Property:CSDMS meeting abstract
From CSDMS
This is a property of type Text.
2
Coastal areas globally face increasing threats from intensified weather events and rising sea levels, leading to challenges such as fluctuations in groundwater levels and salinity intrusions. This presents a significant concern for the Department of Defense (DoD), which manages over 1700 coastal sites worldwide, with several facing heightened vulnerability to these environmental changes. We aim to evaluate the susceptibility of DoD coastal sites to sea-level rise and saltwater intrusion, utilizing the Defense Regional Sea Level (DRSL) database that includes projections for five global sea-level rise scenarios and extreme water events. To achieve this, we have adopted a two-pronged strategy. First, we conduct an in-depth vulnerability analysis considering the current situation, sea-level trends, and topographic elevation. The vulnerability analysis aids in selecting sites for detailed further investigation. Subsequently, we formulate Reduced Order Models (ROMs), including Dynamic Mode Decomposition (DMD) and the Unified Fourier Neural Operator (U-FNO) for sites with a range of vulnerabilities. DMD and U-FNO are selected for their efficiency, enabling faster execution and thousands of runs to assess site vulnerability under future climate scenarios through the century's end. Trained on site-specific mechanistic models, both DMD and U-FNO accurately simulate current groundwater and salinity conditions, providing reliable forecasts of future impacts on DoD sites, utilizing data from the DRSL database and climate model projections. This approach clarifies the immediate risks and facilitates the transfer of essential knowledge throughout DoD's extensive network, fostering a deep understanding of global coastal vulnerabilities. Ultimately, this informs the development of targeted, effective mitigation strategies, safeguarding critical defense infrastructure against the impacts of climate change. +
Coastal communities facing erosion require beach maintenance for property protection and recreation. While some communities may have the means to pay for sand nourishment, others may benefit from their neighbor’s alongshore-transported sediments. If communities expect to free-ride off beach nourishment carried out by a neighbor, incentives favoring inaction may lead to narrower beaches overall. Recent work coupling human and natural systems found that coordination between neighboring communities is preferable economically to each community acting independently. Contrasting past work, we model two communities acting without knowledge of a jointly determined economically optimal nourishment program. Instead, nourishment behavior is triggered by a traditionally imposed minimum beach threshold and bounded by a predefined seaward edge. The goal is not to limit sand loss; rather, nourishment decisions are based on separate or joint benefit-cost assessments for two communities. We compare two management approaches: (1) sequential/decentralized decisions, where the updrift community chooses first and the downdrift community reacts second; and (2) simultaneous/coordinated decisions where both communities make a joint choice. We test how variable up/downdrift property values affect outcomes under these two approaches. Results suggest that communities do not always favor coordinating simultaneously. When both up- and downdrift communities have high property values, sequential/decentralized decisions are favored, leading to updrift over-nourishment to maintain beach width. This enhances alongshore sediment availability, thus providing higher marginal benefits for downdrift communities whom under-nourish. When the property values of the updrift community are low and the property values of the downdrift community are high, however, the outcome results in abandonment of property by the updrift community instead of coordinating with the downdrift community. Overall, we find that the distribution of property values across neighboring communities can be a driver for both strategy selection and the decision-making process.
Coastal ecosystems, infrastructure, and human health are vulnerable to extreme precipitation, flooding, and water-quality impacts. Integrating a hydrologic model (WRF-Hydro) into the Coupled Ocean Atmosphere Wave Sediment Transport modeling system (COAWST), which includes ocean (ROMS), atmosphere (WRF), surface-wave (SWAN, WAVEWATCHIII), sediment (CSTMS), and sea-ice components, offers the potential to investigate compound flooding and the dispersal of contaminants, sediments, and other material at the land-ocean boundary. Here, the new model coupling is described, along with an application to Hurricane Florence.
Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September, 2018, led to breaches of hog-waste lagoons, coal-ash pits, and wastewater facilities. In the weeks following the storm, historic freshwater discharge carrying pollutants, sediment, organic matter, and other debris was released to the coastal ocean, contributing to beach closures, algal blooms, hypoxic conditions, and other ecosystem impacts. The Cape Fear river basin, North Carolina’s largest watershed, is used as a case study. Progress in model coupling applied to this region includes (1) a two-way coupled ROMS and WRF-Hydro simulation in which fluxes between the ocean and hydrology models are computed from the pressure gradient at the ocean-land boundary, and (2) a one-way coupled simulation in which a WRF-Hydro simulation provides river point-source forcing in ROMS. The work as part of the one-way coupled simulation demonstrates how the pathways of land-sourced tracers can be tracked in the coastal ocean; a suite of different flood and wind scenarios are studied and used to map the arrival and departure times of threshold-exceeding contaminants that contribute to swimming advisories and other impacts. Next steps are described for continuing the ocean-hydrology model coupling efforts to improve forecasts of compound flooding and water quality impacts.
Coastal erosion and wetland loss are affecting Louisiana to such an extent that the loss of land between 1932 and 2016 was close to 5,000 km2. To mitigate this decline, coastal protection and restoration projects are being planned and implemented by the State of Louisiana, United States. The Louisiana Coastal Master Plan (CMP) is an adaptive management approach that provides a suite of projects that are predicted to build or maintain land and protect coastal communities. Restoring the coast with this 50-year large-scale restoration and risk reduction plan has the potential to change the biomass and distribution of economically and ecologically important fisheries species in this region. However, not restoring the coast may have negative impacts on these species due to the loss of habitat. This research uses an ecosystem model to evaluate the effects of plan implementation versus a future without action (FWOA) on the biomass and distribution of fisheries species in the estuaries over 50 years of model simulations. By simulating effects using a spatially-explicit ecosystem model, not only can the changes in biomass in response to plan implementation be evaluated, but also the distribution of species in response to the planned restoration and risk reduction projects. Simulations are performed under two relative sea level rise (SLR) scenarios to understand the effects of climate change on project performance and subsequent fisheries species biomass and distribution. Simulation output of eight economically
important fisheries species shows that the plan mostly results in increases in species biomass, but that the outcomes are species-specific and basin-specific. The SLR scenario highly affects the amount of wetland habitat maintained after 50 years (with higher levels of wetland loss under increased SLR) and, subsequently, the biomass of species depending on that habitat. Species distribution results can be used to identify expected changes for specific species on a regional basis. By making this type of information available to resource managers, precautionary measures of ecosystem management and adaptation can be implemented.
Coastal flooding is an increasingly prominent hazard in the northeast United States, causing both property damage and disruption of daily life. Tide gauge records provide historical water level data and are used to estimate current return periods of storm tides (tide level plus storm surge) from both hurricanes and nor’easters. We calculate the interannual joint probability exceedance curves for select tide gauges in the Philadelphia, New Jersey, and New York City megaregion using the quasi‐nonstationary skew surge joint probability method (qn‐SSJPM) from Baranes et al. (2020). Analysis of the probability of storm tides for hurricane versus nor’easter seasons will be discussed, including geographic variations of the storm tide exceedance curves. Results from this study can be compared to storm climatology and used by social scientists and city planners to assess risk associated with the flood hazard in the area. By understanding the ways that probability of storm tide in summer and winter may change in the future, communities can better plan and prepare for future hazards. +
Coastal foredunes are dynamic ecogeomorphic landforms that provide increased resilience for both natural habitats and developed communities. Despite their dynamic nature, dunes can be stabilized with vegetation and are therefore an adaptable nature-based solution that can be utilized for flood risk management. However, coastal habitats are rapidly changing and require modeling support to understand the effectiveness of vegetated dunes under changing environmental conditions. Most existing dune morphology models incorporate vegetation implicitly, using percent cover or plant height to affect sediment accretion and erosion, rather than explicitly simulating ecological processes such as mortality and dispersal. A coupled modeling approach that integrates process-based dune and vegetation models is necessary to better understand plant-sediment-water interactions and manage coastal dune systems. Through this work, we demonstrate the coupling of AeoLiS, a process-based aeolian sediment transport model with GenVeg, a generalized vegetation model under development in Landlab and parameterized with growth, functional morphology, and sand accretion of native and non-native plant species from a common garden experiment in Nehalem Bay State Park, Oregon. This work highlights how vegetation morphology affects dune building and resilience to better inform dune management and restoration actions. +
Coastal landscapes are dynamic, subject to drowning by sea level rise, erosion driven by alongshore transport, and inundation by large storm events. Coastlines are also highly developed. Along the U.S. coasts, communities continuously develop and implement beach management strategies to protect coastal infrastructure and maintain recreational value. From sediment source to sink, littoral cells often span many coastal communities. Even as physical processes grade along these littoral cells, separate communities along this coast possibly enact different management strategies. By expanding upon an existing alongshore-coupled dynamic model of coastal profile and barrier evolution, we analyze the feedbacks between alongshore and cross-shore processes as well as human response to local shoreline change across multiple communities within the same littoral cell. Incorporating the possibility of intercommunity cooperation allows us to valuate variable coastal resilience strategies for communities within a littoral cell, particularly the benefit of coordinated versus uncoordinated activities. Both sediment transport processes and a cost-benefit analysis for each community determine optimal beach management strategies. Model results provide insights useful for understanding coastal processes and planning, allowing for more robust coastal management decisions, which depend upon future rates of sea-level rise. +
Coastal urban estuaries are often impacted by water quality concerns such as bacterial contamination and harmful algal blooms that can negatively impact both human health and local industries. Historically, these impacts have been exacerbated by floods that increase the riverine discharge and alter wind patterns. Due to technical and safety constraints, however, in-situ observations during extreme events are difficult and their exact effect on water quality is typically challenging to determine. As part of a larger effort to understand how water quality changes during and following floods in estuaries around Baltimore, this study analyzed the variability in winds and river discharge. Specifically, this study utilized wind data from the NOAA station at the Frances Scott Key Bridge and river discharge data from multiple USGS river gauges from 2014-2024. Conditions during and following floods versus quiescent periods were compared. Preliminary results included the identification of 52 flooding events, defined as days when the daily discharge was higher than three standard deviations above the mean total daily discharge. During these events, the wind patterns were distinctly more northeasterly compared to low-discharge conditions, when northwesterly winds were more prevalent. Ongoing work includes investigating how variability in winds impacts circulation and residence time using a 3-D numerical hydrodynamic model. +
Coastal-plain depositional systems such as fluvial deltas are archives of past external (allogenic) forcing, such as sea-level variations, and their evolution can be described by two geomorphic boundaries: the alluvial-basement transition or upstream boundary, and the shoreline or downstream boundary. Patterns of landward/seaward migration of the shoreline (i.e., transgression/regression) and the alluvial basement transition (i.e., coastal onlap/offlap) in the rock record are often used for reconstruction of past sea-level changes. Theories for stratigraphic interpretation, however, need to be adapted to deal with internal (autogenic) processes that could play a significant role, but are to date largely unexplored. In particular, in-situ organic matter accumulation via plant growth has generally received little attention despite accounting for a significant volume fraction in most fluvio-deltaic plains and likely affect their response to sea level variations. To fill this knowledge gap, we develop a geometric model for the long-profile evolution of a fluvio-deltaic environment that accounts for sea-level cycles and organic sediment dynamics. The model assumes that sedimentological processes (i.e., inorganic and organic sedimentation) operate to preserve a linear geometry for both the delta plain or topset, and the subaqueous offshore region or forest. Changes in topset length can occur via shoreline transgression/regression, or coastal onlap/offlap, and the magnitude and timing of these changes can be directly related to the amplitude, phase and frequency of the sea-level variations. The model predicts that the maximum organic fraction occurs when the organic matter accumulation rate matches the accommodation rate, an observation consistent with field observations from coal geology. Further, we find that organic matter accumulation during the topset aggradation and organic matter erosion and decay during topset degradation generally results in substantial increase in the coastal onlap/offlap amplitude, which can result in an overestimation of the sea-level variations. These results are consistent with the discrepancy in sea-level amplitude reconstructions between sequence stratigraphic models and geochemical models over the Cretaceous.
Coasts are among the most intensely used environments on the planet, but they also present dynamic and unique hazards including flooding and erosion. Over the next century, these risk are likely to intensify across many coastal localities due to changes in environmental conditions, including sea level rise and changing wave climate patterns as induced by climate change. Managing these hazards and protecting vulnerable areas is challenging and requires an understanding of the behavior of coastal systems and longer-term prediction of their future evolution in the face of a changing climate.
Many existing one-dimensional coastal evolution models can effectively simulate the evolution of coastal environments. However, due to their 1D nature, they are unable to model the additional and combined effects of a variable water level and sea level rise. Hence, a new model, the Coastline Evolution Model 2D (CEM2D), has been built that is capable of simulating these processes.
CEM2D has been built from the 1D parent model – the Coastline Evolution Model (CEM) - that was originally developed by Ashton et al. (2001), Ashton and Murray (2006) and Valvo et al. (2006). CEM2D has been developed accordingly to the underlying assumption and mathematical framework of CEM, but applied over a two-dimensional grid. At the core of this framework is the calculation of longshore sediment transport rates using the CERC formula and Linear Wave Theory. Wave shadowing calculations are also used to ensure that sediment transport is negligible in shadowed areas. The distribution of material across the shoreface is controlled by a steepest descent formula that routes sediment from higher to lower elevations across the domain according to defined thresholds, whilst maintaining the average slope angle.
CEM2D provides a step forward in the field of coastal numerical modelling. It fills a gap between one-dimensional models of shoreline change that provide insights into the fundamental processes that control coastal morphodynamics and more complex and computationally expensive two- and three-dimensional models that are capable of simulating more complex processes and feedbacks. Key applications of CEM2D include improving our understanding of the meso-scale morphodynamic behaviour of coastal systems, their sensitivities to changing environmental conditions and the influence that climate change may have on their evolution over centennial to decadal timescales.
Conservation biologist, modeler, blogger, nature photographer, animal friend, swing dancer, Ecopathologist… All these describe Adrian Dahood, who tragically lost her life along with 33 others in a diving boat fire off the coast of California. She will be remembered fondly, and her legacy as a scientist and policy expert will remain alive within the scientific community. Please check out her photos, blogs, postcards and scientific papers, and I hope she can bring a smile to your face as well. +
Continental and global water models have long been trapped in slow growth and inadequate predictive power, as they are not able to effectively assimilate information from big data. While Artificial Intelligence (AI) models greatly improve performance, purely data-driven approaches do not provide strong enough interpretability and generalization. One promising avenue is “differentiable” modeling that seamlessly connects neural networks with physical modules and trains them together to deliver real-world benefits in operational systems. Differentiable modeling (DM) can efficiently learn from big data to reach state-of-the-art accuracy while preserving interpretability and physical constraints, promising superior generalization ability, predictions of untrained intermediate variables, and the potential for knowledge discovery. Here we demonstrate the practical relevance of a high-resolution, multiscale water model for operational continental-scale and global-scale water resources assessment. (https://bit.ly/3NnqDNB). Not only does it achieve significant improvements in streamflow simulation compared to the established national- and global water models, but it also produces much more reliable depictions of interannual changes in large river streamflow, freshwater inputs to estuaries, and groundwater recharge. As a related topic, we also showcase the value of foundation AI for global environmental change and its benefits for resource management. +
Coupled process-based numerical models have the potential to greatly enhance our understanding of the drivers of coastal change by allowing for detailed simulations of the processes involved within each model core of the coupling. However, producing accurate hindcasts and forecasts with these coupled frameworks can be challenging due to a wide array of parameters that interact nonlinearly across and within the individual model cores and the potentially substantial computational cost that limits both the number and duration of simulations that can be reasonably performed. Additionally, many model parameters (e.g., wave asymmetry and skewness or sediment transport coefficients) that are critical for model calibration are unitless coefficients in the model formulations and thus cannot be readily measured in the field. Here, we use Windsurf, a coupled beach-dune modeling system that includes Aeolis, the Coastal Dune Model, and XBeach, paired with two surrogate neural network models, to produce a pair of hindcasts and forecasts to replicate observed modes of dune and beach morphological change on a developed barrier island on the US Atlantic coast (Bogue Banks, North Carolina). The first neural network aids in the calibration process by allowing for the prediction of Windsurf’s error surface over thousands of potential parameterizations to rapidly identify a potential best calibration before actually running the model. Windsurf is then run within a genetic algorithm to further hone the collection model parameter settings. Once Windsurf is finished running, we use the output to train a second neural network which contains a Long Short-Term Memory (LSTM) layer to produce five-year forecasts of dune crest height and dune toe elevation. We test our results by comparing them to observed data collected in the field between 2016-2020 using Real-Time Kinematic Global Positioning System (RTK-GPS) and find our forecasts (from the hindcasts) produce reasonably accurate predictions of dune morphology change at interannual scale.
Coupling models from different domains (e.g., ecology, hydrology, geology, etc.) is usually difficult because of the heterogeneity in operating system requirements, programming languages, variable names, units and tempo-spatial properties. Among multiple solutions to address the issue of integrating heterogeneous models, a loosely-coupled, serviced-oriented approach is gradually gaining momentum. By leveraging the World Wide Web, the service-oriented approach lowers the interoperability barrier of coupling models due to its innate capability of allowing the independence of programming languages and operating system requirements. While the service-oriented paradigm has been applied to integrate models wrapped with some standard interfaces, this paper considers the Basic Model Interface (BMI) as the model interface. Compared with most modeling interfaces, BMI is able to (1) enrich the semantic information of variable names by mapping the models’ internal variables with a set of standard names, and (2) be easily adopted in other modeling frameworks due to its framework-agnostic property. We developed a set of JSON-based endpoints to expose the BMI-enabled models as web services, through storing variable values in the network common data form file during the communication between web services to reduce network latency. Then, a smart modeling framework, the Experimental Modeling Environment for Linking and Interoperability (EMELI), was enhanced into a web application (i.e., EMELI-Web) to integrate the BMI-enabled web service models in a user-friendly web platform. The whole orchestration was then implemented in coupling TopoFlow components, a set of spatially distributed hydrologic models, as a case study. We demonstrate that BMI helps connect web service models by reducing the heterogeneity of variable names, and EMELI-Web makes it convenient to couple BMI-enabled web service models. +
Crop models are used to simulate crop development, yield and irrigation requirements, but their performance can be influenced by environmental and management conditions such as climate and irrigation strategies. Hence, performing a sensitivity analysis on these models is crucial to identifying influential parameters which informs model calibration. Here, we performed a global sensitivity analysis (Morris Screening method) on crop yield and irrigation on 34 crop parameters using the AquaCrop-OSPy model. This analysis is done for corn in Sheridan, KS under different water treatments (irrigated and rainfed) for varying meteorological scenarios represented by past years annual precipitation (normal-2021, wet-2019 and dry-2002). Thresholds of 0.3t/ha and 20mm are used for yield and irrigation respectively to identify influential parameters. Overall, parameter importance varies for yield and irrigation: parameters related to biomass and yield, root and canopy development, and irrigation strategy are the most influential for yield while those related to irrigation strategy, and root and canopy development are the most influential for irrigation. In general, yield was responsive to fewer parameters in rainfed conditions and simulations with drier meteorological conditions. The normal and wet scenarios have similar influential parameters with varying order of influence for yield under irrigated conditions. However, under rainfed conditions, the normal scenario only has two influential parameters (minimum effective rooting depth and the excess of potential fruits, a parameter related to biomass and yield), while 8 parameters related to biomass and yield production, water stress, and root development are influential during the wet scenario. Yield under irrigated conditions during the wetter years (receiving normal and high precipitation) tends to be impacted by water and temperature stress parameters. The influential parameters will further be analyzed using the Sobol method to calculate each parameter's influence on the output’s variance and interaction with other parameters, and ultimately used to guide model calibration.
Debris flows pose a hazard to infrastructure and human life. However, predicting debris flows remains a challenge due to uncertainty in initiation mechanisms, and the difficultly in appropriately parameterizing the resistance equations that describe flow velocities. Additionally, one of the limitations to progress in modeling debris-flow timing is the lack of empirical data from natural watersheds that can be used for parameter estimation and validation of predictions. Most quantitative measurements of debris flows are conducted in flumes, or unique watersheds where debris flows are known to occur annually, both of which suggest particularly remarkable conditions that may not reflect the majority of conditions where debris flows are manifested. This research addresses those challenges by using measured debris-flow timing in nine watersheds that were burned by a wildfire in 2009 to calibrate and test debris flow model parameterizations. Debris-flow timing was captured using pressure transducers attached to the channel bed. We used a kinematic wave rainfall-runoff model that we developed in python using the landlab environment to model flow timing. We separated the nine study watersheds into two categories: calibration and testing. For the calibration watersheds, model parameters were estimated based on prior research and then changed iteratively using a storm with known rainfall to minimize an objective function of the observed and modeled flow timing. Following hundreds of model realizations, we arrived at a set of best-fit parameters for saturated hydraulic conductivity (Ks) and the Manning’s roughness parameter (n). We found that a single value of Ks could be used in each of the model watersheds because, following wildfires, this parameter is typically reduced to very low values with a relatively small variance. In contrast n varied systematically as a function of upstream contributing drainage area, and thus values of n could be estimated for uncalibrated basins. When Ks and n were applied to test basins without any calibration we found that a reasonable result in estimated debris-flow timing was attained. These results suggest that given the appropriate scaling estimates it may be possible to estimate debris-flow timing within minutes and to capture multiple debris-flow surges separated by several hours.
Debris flows pose a significant threat to downstream communities in mountainous regions across the world, and there is a continued need for methods to delineate hazard zones associated with debris-flow inundation. Here we present ProDF, a reduced-complexity debris-flow inundation model for rapid hazard assessment. We calibrated and tested ProDF against observed debris-flow inundation at eight study sites across the western United States. While the debris flows at these sites varied in initiation mechanism, volume, and flow characteristics, results show that ProDF is capable of accurately reproducing observed inundation extent across different geographic settings. ProDF reproduced observed inundation while maintaining computational efficiency, suggesting the model may be especially applicable in rapid hazard assessment scenarios. +
Debris flows, sediment laden gravity driven fluvial processes, are a common issue in Southern California. They often occur during peak streamflow, making precipitation an important predictor for debris flow activity. However, the low temporal sampling of precipitation data used to calculate streamflow is often insufficient to forecast peak flows accurately. Here, we evaluate the effect of precipitation data resolution on discharge using 30-minute IMERG-early data averaged over different time intervals to model streamflow. We apply the results to a dimensionless discharge threshold model to predict debris flow locations. The streamflow values were calculated with the Distributed Hydrology Soil Vegetation Model and the debris flow model was programmed to be compatible with the Basic Model Interface (BMI). BMI was selected for this project because it standardizes model coupling, which enabled a hydrologic driven landslide model to run efficiently. The landslide model follows Tang et al. (2019) to produce dimensionless discharge and debris flow threshold values for stream segments. This can be used to predict where we would likely see a debris flow based on the given streamflow data. We ran these models with precipitation data of different temporal resolutions and evaluated their effect on dimensionless discharge. The model was able to capture a portion of debris flows using higher temporal resolution precipitation data. Of the 138 stream segments evaluated, 122 were predicted to have a dimensionless discharge value above the calculated thresholds when using 30-minute data, which largely matched observations from aerial imagery. In contrast, lower temporal resolution data did not capture these results. Initial debris flow predictions using high resolution precipitation data coincide in stream segments that experienced landslides. We conclude that high resolution precipitation data is critically important for predicting debris flow events. +
Decision making is a cultural process fundamental to slowing environmental destruction in all its guises. Although crucial to understanding environmental decision making, working toward a viable interdisciplinary model that could be used across problems and sites is not without obstacles. In order for coupled models to capture realistic lag times and interactions between social choices and the environment, algorithms of decision making must incorporate the influence of spatial-temporal local differences. This is especially true for coupled human-earth system models or agent-based models designed to inform policy. Here we provide a case study from the Paraná Delta of Argentina where a neighborhood assembly fights against pollution in the delta caused by an engineering failure. We combine components of a decision making framework with concepts from cultural and geographic theory, and then filter the combination through ethnographic description and interpretation to track how local culture influences decisions, and hence, lag times between actions and outcomes. Although fundamental to human decision making processes, sociocultural dynamics are often left out of formal behavioral modules coupled to environmental models. Through this experiment, we expand the capacity of such a framework for carrying cultural meaning and social interaction. +
Degradation of ice-rich permafrost is caused by rapid Arctic warming. Likely this degradation already has altered the water balance by increasing runoff and flooding. But here we ask, how do the hydrological changes in river systems, in turn, affect the permafrost conditions? How does river flooding affects permafrost thermal state in floodplains and deltas? What if the timing of river flooding changes with Arctic warming?
We develop a first-order heat budget approach to simulate evolving river flood water temperature over the seasonal inundation period. Solar radiation, air temperature and wind control the different components of heat exchange between the atmosphere and the river water surface. An additional term specifically calculates the exchange of heat between the river water and the channel bed and subsurface. Then, this river and flood water temperature is coupled to the Control Volume Permafrost Model (CVPM), which models detailed thermal state of shallow permafrost. We apply the combined model to the Kuparuk river floodplain and delta, a medium-sized river system on the North Slope of Alaska. Results indicate that permafrost underlaying the floodplain warms during inundation, and the active layer thickness (ALT) can increase for more several meters with sustained standing water. Permafrost underlying the floodplain farthest laterally from the main channel is only warmed by the short-lived spring snowmelt flood. We find that earlier arrival of the spring freshet and associated earlier inundation onset, as well as the increase of river discharge can significantly increase subsurface permafrost temperature, and lead to the deepening of the active layer. The sedimentary characteristics of the deposits in the floodplain are an important controls on the response of permafrost thermal state to inundation. River corridors, especially in the continuous zone of permafrost in the Arctic, are increasingly vulnerable to future changes in timing and magnitude of freshwater flooding as a result of earlier spring snowmelt and river breakup, and increasing river discharge.
