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Deep learning (DL) models have been used for rapid assessments of environmental phenomena like mapping compound flood hazards from cyclones. However, predicting compound flood dynamics (e.g., flood extent and inundation depth over time) is often done with physically-based models because they capture physical drivers, nonlinear interactions, and hysteresis in system behavior. Here, we show that a customized DL model can efficiently learn spatiotemporal dependencies of multiple flood events in Galveston, TX. The proposed model combines the spatial feature extraction of CNN, temporal regression of LSTM, and a novel cluster-based temporal attention approach to assimilate multimodal inputs; thus, accurately replicating compound flood dynamics of physically-based models. The DL model achieves satisfactory flood timing (±1 h), critical success index above 60 %, RMSE below 0.10 m, and nearly perfect error bias of 1. These results demonstrate the model's potential to assist in flood preparation and response efforts in vulnerable coastal regions. 

Abstract. Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in which concurrent or successive flood drivers synergize, producing larger impacts than those from individual drivers. However, CF modeling is subject to four main sources of uncertainty: (i) the initial condition, (ii) the forcing (or boundary) conditions, (iii) the model parameters, and (iv) the model structure. These sources of uncertainty, if not quantified and effectively reduced, cascade in series throughout the modeling chain and compromise the accuracy of CF hazard assessments. Here, we characterize cascading uncertainty using linked process-based and machine learning (PB–ML) models for a well-known CF event, namely, Hurricane Harvey in Galveston Bay, TX. For this, we run a set of hydrodynamic model scenarios to quantify isolated and cascading uncertainty in terms of maximum water level residuals; additionally, we track the evolution of residuals during the onset, peak, and dissipation of Hurricane Harvey. We then develop multiple linear regression (MLR) and PB–ML models to estimate the relative and cumulative contribution of the four sources of uncertainty to total uncertainty over time. Results from this study show that the proposed PB–ML model captures “hidden” nonlinear associations and interactions among the sources of uncertainty, thereby outperforming conventional MLR models. The model structure and forcing conditions are the main sources of uncertainty in CF modeling, and their corresponding model scenarios, or input features, contribute to 56 % of variance reduction in the estimation of maximum water level residuals. Following these results, we conclude that PB–ML models are a feasible alternative for quantifying cascading uncertainty in CF modeling. 

This work presents a framework for studying temporal networks using zigzag persistence, a tool from the field of Topological Data Analysis (TDA). The resulting approach is general and applicable to a wide variety of time-varying graphs. For example, these graphs may correspond to a system modeled as a network with edges whose weights are functions of time, or they may represent a time series of a complex dynamical system. We use simplicial complexes to represent snapshots of the temporal networks that can then be analyzed using zigzag persistence. We show two applications of our method to dynamic networks: an analysis of commuting trends on multiple temporal scales, e.g., daily and weekly, in the Great Britain transportation network, and the detection of periodic/chaotic transitions due to intermittency in dynamical systems represented by temporal ordinal partition networks. Our findings show that the resulting zero- and one-dimensional zigzag persistence diagrams can detect changes in the networks’ shapes that are missed by traditional connectivity and centrality graph statistics. 

Characterizing the population density of species is a central interest in ecology. Eastern North America is the global hotspot for biodiversity of plethodontid salamanders, an inconspicuous component of terrestrial vertebrate communities, and among the most widespread is the eastern red-backed salamander,Plethodon cinereus. Previous work suggests population densities are high with significant geographic variation, but comparisons among locations are challenged by lack of standardization of methods and failure to accommodate imperfect detection. We present results from a large-scale research network that accounts for detection uncertainty using systematic survey protocols and robust statistical models. We analysed mark–recapture data from 18 study areas across much of the species range. Estimated salamander densities ranged from 1950 to 34 300 salamanders ha⁻¹, with a median of 9965 salamanders ha⁻¹. We compared these results to previous estimates forP. cinereusand other abundant terrestrial vertebrates. We demonstrate that overall the biomass ofP. cinereus, a secondary consumer, is of similar or greater magnitude to widespread primary consumers such as white-tailed deer (Odocoileus virginianus) andPeromyscusmice, and two to three orders of magnitude greater than common secondary consumer species. Our results add empirical evidence thatP. cinereus, and amphibians in general, are an outsized component of terrestrial vertebrate communities in temperate ecosystems. 

Maritime transportation is crucial to national economic development as it offers a low-cost, safe, and efficient alternative for movement of freight compared to its land or air counterparts. River and channel dredging protocols are often adopted in many ports and harbors of the world to meet the increasing demand for freight and ensure safe passage of larger vessels. However, such protocols may have unintended adverse consequences on flood risks and functioning of coastal ecosystems and thereby compromising the valuable services they provide to society and the environment. This study analyzes the compound effects of dredging protocols under a range of terrestrial and coastal flood drivers, including the effects of sea level rise (SLR) on compound flood risk, vessel navigability, and coastal wetland inundation dynamics in Mobile Bay (MB), Alabama. We develop a set of hydrodynamic simulation scenarios for a range of river flow and coastal water level regimes, SLR projections, and dredging protocols designed by the U.S. Army Corps of Engineers. We show that channel dredging helps increase bottom (‘underkeel’) clearances by a factor of 3.33 under current mean sea level and from 4.20 to 4.60 under SLR projections. We find that both low and high water surface elevations (WSEs) could be detrimental, with low WSE (< -1.22 m) hindering safe navigation whereas high WSE (> 0.87 m) triggering minor to major flooding in the surrounding urban and wetland areas. Likewise, we identify complex inundation patterns emerging from nonlinear interactions of SLR, flood drivers, and dredging protocols, and additionally estimate probability density functions (PDFs) of wetland inundation. We show that changes in mean sea level due to SLR diminish any effects of channel dredging on wetland inundation dynamics and shift the PDFs beyond pre-established thresholds for moderate and major flooding. In light of our results, we recommend the need for integrated analyses that account for compound effects on vessel navigation and wetland inundation, and provide insights into environmental-friendly solutions for increasing cargo transportation.