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32 results for “Reservoir Modeling”
General Lake Model-Aquatic EcoDynamics model parameter set for Falling Creek Reservoir, Vinton, Virginia, USA 2013-2019
The General Lake Model (GLM), an open-source, one-dimensional hydrodynamic model, was used to simulate physical, chemical, and biological variables in Falling Creek Reservoir, Vinton, Virginia, USA between 15 May 2013 and 31 December 2019. GLM (v.3.2.0a3) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires three configuration files to run the model. First, the glm3.nml file configures lake metadata (including hypsometry), meteorological driver data, stream inflow and outflow driver data files, and physical response variables (mixing parameters and sediment heat zones). Second, the aed2_20220111_2DOCpools.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, silica, nitrogen, phosphorus, organic matter, and phytoplankton. Third, the aed2_phyto_pars_4Jan2022.nml file configures all parameters pertaining to phytoplankton dynamics. Meteorological data, two surface stream inflow files, a submerged oxygenation inflow file, and outflow file used in this calibration are also included.
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
Modelled Temperature in Cuerda del Pozo Reservoir
<p>This dataset in NetCDF format stores the modelled temperature of Cuerda del Pozo reservoir produced by Delft3D software.</p>
Stochastic Modelling of Thin Mud Drapes inside Point Bar Reservoirs with ALLUVSIM-GANSim
<p>Here is the dataset and code for the paper by Hu, X et al. (2023, under review). <span>Stochastic Modelling of Thin Mud Drapes inside Point Bar Reservoirs with ALLUVSIM-GANSim,</span> Water Resources Research.</p>
Implementation and sensitivity analysis of a Dam-Reservoir OPeration model (DROP v1.0) over Spain - Supplement
<p>Supplement of the following scientific paper submitted in GMD :</p> <p><strong>Sadki M., Munier S., Boone A., Ricci S. : Implementation and sensitivity analysis of a Dam-Reservoir OPeration model (DROP v1.0) over Spain, Geoscientific Model Development, 2022. </strong></p> <p> </p>
Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)
<p>Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). </p> <p> </p> <p>References:</p> <p>Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.</p>
Fig. 4 in Modeling energy flow in a large Neotropical reservoir: a tool do evaluate fishing and stability
Fig. 4. Simulated Total catch (solid line) and catch values observed (triangles). Simulations performed on ITAIPU-2 model, under constant fishing effort (values were close to 1998). Simulations made in Ecopath with Ecosim (Subroutine: Run Ecossim, module: Results).
Fig. 1 in Modeling energy flow in a large Neotropical reservoir: a tool do evaluate fishing and stability
Fig. 1. Itaipu Reservoir, its tributaries and the upper Paraná River Floodplain upstream (spawning areas for the reservoir migratory fish species).
3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland
<p>This repository contains the supplementary data to the submitted publication titled "3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland" which was submitted to the Journal "Geothermal Energy" (https://geothermal-energy-journal.springeropen.com/). </p>
Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models
<p>This dataset contains the .npy (numpy) files of the simulation models and reference discussed in the paper "Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models".</p> <p>The folders contain all simulation models and reference maps already divided in subregions. Each .npy file is a numpy 2D array with delta IP or delta Sw values. The csv files contain the 3-tuples and the selected model in each.</p> <p>There are two csv files: the first is the dataset used for training the CNN, with 1280 labeled tuples evaluated by a single specialist. The second is the ground-truth, with 164 tuples evaluated by three specialists (in which 2 or more agreed on the selected model), used for validating the models and comparing different approaches.</p> <p>We also provide a Python code to read and visualize the .npy files.</p>
Data and Models of Thermal Density Currents Research in Daheiting Reservoir
<p><span>This database includes the field measurement results from cruise surveys, profile observations, and benthic observations in the Daheiting Reservoir, along with the retrospective model and the average-year model. These data and models are used to study the oxygenation benefits of thermal density currents and their regulation measures.</span></p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Hydraulic model (HEC-RAS) of downstream of Tuttle Creek Reservoir at the confluence of the Big Blue River and the Kansas River near Manhattan, KS
<p>A 2D Hydraulic model (HEC-RAS) for below Tuttle Creek Reservoir at the confluence of the Kansas River and the Big Blue River near Manhattan, KS is presented. Model geometry is based on United States Geological Survey (USGS) 3DEP data (2015), with underwater bathymetry "burned" in using cross-sections sampled in the field in April of 2023. The model was calibrated based on water surface measured during data collection. The hydraulic simulations correspond to streamflows during which fish monitoring data were collected by researchers at Kansas State University (L. Rowley and K. Gido, to be published). Results from the hydraulic model, coupled with a sediment transport model, will be used to study fish and macroinvertabrate ecological response to streamflow.</p>
Data used in the physical-biogeochemical model of Danjiangkou Reservoir
<p>The date set includes meteorological, hydrological, water quality, and organic carbon loading data obtained in 2009 for the Danjiangkou Reservoir in China. The data were used to set the boundary conditions of the physical-biogeochemical model, which was adopted to simulate reservoir methane dynamics in the Danjiangkou Reservoir.</p>
Hydraulic model (HEC-RAS) of downstream of Tuttle Creek Reservoir at the confluence of the Big Blue River and the Kansas River near Manhattan, KS
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Data from: Modeling the impact of Plasmodium falciparum sexual stage immunity on the composition and dynamics of the human infectious reservoir for malaria in natural settings
Malaria transmission remains high in Sub-Saharan Africa despite large-scale implementation of malaria control interventions. A comprehensive understanding of the transmissibility of infections to mosquitoes may guide the design of more effective transmission reducing strategies. The impact of P. falciparum sexual stage immunity on the infectious reservoir for malaria has never been studied in natural settings. Repeated measurements were carried out at start-wet, peak-wet and dry season, and provided data on antibody responses against gametocyte/gamete antigens Pfs48/45 and Pfs230 as anti-gametocyte immunity. Data on high and low-density infections and their infectiousness to anopheline mosquitoes were obtained using quantitative molecular methods and mosquito feeding assays, respectively. An event-driven model for P. falciparum sexual stage immunity was developed and fit to data using an agent based malaria model infrastructure. We found that Pfs48/45 and Pfs230 antibody densities increased with increasing concurrent gametocyte densities; associated with 55–70% reduction in oocyst intensity and achieved up to 44% reduction in proportions of infected mosquitoes. We showed that P. falciparum sexual stage immunity significantly reduces transmission of microscopic (p < 0.001) but not submicroscopic (p = 0.937) gametocyte infections to mosquitoes and that incorporating sexual stage immunity into mathematical models had a considerable impact on the contribution of different age groups to the infectious reservoir of malaria. Human antibody responses to gametocyte antigens are likely to be dependent on recent and concurrent high-density gametocyte exposure and have a pronounced impact on the likelihood of onward transmission of microscopic gametocyte densities compared to low density infections. Our mathematical simulations indicate that anti-gametocyte immunity is an important factor for predicting and understanding the composition and dynamics of the human infectious reservoir for malaria.
Dataset: Risk Transfer Model for Flood Risk Evolution in a Multi-reservoir System
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p> </p> <p>The files consist of:</p> <p> </p> <p>Data:</p> <ul> <li>Figure 11;</li> <li>Figure 12;</li> <li>Figure S1;</li> <li>Figure S4</li> <li>Relative prediction error</li> <li>Reservoir information</li> <li>Streamflow</li> </ul> <p>Model code:</p> <ul> <li>Calculation of entropy</li> <li>Forecasting error simulation model</li> <li>LHS</li> <li>Analytic code of transfer model</li> </ul>
Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling
<p>Description of Research Data and Code<br>This repository contains the data and code associated with the research paper: Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling, currently under review at Water Resources Research.</p> <p>Overview<br>To investigate the risk interdependencies arising from hydraulic interactions in cascading reservoir systems, we developed a risk propagation model using Bayesian networks (see file: Risk_propagation_model). Building on this model, we employed EMODPS to create a robust operational model for the reservoirs (see file: Robust_operation_model). Our goal was to minimize the joint risks of insufficient hydropower output and ecological water shortages while formulating robust operating policies to mitigate system performance degradation in the face of uncertain future runoffs (generated from our runoff simulations, see file: runoff simulation).</p> <p>Additionally, we analyzed the relationship between overall risk and risk at individual reservoir sites using a scenario discovery algorithm to pinpoint scenarios that reveal vulnerabilities (see file: python_project_scenariodiscovery).</p> <p>Acknowledgments<br>This project builds upon the code developed by Giuliani et al. (2016) M3O-Multi-Objective-Optimal-Operations (https://mxgiuliani00.github.io/M3O-Multi-Objective-Optimal-Operations/), Hadka and Reed (2013) BORG MOEA (http://borgmoea.org/), and Kevin Patrick Murphy et al. (2007) Bayesian Network Toolbox (https://www.ipcc.ch/report/ar6/wg1/#InteractiveAtlas). We are grateful to the original authors for their contributions.</p> <p>While we have made modifications and extensions to the original code, we have not altered its license. Users should refer to the original repositories for more details and ensure compliance with the terms of the original authors' licenses.</p>
Dataset accompanying the publication: Reservoir mud releasing may suboptimize fluvial sand supply to coastal sediment budget: Modeling the impact of Shihmen Reservoir case on Tamsui River estuary
<p>Delft3D model input and output files for scenario simulations (Scenario 1, Scenario 2, and Scenario 3)</p>
Data from: What's stirring in the reservoir? modelling mechanisms of henipavirus circulation in fruit bat hosts
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OpenNeuro
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