Nature-based solutions as buffers against coastal compound flooding: Exploring potential framework for process-based modeling of hazard mitigation.

As coastal regions face escalating risks from flooding in a changing climate, Nature-based Solutions (NbS) have garnered attention as promising adaptation measures to mitigate the destructive impacts of coastal flooding. However, the challenge of compound flooding, which involves the combined effects of multiple flood drivers, demands a deeper understanding of the efficacy of NbS against this complex phenomenon. This manuscript reviews the literature on process-based modeling of NbS for mitigating compound coastal flooding and identifies knowledge gaps to enhance future research efforts. We used an automated search strategy within the SCOPUS database, followed by a screening process that ultimately resulted in 141 publications assessing the functionality of NbS against coastal flooding. Our review identified a dearth of research (9 %) investigating the performance of NbS against compound flooding scenarios. We examined the challenges and complexities involved in modeling such scenarios, including hydrologic, hydrodynamic, and ecological feedback processes by exploring the studies that used a process-based modeling framework. Key research gaps were identified, such as navigating the complex environment, managing computational costs, and addressing the shortages of experts and data. We outlined potential modeling pathways to improve NbS characterization in the compound flooding framework. Additionally, uncertainties associated with numerical modeling and steps to bridge the research-to-operation gaps were briefly discussed, highlighting the bottlenecks in operational implementation.

Establishing flood thresholds for sea level rise impact communication.

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States' coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.

Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States.

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks.

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting.

This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of hydrometeorological and hydrodynamic model simulations as well as their complex interactions and cascading effects (e.g., uncertainty propagation) in forecasting compound flooding (CF). Over the past few decades, CF has come to attention across the globe as this natural hazard results from a combination of either concurrent or successive flood drivers with larger economic, societal, and environmental impacts than those from isolated drivers. A warming climate and increased urbanization in flood-prone areas are expected to contribute to an escalation in the risk of CF in the near future. Recent advances in remote sensing and data science can provide a wide range of possibilities to account for and reduce the predictive uncertainties; hence improving the predictability of CF events, enabling risk-informed decision-making, and ensuring a sustainable CF risk governance.

Extreme heat events heighten soil respiration.

In the wake of climate change, extreme events such as heatwaves are considered to be key players in the terrestrial biosphere. In the past decades, the frequency and severity of heatwaves have risen substantially, and they are projected to continue to intensify in the future. One key question is therefore: how do changes in extreme heatwaves affect the carbon cycle? Although soil respiration (Rs) is the second largest contributor to the carbon cycle, the impacts of heatwaves on Rs have not been fully understood. Using a unique set of continuous high frequency in-situ measurements from our field site, we characterize the relationship between Rs and heatwaves. We further compare the Rs response to heatwaves across ten additional sites spanning the contiguous United States (CONUS). Applying a probabilistic framework, we conclude that during heatwaves Rs rates increase significantly, on average, by ~ 26% relative to that of non-heatwave conditions over the CONUS. Since previous in-situ observations have not measured the Rs response to heatwaves (e.g., rate, amount) at the high frequency that we present here, the terrestrial feedback to the carbon cycle may be underestimated without capturing these high frequency extreme heatwave events.

Quantifying Anthropogenic Stress on Groundwater Resources.

This study explores a general framework for quantifying anthropogenic influences on groundwater budget based on normalized human outflow (hout) and inflow (hin). The framework is useful for sustainability assessment of groundwater systems and allows investigating the effects of different human water abstraction scenarios on the overall aquifer regime (e.g., depleted, natural flow-dominated, and human flow-dominated). We apply this approach to selected regions in the USA, Germany and Iran to evaluate the current aquifer regime. We subsequently present two scenarios of changes in human water withdrawals and return flow to the system (individually and combined). Results show that approximately one-third of the selected aquifers in the USA, and half of the selected aquifers in Iran are dominated by human activities, while the selected aquifers in Germany are natural flow-dominated. The scenario analysis results also show that reduced human withdrawals could help with regime change in some aquifers. For instance, in two of the selected USA aquifers, a decrease in anthropogenic influences by ~20% may change the condition of depleted regime to natural flow-dominated regime. We specifically highlight a trending threat to the sustainability of groundwater in northwest Iran and California, and the need for more careful assessment and monitoring practices as well as strict regulations to mitigate the negative impacts of groundwater overexploitation.

Compounding effects of sea level rise and fluvial flooding.

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.