TY - JOUR
T1 - STORM 1.0
T2 - A simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change
AU - Bliss Singer, Michael
AU - Michaelides, Katerina
AU - Hobley, Daniel E.J.
PY - 2018/9/11
Y1 - 2018/9/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85053261375&partnerID=8YFLogxK
U2 - 10.5194/gmd-11-3713-2018
DO - 10.5194/gmd-11-3713-2018
M3 - Article (Academic Journal)
AN - SCOPUS:85053261375
VL - 11
SP - 3713
EP - 3726
JO - Geoscientific Model Development
JF - Geoscientific Model Development
SN - 1991-959X
IS - 9
ER -