Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment

Abstract

[1] Hydrologic implications of global climate change are usually assessed by downscaling appropriate predictors simulated by general circulation models (GCMs). Results from GCM simulations are subjected to a number of uncertainties due to incomplete knowledge about the underlying geophysical processes of global change (GCM uncertainties) and due to uncertain future scenarios (scenario uncertainties). With a relatively small number of GCMs available and a finite number of scenarios simulated by them, uncertainties in the hydrologic impacts at a smaller spatial scale become particularly pronounced. In this paper, a methodology is developed to address such uncertainties for a specific problem of drought impact assessment with results from GCM simulations. Samples of a drought indicator are generated with downscaled precipitation from available GCMs and scenarios. Since it is very unlikely that such small samples resulting from GCM scenarios fit a known parametric distribution, nonparametric methods such as kernel density estimation and orthonormal series methods are used to determine the probability distribution function (PDF) of the drought indicator. Principal component analysis, fuzzy clustering, and statistical regression are used for downscaling the mean sea level pressure (MSLP) output from the GCMs to precipitation at a smaller spatial scale. Reanalysis data from the National Center for Environmental Prediction (NCEP) are used in relating precipitation with MSLP. The information generated through the PDF of the drought indicator in a future year may be used in long-term planning decisions. The methodology is demonstrated with a case study of the drought-prone Orissa meteorological subdivision in India.Citation: Ghosh, S., and P. P. Mujumdar (2007), Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment, Water Resour. Res., 43, W07405,

Modeling Potential Impacts of Climate Change on Streamflow Using Projections of the 5th Assessment Report for the Bernam River Basin, Malaysia

“…It is acknowledged in climate change assessment studies that most GCMs are inherently poor in simulating some of the climate variables compared to others, and therefore, their outputs cannot be used directly in hydrologic modeling studies [45,46]. For this reason, downscaling is used to bridge this gap in impact assessment studies.…”

Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects

“…This uncertainty complicates the accurate interpretation of climate impact assessment. Therefore, many researchers have attempted to quantify the irreducible uncertainty in hydrologic streamflow projections (New et al, 2007;Wilby, 2005;Kingston and Taylor, 2010), low flow (Wilby and Harris, 2006), flooding (Booij, 2005;Kay et al, 2009;Raff et al, 2009;Moradkhani et al, 2010), and drought (Ghosh and Mujumdar, 2007;Mishra and Singh, 2009). Despite substantial effort of previous studies, however, large uncertainty in climate impact studies still remain .…”

An inverse-modelling approach to assess the impacts of climate change in the Seyhan River basin, Turkey / Une approche de modélisation inverse pour évaluer les impacts du changement climatique dans le bassin versant de la Rivière Seyhan, Turquie

“…Monthly precipitation and monthly mean temperature data under the IPCC SRES A2 scenario are prepared for each model for two periods, representing: (a) present conditions, 1961-1990; and (b) future conditions, 2070-2099. Different GCMs under the same SRES scenario generate results that differ considerably (Ghosh & Mujumdar, 2007). We address this uncertainty by averaging outputs of the nine GCMs.…”

“…Exploring a whole spectrum of alternative climate scenarios would be more useful for effective management of water resource systems. The uncertainty of the GCM outputs can be reduced by using an ensemble of simulations, rather than a single experimental result (Ghosh & Mujumdar, 2007). For now, however, the GCM remains the only available tool for detailed modelling of the future climate, and the key challenge to hydrologists is to express GCM results at a scale more relevant to hydrological studies (Prudhomme et al, 2002).…”