Global Circulation Models (GCMs) are a major tool used for climate
change projections under different emission scenarios. However, for
assessing the hydrological impacts of climate change at watershed
scale, GCM outputs cannot be used directly owing to the mismatch in
spatial resolution between the GCMs and hydrological models. Thus a
downscaling scheme (either statistical or dynamical) is employed.
For hydrologic impact assessment studies, statistical downscaling
is usually preferred over dynamical downscaling and among the
variables usually downscaled, precipitation downscaling is more
challenging than downscaling of other climatological variables.
Therefore, this book is specifically focusing on addressing some of
the contemporary issues in statistically downscaling of
precipitation at watershed scale. Studies presented in this book
make use of two widely used downscaling methods i.e. multiple
linear regression and stochastic weather generator. Also, a new
downscaling methodology using Gene Expression Programming (GEP) has
been proposed. The issues discussed and analyzed in this book will
facilitate further research in the area of statistical downscaling
for hydrologic studies.
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