Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India

Abstract

[1] Climate change impact assessment on water resources with downscaled General Circulation Model (GCM) simulation output is characterized by uncertainty due to incomplete knowledge about the underlying geophysical processes of global change (GCM uncertainties) and due to uncertain future scenarios (scenario uncertainties). Disagreement between different GCMs and scenarios in regional climate change impact studies indicates that overreliance on a single GCM with a scenario could lead to inappropriate planning and adaptation responses. This paper focuses on modeling GCM and scenario uncertainty using possibility theory in projecting streamflow of Mahanadi river, at Hirakud, India. A downscaling method based on fuzzy clustering and Relevance Vector Machine (RVM) is applied to project monsoon streamflow from three GCMs with two green house emission scenarios. Possibilities are assigned to all the GCMs with scenarios based on their performance in modeling the streamflow of the recent past (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005), when there are signals of climate forcing. The possibilities associated with different GCMs and scenarios are used as weights in computing the possibilistic mean of the CDFs projected for three standard time slices 2020s, 2050s, and 2080s. The result shows that the value of streamflow at which the CDF reaches 1 reduces with time, which shows the reduction in probability of occurrence of extreme high flow events in future. Historic record of monsoon streamflow of Mahanadi river also shows similar decreasing trend, which may be due to the effect of high surface warming. Reduction in Mahandai streamflow is likely to pose a major challenge for water resources engineers in meeting water demands in future.Citation: Mujumdar, P. P., and S. Ghosh (2008), Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India, Water Resour. Res., 44, W06407,

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

“…Some examples include maps of soil hydrological properties (Martin-Clouaire et al, 2000), remotely sensed soil moisture data (Verhoest et al, 2007), climate modelling (Mujumdar and Ghosh, 2008), subsurface contaminant transport , and streamflow forecasting (Alvisi and Franchini, 2011). Khan et al (2013) and Khan and Valeo (2015) have introduced a fuzzy number based regression technique to model daily DO in the Bow River using abiotic factors with promising results.…”

Probabilistic downscaling of GCM scenarios over southern India

“…Several SDMs have already been used for downscaling rainfall over India, such as relevance Mujumdar and Ghosh, 2008) and support vector machine (Tripathi et al, 2006;Anandhi et al, 2008Anandhi et al, , 2009, fuzzy clustering (Ghosh and Mujumdar, 2007;Mujumdar and Ghosh, 2008) and also conditional random field distribution (Raje and Mujumdar, 2009). In these recent studies, methods are generally validated by examining the quantiles, cumulative and probability distribution functions (CDFs and PDFs) of daily rainfall as well as dry/wet spells' lengths at local scale.…”

“…The purpose of this study is to document the use of CDF-t for providing regional precipitation and surface temperature changes as derived from several medium-term GCM projections under the Special Report on Emission Scenarios (SRES published by the IPCC) A2 scenario (horizons 2040-2060). Most of the recent statistical downscaling studies of future climate scenarios over India were restrained to the monsoon season (Tripathi et al, 2006;Mujumdar and Ghosh, 2008) or to specific watersheds (Ghosh and Mujumdar, 2007;Anandhi et al, 2008Anandhi et al, , 2009). This paper aims to present results from the CDF-t probabilistic downscaling method applied not only to the June-September (JJAS) monsoon period alone but to the full annual cycle, and for a domain covering the whole of southern India.…”

Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff

“…Non-probabilistic methods found in the hydrological literature include those based on fuzzy sets theory (e.g. Dou et al, 1997;Seibert, 1997;Freissinet et al, 1999;Özelkan and Duckstein, 2001;Bárdossy et al, 2006;Zhang et al, 2009) and possibility theory (Martin-Clouaire et al, 2000;Verhoest et al, 2007;Mujumdar and Ghosh, 2008).…”

“…Even though possibility theory has been available for decades, the application of possibilistic calculus in uncertainty analysis is still very rare in hydrology. Examples of this line of work include mappings of soil hydrological properties (Martin-Clouaire et al, 2000), soil moisture retrieval from radar remote sensing data (Verhoest et al, 2007), and modelling uncertainties affecting General Circulation Models and future scenarios in climate change impact evaluation (Mujumdar and Ghosh, 2008).…”