STORM 1.0: A simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change

Michael Bliss Singer*, Katerina Michaelides, Daniel E.J. Hobley

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

6 Citations (Scopus)
190 Downloads (Pure)

Abstract

Assessments of water balance changes, 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 details 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 how the model works and demonstrate its ability to simulate observed historical rainfall characteristics for a small watershed in southeast 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.

Original languageEnglish
Pages (from-to)3713-3726
Number of pages14
JournalGeoscientific Model Development
Volume11
Issue number9
DOIs
Publication statusPublished - 11 Sep 2018

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