Coastal natural and nature-based features: international guidelines for flood risk management

Natural and nature-based features (NNBF) have been used for more than 100 years as coastal protection infrastructure (e.g., beach nourishment projects). The application of NNBF has grown steadily in recent years with the goal of realizing both coastal engineering and environment and social co-benefits through projects that have the potential to adapt to the changing climate. Technical advancements in support of NNBF are increasingly the subject of peer-reviewed literature, and guidance has been published by numerous organizations to inform technical practice for specific types of nature-based solutions. The International Guidelines on Natural and Nature-Based Features for Flood Risk Management was recently published to provide a comprehensive guide that draws directly on the growing body of knowledge and practitioner experience from around the world to inform the process of conceptualizing, planning, designing, engineering, and operating NNBF. These Guidelines focus on the role of nature-based solutions and natural infrastructure (beaches, dunes, wetlands and plant systems, islands, reefs) as a part of coastal and riverine flood risk management. In addition to describing each of the NNBF types, their use, design, implementation, and maintenance, the guidelines describe general principles for employing NNBF, stakeholder engagement, monitoring, costs and benefits, and adaptive management. An overall systems approach is taken to planning and implementation of NNBF. The guidelines were developed to support decision-makers, project managers, and practitioners in conceptualizing, planning, designing, engineering, implementing, and maintaining sustainable systems for nature-based flood risk management. This paper summarizes key concepts and highlights challenges and areas of future research

Go with the flow: Impacts of high and low flow conditions on freshwater mussel assemblages and distribution.

Understanding the drivers of distribution and assemblage composition of aquatic organisms is an important aspect of management and conservation, especially in freshwater systems that are inordinately facing increasing anthropogenic pressures and decreasing biodiversity. For stream organisms, habitat conditions during high flows may be impossible to measure in the field, but can be an important factor for their distribution, especially for less mobile organisms like freshwater mussels. Hence, the objective of this study was to use a two dimensional HEC-RAS model to simulate hydraulic conditions during high and baseline flows (flows approx. 10-600 x and 0.7 x median daily flows respectively) in a 20 km segment in the San Saba River, Texas in combination with existing mussel survey data from 200 sites (collected every 100m) to 1) examine whether hydraulic conditions differed between areas of increased mussel richness and diversity (referred to as hotspots) and other sites, and 2) understand how well site occupancy and species abundance could be explained by hydraulic conditions occurring under different flow conditions. The results showed that richness and diversity hotspots occurred in deeper areas with lower shear stress, stream power, and Froude number during both high and low flows. Occupancy could be predicted with 67-79% accuracy at the site scale and 60-70% accuracy at the mesohabitat scale (∼20 to 1200 m long). In addition, hydraulic conditions across flow scenarios explained up to 55% of variation in species abundances, but predictions were less successful for species often observed to occupy micro-scale flow refuges such as bedrock crevices. The results indicate that pools may serve as important refuge for all species during both high and low flow events, which may be relatively unique to bedrock-dominated systems. Understanding hydraulic conditions that occur at extreme flows such as these is important given that the frequency and magnitude of such events are increasing due to climate change

Table1_Life cycle management of natural infrastructure: assessment of state of practice and current tools.docx

Design alternatives for traditional infrastructure are often compared in terms of expected–and often narrowly defined–costs and benefits to justify the selected plan. Taking a broader life cycle perspective in the benefit-cost evaluation process helps account for potentially rare, indirect, or accruing project benefits. Natural infrastructure design alternatives are generally difficult to compare to conventional alternatives due to their distinctly different costs and benefits. Natural infrastructure differs from conventional infrastructure in terms of performance and benefit development over time, lifespan, materials, intensity of intervention needs, and social and environmental benefits. This paper presents a life cycle framework that expands conventional life cycle analysis to capture other important and relevant aspects of natural and conventional infrastructure, enabling a more complete and equitable comparison of project costs and benefits. The framework consists of four dimensions: risk mitigation performance (e.g., traditional benefit of flood risk management), co-benefits, financial costs (life cycle cost analysis), and environmental costs (life cycle assessment). The framework takes current benefit cost analysis practice for both infrastructure types into account, is informed by existing life cycle evaluation methods and tools and is responsive to the unique needs and characteristics of natural infrastructure. Components of this framework have been advanced elsewhere, including in business product management, asset management, building code development, environmental certifications, ecosystem goods and services accounting, and others, but are generally not developed for natural infrastructure. Our proposed framework provides a roadmap for development of supporting resources to conduct life cycle evaluation for natural infrastructure. Systematically grasping the temporal flow of costs and benefits of natural infrastructure, in comparison to conventional flood risk management projects, will be important as societies address vast infrastructure needs in the face of climate change.</p

S1 Fig -

Simulated depths (m) for an A) low (0.42 m3s-1), B) moderate (5.32 m3s-1), C) moderate-high (32.28 m3s-1), and D) high (361.89 m3s-1) flows in a study segment in the San Saba River, TX. Discharges were modeled in the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) using a two-dimensional unsteady flow model built using survey data collected in 2018. (ZIP)</p

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and magnitude of last exceedance [58].

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and magnitude of last exceedance [58].</p

Data flow diagram describing inputs and outputs of data analysis, including the data used to create a two-dimensional HEC-RAS model to simulate flow conditions in a segment of the San Saba River, TX, USA.

For additional information about construction of the model and data analysis, please see the methods section and S1 Appendix.</p

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (5.32 m3s-1), moderate-high (32.28 m3s-1), and high flow (361.89 m3s-1) in a 20 km segment of the San Saba River, TX, USA.

Variable importance was determined using conditional permutation importance (CPI) because some predictor variables were found to be highly correlated [81]. Note: CPI should not be compared across different RF models.</p

Spearman correlation coefficients (r) between hydraulic variables and mussel indicators at the mesohabitat scale.

Indicators include mussel presence, log(x+1) species’ CPUE, SPUE, Shannon-Wiener diversity, and Simpson’s diversity. Correlations in bold print were significant after Bonferroni adjustment. The adjusted threshold of significance was p (DOCX)</p