Stochastic downscaling of hourly precipitation series from climate
change projections
Abstract
Stochastic precipitation generators (SPGs) are often used to produce
synthetic precipitation series for water resource management. Typically,
an SPG assumes a stationary climate. We present an hourly precipitation
generation algorithm for non-stationary conditions informed by the
Global Climate Model (GCM) forecasted average monthly temperature (AMT).
The physical basis for precipitation formation is considered explicitly
in the design of the algorithm using hourly Pressure Change Events (PCE)
to define the relationship between hourly precipitation and AMT. The
algorithm consists of a multi-variable Markov Chain and a moving window
driven by time, temperature, and pressure change. We demonstrate the
methodology by generating a 100-year, continuous, synthetic hourly
precipitation time series using GCM AMT projections for the Northeast
US. When compared with historical observations, the synthetic results
suggest that future precipitation in this region will be more variable,
with more frequent mild events and fewer but intensified extremes,
especially in warm seasons. The synthetic time series suggests that
there will be less precipitation in the summers, while winters will be
wetter, consistent with other research on climate change projections for
the northeast US. This SPG provides physically plausible weather
ensembles for water resource studies involving climate change.