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25 results for “Rainfall simulator”
Dataset to Manuscript: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Bellè et al. 2021 (Biogeosciences)
<p>Dataset to manuscript: Bellè, S-L., Berhe, A., Hagedorn, F., Santin, C., Schiedung, M., van Meerveld, I. and Abiven, S.: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Biogeosciences, https://doi.org/10.5194/bg-2020-361, 2021. </p> <p>All parameters and variables are described in the "var_names" file.</p>
Rainfall data from WRF simulations for the Atacama Desert for present and mid-Pliocene climate
<p>We provide model output for rainfall from WRF experiments for the present-day and mid-Pliocene climate. These are netCDF files that contain processed data shown in figures of Reyers et al. (accepted). Details on the files and content are listed in the primary data information Reyers_et_al_primary_data_information.pdf Refer to Reyers et al. (2022) for the full information on the data production and interpretation.</p> <p>This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1198. The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 268236062 – SFB1211 "Earth-evolution at the dry limit" (https://sfb1211.uni-koeln.de/).</p> <p><strong>Reference</strong></p> <p>Reyers, M., Fiedler, S., Ludwig, P., Böhm, C., Wennrich, V., and Shao, Y.: On the importance of moisture conveyor belts from the tropical East Pacific for wetter conditions in the Atacama Desert during the Mid-Pliocene, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-72, 2022, accepted.</p>
ASR01 Short-term assessment of effects of burning on infiltration, runoff, and sediment and nutrient loss on Tallgrass Prairie using rainfall simulation, 1989
Rainfall simulation and overland flow experiments were performed on four plots at a single site on Konza from May to August, 1989. Two plots were treated with a late spring burn and two plots were left unburned. Five simulations were performed on burned plots and three simulatons on unburned plots. Each simulation consisted of a “dry run” followed 24 hours later by a 'wet run'. The dry run consisted of rainfall applied at an intesity of approximately 60 mm/hour. The wet run was the same as a dry run, except when the rainfall was complete, overland flow was applied directly at the top of the plots to simulate run off coming from upslope. Measurements taken include overland flow velocity, water application rate, runoff, hydrograph, water flow depth, sediment content, nitrogen and phosphorus content and percent ground cover (See A.B. Duell, Effects of burning on infiltration, overland flow, and sediment loss on tallgrass prairie, M.S. thesis, Kansas State University, 82pp. for further details).
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Existing rainfall events to parameterise rainfall event simulations
<p>Existing radar rainfall events from the High Moorsley weather radar, and corresponding simulated events, using spatiotemporal event properties to parameterise simulations.</p>
Mechanisms for a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China: observation analysis and nested very-large-eddy simulation with the WRF Model
<p>A video shows the processes of a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China simulated by WRF nested very-large-eddy simulation.</p>
CCSM4 LR and HR Model Simulations for GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall"
<p>This archive contains model simulations of precipitation used in the GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall" (Zhang et al., 2021). These model simulations are standard control simulations based on the Community Climate System Model Version 4.0 (CCSM4) using eddying (HR) and eddy-parameterizing (LR) ocean component models. </p> <p><em><strong>In order to properly acknowledge those who have worked hard to create those data sets we do require that if this data is used in a publication that coauthorship be offered to those involved in the data creation. Please contact the author Dr. Wei Zhang (email: wz19@princeton.edu) for any further questions or potential collaboration. </strong></em></p>
Effects of Grass Cover on the Overland Soil Erosion Mechanism under Simulated Rainfall
<p><span>Grass cover has a complex influence on overland soil erosion. This study quantified the impact of grass cover on overland soil erosion using a dimensionless water flow path index. It systematically analyzed the response mechanism among overland soil erosion, slope gradient, rainfall intensity, and hydrodynamic parameters, aiming to identify the optimal hydrodynamic parameters capable of characterizing overland soil erosion. A predictive model for soil erosion was constructed based on general dimensionless water flow intensity parameters, comprehensively evaluating the mechanism of soil erosion on grass-covered overland under simulated rainfall conditions. The results indicate that the model constructed using dimensionless parameters exhibits strong adaptability and can be effectively validated in other experiments.</span></p>
Innovatory rainfall simulator design – A concept of moving storm automation DATA
<p>Rainfall simulator discharge data for two slope conditions, two storm movement directions, and three different velocities.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
Major and trace element composition of IODP Site U1429 and Yellow River sediments and simulated rainfall changes in East-Southeast Asia during past 300 ka
<p>This dataset includes major and trace element composition of IODP Site U1429 and Yellow River surface sediments. They are all analyzed based on the clay-sized fractions. It also includes the data of simulated rainfall changes in the northern and southern China, as well as in Southeast Asia during past 300 ka.</p>
A microwave link simulation dataset for rainfall observation applications
<p>A microwave link simulation dataset is developed for theoretical research of rainfall observation applications. The dataset is built based on electromagnetic propagation theory attenuation effects with measured raindrop size distribution data from a PARSIVEL disdrometer and air temperature, air pressure and humidity data from nearby weather stations. Frequencies from 6 to 180 GHz, horizontal or vertical polarization, link lengths from 0.1 km to 20 km, and quantization resolutions from 0 to 1 dB are all fully considered for various existing and future complications. The dataset can be utilised to validate the performance of the microwave link-based rainfall inversion process algorithm in various equipment configurations and to assess the potential of microwave link-based rainfall monitoring technology in future high-frequency communication infrastructure conditions on a theoretical level with a very low cost.</p>
Supporting data 3 for "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?"
<p>Selected variables from a Geophysical Fluid Dynamics Laboratory's (GFDL) global atmospheric model version 4.0 (AM4) for a horizontal resolution of ~25 km (c384). Details of the simulations are documented in a manuscript titled "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?" to be submitted to the Geophysical Research letters.</p>
Supporting data 2 for "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?"
<p>Selected variables from a Geophysical Fluid Dynamics Laboratory's (GFDL) global atmospheric model version 4.0 (AM4) for a horizontal resolution of ~50 km (c192). Details of the simulations are documented in a manuscript titled "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?" to be submitted to the Geophysical Research letters.</p>
Supporting data 1 for "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?"
<p>Selected variables from a Geophysical Fluid Dynamics Laboratory's (GFDL) global atmospheric model version 4.0 (AM4) for a horizontal resolution of ~100 km (c96). Details of the simulations are documented in a manuscript titled "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?" to be submitted to the Geophysical Research letters.</p>
Data for "Disdrometer measurements under Sense-City rainfall simulator"
<p>The data set corresponds the data presented in the data paper : “Disdrometer measurements under Sense-City rainfall simulator“ which is published in Earth System Science Data” (https://www.earth-system-science-data.net/).</p> <p>More details can be found in the Read_me.txt file and in the paper.</p>
The observed and simulated datasets of "21·7" Henan extremely heavy rainfall event
<p>The uploaded files are the observed and simulated datasets of “21·7” Henan extremely heavy rainfall event, including OTT disdrometers, Radar, national and regional gauges, and WRFOUT files. </p>
Arid Hydrology Research Area (AHRA) rainfall simulator data
<p>Much of the arid and semiarid western United States is affected by flash flooding associated with infrequent extreme rain events. Floods have large social and economic consequences for communities and individuals. Thus, flood hazard assessment is important to reduce flood-related risks and support sustainable development. Flood prediction relies heavily on hydrologic models, which are plagued by substantial uncertainties. To improve model performance, the Southern Sandoval County Arroyo Flood Control Authority (SSCAFCA) established an outdoor laboratory called the Arid Hydrology Research Area (AHRA) in 2022. AHRA is situated on a 1.5-ha parcel of land in Rio Rancho, New Mexico. The site is used to conduct runoff experiments using a rainfall simulator, a device that can reproduce intense precipitation events often observed during the summer Monsoon. This dataset contains runoff and infiltration time series for test plots covering a range of soil textures ranging from sand to silty clay. Experiments were designed to assess the impact of soil texture, antecedent moisture conditions, and physical soil crusts on the plot-scale runoff response. </p>
Arid Hydrology Research Area (AHRA) rainfall simulator data
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