Abstract: Mitigation of localized pluvial flooding is one of the major resiliency goals in urban environments, and Green Stormwater Infrastructure (GSI) has the potential to deliver such an outcome. However, there is a lack of systematic approaches to prioritize investment in different candidate areas. This study provides a framework to identify vulnerable stormwater drainage inlets and their contributing areas, prioritize them, identify dominant factors in their selection, assess the potential of GSI in mitigating their overflows, and compare the impact and its cost to gray infrastructure upgrade alternatives. Using SWMM 5.1.013, decision trees, and a volumetric-based assessment of GSI overflow capture, we applied the framework to the City of Dallas, Texas, for three design storms with three GSI practices— bioretention cells, raingardens, and rainwater harvesting tanks. Results showed that there was a significant increase in the number of overflowing stormwater drainage inlets, referred to as hotspots, and their contributing subwatersheds, referred to as opportunity areas, with more intense storms especially in problematic watersheds. Also, prioritization results provided a series of maps to rank the opportunity areas based on overflow severity, recurrence of the overflows, and GSI availability. Moreover, classification results showed that inlet features, especially the inlet depth, were the dominant factors in the identification of the non-problematic inlets. Finally, the GSI impact assessment showed substantial overflow mitigation even at the “very high” severity levels when GSI is comprehensively deployed across opportunity areas. Despite gray infrastructure upgrades yielding higher reduction levels, their cost per cubic meter was higher than GSI. Therefore, a combination of GSI and gray results in maximum overflow reduction at a lower cost compared to common practices.

Abstract: Drawing on advances in nonstationary frequency analysis and the science of causation and attribution, this study employs a newly developed nonstationary stochastic paired watershed approach to determine the effect of forest harvesting on snowmelt-generated floods. Moreover, this study furthers the application of stochastic physics to evaluate the environmental controls and drivers of flood response. Physically-based climate and time-varying harvesting data are used as covariates to drive the nonstationary flood frequency distribution parameters to detect, attribute, and quantify the effect of harvesting on floods in the snow-dominated Deadman River (878 km2) and nested Joe Ross Creek (99 km2) watersheds. Harvesting only 21% of the watershed caused a 38% and 84% increase in the mean but no increase in variability around the mean of the frequency distribution in the Deadman River and Joe Ross Creek, respectively. Consequently, the 7-year, 20-year, 50-year, and 100-year flood events became approximately two, four, six, and ten times more frequent in both watersheds. An increase in the mean is posited to occur from an increase in moisture availability following harvest from suppressed snow interception and increased net radiation reaching the snowpack. Variability was not increased because snowmelt synchronization was inhibited by the buffering capacity of abundant lakes, evenly distributed aspects, and widespread spatial distribution of cutblocks in the watersheds, preventing any potential for harvesting to increase the efficiency of runoff delivery to the outlet. Consistent with similar recent studies, the effect of logging on floods is controlled not only by the harvest rate but most importantly the physiographic characteristics of the watershed and the spatial distribution of the cutblocks. Imposed by the probabilistic framework to understanding and predicting the relation between extremes and their environmental controls, commonly used in the general sciences but not forest hydrology, it is the inherent nature of snowmelt-driven flood regimes which cause even modest increases in magnitude, especially in the upper tail of the distribution, to translate into surprisingly large changes in frequency. Contrary to conventional wisdom, harvesting influenced small, medium, and very large flood events, and the sensitivity to harvest increased with increasing flood event size and watershed area.

Abstract: Leaky dams are an increasingly popular natural flood management measure, yet their impacts on flood peak magnitude have not yet been empirically quantified for a range of event types and magnitudes, even at the stream scale. In this study, the novel application of a transfer function noise modelling approach to empirical Before-After-Control-Impact stage data from an upland catchment allowed leaky dam effectiveness in reducing flood peak magnitude to be quantified. Flood peak stage and discharge magnitude changes were assessed from empirical data for 50 single and multi-peaked high flow events with return periods ranging from less than one year to six years. Overall, event peak magnitude was significantly reduced following the installation of eight leaky dams on the impact stream. Effectiveness was highly variable, but on average, flood peak magnitude was reduced by 10% for events with a return period up to one year. Some of the variability was explained by the size of the event and whether it was a single or multi-peaked event. This finding emphasises the need to manage expectations by considering both a range of event magnitudes and types when designing or assessing leaky dam natural flood management schemes.