Flood Prediction with Causal Analysis
The traditional method of curve fitting, in order to predict low probability flood events, does not utilize any considerations of the physical processes which determine flood flows. Causal analysis is a disaggregation-aggregation method of data analysis and flood prediction. Raw extreme event data are partitioned into subsets. These subsets are characterized by the physical processes which cause the observed floods, i. e., low pressure storms, thunder-showers, etc. Once the subsets are determined and the data are partitioned, then distributions are fitted to each subset. By aggregating the subset distributions, the joint probability distribution of all flood causal processes may be determined. Knowledge of the joint probability distribution allows estimation of low probability events either by sampling or by theory. Identification of the probability distribution of independent meteorologic events, i. e., hurricanes, thunderstorms, etc., form the building blocks of the causal analysis joint probability predictive model. Thus, regionalized information may be employed in data scarce regions to aid in the predictive model formulation. This partitioning-aggregation procedure considers both physical and statistical principles and thus is an improvement over purely statistical techniques. Causal analysis is compared to the more simple curve-fitting method with a case study of a humid mountain river environment.
Key WordsFlood prediction flood modeling extreme event analysis
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