Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
113
datasets available to search
ShareScore release 0.7.1
Dataset results
113 results for “Hydrological Model”
Model output for "Catchment coevolution and the geomorphic origins of variable source area hydrology"
<p>Model output supporting "Catchment coevolution and the geomorphic origins of variable source area hydrology" for submission to Water Resources Research. The Python package DupuitLEM v1.1-alpha (DOI: 10.5281/zenodo.7620978) contains the models and scripts used to generate and post-process output.</p>
Output from SHUD hydrological model for Waerma Watershed, Gansu, China
<p>The Waerma watershed is a headwater of the Yellow River, with an area of 9.8 $km^2$, located near Waerma Village, about 20 kilometers northwest of Maqu County in Gansu province of China. </p> <p> </p>
Data set supporting publication "Hydrological Coupling and Decoupling of Hydric Hemi-boreal Forest Sites Inferred from Soil Water Models and Tree-Ring Chronology" by Kalvāns A. and Dauškane I. accepted for publication in the scientific journal Forests
<p>These files are supporting information for the article:</p> <p>Kalvāns, Daukškane (<em>accepted</em>). Hydrological Coupling and Decoupling of Hydric Hemi-boreal Forest Sites Inferred from Soil Water Models and Tree-Ring Chronology. <em>Forests</em></p> <p>The data set comprises following elements:</p> <ol> <li>Hydrus-1D soil water model setup files and calculation results ([1_Hydrus_1D_hydric_forest_soil_water_model_instances.zip]) for two study plots and 12 model instances, with following naming convention: [{Site Identifier}__{proportion of active leaf area index}_kLAI__{forced groundwater exfiltration rate cm/day}_SeepIn_const]. That is model instance named [P1__0.4_kLAI__0.05_SeepIn_const.h1d], considers the study site Plot_1, the active leafe area proportion is 0.4 and it has applied constant rate of groundwater exfiltration at the base of the soil column of 0.05 cm/day. The models are forced by E-OBS v26.0e data set for the period from 1980-01-01 to 2022-06-30.</li> <li>Black alder <em>Alnus glutinosa</em> tree ring chronologies for the two study plots ([2_tree_ring_data.zip])</li> <li>Soil and ground-water observation time series for the two study plots ([3_soil_ground_water_observations.zip])</li> </ol>
A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble
<p>Supporting information for manuscript titled "A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble"</p><p>Datasets S1. Early to Middle Miocene NetCDF files: E2MMIO280.nc, E2MMIO400.nc, E2MMIO560.nc, E2MMIO850.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S2 Middle to Late Miocene NetCDF files: M2LMIO280.nc, M2LMIO400.nc, M2LMIO560.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S3 Preindustrial NetCDF files: PI contains MioMIP1 climate variables used to make manuscript figures.</p><p>Dataset S4 CSV file MioMIP_MAP_compilation contains newly revised miocene reconstructed mean annual precipitation from proxies. </p>
Data for "A Stepwise Clustered Hydrological Model for Addressing the Autocorrelation Structure of Streamflow in Irrigated Watersheds"
<p>This data set contains hydrological input (goundwater depth, streamflow and irrigation) for the study of "<strong>A Stepwise Clustered Hydrological Model for Addressing the Autocorrelation Structure of Streamflow in Irrigated Watersheds</strong>"</p> <p>For more information please contact the author.</p>
Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods
<p>Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this. In the case of floods, for instance, data collection from social media can be triggered by a severe weather forecast and/or a flood prediction. By way of contrast, in this paper we explore the possibility of having an entirely independent flood monitoring system which is based completely on social media, and which is completely self-activated. This independence and self-activation would bring increased robustness, as the system would not depend on other mechanisms for forecasting. We observe that social media can indeed help in the early detection of some flood events that would otherwise not be detected until later, albeit at the cost of many false positives. Overall, our experiments suggest that social media signals should only be used to complement existing monitoring systems, and we provide various explanations to support this argument.</p> <p>This dataset contains the jupyter notebook and related data for running experiments described in the paper along with an additional table with full list of events considered</p>
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) - Data Set
<p>GENERAL INFORMATION</p> <p>The data and scripts used for the analysis of the paper "Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – Towards an improved representation of mountain water resources in global assessments"</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p><strong>Hanus, S., Schuster, L., Burek, P., Maussion, F., Wada, Y., and Viviroli, D.: Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments, Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, 2024.</strong></p> <p>DATA & FILE OVERVIEW</p> <p>please have a look at readme.txt </p> <p>Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)</p>
Coupling deep learning and physically-based hydrological models for monthly streamflow predictions
<p>Revision in journal Water Resources Research, Paper # <strong><span>2023WR035618R</span></strong></p>
Supplementary material 1 from: Dang NA, Jackson BM, Tomscha SA, Lilburne L, Burkhard K, Tran DD, Phi LH, Benavidez R (2022) Guidelines and a supporting toolbox for parameterising key soil hydraulic properties in hydrological studies and broader integrated modelling. One Ecosystem 7: e76410. https://doi.org/10.3897/oneeco.7.e76410
Supplementary Material S1
Supplementary material 2 from: Dang NA, Jackson BM, Tomscha SA, Lilburne L, Burkhard K, Tran DD, Phi LH, Benavidez R (2022) Guidelines and a supporting toolbox for parameterising key soil hydraulic properties in hydrological studies and broader integrated modelling. One Ecosystem 7: e76410. https://doi.org/10.3897/oneeco.7.e76410
Supplementary Material S2
Hypothesis testing the difference between two Nash-Sutcliffe Efficiencies (NSEs) for comparing the hydrologic model performance
<p>There are data and raw code for testing the hypothesis testing the difference between two Nash-Sutcliffe Efficiencies (<em>NSEs</em>) in our research</p>
The hydrological fluxes of the Upper Brahmaputra River Basin constrained by a multi-physics ensemble (MPE) modeling approach
<p>The data represent monthly hydrological fluxes for four sub-basins within the Upper Brahmaputra River Basin, where yyyy is the year, mm is the month, MPE is the multi-physics ensemble, P is the precipitation, R is the runoff, and ET is the evapotranspiration. The unit is mm. The upper-bounds and lower-bounds represent the upper and lower bounds of the hydrological fluxes constrained by the MPE, respectively.</p> <p> </p> <p>Reference:<br>Lei, X, P. Lin*, H. Zheng, K. Yang, W. Liu, C. Miao, K. Wang, J. Wang: A multi-physics ensemble modeling approach to constraining the uncertainty of hydrological fluxes in sparsely-gauged river basins. Geophysical Research Letters, (submitted), 2024.<br>Contact:<br>xiangyonglei@stu.pku.edu.cn; peironglinlin@pku.edu.cn</p>
Dataset to develop the Generalized Linear Models in "THE CRITICAL ROLE OF HYDROLOGICAL DISTANCE IN SHAPING NUTRIENT DYNAMICS ALONG THE WATERSHED-LAKE CONTINUUM"
Open the record for dataset details and reuse information.
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models
<p>The zip file contains a synthetic dataset that was used to construct the surrogate model.</p>
Impact of uncertainty in precipitation forcing datasets on the hydrologic budget of an integrated hydrologic model in mountainous terrain
Open the record for dataset details and reuse information.
Lund-Potsdam-Jena Wetland Hydrology and Methane DGV Model (LPJ-WHyMe v1.3.1)
This model product provides the Fortran 77 source code for the Lund-Potsdam-Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ-WHyMe v1.3.1), auxiliary C++ routines, ASCII and NetCDF input data, and NetCDF example output data. LPJ-WHyMe v1.3.1 simulates peatland hydrology, permafrost dynamics, peatland vegetation, and methane emissions.The model processes can be simulated on an area-averaged 0.5 or 1.0 degree grid cell basis at global, regional, or site scales and on a daily, monthly, or annual time step as appropriate. Input driver data are monthly mean air temperature, total precipitation, percentage of full sunshine, annual atmospheric CO2 concentration, and soil texture class. The simulation for each grid cell begins from "bare ground", requiring a "spin up" (under non-transient climate) of ca. 1,000 years to develop equilibrium vegetation, carbon, and soil structure. Model simulations compare favorably, with some exceptions, to field observations collected from peatland sites (e.g., Degero, Sweden; Lakkasuo, Finland; BOREAS Northern Study Area, Canada; and others) and non-peatland sites (e.g., Point Barrow, Alaska, and Spasskaya, Siberia). LPJ-WHyMe is a further development of LPJ-WHy, which dealt with the introduction of permafrost and peatlands into LPJ. Implementing peatlands in LPJ required the addition of two new plant functional types (PFTs) (flood tolerant C3 graminoids and Sphagnum mosses) to the already existing ten PFTs, the introduction of inundation stress for non-peatland PFTs, a slow-down in decomposition under inundation, and the addition of a root exudates pool. LPJ-WHyMe v1.3.1 adds a methane model subroutine. This model product has one compressed data file (*.zip) and seven companion files.
Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling
This dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system.
Monthly gridded Global Land Data Assimilation System (GLDAS) from Noah-v3.3 land hydrology model for GRACE and GRACE-FO over nominal months
The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format. Currently, the days included in these monthly anomaly computation are same as GRACE-FO monthly Level-2 RL06.3 JPL solutions.
Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies
The NCAR LSM 1.0 is a land surface model developed by Gordon Bonan to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate.Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide (ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Users_Guide.pdf ), the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files (ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Bckgrnd_Application_Info.pdf and ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Analyzed-Data.pdf.
Supplementary Data: Measured values of 13 flood events in the upper watershed of Qingshan Hydrological Station and their simulation results by two models
<p>The files in this record contain measured data of 13 flood events and simulated flood processes from the XAJ and CM-XAJ models considered for publication in Water Resources Research.</p><p> </p><p>The files consist of:</p><p> </p><p>Measured values for 13 flood events of Qingshan Hydrological Station;</p><p>Source code (incomplete) and results of the XAJ Model;</p><p>Source code (incomplete) and results of the CM-XAJ Model;</p><p>Source code of Genetic Algorithm with recourse model.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.