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201 results for “StreamFlow”
Streamflow data for outlet from Navajo meadow, 1994 - 2014
This is a summary of discharges from the stream draining Navajo meadow in the Green Lakes Valley. The samples were collected from the stream 5 m upstream of outlet from Navajo meadow. Data includes daily summaries of flows calculated for a 24 hour day starting at 12:00 MST on the date recorded.
Streamflow data for Green Lake 5 outlet, 2006 - 2017
This is a summary of discharges from the stream draining Green Lake 5 in the Green Lakes Valley. The samples were collected from the Creek 15 m downstream of outlet from Green Lake 5. Data includes daily summaries of flows calculated for a 24 hour day starting at 12:00 MST on the date recorded.
Winter inputs buffer streamflow sensitivity to snowpack losses in the Salt River Watershed in the Lower Colorado River Basin
Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production.
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>
Enhanced modulation of streamflow flash droughts by reservoir operations in India
Open the record for dataset details and reuse information.
Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"
<p>Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"</p> <p>Yuhang Zhang1, Aizhong Ye1*, Phu Nguyen2, Bita Analui2, Soroosh Sorooshian2, Kuolin Hsu2</p> <p>1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.</p> <p>2 Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA.</p> <p>## Dataset </p> <p>Streamflow simulations from one observed precipitation (CMA) and three satellite precipitation products (PDIR, IMERG-F, and GSMaP) for 522 sub-basins.</p> <p>- Q-CMA (streamflow reference)<br> - Q-PDIR (uncorrected)<br> - Q-IMERGF (uncorrected)<br> - Q-GSMAP (uncorrected)</p> <p>### Data structure</p> <p>- Head section (row1-row5)<br> - SubNO: 522 <br> - BeginT: 2003-01-01 00:00 <br> - EndT: 2019-12-31 00:00 <br> - Interval: 1440s (daily)<br> - Revise: 10 (scaling factor to keep int datatype)<br> - Point1 Point2 ... (Subbasin No.)<br> - Data section<br> - 6209 rows, 522 cols</p> <p>## Results</p> <p>Two post-processing model results for test period (2015-1-1 to 2018-12-31).</p> <p>### Data structure</p> <p>- 1462 rows, every row denotes each day from 2015-1-1 to 2018-12-31</p> <p>- 100 columns, every column denotes each quantile from 0.005 to 0.995, total 100 quantiles.</p> <p>### qrf-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p>### lstm-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p> </p>
Data for Streamflow Prediction: Comparison of SWAT vs. Random Forest Models in Diverse Catchments
<p>This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time series prediction across diverse catchments, and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [<em>NSE</em>] > 0.5), whereas SWAT generally yielded unsatisfactory results (<em>NSE</em> < 0.5) and tended to overestimate daily streamflow by up to 27% (<em>PBIAS</em>). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.</p> <p>This repository contains the input data used for building the RF and SWAT models and the files describing the modeling results.</p> <p>The corresponding Zenodo code repository is available at <a href="../doi/10.5281/zenodo.11064973" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.11064973</a>.</p>
Datasets for: The component and future trend of streamflow in the Yarlung Tsangpo River
<p>The datasets are used for the submitted article: The component and future trend of streamflow in the Yarlung Tsangpo River, including the input data for hydrological model's calibration and the simulation results in the study.</p>
Higher-order Internal Modes of Variability Imprinted in Year-to-year California Streamflow Changes
<p>This is the dataset associated with our manuscript "Higher-order Internal Modes of Variability Imprinted in Year-to-year California Streamflow Changes". It includes BCSD-CMIP6 simulations and corresponding streamflow projections. </p> <p>CMIP6 models:</p> <ul> <li>ACCESS-ESM1-5</li> <li>CNRM-ESM2-1</li> <li>EC-Earth3</li> <li>IPSL-CM6A-LR</li> <li>MPI-ESM1-2-LR</li> <li>MIROC6</li> </ul>
Data presented in: Glacier recession and the response of summer streamflow in the Pacific Northwest United States, 1960‐2099
<p>This is an archive of data that is presented and described in:</p> <p>Glacier recession and the response of summer streamflow in the Pacific Northwest United States, 1960‐2099</p> <p><a href="https://doi.org/10.1029/2017WR021764">https://doi.org/10.1029/2017WR021764</a></p>
Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0
<p>This repository contains the data and software used for the study "Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0." The data and model were used to simulate an extreme low-water event in the River Rhine Basin.</p> <p>This archive contains the following:</p> <p><strong>wrf_hydro_nwm_public-5.2.0.tar.gz</strong>: Model code of the hydrological model WRF-Hydro. (Source: https://ral.ucar.edu/projects/wrf_hydro/model-code)</p> <p><strong>ERA5_Dataset_2016_2018.zip</strong>: ERA5 Reanalysis data and modified for the domain of the project, it includes all the mandatory variables necessary to run the model (Source:https://doi.org/10.24381/cds.bd0915c6)</p> <p><strong>GRDC_HydrologicalData_Rhine_Basin.zip</strong>: Daily stremaflow from gauges in the Rhine River from the Global Runoff Dataset Center (GRDC) (Source: https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Stations)</p> <p><strong>RegriddedFormatData.sh: </strong>Bash file to create the the input data for the regridding scripts using the ERA5 data. (Source: Campoverde, A.)</p> <p><strong>ESMFregrid_NLDAS.tar.gz</strong>: Earth System Modeling Framework (ESMF) Regridding Scripts (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#regridding2)</p> <p><strong>wrf_hydro_arcgis_preprocessor-5.2.0.tar.gz</strong>: ArcGIS tools for preparing WRF-Hydro Routing Grids. (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#preprocessing1)</p> <p><strong>eu_dem_3s.zip</strong>: Digital Elevation Model for Europe from HydroSHEDS - 3" (~90m) (Source: https://www.hydrosheds.org/hydrosheds-core-downloads)</p> <p><strong>S2GLC_Europe_2017_v1.2_grey.zip</strong>: Land Cover data used to create spatially distributed hydrological parameters from WRF-Hydro (RETDEPRTFAC, REFKDT, SLOPE) (Source: https://s2glc.cbk.waw.pl/extension)</p> <p><strong>WRF_Hydro_Setup_withLakeScheme.zip</strong>: Necesary files for WRF-Hydro model when considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>WRF_Hydro_Setup_withoutLakeScheme.zip</strong>: Necessary files for the WRF-Hydro model when <strong>not</strong> considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>ERA5_Land_Soil_Moisture_Dataset_2016_2018.zip</strong>: ERA5 Land data for the domain of the project includes the values of soil moisture for the period 2016-2018. (Source:https://doi.org/10.24381/cds.e2161bac)</p> <p>With this repository, we aim to provide to the comminity the necessary tools to replicate the experiments that led to the calibration of WRF-Hydro and the results of our study. </p> <p> </p>
Streamflow depletion for multiple wells pumping in BX and Peace region
<p>This code is used to generate streamflow depletion assessment for comparison between the numerical model and analytical depletion functions for multiple well pumping scenarios. </p>
The streamflow and water quality data for two catchments in the Bavarian Forest National Park, Germany
<p>This dataset was used in a study to support modeling simulations of catchment runoff and nitrogen export in streams following forest dieback caused by bark beetles.</p> <p>Große Ohe data were provided by the Bavarian Environment Agency (LFU) (https://www.lfu.bayern.de/wasser/gewaesserqualitaet_fluesse/messnetze/index.htm).<br>The data are available from the Gewässerkundlicher Dienst:<br>For runoff:<br>https://www.gkd.bayern.de/en/rivers/discharge/passau/taferlruck-17413000<br>For runoff chemistry:<br>https://www.gkd.bayern.de/de/fluesse/chemie/passau/taferlruck-messstation-11801/gesamtzeitraum</p> <p>Forellenbach data were provided by the Federal Environment Agency (UBA).<br>The Forellenbach catchment is part of the Integrated Monitoring Programme under the UNECE Convention on Long-Range Transboundary Air Pollution (1979). https://www.umweltbundesamt.de/themen/luft/messenbeobachtenueberwachen/medienuebergreifendes-monitoring-in-der#icp-integrated-monitoring</p> <p>The main runoff and runoff chemistry data used in the study are shown in the file, and please see original data sources for more information.</p>
Pedler creek streamflow generation: Model input files for Pedler Creek
<p>This repository contains supplementary material and all of the model input files for the results presented in the article Taking theory to the field: streamflow generation mechanisms in an intermittent Mediterranean catchment</p>
The streamflow discharge, water chemical and isotopic data of the Binggou and Yakou catchments in 2017
<p>This is the streamflow discharge, water chemical and isotopic data of the Binggou and Yakou catchments collected in 2017</p>
Data and analysis for "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"
<p>This contains all of the necessary data and code to reproduce the results of the manuscript submitted to the Journal of</p> <p>Hydrometeorology entitled "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"</p>
Victorian Water and Climate dataset: long-term streamflow, climate, and vegetation observation records and catchment attributes
<p>This dataset contains streamflow, climate, and vegetation data for 155 minimally impaired catchments in Victoria, Australia. </p> <p>What's included:</p> <p>- Streamflow long-term observation records in daily, monthly, and hydroannual (based on March to February water year) resolution</p> <p>- Catchment climate characteristics in daily, monthly, and hydroannual resolution</p> <p>- Catchment vegetation characteristics in monthly resolution and actual evapotranspiration estimate in monthly and hydroannual resolution</p> <p>- Catchment boundaries</p> <p>- Catchment attributes (topographic, geological, soil, groundwater, vegetation, and human impacts characteristics)</p> <p>- Hydroclimatic characteristics for 3 different periods.</p> <p>Precipitation (p), streamflow (q), and PET (pet) data and their derivatives are in mm (i.e. normalised by catchment area). </p> <p>A paper containing a detailed description of the data including units, data sources, and processing details is submitted, please contact Margarita Saft for a private copy of the draft.</p>
Supporting CESM output for "The effect of plant physiological responses to rising CO2 on global streamflow" (Fowler et al.; 2019)
<p>Thirty years of CESM soil moisture, rain, and snow data is collected here based on each experiment contained within Fowler et al. (2019). These data were used to produce Figure 3 of that work. Other supporting data including daily runoff and river discharge is archived elsewhere, while analysis scripts and more information can be found here: https://github.com/megandevlan/Physiology-Streamflow.git. </p>
Streamflow Predictions using Machine Learning with Data Reformation
<p>Streamflow Predictions using Machine Learning with Data Reformation</p>
Streamflow data
<p>This is global streamflow dataset.</p>
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