Abstract
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.
Highlights
Models of watershed response, water balance, and landscape evolution require characterization of the driving climate, spatially explicit rainfall fields over a time series
To fill this research gap, we have developed the STOchastic Rainfall Model (STORM), which generates time series of spatially explicit rainfall fields over a gridded domain and spatially uniform time series of evaporative demand
STORM was introduced elsewhere to explore rainfall patterns and processes within a small drainage basin in the southwest US (Singer and Michaelides, 2017), but here we provide the relevant detail about the model initialization, operation, and evaluation, and describe various improvements that have been made since its initial appearance in the scientific literature
Summary
Models of watershed response (rainfall–runoff), water balance, and landscape evolution require characterization of the driving climate, spatially explicit rainfall fields over a time series. GCM output and reanalysis data products do not allow for the flexibility to assess watershed responses to a wide range of potential regional climate changes that could impact runoff/flood regimes, groundwater recharge, the water balance between plants and the hydrologic cycle, and basin-wide erosion and topographic development. This is especially true in regions where orography and other complicating land surface dynamics affect rainfall fields. We provide links to open-source code for STORM in two forms (Matlab and Python), along with sample input data and parameters
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