Model:SpatialPrecipitationDistribution

Edit Model information

SpatialPrecipitationDistribution

Metadata

Summary Contact Technical specs In/Output Process Testing Other Component info Also known as Model type Tool Model part of larger framework LandLab Note on status model Date note status model Incorporated models or components: Spatial dimensions 2D Spatial extent Landscape-Scale, Watershed-Scale Model domain Terrestrial, Hydrology, Climate One-line model description Generate random sequence of spatially-resolved precipitation events Extended model description A component to generate a sequence of spatially resolved storms over a grid, following a lightly modified version (see below) of the stochastic methods of Singer & Michaelides, Env Res Lett 12, 104011, 2017, & Singer et al., Geosci. Model Dev., accepted, 10.5194/gmd-2018-86. The method is heavily stochastic, and at the present time is intimately calibrated against the conditions at Walnut Gulch, described in those papers. In particular, assumptions around intensity-duration calibration and orographic rainfall are "burned in" for now, and are not accessible to the user. The various probability distributions supplied to the various run methods default to WG values, but are easily modified. This calibration reflects a US desert southwest "monsoonal" climate, and the component distinguishes (optionally) between two seasons, "monsoonal" and "winter". The intensity-duration relationship is shared between the seasons, and so may prove useful in a variety of storm-dominated contexts. The default is to disable the orographic rainfall functionality of the component. However, if orographic_scenario == 'Singer', the component requires a 'topographic__elevation' field to already exist on the grid at the time of instantiation. The component has two ways of simulating a "year". This choice is controlled by the 'limit' parameter of the yield methods. If limit == 'total_rainfall', the component will continue to run until the total rainfall for the season and/or year exceeds a stochastically generated value. This method is directly comparable to the Singer & Michaelides method, but will almost always result in years which are not one calendar year long, unless the input distributions are very carefully recalibrated for each use case. If limit == 'total_time', the component will terminate a season and/or year once the elapsed time exceeds one year. In this case, the total rainfall will not correspond to the stochastically generated total. You can access the actual total for the last season using the property `(median_)total_rainfall_last_season`. Note that this component cannot simulate the occurrence of more than one storm at the same time. Storms that should be synchronous will instead occur sequentially, with no interstorm time. This limitation means that if enough storms occur in a year that numstorms*mean_storm_duration exceeds one year, the number of simulated storms will saturate. This limitation may be relaxed in the future. The component offers the option to modify the maximum number of storms simulated per year. If you find simulations encountering this limit too often, you may need to raise this limit. Conversely, it could be lowered to reduce memory usage over small grids. However, in increasing the value, beware - the component maintains two limit*nnodes arrays, which will chew through memory if the limit gets too high. The default will happily simulate grids up to around 50 km * 50 km using the default probability distributions. Keywords: precipitation, stochastic precipitation, Name Daniel Hobley Type of contact Institute / Organization Postal address 1 Postal address 2 Town / City Boulder Postal code 80309 State Colorado Country United States Email address daniel.hobley@colorado.edu Phone Fax Supported platforms Unix, Linux, Mac OS, Windows Other platform Programming language Python Other program language Code optimized Single Processor Multiple processors implemented Nr of distributed processors Nr of shared processors Start year development 2017 Does model development still take place? No If above answer is no, provide end year model development 2020 Code development status Only maintenance When did you indicate the 'code development status'? 2020 Model availability As code Source code availability (Or provide future intension) Through web repository Source web address https://github.com/landlab/landlab/tree/master/landlab Source csdms web address Program license type BSD or MIT X11 Program license type other Memory requirements Typical run time Describe input parameters grid : ModelGrid A Landlab model grid of any type. number_of_years : int The number of years over which to generate storms. orographic_scenario : {None, 'Singer', func} Whether to use no orographic rule, or to adopt S&M's 2017 calibration for Walnut Gulch. Alternatively, provide a function here that turns the provided elevation of the storm center into a length-11 curve weighting to select which orographic scenario to apply. Input format ASCII Other input format Describe output parameters "rainfall__flux": { "dtype": float, "intent": "out", "optional": False, "units": "mm/hr", "mapping": "node", "doc": "Depth of water delivered per unit time in each storm", } "rainfall__total_depth_per_year": { "dtype": float, "intent": "out", "optional": False, "units": "mm/yr", "mapping": "node", "doc": "Depth of water delivered in total in each model year", } "topographic__elevation": { "dtype": float, "intent": "in", "optional": True, "units": "m", "mapping": "node", "doc": "Land surface topographic elevation", } Output format ASCII Other output format Pre-processing software needed? No Describe pre-processing software Post-processing software needed? No Describe post-processing software Visualization software needed? No If above answer is yes Other visualization software Describe processes represented by the model Describe key physical parameters and equations Describe length scale and resolution constraints Describe time scale and resolution constraints Describe any numerical limitations and issues Describe available calibration data sets Upload calibration data sets if available: Describe available test data sets Upload test data sets if available: Describe ideal data for testing Do you have current or future plans for collaborating with other researchers? Is there a manual available? No Upload manual if available: Model website if any https://landlab.github.io Model forum / discussion board https://github.com/landlab/landlab/issues Comments This part will be filled out by CSDMS staff OpenMI compliant Not yet"Not yet" is not in the list (Yes, No but planned, No but possible, No not possible) of allowed values for the "Code openmi compliant or not" property. BMI compliant Not yet"Not yet" is not in the list (Yes, No but planned, No but possible, No not possible) of allowed values for the "BMICompliant" property. WMT component Not yet"Not yet" is not in the list (Yes, In progress, No but possible, No not possible) of allowed values for the "Code CMT compliant or not" property. PyMT component Not yet"Not yet" is not in the list (Yes, In progress, No but possible, No not possible) of allowed values for the "Code PyMT compliant or not" property. Is this a data component Can be coupled with:

Model info Authors Daniel Hobley Source code Go to external source code site Model citations Nr. of publications: -- Total citations: 0 h-index: --"--" is not a number. m-quotient: 0 QR-code Link to this page Other models by author ChiFinder FastscapeEroder LakeMapperBarnes PerronNLDiffuse PotentialityFlowRouter SedDepEroder SinkFiller SpatialPrecipitationDistribution SteepnessFinder

References

Automatically enter Reference by DOI

Manually enter Reference

Nr. of publications:	--
Total citations:	0
h-index:	--"--" is not a number.
m-quotient:	0

See more publications of SpatialPrecipitationDistribution