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201 results for “StreamFlow”
Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)
<p><strong>1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong></p> <p>CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,</p> <p>CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,</p> <p>CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:</strong></p> <p>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):</strong></p> <p>Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt</p> <p>GRDC_number_WaterGAP_ArcID_220_stations.txt</p> <p><strong>4) SDHIs for four GRDC gauging stations:</strong></p> <p>time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt</p> <p><strong>5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p> </p>
Streamflow and rain event data from the Tromble experimental watershed weir in the Jornada Basin, southern New Mexico USA, during 2 periods: 1977-1985 and 2003-2011
This data package contains long-term hydrologic event data collected in the Tromble experimental watershed in the Jornada Basin of southern New Mexico, USA. Streamflow and precipitation data were collected at an instrumented flume below a 4.7 hectare shrub-dominated catchment during two periods: 1977-1985 and 2003-2011. This dataset was assembled to evaluate the effects of changes in rainfall on runoff in this Chihuahauan desert watershed. Four data files are available here, including event precipitation totals and sub-hourly runoff details for hydrologic events during both time periods. Variables measured include event precipitation, stage heights at the flume, and calculated discharge rates. This study is complete. For further information, refer to: Turnbull, L., A. J. Parsons, J. Wainwright, and J. P. Anderson. "Runoff responses to long-term rainfall variability in a shrub-dominated catchment." Journal of arid environments 91 (2013): 88-94.
Streamflow data for Como creek, 2006 - 2014
This is a summary of discharge from the Como Creek catchment located on and below the southeast flank of Niwot Ridge (40°N, 105°W) in Colorado, USA, approximately 4–9 km east of the Continental Divide. The catchment is 5.36 km2 in area and ranges in elevation from 2900 to 3560 m. Discharge is calculated from tenminute data from a pressure transducer located in a stilling well, and weekly stage and velocity measurements from the center of the weir. Measurements began in 2004 and continue through 2014, with a gap in 2013 during which the pressure transducer was damaged.
SEAS5/System4-LARSIM_ME Seasonal Streamflow Forecasts for German Waterways
<p>The datasets provided were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em> “, work package 4, task 2 “<em>Analysis of the impact of changes in precipitation attributes from short to medium and climatic ranges</em>” and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Meißner et al. 2017, in Deliverable 4.2 „ <em>The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions</em> “ (Arnal et al. 2017) and in Deliverable 4.3 “<em>Forecast skill developments</em>” (Weerts et al. 2019). The aim was to evaluate the potential skill of seasonal streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As seasonal meteorological forecast data the reforecast dataset from ECMWF’s Seasonal Forecast System 4 (S4 hereafter) (Molteni et al. 2011) as well as from the fifth generation of ECMWF’s Seasonal forecasting system SEAS5 (ECMWF 2017, Johnson et al. 2018, Owens & Hewson 2018) of the period 1981 – 2016 were used.</p> <p>The horizontal resolution of System 4 is approximately 80 km. In operational mode the ensemble consists of 51 members generated by using an ensemble of initial conditions and by the use of stochastic physics. Re-forecasts starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast. For the period 1981 – 2011 the ensemble size varies between 15 members (initialization months January, March, April, June, July, September, October, December) and 51 members (for the remaining months). Since 2012, the ensemble size is 51 members all over the year (operational forecasts).</p> <p>The fifth generation of ECMWF seasonal forecasting system SEAS5 replaced System 4 in November 2017. The horizontal resolution of the model is 0.4°x0.4° (approx. 36 km). The ensemble consists of 51-members created using a combination of Sea Surface Temperature SST and atmospheric initial condition perturbations and the activation of stochastic physics (ECMWF 2017). Re-Forecasts with the ensemble size of 25 members starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>The precipitation and temperature data, used to force the hydrological model in simulation mode up to the initialization of the particular forecast, is taken from the E-OBS dataset, version 18 (Haylock et al. 2008). The downward surface solar radiation is extracted from the ERA-Interim reanalysis (Dee et al. 2011) for the period 1979-2018. For further details on data processing see Meißner et al. (2017).</p> <p>As meteorological seasonal forecasts tend to drift towards the model climate with increasing lead-time, the outputs daily total precipitation and air temperature from S4, interpolated to a 50 km x 50 km grid (multiple of the 5 km x 5 km model grid) and from SEAS5, interpolated to a 25 km x 25 km grid, respectively, were drift-corrected with the meteorological observation dataset used for the baseline simulation. As drift correction method the quantile-quantile method (Piani et al. 2010) was used. We corrected daily values of the different variables on a monthly basis, which means each daily value of the same month is corrected by the same scaling. Separate drift correction factors were estimated for each forecast initialization date (calendar month) and monthly lead time (month 1 to month 7) based on the reforecast datasets. In the final step the corrected precipitation and temperature were downscaled to the 5 km by 5 km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong><em>q_obs(time=24472, stations=8</em>)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_EOBS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the EOBS dataset and ERA-Interim stored as variable <em><strong>q_sim (time=13880, stations=8)</strong></em>. Period 1979-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=13880, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_System4_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System 4 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored as variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=51, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension stations.</p> <pre><code>loat q_fcast_ens(time=432, lead_time=215, realization=51, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_SEAS5_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System SEAS5 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=25, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension ensemble member, fourth dimension stations.</p> <pre><code>float q_fcast_ens(time=432, lead_time=215, realization=25, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Arnal, L., H. Cloke, L. Magnusson, B. Klein, D. Meissner, A. de Tomas, J. Hunink, I. Pechlivanidis, L. Crochemore, S. Suarez, A. Solera, J. Andreu, J. Knight, F. Liggins, A. Weerts, M. H. Ramos & G. Thirel (2017): The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions. Deliverable 4.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf</a></p> <p>Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Thepaut & F. Vitart (2011): The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137(656), 553-597</p> <p>ECMWF (2017): SEAS5 user guide - Version 1.1. ECMWF, Reading, UK</p> <p>Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones & M. New (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 113(D20), D20119</p> <p>Johnson, S. J., T. N. Stockdale, L. Ferranti, M. A. Balmaseda, F. Molteni, L. Magnusson, S. Tietsche, D. Decremer, A. Weisheimer, G. Balsamo, S. Keeley, K. Mogensen, H. Zuo & B. Monge-Sanz (2018): SEAS5: The new ECMWF seasonal forecast system. Geosci. Model Dev. Discuss. 2018, 1-44</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401-6423</p> <p>Molteni, F., T. Stockdale, M. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer & F. Vitart (2011): The new ECMWF seasonal forecast system (System 4). ECMWF Research Department Technical Memorandum n. 656, Shinfield Park, Reading</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Weerts, A., F. Silvestro, L. Magnusson, B. Klein, I. Pechlivanidis, F. Wetterhall, D. Lavers, E. Gascon, J. Day, S. Hagelin, M. Lindskog & B. van Osnabrugge (2019): Forecast skill developments. Deliverable 4.3, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811</p>
ENS-LARSIM_ME Monthly Streamflow Forecasts for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim was to evaluate the potential skill of monthly streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As meteorological forcing data to calculate monthly flow forecasts the extended-range forecasts from ECMWF-ENS are applied. Twice a week (Monday and Thursday), the ENS model is extended to a lead time up to 46 days by ECMWF. The horizontal resolution for the first 15 days is 0.2°x0.2° (approx. 18 km) and from day 15 to day 46 it is 0.4°x0.4° (approx. 36 km). The ensemble consists of 1 control forecasts and 50 perturbed members, made from slightly different initial atmospheric and oceanic conditions (Owens & Hewson 2018). Re-Forecasts are generated with the same model as used for the operational forecasts for the past 20 years, starting on the same day and month as each real time forecast. The ensemble size is 11-member, which means that in total 20 years x 11 members = 220 forecasts are available for each real time forecast date. The re-forecasts are also created twice a week (Mondays and Thursdays) and are available a week in advance.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>Real-time meteorological station data (precipitation, temperature and global radiation) was interpolated to the 5 km x 5 km model grid and used as meteorological forcing to initialize LARSIM-ME at the forecast date.</p> <p>Re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 generating re-forecasts of the last 20 years were used. As station density of real-time meteorological station data is limited before 2000, only reforecasts with a forecast date after 1999 were considered. Daily total precipitation, daily mean air temperature and global radiation of the reforecast dataset of ECMWF-ENS were interpolated to the 5kmx5km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong>q_obs(time=24472, stations=8)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_SYNOP_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from real-time meteorological station data stored as variable float <em><strong>q_sim(time=6210, stations=8)</strong></em>. Period 2000-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=6210, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_ENS_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature, precipitation and global radiation of the ECMWF ENS re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 with a lead time of 46 days. Forecast dates 2nd January 2000 to 9th March 2016, in total 1699 forecasts. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=1699, lead_time=46, realization=11, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station.</p> <pre><code>float q_fcast_ens(time=1699, lead_time=46, realization=11, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
California Streamflow Projection Dataset
<p>This dataset consists of streamflow projection data for 20 basins in California. The files with 'RCP8.5' are for future projection under RCP8.5 forcing and 'hist' represents the projection with historical forcing. </p>
Data - Quantifying dynamic linkages between precipitation, groundwater recharge, and streamflow using ensemble rainfall‐runoff analysis
<p>The data support the analysis conducted in "Quantifying Dynamic Linkages Between Precipitation, Groundwater Recharge, and Streamflow Using Ensemble Rainfall‐Runoff Analysis", accepted for publication in Water Resources Research (https://doi.org/10.1029/2024WR037821) by Huibin Gao, Qin Ju, Dawei Zhang, Zhenlong Wang, Zhenchun Hao, and James Kirchner.</p> <p> </p>
Supporting Documents for "The DESC Catchments: Long-term Monitoring of Inland Precambrian Shield Catchment Streamflow and Water Chemistry in Central Ontario, Canada"
<p>Supporting documents for "<strong>The DESC Catchments: Long-term Monitoring of Inland Precambrian Shield Catchment Streamflow and Water Chemistry in Central Ontario, Canada" </strong> that are otherwise not readily accessible. These include historical government reports and relevant laboratory method descriptions from the Ontario Ministry of Environment (Canada). Reports List includes a listing of the 14 documents.</p>
Data from: Watershed classification predicts streamflow regime and organic carbon dynamics in the Northeast Pacific Coastal Temperate Rainforest
<p class="Abstract">Watershed classification has long been a key tool in the hydrological sciences, but few studies have been extended to biogeochemistry. We developed a combined hydro-biogeochemical classification for watersheds draining to the coastal margin of the Northeast Pacific coastal temperate rainforest (1,443,062<i> </i>km<sup>2</sup>), including 2,695 small coastal rivers (SCR) and 10 large continental watersheds. We used cluster analysis to group SCR watersheds into 12 types, based on watershed properties. The most important variables for distinguishing SCR watershed types were evapotranspiration, slope, snowfall, and total precipitation. We used both streamflow and dissolved organic carbon (DOC) measurements from rivers (<i>n</i> = 104 and 90 watersheds respectively) to validate the classification. Watershed types corresponded with broad differences in streamflow regime, mean annual runoff, DOC seasonality, and mean DOC concentration. These links between watershed type and river conditions enabled the first region-wide empirical characterization of river hydro-biogeochemistry at the land-sea margin, spanning extensive ungauged and unsampled areas. We found very high annual runoff (mean > 3000 mm, <i>n</i> = 10) in three watershed types totaling 59,024 km<sup>2</sup> and ranging from heavily glacierized mountain watersheds with high flow in summer to a rain-fed mountain watershed type with high flow in fall-winter. DOC hotspots (mean > 4 mg L<sup>-1</sup>, <i>n</i> = 14) were found in three other watershed types (48,557 km<sup>2</sup>) with perhumid rainforest climates and less-mountainous topography. We described four patterns of DOC seasonality linked to watershed hydrology, with fall-flushing being widespread. Hydro-biogeochemical watershed classification may be useful for other complex regions with sparse observation networks.</p>
Streamflow data
<p>This dataset contains annual data for global 15 rivers and 4 synthetic data series with a log-normal distribution.</p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
Data and scripts for Alameda streamflow prediction paper
<p>These are the data and scripts that accompany the Alameda streamflow prediction paper titled:</p> <p>"Streamflow prediction at the intersection of physics and machine learning: a case study of two Mediterranean-climate watersheds"</p>
Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities
<p>These codes are used in the paper entitled"Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities" which is submitted to the Journal of Water Resources Research. The code for the differentiable parameter learning (DPL) model can be downloaded at https://doi.org/10.5281/zenodo.7091334. The code for LSTM to reproduce our analysis is available at <a href="https://github.com/neuralhydrology/neuralhydrology">https://github.com/neuralhydrology/neuralhydrology</a>. The SWORD database utilized in our study can be accessed at https://zenodo.org/records/10013982. The geometric dataset of the global river attribute information for every river reach, the values of all pressure indicators (DOF, DOR,SED, USE, RDD and URB) and the values for the CSI are available at https://doi.org/10.6084/m9.figshare.7688801.</p>
HydroPenIndia: A catalogue of streamflow metrics, meteorological time series and catchment attributes of Peninsular India
<p><em>HydroPenIndia, </em>a catalogue of streamflow metrics, hydro-meteorological time series and landscape attributes of 204 catchments of Peninsular India is introduced. This catalogue consists of daily hydro-meteorological time series (rainfall, soil moisture, potential evapotranspiration, actual evapotranspiration, maximum temperature, minimum temperature, longwave radiation, shortwave radiation, wind speed and humidity) for a period of 36 years from 1980-2015. The time series of 26 streamflow metrics, 13 topographic metrics, 12 climate indices, 15 hydrologic signatures, 8 land cover descriptors, 6 geologic characteristics, 6 soil characteristics (see table 8) and 13 human intervention indices are also included in this dataset. <em>HydroPenIndia</em> is an initiative to encourage hydrologists to advance knowledge of hydrological processes by contributing to fundamental research questions on Indian catchments. Free availability of the dataset will provide access to global users to represent India in large-sample hydrology studies. <em>HydroPenIndia</em> is derived from multiple databases to help researchers start their research without wasting time on collecting and processing datasets. This catalogue will motivate researchers to solve pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unravelling regional scale hydrologic functioning and climate change impact assessment over Peninsular India.</p>
Daily streamflow at 0.5 deg resolution generated using 0.22 deg runoff from the historical (1986-2005) and two future scenarios' (RCP 4.5 and 8.5, 2081-2100) simulations of CanRCM4 for its North American domain
<p>These data are provided in support of the following manuscript which is currently (July 2024) under revision for the <strong>Hydrology and Earth System Science</strong> journal. The details about how these data were generated can be found in this manuscript (or its final accepted version, hoping our manuscript will be accepted). <br><br><a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/</a><br><br>The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model<br>Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha <br><br>The netcdf files provide here contain two variables.<br><br>1) streamflow, variable name is fout_land, units are m3/s<br>2) flow velocity, variale name is velocity, units are m/s<br><br><br></p>
Large repeated measures experiments of acoustic Doppler current profiler (ADCP) streamflow measurements under steady flow conditions (Chauvan 2016 regatta)
<p>From 8 to 10 November 2016, 50 laboratories or teams (from 8 different countries) with 50 ADCPs, simultaneously conducted more than 600 ADCP discharge measurements in steady flow conditions (around 14 m<sup>3</sup>/s released by a dam), during three half-days, over 500 m along the Taurion River at Saint-Priest-de-Taurion, France. 26 cross-sections with various shapes and flow conditions, and more or less favourable conditions, were distributed along the river. A specific experiment procedure, which consisted of circulating every team over half of the cross-sections, was implemented in order to quantify the impact of site selection on the discharge measurement<br> uncertainty.</p> <p>The experimental design is presented in Despax et al (2017) available at <a href="https://irsteadoc.irstea.fr/cemoa/PUB00055007">https://irsteadoc.irstea.fr/cemoa/PUB00055007</a></p> <p>Despax, A., Hauet, A., Le Coz, J., Dramais, G., Blanquart, B., Besson, D., & Belleville, A. (2017). Inter-laboratory comparison of discharge measurements with Acoustic Doppler Current Profilers Chauvan field experiments. 8, 9 and 10th November 2016. (Technical report). Lyon, France: Groupe Doppler. (92 p.)</p> <p> </p>
Analysis of streamflow and sea surface salinity near megadeltas
<p>1. A 3-D .mat file of global HyMap streamflow from 2015 April to 2019 October. This is a gridded file (latxlonxtime format) but the data are monthly accumulated (from original hourly scale). </p> <div> <div><strong><span>global_Qs_monthly_2015_2021.mat</span></strong> <div> <div> </div> <div>2. A 3-D .mat file of global LIS surface water storage from 2015 April to 2019 October. This is again a gridded file (latxlonxtime format) but the data are monthly accumulated (from original hourly scale). </div> <div> </div> <div><span><strong>global_SWS_monthly_2015_2021.mat</strong></span></div> <div> </div> <div>3. A 3-D .mat file of the global SMAP SSS data from 2015 April to 2019 October. Same grid-spacing (latxlonxtime format) and the data are monthly accumulated (from original hourly scale). </div> <div><strong><span>global_SSS_monthly_2015_2021.mat<br></span></strong></div> <div> </div> <div>4. The hycom precipitation, temperature and wind speed variables (processed as monthly and grids - the grid spacings are not the same as the other files above)</div> <div><strong><span>hycom_P_T_W.mat<br></span></strong></div> <div> </div> <div>5. HyMap basins TIF file </div> <div><span><strong><span>hymap_basins.tif<br></span></strong></span></div> <div><span> </span></div> <div><span>6. This is the LIS input file.</span></div> <div><span><strong><span><a title="https://gcc02.safelinks.protection.outlook.com/?url=http%3A%2F%2Flis_input_010_hybrid.nc%2F&data=05%7C02%7CSujay.V.Kumar%40nasa.gov%7C62aa4a84913a4fd4c81108dced2e699e%7C7005d45845be48ae8140d43da96dd17b%7C0%7C0%7C638646030805666788%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=gxZbVR3D6AZAxaoj4e4CKb4uzfDdyWMFq3TaEv8Hr4w%3D&reserved=0" href="https://gcc02.safelinks.protection.outlook.com/?url=http%3A%2F%2Flis_input_010_hybrid.nc%2F&data=05%7C02%7CSujay.V.Kumar%40nasa.gov%7C62aa4a84913a4fd4c81108dced2e699e%7C7005d45845be48ae8140d43da96dd17b%7C0%7C0%7C638646030805666788%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=gxZbVR3D6AZAxaoj4e4CKb4uzfDdyWMFq3TaEv8Hr4w%3D&reserved=0">lis_input_010_hybrid.nc</a><br></span></strong></span></div> <div><span> </span></div> <div><span>7. This a .CSV file with some of the outputs </span></div> <div><span><strong><span>basin_data_deltas_taylor.csv<br></span></strong></span></div> </div> </div> </div> <p> </p>
Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States
<p>This dataset contains information of 432 study catchments used in the WRR paper "Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States", including shapefile, hydrological signatures, catchment descriptors, and catchment classification (the primary class of each catchment).</p>
S1 What Are the Key Drivers Controlling the Qualityof Seasonal Streamflow Forecasts?
<p>Recent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydroclimatic services. Given that many water-related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35,400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic-hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions.</p>
The global water resources and use model WaterGAP v2.2e: streamflow calibration and evaluation data basis
<p>The data collection covers the streamflow data used for calibrating and validating the global water use and availability model WaterGAP v2.2e. The collection is a result of a data selection, assessment and merge effort from three data sources (GRDC, GSIM, ADHI) for in total 1509 stations. The stations are co-registered to the DDM30 (Döll & Lehner 2002) according their best hydrological fit.</p> <p>Please see the readme.md for details. Version 1.1 contains now the shapefiles as a zip instead single files which makes downlad more convenient.</p> <p> </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
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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.