Nonlinear Interactions of Sea‐Level Rise and Storm Tide Alter Extreme Coastal Water Levels: How and Why?

Sea-level rise (SLR) increasingly threatens coastal communities around the world. However, not all coastal communities are equally threatened, and realistic estimation of hazard is difficult. Understanding SLR impacts on extreme sea level is challenging due to interactions between multiple tidal and non-tidal flood drivers. We here use global hourly tidal data to show how and why tides and surges interact with mean sea level (MSL) fluctuations. At most locations around the world, the amplitude of at least one tidal constituent and/or amplitude of non-tidal residual have changed in response to MSL variation over the past few decades. In 37% of studied locations, “Potential Maximum Storm Tide” (PMST), a proxy for extreme sea level dynamics, co-varies with MSL variations. Over all stations, the median PMST will be 20% larger by the mid-century, and conventional approaches that simply shift the current storm tide regime up at the rate of projected SLR may underestimate the flooding hazard at these locations by up to a factor of four. Micro- and meso-tidal systems and those with diurnal tidal regime are generally more susceptible to altered MSL than other categories. The nonlinear interactions of MSL and storm tide captured in PMST statistics contribute, along with projected SLR, to the estimated increase in flood hazard at three-fourth of studied locations by mid-21st century. PMST is a threshold that captures nonlinear interactions between extreme sea level components and their co-evolution over time. Thus, use of this statistic can help direct assessment and design of critical coastal infrastructure

Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products

Accurate prediction and assessment of extreme flood events are crucial for effective disaster preparedness, response, and mitigation strategies. One crucial factor influencing the intensity and magnitude of extreme flood events is precipitation. Precipitation patterns, particularly during intense weather phenomena such as hurricanes, can play a significant role in triggering widespread flooding over densely populated areas. Traditional flood prediction models typically rely on single-source precipitation data, which may not adequately capture the inherent variability and uncertainty associated with extreme events due to certain limitations in the precipitation generation framework, availability, or both spatial and temporal resolutions. Moreover, in coastal regions, the complex interaction between local precipitation, river flows, and coastal processes (i.e., storm tide) can result in compound flooding and amplify the overall impact and complexity of flooding patterns. This study presents an implementation of the global copula-embedded Bayesian model averaging (BMA) (Global Cop-BMA) framework for improving the accuracy and reliability of extreme flood modeling. The proposed framework integrates a collection of precipitation products with different spatiotemporal resolutions to account for uncertainty in forcing data for hydrodynamic modeling and generating probabilistic flood inundation maps. The methodology is evaluated with respect to Hurricane Harvey, which was a catastrophic weather event characterized by intense precipitation and compound flooding processes over the city of Houston in the state of Texas in 2017. The results show a significant improvement in predictive accuracy compared to those based on a single precipitation product (e.g., the Nash–Sutcliffe efficiency (NSE) performance of a single quantitative precipitation estimation (QPE) is in the range of 0.695 to 0.846, while the Cop-BMA yields an NSE of 0.858), demonstrating the merits of the Global Cop-BMA approach. Furthermore, this research extends its impact by generating probabilistic flood extension maps that account not only for the primary influence of precipitation as a flood driver but also for the intricate nature of compound flooding processes in coastal environments.</p

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

Estimation of Historic Flows and Sediment Loads to San Francisco Bay, 1849 – 2011

River flow and sediment transport in estuaries influence morphological development over decadal and century time scales, but hydrological and sedimentological records are typically too short to adequately characterize long-term trends. In this study, we recover archival records and apply a rating curve approach to develop the first instrumental estimates of daily delta inflow and sediment loads to San Francisco Bay (1849 – 1929). The total sediment load is constrained using sedimentation/erosion estimated from bathymetric survey data to produce continuous daily sediment transport estimates from 1849 to 1955, the time period prior to sediment load measurements. We estimate that ~55% (45 – 75%) of the ~1500±400 million tons (Mt) of sediment delivered to the estuary between 1849 and 2011 was the result of anthropogenic alteration in the watershed that increased sediment supply. Also, the seasonal timing of sediment flux events has shifted because significant spring-melt floods have decreased, causing estimated springtime transport (April 1st to June 30th) to decrease from ~25% to ~15% of the annual total. By contrast, wintertime sediment loads (December 1st to March 31st) have increased from ~70% to ~80%. A ~35% reduction of annual flow since the 19th century along with decreased sediment supply has resulted in a ~50% reduction in annual sediment delivery. The methods developed in this study can be applied to other systems for which unanalyzed historic data exist

Linking statistical and hydrodynamic modeling for compound flood hazard assessment in tidal channels and estuaries

A method to link bivariate statistical analysis and hydrodynamic modeling for flood hazard estimation in tidal channels and estuaries is presented and discussed for the general case where flood hazards are linked to upstream riverine discharge Q and downstream ocean level, H. Using a bivariate approach, there are many possible combinations of Q and H that jointly reflect a specific return period, T, raising questions about the best choice as boundary forcing in a hydrodynamic model. We show, first of all, how possible Q and H values depend on whether the definition of T corresponds to the probability of exceedance of “H OR Q” or “H AND Q”. We also show that flood hazards defined by “OR” return periods are more conservative than “AND” return periods. Finally, we introduce a new composite water surface profile to represent the spatially distributed hazard for return period T. The composite profile synthesizes hydrodynamic model results from the “AND” hazard scenario and two scenarios based on traditional univariate analysis, a “Marginal Q” scenario and a “Marginal H” scenario

Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998–2063) and mid-future (2018–2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950–2015) if adaptation measures are not implemented

Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998–2063) and mid-future (2018–2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950–2015) if adaptation measures are not implemented

Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts

The rapid shrinkage of Lake Urmia, one of the world's largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia's contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin's water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998-2002). The collapse was caused by a marked overshoot of the basin's hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr−1) during dry conditions to 5400 mcm yr−1 during wet periods with the average being 4100 mcm yr−1. Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia