Property:Describe available calibration data
From CSDMS
This is a property of type Text.
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A total of 17 flow problems and 15 water quality transport problems are presented in WASH123D. These example problems can serve as templates for users to apply WASH123D to research problems or practical field-scale problems. For the 17 flow examples, the following objectives are achieved: Seven to demonstrate the design capability of WASH123D using seven different flow modules; Four to show the needs of various approaches to simulate various types of flow (critical, subcritical, and supercritical) in river networks and overland regime; and Five to illustrate some realistic problems using WASH123D. +
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An application dataset for the Le Sueur Basin is included as part of the source file download with all associated calibration and validation data. +
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An application dataset for the Minnesota River Basin and Clear Creek/Tushar Mountains is included as part of the source file download. +
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An intercomparison of the Matlab and Python versions of DeltaRCM is in the works. +
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Biomass productivity and marsh accretion rates from 1984-present, described in Morris et al. (2002). +
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Calibration must be based on external hydraulic and channel morphology data. Optimal parameters can be determined by test simulations. +
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Cliffs is benchmarked as described in: E. Tolkova. Land-Water Boundary Treatment for a Tsunami Model With Dimensional Splitting
Pure and Applied Geophysics, Vol. 171, Issue 9 (2014), pp. 2289-2314
Examples of modeling with Cliffs (complete set-ups included) are described in Cliffs User Manual at http://arxiv.org/abs/1410.0753 +
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Cohen, S., G. Willgoose, and G. Hancock (2008), A methodology for calculating the spatial distribution of the area-slope equation and the hypsometric integral within a catchment, J. Geophys. Res., 113, F03027, doi:10.1029/2007JF000820. +
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Data available from 1986-88 field experimentation (in compendium) using rainfall simulation. Also, validation data sets are available from USLE database. +
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Default parameter and input files will produce steady state landscape with stream power erosion and mass wasting +
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Example and validation datasets are available on the github page. +
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Experiment data for steady channel flow can be found in:
Sumer, B. M., Kozakiewicz, A., Fredsoe, J., Deigaard, R., 1996. Velocity and concentration profiles in sheet-flow layer of movable bed. Journal of Hydraulic Engineering, (1996) 549-558.
Experiment for oscillatory flow can be found in:
O'Donoghue, T., Wright, S., 2004. Concentrations in oscillatory sheet flow for well sorted and graded sands. Coastal Engineering 50 (2004) 117-138. +
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FVCOM was originally developed for the estuarine flooding/drying process in estuaries and the tidal-, buoyancy- and wind-driven circulation in the coastal region featured with complex irregular geometry and steep bottom topography. This model has been upgraded to the spherical coordinate system for basin and global applications. A non-hydrostatic version of FVCOM has been coded and is being tested.
See also website for model validations. +
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Have successfully tested the model on the Colorado river shelf system, and along analogue models. +
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ILAMB has integrated testing of overall scores on a coarsened subset of observational data which runs via Azure pipelines. +
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In one application, the rate of change in the model has been calibrated to a state data set averaging shoreline change over 50 years (from the North Carolina Department of Transportation; see Slott et al., 2007). Numerous other shoreline change data sets are available, based on surveys of various sorts, aerial photography, and recently LIDAR (e.g. Lazarus and Murray, 2007). +
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Included with the ZIP file +
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Laboratory experiments; long-term surveyed rivers; long profiles of transport-limited rivers +
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Like most morphodynamical models the user is to supply long-term coastal change data from measured data. +
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Long term sediment routine:
*Syvitksi & Milliman, Journal of Geology, 115, 2007.
Short term sediment routine:
*Morehead et al., Global and Planetary Change, 39, 2003. +
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Model description and calibration can be found in:
Leonardi, N., and S. Fagherazzi (2014), How waves shape salt marshes, Geology , doi:10.1130/G35751.1.
Leonardi, N., and S. Fagherazzi (2015), Local variability in erosional resistance affects large scale morphodynamic response of salt marshes to wind waves, Geophysical Research Letters, 2015GL064730, doi:10.1002/2015GL064730. +
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Model is designed and calibrated for Alaska Coastal Plain.
We calibrated the model against temperature data in the subsurface from the Drew point, AK, USGS meteorological station. +
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Module tested here: https://www.geosci-model-dev.net/11/4873/2018/ +
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National Hydrography Dataset (NHD) flowline datasets were used to evaluated the model. +
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No calibration data sets. We validate the model against available analytical solutions and use it to analyze the system behavior under a general base-level fall and base-level rise. See Lorenzo-Trueba et al. 2012. +
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No specific calibration data sets available. GENESIS has enjoyed many practical applications within the engineering and scientific community, results routinely published in proceedings of coastal conferences. +
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Not readily available; theoretical experiments are available as examples. +
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One test facies succession CSDMStestSection.txt and one test facies code, colour code and name file CSDMStestSectionLithoCol.txt +
One test facies succession CSDMStestSection.txt and one test facies code, colour code and name file CSDMStestSectionLithoCol.txt +
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Proof of concept was applied for the Ganges-Brahmaputra delta system +
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Requires calibration for relationship between:
# soil plate volume and tree diameter;
#soil plate width and tree diameter;
#soil plate depth and tree diameter.
These relationships have been calibrated based on field data in the Southern Blue Ridge (Appalachian Mountains). +
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SBEACH was "validated" using a large suite of laboratory and field data sets. See SBEACH Report 4: Cross-Shore Transport Under Random Waves and Model Validation with SUPERTANK and Field Data. http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=Software;31 +
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SPF and the earlier models from which it was developed have been extensively applied in a wide variety of hydrologic and water quality studies (3,4), including pesticide runoff model testing (5), aquatic fate and transport model testing (6,7), and analyses of agricultural best management practices (8,9). An application of HSPF in a screening methodology for pesticide review is described by Donigian et al. (10). In addition, HSPF has been validated with both field data and model experiments, and has been reviewed by independent experts (11-20).
The Stream Transport and Agricultural Runoff for Exposure Assessment Methodology (STREAM) applies the HSPF program to various test watersheds for five major crops in four agricultural regions in the United States, defines a "representative" watershed based on regional conditions and an extrapolation of the calibration for the test watershed, and performs a sensitivity analysis on key pesticide parameters to generate cumulative frequency distributions of pesticide loads and concentrations in each regions. The resulting methodology requires the user to evaluate only the crops and regions of interest, the pesticide application rate, and three pesticide parameters -- the partition coefficient, the soil/sediment decay rate, and the solution decay rate.
The EPA Chesapeake Bay Program has been using the HSPF model as the framework for modeling total watershed contributions of flow, sediment, and nutrients (and associated constituents such as water temperature, DO, BOD, etc.) to the tidal region of the Chesapeake Bay (21,22). The watershed modeling represents pollutant contributions from an area of more than 68,000 sq. mi., and provides the input to drive a fully dynamic three-dimensional, hydrodynamic/water quality model of the Bay. The watershed drainage area is divided into land segments and stream channel segments. The land areas modeled include forest, agricultural cropland (conventional and conservation tillage systems), pasture, urban (pervious and impervious areas), and uncontrolled animal waste contributions. The stream channel simulation includes flow routing and oxygen and nutrient biochemical modeling (through phytoplankton) in order to account for instream processes affecting nutrient delivery to the Bay.
Currently, buildup/washoff type algorithms are being used for urban impervious areas, potency factors for all pervious areas, and constant (or seasonally variable) concentrations for all subsurface contributions and animal waste components. Enhancements are underway to utilize the detailed process (i.e. Agrichemical modules) simulation for cropland areas to better represent the impacts of agricultural BMPs and to include nitrogen cycling in forested systems to evaluate the impacts of atmospheric deposition of nitrogen on Chesapeake Bay. The watershed modeling is being used to evaluate nutrient management alternatives for attaining a 40% reduction in nutrient loads delivered to the Bay, as defined in a joint agreement among the governors of the member states.
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See 'test' folder +
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See paper that describes the simulation of Tualatin River Basin as a case study. Lin et al., 2022. +
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See readme file and the associated published paper: https://doi.org/10.1086/684223.
Calibration must be performed on a site-by-site basis; the provided data do not permit calibration for our sites, but we do include the calibrated parameters and explain our methods. +
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See testing description on github. +
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See the following studies:
http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;276&g=46
http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;277&g=46
http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;278&g=46 +
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See two papers:
Cui (2007a) http://dx.doi.org/10.1029/2006WR005330 Cui (2007b) http://dx.doi.org/10.1002/rra.1012 A manuscript with regard to its application to the Waipaoa River, NZ is currently underway by Basil Gomez and others. +
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See:
Croley, T. E., II, C. He, and D. H. Lee, 2005. Distributed-parameter large basin runoff model II: application. Journal of Hydrologic Engineering, 10(3):182-191.
C.He, and Croley, T.E., 2007. Application of a distributed large basin runoff model in the Great Lakes basin. Control Engineering Practice, 15(8): 1001-1011. +
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Separate publications. +
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TOPMODEL calibration procedures are relatively simple because it uses very few parameters in the model formulas. The model is very sensitive to changes of the soil hydraulic conductivity decay parameter, the soil transmissivity at saturation, the root zone storage capacity, and the channel routing velocity in larger watersheds. The calibrated values of parameters are also related to the grid size used in the digital terrain analysis. The timestep and the grid size also have been shown to influence TOPMODEL simulations. +
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Tested against river temperature observations of the Kuparuk river on the North Slope of Alaska (described in Zheng et al., 2019). +
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Testing described in various papers (see web page) +
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The codes have been validated against laboratory experiments which are described in Eke's MS thesis. The results are summarized in her thesis and in a conference paper that she presented in Vancouver. +
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The component is unit tested against known analytical solutions as part of Landlab. +
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The experiments by Wong et al. (2007) were used to determine a relation to express the elevation of the maximum probability of particle entrainment in bedload transport to the characteristics of the flow and of the sediment.
The Wong et al. (2007) dataset is available in the github repository with the comparison between model results and experimental data. +
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The model has been benchmarked against analytical solutions for simple cases, such as fluvial slope-area scaling and parabolic to parabolic-planar hillslope form under uniform erosion, materials, and climate. Testing and calibration of some of the individual components (e.g., linear and nonlinear soil creep, stream-power fluvial erosion law, etc.) have been reported in the literature (for a review, see Tucker and Hancock, 2010). Testing of the full coupled model using natural experiments (Tucker, 2009) is ongoing. +
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The model has been compared qualitatively with Clarke and Werner (2004), Gallagher et al 1998, 2003 and 2005 and various Hay papers. +
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The model is tested against the known analytical solution (see Adams et al., 2017 for more details). It is unit-tested against this analytical solution every time Landlab is updated. +
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The model is tested against three known analytical solutions (see Shobe et al., 2017 for details). +
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The model is validated against a set of laboratory experiments that have been performed in a unidirectional flume. +
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The model produces virtual core-logs which can be compared with Ocean Drilling Program and other descriptive core logs and with outcrops. +
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The model was zeroed on the pre-1930 Minnesota River between Mankato and Jordan, Minnesota, using data available in the literature and on the USGS website. +
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The test is available in the main function of the code +
The user guide covers essential features of gospl, mostly in the form of interactive Jupyter notebooks and Python scripts.
https://gospl.readthedocs.io/en/latest/user_guide/index.html +
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There are few test on Rio Corda, a ten square kilometer catchment in Trentino, Italy. Simulations on Little Washita will be made available soon, too. +
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There are several idealized and realistic test cases. Some of the idealized test cases have quasi-analytical solutions. +
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There are several idealized and realistic test cases. Some of the idealized test cases have quasi-analytical solutions. +
There are several idealized and realistic test cases. Some of the idealized test cases have quasi-analytical solutions. +
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There are several idealized and realistic test cases. Some of the idealized test cases have quasi-analytical solutions. +
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This is not a model with adjustable parameters so calibration is not required. Example datasets are available +
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This model is not calibrated, but uses physically-based (measurable) parameters and measured data for validation. See next box. +
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This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
This model/component is typically not calibrated to fit data, but is run with a best guess or measured value for each input parameter. +
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To calibrate and validate the mass-balance model, OGGM relies on mass-balance observations provided by the World Glacier Monitoring Service +
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To get some additional info in regards to how to use eSCAPE a series of examples and tutorials is provided in the docker container (Geodels escape-docker) and is also available for download from the eSCAPE-demo repository (https://github.com/Geodels/eSCAPE-demo). +
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TopoFlow is typically not calibrated to fit data, but is run with best guesses of the physical parameters. +
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Topographic analysis: no calibration required. +
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Topographic analysis; no calibration required. +
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Topographic analysis; no calibration required. +
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Validation references can be found in: https://sites.google.com/view/olaflowcfd/numerical-model/references/references-internal +
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We have tested the model for different permafrost observation sites for Alaska(USA) and Siberia(Russia). Typically, the model results show good correlation with measured data (if observations are accurate). +
We used the HYDE 3.1 dataset and the USDA's CropScape dataset for validation.
See: Haney, N., Cohen, S. (2015), Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model. Applied Geography, 62: 366-376. +
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examples provided along with the documentation
http://github.com/badlands-model/Badlands-doc/releases +
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mosartwmpy was calibrated against historical MOSART-WM 30 year model runs. +
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see Mudd et al. (2009) ECSS v 82(3) 377-389
Calibrated from South Carolina sediment cores +
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see the user document +
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