Floods, the most common natural hazard in the world, cause serious loss in terms of lives, buildings, and infrastructures. As a consequence, the need for flood risk assessment has become critical. Using a semi-quantitative model and fuzzy analytic hierarchy process (FAHP) weighting approach, this paper assessed flood risk in the Dongting Lake region, Hunan Province, Central China, an area where flood hazards frequently occur. The model was designed using spatial multi-criteria analysis (SMCA) techniques in a Geographic Information System (GIS). A GIS database of indicators for the evaluation of hazard and vulnerability was created. Each indicator was analyzed, standardized, and weighted; after which, the weights of the indicators were combined to obtain the final flood risk index map. Using the flood risk index, the study area was classified into five categories of flood risk: very low, low, medium, high, and very high. The high and very high risk zones are mainly concentrated in the northern and central plains. The results obtained can provide useful information for decision makers and insurance companies.

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.

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