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Dataset results
113 results for “Hydrological Model”
Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
<p>We built this dataset to assess the impacts of hydropower plants on the distribution of an endemic and imperiled freshwater turtle with very unique ecological requirements, the Williams' side-necked turtle (<em>Phrynops</em> <em>williamsi</em>). To prevent and mitigate impacts, we prioritized sites for species conservation by classifying planned HPP locations according to their predicted adverse effects on species distribution. The dataset has two files: i) species occurrence records and ii) hydropower plant data. The first dataset was fully built by the authors and the second was modified from the Brazilian Electricity Regulatory Agency (ANEEL) georeferenced data system.</p>
Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"
<p>Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"</p>
Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
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The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales
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[Model outputs] Identifying major hydrologic change drivers in a highly managed transboundary endorheic basin: integrating hydro‐ecological models and time‐series data mining techniques
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Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
MGB hydrological model for Paraná Basin
<p>This dataset contains model code, input and output data for the MGB hydrological model applied to the Upper Paraná Basin.</p> <p>Output files (QTUDO_XXX.QBI) are binary ones (single format) containing daily discharges for all model unit-catchments for each simulation scenario.</p> <p>Simulation scenarios are a combination of storage representation and operation rule types.</p> <p> </p> <p>Storage representation types:</p> <p>L=Lumped reservoir</p> <p>E=Equally distributed reservoir</p> <p>V=Variably distributed reservoir</p> <p> </p> <p>Operation rules:</p> <p>T=Three points rule</p> <p>Tg=rule T with global-based configuration</p> <p>R=regression-based rule</p> <p>Rg=rule R with global-based configuration</p> <p>S=inflow-based rule (based on Shin et al. 2019 WRR)</p> <p>Sg=rule S with global-based configuration</p> <p> </p> <p>Additionally, simulation scenarios with pristine conditions (i.e., without reservoirs) and without floodplains are also provided.</p> <p> </p> <p>For further details please contact Ayan Fleischmann at 'ayan.fleischmann at gmail.com'.</p> <p> </p>
A Dynamic Socio-hydrological Model of The Irrigation Efficiency Paradox
<p>The dataset for the journal article entitled: A Dynamic Socio-hydrological Model of The Irrigation Efficiency Paradox</p>
Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China
<p>The attached is the dataset assoicated with the paper titled "Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China" which was submitted to Journal of Earth and Space Science.</p><p>The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth System Models (ESMs). In this paper, historical LS3MIP hydrological variables including precipitation (PR), evapotranspiration (ET), soil moisture (SM), total runoff (Ro), and snow cover fraction (SCF) were extensively evaluated with various high-quality reference datasets over Chinese mainland. The six ESMs in LS3MIP were driven by four meteorological forcing datasets. The results indicated that the LS3MIP multi-model means (MMEs) of most variables are underestimated overall, while they show high spatial consistency in term of linear trends, with the percentage area ranging 56% ~ 85% between simulations and reference datasets. After computing and ranking multi statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases), it is found that the CESM2 model produces the best performance of land surface hydrological variables, while as the meteorological forcing dataset GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) was then used to trace sources of the uncertainty of the LS3MIP hydrological variables for 1900–2012 (1948–2012 for Ro). In ANOVA, the simulation uncertainties may be decomposed into three sources: model, atmospheric forcing datasets and their interactions. In LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall although it shows regional differences, and the dependence of uncertainty on the model differs among hydrological regimes. This highlights the urgent requirements to improve the land surface model representation in future research.</p>
HRFMD (Hydrological model based Random Forest Model Diagnostics) results
<p>Results accompanying the publication titled: Advancing Hydrological Model Diagnostics: An Exploratory Approach Using Random Forest Models and Large-sample Catchment Dataset</p>
Dataset associated with "Emulating subglacial hydrology in ice sheet models with deep learning methods" by Verjans and Robel.
<p>See Readme file for descriptions.</p>
Coupling deep learning and physically-based hydrological models for monthly streamflow predictions
<p>Revision in journal Water Resources Research, Manuscript number: <strong><span>2023WR035618R</span></strong></p> <p><strong>Abstract:</strong><strong> </strong>This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically-based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. <span>It</span> is then driven by bias corrected general circulation model (GCM) <span>prediction</span>s to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future <span>monthly </span>streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead <span>reservoir </span>inflows <span>for the Danjiangkou Reservoir in China. </span>The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. <span>Additional tests indicate that hybrid</span> model<span>s based on CNN-GRU </span>outperform <span>those based on</span> <span>LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model</span> greatly reduce<span>s</span> the <span>computation </span>burden of rolling prediction<span>. It also </span>saves decision-makers the time and effort of trying different combinations of predictors<span>, which is indispensable when building DL models. Overall</span>, the new hybrid model <span>demonstrates great potential</span> for monthly streamflow prediction <span>where</span> training data are limited.</p> <p><strong><span>Keywords:</span></strong> <span>monthly streamflow prediction; deep learning; </span><span>physically-based distributed hydrological model; </span><span>VIC model; soil moisture; hybrid model </span></p>
Model output for "Groundwater affects the geomorphic and hydrologic properties of coevolved landscapes"
<p>Model output supporting "Groundwater affects the geomorphic and hydrologic properties of coevolved landscapes" in JGR Earth Surface, DOI:10.1029/2021JF006239. The Python package DupuitLEM v1.0-beta (DOI:10.5281/zenodo.5522828) contains the models and scripts used to generate and post-process output.</p>
Sediment supply effects in hydrology-sediment modelling of an Alpine basin
<p>Discharge, sediment and precipitation data used in the pubblication "Sediment supply effects in hydrology-sediment modelling of an Alpine basin" by Battista et al., WRR, in review.</p>
Dataset used for paper "Evaluating simplifications of subsurface process representations for field-scale permafrost hydrology models"
<p>No description provided.</p>
Data from Climate adaptability in hydrological models: variable storage capacity to improve performance under contrasting climates.
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Processed data for the manuscript: Promoting Multi-Task Learning as a General Approach for Deep-Learning-based Hydrological Models
<div> <div>Below is a brief overview of the processed data in this repository:</div> <br> <div>- camels_streamflow: This directory contains streamflow data for CAMELS basins covering the period from January 1, 2015, to December 31, 2021. We have not included the original CAMELS dataset, which contains attributes, meteorological forcing, and streamflow data from January 1, 1980, to December 31, 2014, as it can be easily downloaded from the CAMELS website (https://gdex.ucar.edu/dataset/camels.html) and is too large for us to upload to Zenodo.</div> <div>- modiset4camels: This directory includes multiple versions of basin-mean Evapotranspiration (ET) data retrieved from the MOD16A2 data product. The dataset spans from January 1, 2001, to December 31, 2021, with an 8-day temporal resolution.</div> <div>- nldas4camels: This directory contains basin-mean daily meteorological forcing data from the NLDAS-2 dataset, obtained via Google Earth Engine (GEE). The dataset covers the period from January 1, 2001, to December 31, 2021.</div> <div>- smap4camels: This directory features basin-mean Soil Moisture (SSM) data from the NASA-USDA Enhanced SMAP Global Soil Moisture dataset, covering the period from April 2, 2015, to October 3, 2021. The dataset provides SSM measurements at a 5 cm depth. Additionally, we provide basin-mean daily SMAP L4 data spanning from April 1, 2015, to December 31, 2023.</div> </div>
Supporting data for: A 250-year European drought inventory derived from ensemble hydrologic modelling
<p>Supporting data for visualization of 250-year (1766-2015) inventory of European meteorological, hydrological and agricultural droughts derived from ensemble simulations of the mesoscale Hydrological Model (mHM)</p>
Supporting data for "Quantifying process connectivity with transfer entropy in hydrologic models" [Paper #2018WR024555]
<p>This tarball contains the processed datasets used for the analysis of the process networks. It also contains the shapefiles used to define the domains of each sub-region.</p>
Codes and dataset used in the manuscript entitled "Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions"
<p>This contains the codes and dataset for the manuscript entitled "Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions". Detailed information about the dataset is described in the Readme.txt file.</p>
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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)
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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.