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8 results for “surface runoff”
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
Data: Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet
<h1>Data repository associated with the manuscript 'Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet', Nicolas Jullien, Andrew J. Tedstone, Horst Machguth, (under review in the Journal of Glaciology)</h1> <h2> </h2> <h2>Introduction:</h2> <p>We provide a short description of each file present in this data repository, and flag to the corresponding reference when applicable. Please cite the appropriate references when using these data.</p> <h2> </h2> <h2>Data:</h2> <h3>In this repository:</h3> <ul> <li>'Ice_Layer_Output_Thicknesses_Likelihood_2010_2018_jullienetal2021_modified.csv'. Modified 2010-2018 ice slabs thickness retrievals from Jullien et al., (2023) where ice slabs thickness > 16 m thick and < 1 m thick are retained, and flight-lines not holding ice slab were set to hold an ice content of 0 m thick.</li> <li>'master_maps.zip'. Raster files. Surface hydrology connectivity map over the Greeland Ice sheet, first presented in Tedstone and Machguth (2022). The easiest way to handle this dataset is to use the 'master_map_GrIS_mean.vrt' file.</li> <li>'MARv.3.14_MoA_2000_2012.nc'. Melt over accumulation from 2000 to 2012 extracted from MARv3.14. See file '<a title="melt_over_accumulation_calculations.py" href="https://github.com/jullienn/IceSlabs_SurfaceRunoff/blob/main/melt_over_accumulation_calculations.py">melt_over_accumulation_calculations.py</a>' in the code repository for post processing analysis.</li> <li>'RunoffLimits.zip'. '.csv' files. Maximum visible runoff limits in 2012 and 2019, sorted for each boxes generated by Tedstone and Machguth (2022). Each '.csv' file stores the data points coordinates (Geographical Reference System: WGS 84 / NSIDC Sea Ice Polar Stereographic North (EPSG:3413)) of the maximum visible runoff limit retrievals after filtering out the outliers. The maximum visible runoff limits where first presented in Tedstone and Machguth (2022).</li> </ul> <h3>Used in this study but from other datasets:</h3> <ul> <li>The ice slabs extent and ice slabs thickness were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426</li> <li>The radargrams displayed in Fig. 5c-f were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426. The following files were used: <ul> <li>'L1_may12_03_1_aggregated.pickle'</li> <li>'L1_may12_03_2_aggregated.pickle'</li> <li>'20100508_01_114_115_Depth_CORRECTED.pickle'</li> <li>'20140424_01_002_004_Depth_CORRECTED.pickle'</li> <li>'20180427_01_170_172_Depth_CORRECTED.pickle'</li> </ul> </li> <li>The surface topography present in Fig. 5g are 10 m resolution mosaics from the ArcticDEMv3 (Porter et al., 2018), and accessible at: https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/</li> <li>The winter time strain rates map displayed in Fig. 5h were first presented in Poinar and Andrews (2021), and are accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82127</li> </ul> <p> </p> <h2>References:</h2> <p>Jullien, N., Tedstone, A. J., Machguth, H., Karlsson, N. B., & Helm, V. (2023). Greenland Ice Sheet Ice Slab Expansion and Thickening. <em>Geophysical Research Letters</em>, <em>50</em>(10), e2022GL100911. https://doi.org/10.1029/2022GL100911</p> <p>Poinar, K., & Andrews, L. C. (2021). Challenges in predicting Greenland supraglacial lake drainages at the regional scale. <em>The Cryosphere</em>, <em>15</em>(3), 1455–1483. https://doi.org/10.5194/tc-15-1455-2021</p> <p>Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M., Jr., Williamson, C., … Bojesen, M. (2018). <em>ArcticDEM, Version 3</em> (Version V1) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/OHHUKH</p> <p>Tedstone, A. J., & Machguth, H. (2022). Increasing surface runoff from Greenland’s firn areas. <em>Nature Climate Change</em>. https://doi.org/10.1038/s41558-022-01371-z</p>
Tampere - Surface runoff in relation to precipitation
<p>Results of the computed runoff coefficients from two methods in Vuores neighbourhood of Tampere: 1. direct runoff measurement from a stream; 2. process-based hydraulic modelling for storms with 1-, 10- and 100-year return period with and without the NBS interventions.</p>
Data from: Feedbacks between shallow groundwater dynamics and surface topography on runoff generation in flat fields
In winter, saturation excess (SE) ponding is observed regularly in temperate lowland regions. Surface runoff dynamics are controlled by small topographical features that are unaccounted for in hydrological models. To better understand storage and routing effects of small scale topography and their interaction with shallow groundwater under SE conditions, we developed a model of reduced complexity to investigate SE runoff generation, emphasizing feedbacks between shallow groundwater dynamics and mesotopography. The dynamic specific yield affected unsaturated zone water storage, causing rapid switches between negative and positive head and a flatter groundwater mound than predicted by analytical agro-hydrological models. Accordingly, saturated areas were larger and local groundwater fluxes smaller than predicted, leading to surface runoff generation. Mesotopographic features routed water over larger distances, providing a feedback mechanism that amplified changes to the shape of the groundwater mound. This in turn enhanced runoff generation, but whether it also resulted in runoff events depended on the geometry and location of the depressions. Whereas conditions favourable to runoff generation may abound during winter, these feedbacks profoundly reduce the predictability of SE runoff: statistically identical rainfall series may result in completely different runoff generation. The model results indicate that waterlogged areas in any given rainfall event are larger than those predicted by current analytical groundwater models used for drainage design. This change in the groundwater mound extent has implications for crop growth and damage assessments.
The simulation results of runoff components and estimated surface water demands for the Yarkant River basin
<p>This dataset contains two directories as below. </p> <p>1. Hydrological_Simulation</p> <p>Historical_runoff_simulation.csv : the four runoff components at monthly scale (km^3/month) aggregated from daily runoff simulation results for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP1-2.6 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP2-4.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP5-8.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "48 experiments" contains the four runoff components at monthly scale (km^3/month) simulated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p> <p>2. Water_Demand_Estimation</p> <p>Historical_water_demand_estimation.csv : the monthly water demands (km^3/month) from irrigation (including wheat, cotton, and corn) and other sectors for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP1-2.6 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP2-4.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP5-8.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "48 experiments" contains the monthly water demands (km^3/month) estimated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p>
The formation process, mechanism, and attribution of urban impervious surface thermal runoff
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Data from: Feedbacks between shallow groundwater dynamics and surface topography on runoff generation in flat fields
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A soil moisture-dependent model to simulate water table depth and proportions of surface and subsurface runoff and its validation at basin scale
<p>The data is the simulations of the SMD-model, Sy-mdoel, adn Noah-MP in three basins in China and the USA. The vaiables are monthly water table depth, soil moisture, subsurface and total runoff. </p>
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