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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-86
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 14 May 2018

Model description paper | 14 May 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

STORM: A simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change

Michael Bliss Singer1,3, Katerina Michaelides2,3, and Daniel E. J. Hobley1 Michael Bliss Singer et al.
  • 1School of Earth and Ocean Sciences, Cardiff University, Cardiff, United Kingdom
  • 2School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
  • 3Earth Research Institute, University of California Santa Barbara, Santa Barbara, USA

Abstract. Assessments of water balance, watershed response, and landscape evolution to climate change require representation of spatially and temporally varying rainfall fields over a drainage basin, as well as the flexibility to simply modify key driving climate variables (evaporative demand, overall wetness, storminess). An empirical-stochastic approach to the problem of rainstorm simulation enables statistical realism and the creation of multiple ensembles that allow for statistical characterization and/or time series of the driving rainfall over a fine grid for any climate scenario. Here we provide detail on the STOchastic Rainfall Model (STORM), which uses this approach to simulate drainage basin rainfall. STORM simulates individual storms based on Monte Carlo selection from probability density functions (PDFs) of storm area, storm duration, storm intensity at the core, and storm center location. The model accounts for seasonality, orography, and the probability of storm intensity for a given storm duration. STORM also generates time series of potential evapotranspiration (PET), which are required for most physically based applications. We explain the how the model works and demonstrate its ability to simulate observed historical rainfall characteristics for a small watershed in SE Arizona. We explain the data requirements for STORM and its flexibility for simulating rainfall for various classes of climate change. Finally, we discuss several potential applications of STORM.

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Short summary
For various applications, a regional or local characterization of rainfall is required, particularly at the watershed scale, where there is spatial heterogeneity. Furthermore, simple models are needed that can simulate various scenarios of climate change including changes in seasonal wetness and rainstorm intensity. To this end, we have developed the STOchastic Rainstorm Model (STORM). We explain its developments and data requirements, and illustrate how it simulates rainstorms over a basin.
For various applications, a regional or local characterization of rainfall is required,...
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