In this paper, we describe an experiment in which the position of scientists with respect to flood risk management is fundamentally changed. Building on a review of three very different approaches to engaging the public in science, we contrast the normal way in which science is used in flood risk management in England and Wales with an experiment in which knowledge regarding flooding was co-produced. This illustrates a way of working with experts, both certified (academic natural and social scientists) and noncertified (local people affected by flooding), for whom flooding is a matter of concern, and where the event, flooding, is given agency in the experiment. We reveal a deep and distributed understanding of flood hydrology across all experts, certified and uncertified, involved in the experiment. This did not map onto the conventional dichotomy between 'universal' scientific expertise and 'local' lay expertise. By working with the event we harnessed, produced and negotiated a new and collective sense of knowledge, sufficient in our experiment to make a public intervention in flood risk management in our case-study location. The manner in which the academic scientists involved in the practice of their science were repositioned was radical as compared with normal scientific method. It was also radical for a more fundamental reason: the purpose of our experiment became as much about creating a new public capable of making a political intervention in a situation of impasse, as it was about producing the solution itself. The practice of knowledge generation, the science undertaken, worked with the hybridisation of science and politics rather than trying to extract science from it. key words flood risk management flooding scientific method participation co-production hybridisation
A rising exposure to flood risk is a predicted consequence of increased development in vulnerable areas and an increase in the frequency of extreme weather events due to climate change. In the face of this challenge, a continued reliance on engineered at-a-point flood defences is seen as both unrealistic and undesirable. The contribution of 'soft engineering' solutions (e.g. riparian forests, wood in rivers) to integrated, catchment scale flood risk management has been demonstrated at small scales but not larger ones. In this study we use reduced complexity hydrological modelling to analyse the effects of land use and channel changes resulting from river restoration upon flood flows at the catchment scale. Results show short sections of river-floodplain restoration using engineered logjams, typical of many current restoration schemes, have highly variable impacts on catchment-scale flood peak magnitude and so need to be used with caution as a flood management solution. Forested floodplains have a more general impact upon flood hydrology, with areas in the middle and upper catchment tending to show reductions in peak magnitude at the catchment outflow. The most promising restoration scenarios for flood risk management are for riparian forest restoration at the sub-catchment scale, representing 20-40% of the total catchment area, where reductions in peak magnitude of up to 19% are observed through de-synchronization of the timings of sub-catchment flood waves. Sub-catchment floodplain forest restoration over 10-15% of total catchment area can lead to reductions in peak magnitude of 6% at 25 years post-restoration.
This paper suggests that eomputer simulation modelling can offer opportunities for redistributing expertise between science and affected publies in relation to environmental problems. However, in order for scientific modelling to contribute to the eoproduetion of new knowledge elaims about environmental processes, seientists need to reposition themselves with respect to their modelling practices. In the paper we examine a proeess in which two hydrologieal modellers became part of an extended research collective generating new knowledge about flooding in a small rural town in the UK. This process emerged in a projeet trialling a novel participatory researeh apparatus-eompeteney groups-aiming to harness the energy generated in publie controversy and enable other than scientific expertise to contribute to environmental knowledge. Analysing the proeess repositioning the seientists in terms of a dynamic of 'dissociation' and 'attachment', we map the ways in whieh prevailing alignments of expertise were unravelled and new connections assembled, in relation to the matter of concern. We show how the redistribution of knowledge and skills in the extended research collective resulted in a new eomputer model, embodying the coproduced flood risk knowledge.
Testing competing conceptual model hypotheses in hydrology is complicated by uncertainties from a wide range of sources, which result in multiple simulations that explain catchment behaviour. In this study, the limits of acceptability uncertainty analysis approach used to discriminate between 78 competing hypotheses in the Framework for Understanding Structural Errors for 24 catchments in the UK. During model evaluation, we test the model's ability to represent observed catchment dynamics and processes by defining key hydrologic signatures and time step-based metrics from the observed discharge time series. We explicitly account for uncertainty in the evaluation data by constructing uncertainty bounds from errors in the stage-discharge rating curve relationship. Our study revealed large differences in model performance both between catchments and depending on the type of diagnostic used to constrain the simulations. Model performance varied with catchment characteristics and was best in wet catchments with a simple rainfall-runoff relationship. The analysis showed that the value of different diagnostics in constraining catchment response and discriminating between competing conceptual hypotheses varies according to catchment characteristics. The information content held within water balance signatures was found to better capture catchment dynamics in chalk catchments, where catchment behaviour is predominantly controlled by seasonal and annual changes in rainfall, whereas the information content in the flow-duration curve and time-step performance metrics was able to better capture the dynamics of rainfall-driven catchments. We also investigate the effect of model structure on model performance and demonstrate its (in)significance in reproducing catchment dynamics for different catchments.
Efficient incorporation of channel cross-section geometry uncertainty into regional and global scale flood inundation models, Journal of Hydrology (2015), doi: http://dx. AbstractThis paper investigates the challenge of representing structural differences in river channel crosssection geometry for regional to global scale river hydraulic models and the effect this can have on simulations of wave dynamics. Classically, channel geometry is defined using data, yet at larger scales the necessary information and model structures do not exist to take this approach. We therefore propose a fundamentally different approach where the structural uncertainty in channel geometry is represented using a simple parameterization, which could then be estimated through calibration or data assimilation. This paper first outlines the development of a computationally efficient numerical scheme to represent generalised channel shapes using a single parameter, which is then validated using a simple straight channel test case and shown to predict wetted perimeter to within 2% for the channels tested. An application to the River Severn, UK is also presented, along with an analysis of model sensitivity to channel shape, depth and friction. The channel shape parameter was shown to improve model simulations of river level, particularly for more physically plausible channel roughness and depth parameter ranges. Calibrating channel Manning's coefficient in a rectangular channel provided similar water level simulation accuracy in terms of Nash-Sutcliffe efficiency to a model where friction and shape or depth were calibrated. However, the calibrated Manning coefficient in the rectangular channel model was ~2/3 greater than the likely physically realistic value for this reach and this erroneously slowed wave propagation times through the reach by several hours. Therefore, for large scale models applied in data sparse areas, calibrating channel depth and/or shape may be preferable to assuming a rectangular geometry and calibrating friction alone.
The rate of progress in quantitative modelling since the 1950s has been such that application of sophisticated computer models to a wide range of geoscientific problems is now routine. It is generally held that by making such models more physically (physics) based, their explanatory power and predictive reliability are enhanced. This formulation, a model-theoretic approach, assumes accurate knowledge of the properties, states and relationships between all of the objects that are known to matter within the system of interest but, simultaneously, an incomplete understanding of the totality that this knowledge creates. In hydrological modelling, this translates into a severe dependence upon the data models that are needed to make a hydrological model work. The opposite extreme is a model-data approach in which measurements become the basis of generic relationships. Even in the most heavily data-derived cases (eg, neural network forecasting of river flows) these data models can be shown implicitly to have a theoretical content. Thus, both model-theoretic and model-data approaches sit within a general class of modelling, best labelled as ‘data-theoretic’. Here, we illustrate this point and advocate an approach that is knowledge-theoretic rather than data-theoretic, to capture the much richer sources of knowledge available to the modeller. These sources include third-party reports, personal recollections and diaries, old photographs and press articles, opinions, etc, which are, by convention, either excluded from analysis, or simply added into descriptions of model results at the point of dissemination and consultation of model findings. We conclude by noting that this approach to hydrological modelling fits into current thinking that the process by which publics engage with knowledge must be moved upstream. Here, the production of scientific knowledge comes to include not just scientists and specialists, but also those people for whom model predictions make a material difference.
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