Downscaling GCM data for climate change impact assessments on rainfall: a practical application for the Brahmani-Baitarani river basin
Other literature type
Dahm, R. J.
Singh, U. K.
Sperna Weiland, F. C.
Singh, S. K.
Singh, M. P.
(issn: 1607-7938, eissn: 1607-7938)
The delta of the Brahmani-Baitarani river basin, located in the
eastern part of India, frequently experiences severe floods. For
flood risk analysis and water system design, insights in the
possible future changes in extreme rainfall events caused by climate
change are of major importance. There is a wide range of statistical
and dynamical downscaling and bias-correction methods available to
generate local climate projections that also consider changes in
rainfall extremes. Yet the applicability of these methods highly
depends on availability of meteorological observations at local
level. In the developing countries data and model availability may
be limited, either due to the lack of actual existence of these data
or because political data sensitivity hampers open sharing.
We here present the climate change analysis we performed for the
Brahmani-Baitarani river basin focusing on changes in four selected
indices for rainfall extremes using data from three
performance-based selected GCMs that are part of the 5th Coupled
Model Intercomparison Project (CMIP5). We apply and compare two
widely used and easy to implement bias correction approaches. These
methods were selected as best suited due to the absence of reliable
long historic meteorological data. We present the main changes –
likely increases in monsoon rainfall especially in the Mountainous
regions and a likely increase of the number of heavy rain days. In
addition, we discuss the gap between state-of-the-art downscaling
techniques and the actual options one is faced with in local scale
climate change assessments.