No abstract

No 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,

No abstract

No abstract

No 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,

[1] A fuzzy optimization model is developed for the seasonal water quality management of river systems. The model addresses the uncertainty in a water quality system in a fuzzy probability framework. The occurrence of low water quality is treated as a fuzzy event. Randomness associated with the water quality indicator is linked to this fuzzy event using the concept of probability of a fuzzy event. In most water quality management models the risk level for violation of a water quality standard is constrained by a preassigned value through a chance constraint. In the fuzzy risk approach a range of risk levels is specified by considering a fuzzy set of low risk, instead of using a chance constraint. Thus the two levels of uncertainty, one associated with low water quality and the other with low risk, are quantified and incorporated in the management model. The model takes into account the seasonal variations of river flow to specify seasonal fraction removal levels for the pollutants. Fuzzy sets of low water quality are considered in each season. The membership functions of these fuzzy sets represent the degree of low water quality associated with the discrete states of water quality in a season. The fuzzy risk of low water quality in a season at a checkpoint in the river system is expressed in terms of the degree of low water quality and the steady state probabilities associated with discrete river flows. Considering the fuzzy goals of the pollution control agency and dischargers, and the crisp constraints, the water quality management problem is formulated as a fuzzy optimization model. The model solution gives seasonal fraction removal levels for the pollutants. Application of the fuzzy optimization model is illustrated with a hypothetical river system.