Impact of floodplain and Stage 0 stream restoration on flood attenuation and floodplain exchange during small frequent storms

River flooding impacts human life and infrastructure, yet provides habitat and ecosystem services. Traditional flood control (e.g., levees, dams) reduces habitat and ecosystem services, and exacerbates flooding elsewhere. Floodplain restoration (i.e., bankfull floodplain reconnection and Stage 0) can also provide flood management, but has not been sufficiently evaluated for small frequent storms. We used 1D unsteady Hydrologic Engineering Center's River Analysis System to simulate small storms in a 5 km-long, second-order generic stream from the Chesapeake Bay watershed, and varied % channel restored (starting at the upstream end), restoration location, restoration bank height (distinguishes bankfull from Stage 0 restoration), and floodplain width/Manning's n. Stream restoration decreased (attenuated) peak flow up to 37% and increased floodplain exchange by up to 46%. Floodplain width and % channel restored had the largest impact on flood attenuation. The incremental effects of new restoration projects on flood attenuation were greatest when little prior restoration had occurred. By contrast, incremental effects on floodplain exchange were greatest in the presence of substantial prior restoration, setting up a tradeoff. A similar tradeoff was revealed between attenuation and exchange for project location, but not bank height or floodplain width. In particular, attenuation and exchange were always greater for Stage 0 than for bankfull floodplain restoration. Stage 0 thus may counteract human impacts such as urbanization.     

Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.