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201 results for “StreamFlow”
Streamflow Predictions using Machine Learning with Data Reformation
<p>Reference: Tran, Vinh Ngoc, Valeriy Y. Ivanov, and Jongho Kim. "Data reformation–A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes." <em>Advances in Water Resources</em> 182 (2023): 104569. https://doi.org/10.1016/j.advwatres.2023.104569</p>
Figures for 'Global streamflow modelling using process-informed machine learning'
<p>High-quality figures for the article 'Global streamflow modelling using process-informed machine learning' (<a href="https://doi.org/10.2166/hydro.2023.217">https://doi.org/10.2166/hydro.2023.217</a>).</p>
Winter inputs buffer streamflow sensitivity to snowpack losses in the Salt River Watershed in the Lower Colorado River Basin
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Code, data and results for manuscript "A parsimonious empirical approach to streamflow recession analysis and forecasting"
<p>This repository hosts the supplementary materials associated with the paper:<br> > Delforge, D., Muñoz-Carpena, R., Van Camp, M. Vanclooster, M. (2020), A parsimonious empirical approach to streamflow recession analysis and forecasting (accepted at Water Resources Research - 29-01-2020).</p> <p>This data set contains streamflow and recession data, a python code file and a Jupyter notebook illustrating how to apply the EDM-Simplex method to forecast the recession, and the outputs of the global sensitivity analysis. All files are documented in the readme.md Markdown files. </p> <p>Streamflow data were obtained from the Aqualim portal (<a href="http://aqualim.environnement.wallonie.be/">http://aqualim.environnement.wallonie.be/</a>) of the "Service Public de Wallonie" and shared with their kind permission. This work is part of a Ph.D. supported by a FRIA grant from the Fund for Scientific Research (FSR-FNRS, Belgium). The authors acknowledge University of Florida Research Computing for providing computational resources and support that have contributed to the research results stored in this repository. URL: <a href="http://researchcomputing.ufl.edu">http://researchcomputing.ufl.edu</a>.</p>
Models and Predictions for "The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction"
<p><strong>Models and Predictions</strong></p> <p>This dataset contains the trained XGBoost and EA-LSTM models and the models' predictions for the paper <a href="https://github.com/gauchm/ealstm_regional_modeling"><em>The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction</em></a>.</p> <p>For each input sequence length (10, 30, 100, 270*, 365*) and each combination of model (XGBoost, EA-LSTM), training years (3, 6, 9), number of basins (13, 26, 53, 265, 531), and seed (111-888), there are five folders. Each corresponds to a random basin sample (for 531 basins there's only one folder, since it's all basins).<br> In each folder, there are three files:</p> <ul> <li><span class="math-tex">\(\texttt{model.pkl}\)</span> (XGBoost) or <em><span class="math-tex">\(\texttt{model_epoch30.pt}\)</span></em> (EA-LSTM), which stores the pickled trained model</li> <li><em><span class="math-tex">\(\texttt{xgboost_seedNNN.p}\)</span></em> or <em><span class="math-tex">\(\texttt{ealstm_seedNNN.p}\)</span></em>, which stores a pickled dictionary that maps each basin to the DataFrame of predicted and actual daily streamflow.</li> <li><span class="math-tex">\(\texttt{attributes.db}\)</span>, which stores static catchment attributes needed for inference.</li> </ul> <p>In addition to each folder, there is a SLURM submission script called <em><span class="math-tex">\(\texttt{<foldername>.sbatch}\)</span></em> that was used to create and evaluate the model in the folder.</p> <p> </p> <p>* sequence lengths 270 and 365 only contain data for EA-LSTM.</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>
Data for 'Sustainability of Irrigation and Streamflow in the Western US'
<p>Materials required to reproduce the analysis reported in the paper.</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>
Supporting data for: Where and When Does Streamflow Regulation Significantly Affect Climate Change Outcomes in the Columbia River Basin?
<p>Unregulated and regulated streamflow statistics presented in: Where and When Does Streamflow Regulation Significantly Affect Climate Change Outcomes in the Columbia River Basin?</p>
Enhancing Streamflow Prediction through Multi-model Ensemble Framework and Machine Learning Techniques
<p>This file contains python code used in this study and data used to plot figures. </p>
Simulated streamflow datasets under different future scenarios for near-future (NF, 2031-2060) and far-future (FF, 2071-2100) periods.
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Groundwater Storage in High Alpine Catchments and its Contribution to Streamflow
<p>Continuously recorded meteorological and hydrological data from the Vallon de Rechy research site.</p>
Seasonal growth potential of Oncorhynchus mykiss in streams with contrasting prey phenology and streamflow
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BOREAS HYD-09 Streamflow Data
These streamflow data were collected by the HYD-09 science team to support its research into meltwater supply to the soil during the spring melt period. These data were also collected for HYD-09's research into the evolution of soil moisture, evaporation, and runoff from the end of the snowmelt period through freeze up. Data were collected in the BOREAS SSA and NSA from April until October in 1994, 1995, and 1996. Gauges SW1 and NW1 were operated year-round; however, data may not be available for both gauges for all 3 years.
The role of snowmelt, glacier melt and rainfall in streamflow dynamics on James Ross Island, Antarctic Peninsula
<p>Description: The file in this record represents a supplementary data for the journal paper <br>Ondřej Nedělčev, Michael Matějka, Kamil Láska, Zbyněk Engel, Jan Kavan, and Michal Jenicek (2024):<br>The role of snowmelt, glacier melt and rainfall in streamflow dynamics on James Ross Island, Antarctic Peninsula, DOY: 10.5281/zenodo.11001370 </p> <p>The file contains simulations of the HBV-light rainfall-runoff model for Triangular catchment on James Ross Island.<br>The model simulated different water balance components, such as runoff, snow water equivalent, galcier water equivalent, <br>evapotranspiration and groundwater storage for the study period 2010/11–2020/21.</p>
Supported Datasets for the Research Titled: "Impacts of Climate and Land Use Changes on Streamflow in the Mun-Chi River Basin, the Largest Tributary of the Mekong River"
<p><strong>Supported Datasets for the Research Titled: "Impacts of Climate and Land Use Changes on Streamflow in the Mun-Chi River Basin, the Largest Tributary of the Mekong River"</strong></p> <p>Abstract: </p> <p><span>The impact of climate change and human activities poses significant challenges in the tropical region of Southeast Asia, specifically within the Mun-Chi River Basin, the largest tributary of the Mekong River in Thailand. The bias-corrected MPI-ESM1-2-LR, the most appropriate Global Climate Model (GCM) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) for projecting Mun-Chi River flow, represent future climate variations in this basin. The analysis reveals forthcoming transformations in future land use, with cropland areas transitioning into forests and urban areas. While the projected annual streamflow contributing to the Lower Mekong River is expected to slightly increase by up to 4%, with 67% attributed to climate change and 33% to land-use change, temporal variations in the future flow regime reveal a wetter wet season and a drier dry season in this catchment. During the wet season, streamflow is projected to rise by 5% to 18% in 2023-2035 and 10% to 24% in 2036-2050. In contrast, the dry season is expected to experience a decrease of -3% to -9% in 2023-2035 and -6% to -17% in 2036-2050. Projected streamflow fluctuations are more pronounced in mountainous areas and upstream tributaries. These seasonal contrasts highlight the potential impact of more severe drought during the dry season and more severe flooding during the wet season. These potential increases in extreme hydrological events present challenges for efficient water resource management in this watershed and downstream countries. Consequently, effective water regulation and land-use policies are deemed crucial for sustainable management in the Mun-Chi River Basin.</span></p>
Hourly Balancing Authority Transfers, Streamflows, and Climate Data for Carolinas Region
<p>The data contain the electricity transfers and relevant indicators associated with nine exchanges between balancing authorities in the Carolinas region. The information comes from the following sources:</p> <p>Hydrology Dataset from USGS:</p> <p>CPLE<span> </span>02084557- Van Swamp near Hoke, NC</p> <p><span> </span>02089000- Neuse River near Goldsboro, NC</p> <p><span> </span>02087324-Crabtree Creek at US 1 at Raleigh, NC</p> <p>DUK<span> </span>0212427947- Reedy Creek at SR2803 near Charlotte, NC</p> <p><span> </span>0212430653- McKee Creek at SR2804 near Wilgrove, NC</p> <p><span> </span>02124080- Clarke Creek near Harrisburg, NC</p> <p>SC<span> </span>02171500- Santee River near Pineville, SC</p> <p><span> </span>02131010- Pee Dee River below Pee Dee, SC</p> <p><span> </span>02130980- Black Creek near Quinby, SC</p> <p>SCEG<span> </span>02175500- Salkehatchie River near Miley, SC</p> <p><span> </span>02176500- Coosawhatchie River near Hampton, SC</p> <p>YAD<span> </span>02121500- Abbotts Creek at Lexington, NC</p> <p>CPLW<span> </span>02140991- Johns River at Arneys Store, NC</p> <p>*CPLE = Duke Energy Progress East; DUK = Duke Energy Carolina; SC = Santee Cooper; SCEG = South Carolina Electric & Gas Company; YAD = Yadkin, Inc.; CPLW = Duke Energy Progress West</p> <p> </p> <p>Balancing Authority Data</p> <p><span>Nugent J, Chini C M, Peer R A M and Stillwell A S 2023 Monthly virtual water transfers on the U.S. electric grid Environ. Res. Infrastruct. Sustain. 3 035006</span></p> <p><span>Balancing Authority Climate Data</span></p> <p>Burleyson C, Thurber T and Vernon C 2023 Projections of hourly meteorology by balancing authority based on the IM3/HyperFACETS thermodynamic global warming (TGW) simulations (v1.0.0) [Data set] MSD-LIVE Data Repos.</p> <p>NOAA National Centers for Environmental Information 2024 U.S. Air Force 14th weather squadron (2013): United States Air Force 14th weather squadron surface weather observations (restricted). NCEI DSI 9966</p> <p> </p>
A Retrospective Analysis of the Role of Snow in the 2021 Western U.S. Streamflow Drought
<p>This repository contains scripts and data used in the paper entitled "A Retrospective Analysis of the Role of Snow in the 2021 Western U.S. Streamflow Drought".</p>
Dataset of observed streamflow and calculated base flow, groundwater runoff and groundwater discharge of the Zhizdra River, Russia (1958–2016)
<p>This dataset accompanies the paper, titled “A physically based model of a two-pass digital filter for groundwater runoff separation from streamflow time series”, submitted to Water Resources Research.</p> <p>The dataset includes the daily river runoff of the Zhizdra River, Russia, during 1958–2016. Using this dataset, we validate the proposed two-pass digital filter model to separate groundwater runoff from streamflow. This dataset also includes the calculated base flow, groundwater runoff and groundwater discharge to the Zhizdra River, Russia, during 1958–2016.</p>
long-term reconstructed streamflow across the LMR basin
<p>This is the reconstructed streamflow results across gauging stations in the LMR basin from 1000 to 2012. The results will be made publicly available upon the publication of the manuscript.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.