</li><li>EvC: Accumulated real evaporation from canopy (intercepted precipitation) in mm</li><li>EvN:Accumulated real sublimation from snow in mm</li><li>EvS: Accumulated real evapotranspiration ration from soil in mm</li><li>Exp: Accumulated direct runoff in mm</li><li>Fus: Accumulated snowmelt in mm</li><li>HSol1: Instantaneous soil moisture in surface layer 1 (upper 25 cm) in mm</li><li>HSol2: Instantaneous soil moisture in deep layer 2 in mm</li><li>Inf: Accumulated infiltration from surface into soil in mm</li><li>Per: Accumulated aquifer recharge (from soil to groundwater) in mm</li><li>Qlat: Accumulated lateral flow (horizontal movement of water between cells) in mm</li><li>Tmn: Minimum temperature in ºC</li><li>Tmx: Maximum temperature in ºC</li></ul><p>There are also some other grid files (Tot_XXX.asc) related to the initial and final conditions of the state variables or internal conditions of the model.</p><p><strong>References</strong></p><p>Aguilar, C., Polo, M.J., 2011. Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale. Hydrol. Earth Syst. Sci. 15, 2495–2508. doi: 10.5194/hess-15-2495-2011.</p><p>Bergillos, R.J., Rodríguez-Delgado, C., Millares, A., Ortega-Sánchez, M., Losada, M.A., 2016. Impact of river regulation on a Mediterranean delta: assessment of managed versus unmanaged scenarios. Water Resour. Res. 52 (7), 5132–5148.</p><p>Castillo, A. 2008. Manantiales de Andalucía. Agencia Andaluza del agua, Consejería de Medio Ambiente, Junta de Andalucía, Sevilla, 410 pp.</p><p>Herrero, J., Polo, M.J., Moñino, A., Losada, M.A., 2009. An energy balance snowmelt model in a Mediterranean site. J. Hydrol. 371 (1-4), 98–107.</p><p>Herrero, J., Millares, A., Aguilar, C., Egüen, M., Losada, M.A., 2014. Coupling spatial and time scales in the hydrological modelling of mediterranean regions: WiMMed, in: CUNY Academic Works. In: Presented at the International Conference on Hydroinformatics, p. 8. ITGE-Junta de Andalucía: Atlas Hidrogeológico de Andalucía. Madrid, 216 pp., ISBN: 84-7840-351-5, available at: http: //aguas.igme.es/igme/publica/libros1 HR/libro110/lib110.htm, last access: 18 March 2012, 1998</p><p>Millares, A., 2008. Integración del caudal base en un modelo distribuido de cuenca. Estudio de las aportaciones subterráneas en ríos de montaña. University of Granada.</p><p>Millares, A., Polo, M.J., Losada, M.A., 2009. The hydrological response of baseflow in fractured mountain areas. Hydrol. Earth Syst. Sci. 13 (1261–1271), 2009.</p><p>Millares, A., Díez-Minguito, M., Moñino, A., 2019. Evaluating gullying effects on modeling erosive responses at basin scale. Environ. Modell. Software 111, 61–71. Millares, A., Herrero, J., Bermúdez, M., Leiva, J.F., Cantalejo, M., 2020. Long-term modelling of soil loss and fluvial transport processes in a mountainous semi-arid basin, southern Spain, in: River Flow 2020 - Twentieth International Conference on Fluvial Hydraulic. Delf, Netherlands.</p><p>Moreno-Llorca, R., Vaz, A. S., Herrero, J., Millares, A., Bonet-García, F. J., & Alcaraz-Segura, D. 2020. Multi-scale evolution of ecosystem services' supply in Sierra Nevada (Spain): An assessment over the last half-century. <i>Ecosystem Services</i>, <i>46</i>, 101204.</p><p>Muñoz Carpena, R., Ritter Rodriguez, A., 2005. Hidrología Agroforestal. Mundiprensa.</p><p>Pérez-Palazón, M. J., Pimentel, R., Herrero, J., & Polo-Gómez, M. J. 2014. Analysis of snow spatial and temporary variability through the study of terrestrial photography in the Trevelez river valley. In <i>Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI</i> (Vol. 9239, pp. 358-368). SPIE.</p><p>Rodríguez, J. A. 2008. Sistema de Inferencia Espacial de Propiedades Físico-Químicas e Hidráulicas de los Suelos de Andalucía. Herramienta de Apoyo a la Simulación de Procesos Agro-Hidrológicos a Escala Regional. Informe Final. Empresa Pública Desarrollo Agrario y Pesquero, Consejería de Agricultura y Pesca, Sevilla.</p><p> </p><p> </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>
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>
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>
The use of GRDC gauging stations for calibrating large-scale hydrological models
<p>The Global Runoff Data Centre provides time series of observed discharges that are very valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology which, though investigated several times, has not been satisfactorily solved. Grid-based hydrological models need to fit the reported station location to the river network depending on the resolution, to compare simulated discharge with observed discharge. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the wrong basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcseconds) river network, and we provide source codes and high- and low-resolution watershed boundaries.</p> <p>Same as release on Github: https://github.com/iiasa/CWATM_grdc_calibration_stations/releases/tag/V1.0</p>
Making the Most out of a Hydrological Model Dataset: Sensitivity Analyses to Open the Model Black-Box (data and code)
<p>This is "data and code" repository for the Water Resources Research Article 2017WR020401 by Borgonovo et al. (2017): "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box". Each sub-directory contains the Matlab or R scripts to reproduce all paper plots. </p> <p>Note, that the data of this repository (i.e. under ./data_input ) are identical to the data analysed by Rakovec et al. (2014).</p> <p>References:</p> <ul> <li>Borgonovo, E., Lu, X., Plischke, E., Rakovec, O. and Hill, M. C. (2017), Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2017WR020767</li> <li>Rakovec, O., M. C. Hill, M. P. Clark, A. H. Weerts, A. J. Teuling, and R. Uijlenhoet (2014), Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, doi:10.1002/2013WR014063.</li> </ul>
Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"
<p>This dataset contains observations and model output used in </p> <p>Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Data for: Antarctic wide subglacial hydrology modeling
Open the record for dataset details and reuse information.
VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
<p>## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model</p> <p>Date updated: June 28, 2021</p> <p>Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2)<br> 1. Department of Civil and Environmental Engineering, UCLA<br> 2. Department of Geography, UCLA<br> Author contact info: jschap@g.ucla.edu</p> <p>The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes.</p> <p>v1.6c is the same as v1.6, except that the image mode parameters have been updated to reflect the changes made to the classic mode parameters (e.g. r0 and rmin are different, and albedo, fcanopy, and LAI are calculated based on snow-free values).</p> <p>## Overview</p> <p>VICGlobal is a dataset that can be used to run the Variable Infiltration Capacity (VIC) hydrological model over regional to continental scales. The dataset is at 1/16 degree resolution and has latitudinal coverage from -60 to 85 degrees. All files are referenced to the WGS84 ellipsoid and datum (EPSG code 4326).</p> <p>The vegetation parameter file uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (2013; 2015); however, the monthly average LAI, canopy fraction, and albedo values for each land cover type are calculated based on MODIS observations from 2017, using the method of Bohn and Vivoni (2019).</p> <p>There are two vegetation libraries: one for the northern hemisphere, and one for the southern hemisphere, in order to account for the seasonality of LAI, canopy fraction, and albedo.</p> <p>WARNING: although it appears small in compressed form, the image driver parameter input file, VICGlobal_params.nc, is about 140 GB when unzipped. Users are encouraged to use the image mode parameters that are already split by continent. For example, the parameter file for Africa is about 19 GB.</p> <p>A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/).</p> <p>Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631</p> <p>## List of contents</p> <p>Inputs for VIC-4 or the VIC-5 Classic Driver<br> * Soil parameter file<br> * Vegetation parameter file<br> * Elevation band file<br> * Vegetation library files (one each for the northern and southern hemispheres)</p> <p>Inputs for the VIC-5 Image Driver<br> * Parameter file (global)<br> * Domain file (global)<br> * Parameter files for each continent<br> * Africa<br> * Australia<br> * Eurasia (except Kamchatka)<br> * Kamchatka<br> * North America<br> * Oceania (New Zealand and nearby islands)<br> * South America<br> * Domain files for each continent<br> * GeoTiffs with continent masks</p> <p>Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided.</p> <p>## References</p> <p>* Bohn and Vivoni (2019). MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. https://www.nature.com/articles/s41597-019-0150-2</p> <p>* Livneh et al. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. https://www.nature.com/articles/sdata201542</p> <p>* Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P. and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Clim., 26(23), 9384–9392, doi:10.1175/JCLI-D-12-00508.1, 2013.</p> <p>* Zeng (2001). Global Vegetation Root Distribution for Land Modeling. Journal of Hydrometeorology. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2</p>
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </p>
Identification and Regionalization of Streamflow Routing Parameters for the HLM Hydrological Model in Iowa
<p>The tables contained the metrics and the peak flows estimated using HLM under three different parameterizations. </p>
The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum
<p>The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models.</p> <p>Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models:</p> <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th><strong>Institution</strong></th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table> <p> </p> <p>Climate change scenarios and periods:</p> <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> <p>The datasets consist of time series for the parameters of:</p> <ul> <li><strong>Ice thickness</strong> - average ice thickness in the Curonian Lagoon;</li> <li><strong>Meteorological data</strong> - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li><strong>Nemunas River discharge</strong> - simulated average daily values for the discharge and water temperature;</li> <li><strong>Salinity</strong> - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li><strong>Water fluxes</strong> - through four predefined cross-sections in the Curonian Lagoon;</li> <li><strong>Water level</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li><strong>Water residence time</strong> - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li><strong>Water temperature</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> <p>Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.</p>
Catchment and hydrological model instance matrices
<p>This dataset contains catchment and hydrological model matrices for 2116 non-U.S. catchments from the CARVAN dataset and 1 million GR4J model instances. GR4J model instance parameters are also provided. Code to use the data is available at https://github.com/stsfk/indexing_catchment_model. The dataset is created in the paper "The Profiling and pairing catchments and hydrological models with latent factor model".</p>
The exemplary basin modeling with Global Hydrologic Data Cloud (GHDC) and SHUD model.
<p><strong>GHDC</strong> = Global Hydrological Data Cloud</p> <p><strong>SHUD</strong> = Simulator of Hydrologic Unstructured Domains</p> <p> </p> <p>Files in the package:</p> <p> </p> <ul> <li> <p>Conestoga - The data retrived from GHDC and the modeling result by SHUD model, for Gonestoga, Pennsylvania, USA.</p> </li> <li> <p>Gummara - The data retrived from GHDC and the modeling result by SHUD model, for Gummara, Ethiopia</p> </li> <li> <p>heihe - The data retrived from GHDC and the modeling result by SHUD model, for Heihe Headwater, Gansu, China</p> </li> <li> <p>threeBasin - The R analysis code for the three basin simulations, including loading data and visualization.</p> </li> </ul> <p> </p> <p>The file structure in each basin folder:</p> <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><strong>TSD</strong></td> <td>Time-series forcing files.</td> </tr> <tr> <td> </td> <td><strong>GCS</strong></td> <td>Subfolder, terriestial data in GCS</td> </tr> <tr> <td> </td> <td><strong>PCS</strong></td> <td>Subfolder, terriestial data in PCS.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/landuse.tif</td> <td>Landuse raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/soil.tif</td> <td>Soil classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/geology.tif</td> <td>Geology classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during pre-processing.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_watershed_delineation</td> <td>Watershed delineation, include boundary, river, pourpoint.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/dem_buf</td> <td>The DEM, and buffer zone.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_LDAS</td> <td>The coverage of reanalysis grid.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Landuse</td> <td>The landuse classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Soil</td> <td>The soil classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/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><strong>Figure</strong></td> <td>The figures during model deployment</td> </tr> <tr> <td> </td> <td><strong>input</strong></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><strong>SHUD_model</strong></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>
Numerical model of the Messinian Mediterranean combining hydrological water balance, river erosion, and flexural isostasy: TISC code and input dataset for the Lago-Mare
Open the record for dataset details and reuse information.
Saddle catchment vegetation cover for Distributed Hydrology Soil Vegetation Model (DHSVM), 2 meter, 2019
The Saddle Catchment of the Niwot Ridge LTER is subject to observed and modeled hydrologic connectivity and nuances that is influenced by the variable vegetation across the area. This file was specifically intended to update the Distributed Hydrology Soil Vegetation Model (DHSVM) input file used to simulate vegetation within the Saddle Catchment, as an ongoing method for evaluating modeled hydrologic connectivity. Thus, high resolution vegetation information was used from existing National Ecological Observatory Network (NEON) datasets. NEON has developed a suite of vegetation datasets including a 1m spatial resolution Airborne Observation Platform (AOP) Total Biomass data product. This product is an orthorectified (UTM projection) raster product derived from NEON AOP Imaging Spectrometer (NIS) reflectance data (DP3.30016.001). This vegetation layer was chosen because it visually reflected the differences in vegetation across the Saddle Catchment at a much higher resolution than previous DHSVM vegetation files. Using this gridded biomass product, different vegetation types were manually assigned and translated into to DHSVM vegetation types/codes. The assigning and translating of vegetation types was done by consulting NWT LTER individuals who specialize in the vegetative and soil properties within the Saddle Catchment and using available point-vegetation data from the Saddle Catchment sensor node network. The final product was upscaled to a 2m resolution using R (to match all other DHSVM input files).
Winter snow depths for initializing a glacio-hydrological model in high mountain Chile
<p>The following dataset consists of the forcings, initial conditions, model grids and parameters used to run the TOPKAPI-ETH model (<em>Finger et al., 2011; Ragettli and Pellicciotti, 2012</em>) for the Rio Yeso catchment of central Chile (33.44°S, 69.93°W - <em>Burger et al., 2018</em>). The data and model grids were used to investigate the importance of accurate snow depth maps for initialising the physically-oriented model in a high elevation catchment - For a manuscript submitted to Water Resources Research (WRR) - January 2020. </p> <p> </p> <p>Data and file repository for the submitted article:<br> %-------------------------------------------------------------<br> %-------------------------------------------------------------</p> <p> On the utility of optical satellite winter snow depths for modelling the<br> glacio-hydrological behaviour of a high elevation, Andean catchment.</p> <p>Thomas E. Shaw1, Alexis Caro1,2, Pablo Mendoza3, Álvaro Ayala4, Francesca Pellicciotti5,6, Simon Gascoin7, James McPhee1,3</p> <p>1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile<br> 2 Univ. Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement (IGE, UMR 5001), Grenoble, France<br> 3 Department of Civil Engineering, Universidad de Chile, Santiago, Chile<br> 4 Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile<br> 5 Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland<br> 6 Department of Geography, Northumbria University, Newcastle, UK<br> 7 CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France</p> <p>%-------------------------------------------------------------<br> %-------------------------------------------------------------<br> The following sub-folders are separated into forcings, grids, initial model conditions, model files and parameters.</p> <p>This file describes briefly the contents of each sub-folder.</p> <p>%-------------------------------------------------------------<br> FORCINGS:</p> <p>CloudCover_TPK.csv - A timeseries of hourly cloud cover fraction (-) derived NASA POWER archives.<br> Discharge_TPK.csv - A timeseries of hourly discharge (m3 s-1) from the outlet station F_TdP.<br> Master_Data_TPK.mat - a Matlab structure (written in version 2017a) for all data availble to the catchment for the considered model period.<br> Precipitation_TPK.csv - A timeseries of hourly precipitation (mm/hr) from AWS TdP.<br> Temperature_TPK.csv - A timeseries of hourly temperature (degC) from AWS TdP. <br> TemperatureGradient_TPK.csv - A timeseries of calibrated temperature gradients based upon forcing from AWS TdP.</p> <p>%-------------------------------------------------------------<br> GRIDS:</p> <p>42 ascii files for various grids (primary or secondary) use to derive the .TES file (see TOPKAPI-ETH sub-folder) for running the model.<br> Associated projection (.prj) files are given.<br> Naming convention is provided in the manual (see TOPKAPI-ETH sub-folder) except:<br> rdy_SoilDepth.asc - An adjusted top layer soil depth map based upon Ragettli et al. (2012).<br> rdy_debris_v.asc - A debris thickness map for Piramde Glacier and the tongue of Bello Glacier. Values adjusted slightly from Ayala et al. (2016) to account for areas that are not debris, but bedrock (Bello Glacier).</p> <p>%-------------------------------------------------------------<br> INITIAL_CONDITIONS:</p> <p>Sub-folder 'Albedo': <br> Albedo_Pleiades.asc - An albedo map derived from the model spin up and limited to the snow-covered pixels of the Pléiades snow depth map.<br> Sub-folder 'Snow':<br> XXX_snow_mmwe.asc - A snow water equivalent map (mm w.e.) given by the initialisation method 'XXX' (Pléiades, TOPO or DBSM). TPK is derived solely from the model spin up (an input grid not required). <br> XXXeq_snow_mmew.asc - As above, though considering the equal means approach described in the manuscript. TPK included here.<br> Sub-Folder 'SpinUp_State':<br> 201709040000.stt - The system state file that contains information on the equiblibrium state of catchment (as read by the model upon initialisation). Running the model with a spinup shuld call upon this file within the command prompt.</p> <p><br> %-------------------------------------------------------------<br> PARAMETERS:</p> <p>Sub-folder 'Calibration'<br> TPK_ParameterAllocation - A Matlab script for the establishing the Monte Carlo parameter simulation and running the model n times. The current script is considered for soil parameters.<br> Sub-folder 'Sensitivity'<br> TPK_Sensivity_Analysis - A Matlab script for establishing the upper and lower boundaries of parameter/forcing sensitivities for a one-at-a-time analysis.</p> <p> </p> <p>%-------------------------------------------------------------<br> RESULTS:</p> <p>Model_Output_Comparison.mat - A matlab file with output grids and vectors for model intercomparisons (i.e. Pléiades (Pléiades-Uncertainty and Pléiades+Uncertainty), TOPO, TPK, DBSM + equal means equivalents). Files are:<br> All_S - Daily snow mass balance grids (mm w.e.)<br> Bias_Month - Monthly bias (row) of modelled vs measured streamflow at F_aP site for each model run (column).<br> Date_Daily - Numeric date of daily grids<br> DateTPK - Numeric date of hourly model simulations<br> Gla_Map - Daily cumulative glacier modelled mass balance grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation<br> GMB_Bello - Cumulative modelled mass balance (mm w.e.) of Bello Glacier AWS grid cell<br> GMB_Piramide - Cumulative modelled mass balance (mm w.e.) of Piramide Glacier AWS grid cell<br> GMB_Yeso - Cumulative mass modelled balance (mm w.e.) of Yeso Glacier AWS grid cell<br> KGE_Month - KGE values per month (row) and for each model run (column)<br> M3AP - Measured streamflow at F_aP<br> M3TP - Measured streamflow at F_TdP<br> MeltG_Avg_all - Mean hourly ALL-glacier melt rate (mm w.e./hr) for each model run (column)<br> MeltS_Avg_all - Mean hourly catchment-wide melt rate (mm w.e./hr) for each model run (column)<br> MOD_SnowCC - Daily MODIS snow cover fraction<br> Model_Name - .... well, its the name of the model run :=)<br> MODIS_SLE - The calculated Snow Line Elevation (m a.s.l.) for each daily MODIS scene<br> PlanetSLE - As above, but for PlanetScope images (17 days total)<br> Planet_SnowObs - The numeric dates of the equivalent PlanetSLE data<br> Q_Mod_aP - The modelled hourly streamflow at F_aP<br> Q_Mod_TdP - The modelled hourly streamflow at F_TdP<br> Q_Prc_Month - The percentage difference in monthly (row) modelled-measured streamflow by model run (column)<br> R_Month - Correlation values per month (row) and for each model run (column)<br> RelVar_Month - Relative variance per month (row) and for each model run (column)<br> Snow_Map - Daily snow water equivalent grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation<br> SnowCC - The hourly modelled snow cover fraction for the catchment for each model run (column)<br> TPKSLE - The daily modelled TOPKAPI-ETH model SLE from each model run (column)</p> <p> </p> <p>%-------------------------------------------------------------<br> TOPKAPI-ETH:</p> <p>FIUME - A generic file type that is called by the model to ID the name of the study site. I this case 'rdy'.<br> rdy.TES - A vectorised file of all grids required by the model to run.<br> rdy.TPK - The TPK parameter and command file. This is adjusted to change input parameters and forcing files etc. The current file is the optimised version for this catchment.<br> TManual_Aug2013.pdf - A PDF instruction file (semi-complete) for the model written by Stefan Rimkus (2013). The naming conventions and grid names are given here.</p> <p> </p> <p> </p> <p>CITED MATERIAL REGARDING THE MODEL</p> <p><strong>Burger, F., Ayala, A., Farias, D., Shaw, T. E., Macdonell, S., Brock, B., McPhee, J., Pellicciotti, F. (2018a). Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment: understanding the role of debris cover in glacier hydrology. Hydrological Processes, SI-Latin(January), 1–16. <a href="https://doi.org/10.1002/hyp.13354">https://doi.org/10.1002/hyp.13354</a></strong></p> <p><strong>Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., & Burlando, P. (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resources Research, 47(7), 1–14. <a href="https://doi.org/10.1029/2010WR009824">https://doi.org/10.1029/2010WR009824</a></strong></p> <p><strong>Ragettli, S., & Pellicciotti, F. (2012). Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters. Water Resources Research, 48(3), n/a-n/a. <a href="https://doi.org/10.1029/2011WR010559">https://doi.org/10.1029/2011WR010559</a></strong></p> <p> </p>
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