The Performance of Natural Flood Management at the Large Catchment-Scale: A Case Study in the Warwickshire Stour Valley

The limited understanding of Natural Flood Management (NFM) performance, especially at large hydrological scales, is considered a critical barrier for the further funding and implementation of these nature-based solutions to the increasing international problem of flooding. The publications of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report and Environment Agency’s National Flood and Coastal Erosion Risk Management Strategy (NFCERMS) for England have shown that extreme weather, including increased likelihood of high magnitude flood events, will occur and will require more novel management methods. This study focused on the ability of co-designed NFM measures to ameliorate downstream fluvial flooding by attenuating catchment response through a highly spatially distributed network of attenuating and roughening measures. Performance was characterised by the ability of NFM to attenuate flood peaks at different spatial scales across a large (187 km2) dendritic catchment, including the lowering of flood peaks and delaying the time-to-peak. Using a coupled modelling methodology and applying it to the upper Stour Valley, Warwickshire-Avon, UK, a rural response to the application of a set of NFM interventions was developed using the hydrodynamic model Flood Modeller Pro and XPSWMM ©. The method demonstrated a means of incorporating local knowledge in a realistic set of NFM schemes, tested to multiple flood risk scenarios (including climate change). Under frequent, smaller design storm events (e.g., Index Flood (QMED) and 3.3% AEP), flood peaks were lowered across all hydrological scales tested (5.8 km2 to 187 km2). As the design flood event severity increases, impact from upstream NFM attenuation on downstream peak response diminished significantly, especially at the largest hydrological scales. However, even at the largest hydrological scale, delays in time-to-peak were noted, increasing the ability of downstream communities to respond and enact flood preparation activities, thus increasing resilience to potential flooding events. While the benefits were limited to large flood events, the modelling indicated that NFM has the potential to reduce downstream flood risk. However, greater integration of observed data to improve model confidence and reduce uncertainty in modelled events is needed, especially the uncertainty associated with using single peaked design storm events from the Flood Estimation Handbook (FEH). This paper proposes a future Before–After Control–Impact (BACI) monitoring programme that could be integrated with models and applied across non-tidally influenced catchments seeking to empirically test the hydrological performance of in-situ NFM

Bayesian outlier detection in INGARCH time series

INGARCH models for time series of counts arising, e.g., in epidemiology assume the observations to be Poisson distributed conditionally on the past, with the conditional mean being an affinelinear function of the previous observations and the previous conditional means. We model outliers within such processes, assuming that we observe a contaminated process with additive Poisson distributed contamination, affecting each observation with a small probability. Our particular concern are additive outliers, which do not enter the dynamics of the process and can represent measurement artifacts and other singular events influencing a single observation. Such outliers are difficult to handle within a non-Bayesian framework since the uncontaminated values entering the dynamics of the process at contaminated time points are unobserved. We propose a Bayesian approach to outlier modeling in INGARCH processes, approximating the posterior distribution of the model parameters by application of a componentwise Metropolis- Hastings algorithm. Analyzing real and simulated data sets, we find Bayesian outlier detection with non-informative priors to work well if there are some outliers in the data

Balancing people, planet, and profit in urban food waste management

Food waste is a complex problem and critical challenge for the sustainable development of circular economies, with interconnected social, environmental, and economic impacts. Supporting the identification of strategies that best minimise these impacts on people, planet and profit, this paper explores the dynamic impacts of food waste management options on the triple bottom lines of sustainable development in urban circular economies. We present a system dynamics model of the urban agri-food supply chain. This model simulates the fluxes of food and food waste throughout the supply chain, as well as their impacts on economy (i.e., costs and benefits for each sector and the broader economy), society (i.e., food insecurity) and environment (i.e., water, energy, and carbon footprints). Using Bristol city in the United Kingdom as a case-study, we evaluate the impacts of seven food waste management options (i.e., reduction, redistribution, animal feed, anaerobic digestion, composting, incineration, and landfilling). The results show that food waste reduction in consumer sectors (i.e., households and hospitality and food services) and redistribution in supply sectors (i.e., primary production and manufacture) offer the greatest benefits for the environment, society, and economy. For the retail sector, both reduction and redistribution options are highly favourable. Although these options can potentially have some adverse economic effects on the supply side due to a reduction in demand, their considerably high benefits make them high-reward, low-risk options. We thus conclude that food waste reduction and redistribution are the only options with a clear triple-win for people, planet and profit. This paper makes a significant contribution by introducing a robust quantitative model and a novel triple bottom line framework for sustainable food waste management in urban circular economies