Property:CSDMS meeting abstract
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Geophysical datasets, thermal modelling, and drilling data suggest that most Arctic shelves are underlain by submarine permafrost due to their exposure during the glacial low water stands. The degradation of subsea permafrost depends on the duration of inundation, warming rate, the coupling of the seabed to the atmosphere from bottom-fast ice, and brine injections into the seabed. The impact of brine injections on permafrost degradation is dependent on seawater salinity, which changes seasonally in response to salt rejection from sea ice formation and terrestrial freshwater inflows. The relative importance of the upper boundary conditions responsible for permafrost table degradation rates, however, remain poorly understood. This study evaluates the effects of changing upper boundary conditions on subaquatic permafrost thaw rates using CRYOGRID, a one-dimensional heat diffusion model, which was extended to include coupled dissolved salt diffusion. More specifically, the impacts of using a seasonally varying seabed temperature function compared to a mean annual seabed temperature for both freshwater and saline water bodies were assessed. For saline conditions, the effects of different salinity regimes at the seabed, including mean annual concentrations and seasonal variations. Daily observations of seabed temperature and electrical conductivity from 01-09-2008 to 31-08-2009 offshore of Muostakh Island in Siberia were used to set up the upper boundary conditions for the base case model runs. For saline water bodies, sensitivity analyses for mean annual salt concentrations and seabed sediment type were also performed. In all model runs, a steady-state heat conduction function was used to calculate the initial ground thermal regime prior to inundation. The initial state of permafrost was assumed to contain no salt and the ramp-up time from a terrestrial to a sub-aquatic upper boundary condition was one year for all simulations. Generally, it was found that using a mean annual seabed temperature overestimates subaquatic permafrost thaw for shallow freshwater by approximately 2 metres after 65 years of inundation. Seasonal variation of the seabed temperature led to seasonal freezing and thawing of the sea bed. However, for water bodies with high mean annual concentrations of salt (i.e. 420 moles NaCl/m3), it was found that the difference between using mean annual versus seasonally varying seabed temperatures was negligible. Dissolved salts below the seabed depress the pore water freezing point sufficiently to prevent ice formation in the near-surface sediment despite sub-zero winter temperatures. Given the current trend of freshening in the Arctic Ocean, we expect seasonal freezing of the seabed to be more common for newly submerged permafrost caused by coastal erosion, and thus potentially leading to slower permafrost table degradation rates.
Glacial erosion has shaped many high mountain belts during the cold periods of the Late Cenozoic. Theoretical models of glacial erosion generally link the pace of erosion to some subglacial properties including basal sliding, basal thermal regime, and effective water pressure. The energy balance of glaciers is a strong control on these properties and therefore, has a potential impact on glacial erosion. Specifically, the geothermal heat from the bedrock can potentially control the patterns and rates of glacial erosion by changing the basal temperature and the supply of meltwater to the subglacial water system. Here, we investigate the impact of geothermal heat flow on glacial erosion using a coupled model of erosion and ice dynamics. The rate of glacial erosion is modeled as a linear function of the basal sliding velocity. The ice flow is modeled using the Parallel Ice Sheet Model (PISM). PISM solves the conservation of energy using an enthalpy-based scheme, and it links basal sliding to subglacial hydrology through a pseudo-plastic basal resistance model. We model glacial erosion over a synthetic glacial landscape using a range of values for geothermal heat flux. Preliminary results demonstrate that higher geothermal heat flux can increase the total amount of erosion significantly by accelerating the rate of basal sliding and expanding the area of sliding into higher elevations. +
Glacial isostatic adjustment creates characteristic patterns of relative sea level change (RSL) as a function of distance to melting ice sheets (Clark et al., 1978). During the last termination and through the Holocene, regions formerly covered by large ice sheets experienced rapidly falling RSL due to the processes involved in glacial isostatic adjustment (GIA), primarily isostatic uplift. Surrounding this region of uplift is a narrow band that similarly records RSL fall but is interrupted at sometime during the Holocene by a period of sea level rise (i.e. a transgressions) culminating in a high stand. Holocene transgressions and highstands have been well documented in many locations including Norway, Canadian Atlantic coast, the Canadian Pacific coast, Svalbard, the Baltic Sea, and the British Isles (Forman, 2004; Smith et al., 2011; Shugar et al., 2014; Shennan et al., 2018; Vacchi et al., 2018; Rosentau et al., 2021; Creel et al., 2022). We investigate the origins of these Holocene transgressions using GIA/sea level modeling and test the hypothesis that they are the direct result of solid Earth deformation. Our modeling results highlight a unique pattern of solid Earth deformation in which the region of subsidence (peripheral bulge) surrounding the ice sheet migrates first towards and then away from the melted ice mass. We show how this effect, we term ’reverse migration’, is the direct result of the contrast in viscosity between the upper and lower mantle. We compare our GIA model predictions of RSL change to 1) RSL data since the last glacial maximum and 2) constrains on the transgression magnitude in Norway and eastern Canada. Both tests show a preference for GIA models that include a mantle with a substantial (1-2 orders of magnitude) increase in vicosity with depth. This suggests that, in contrast to the conventional view that Holocene transgressions record GMSL temporarily outpacing isostatic uplift, solid Earth deformation and specifically reverse migration played an important role in generating nearfield Holocene transgressions. Finally, by comparing GIA model results to RSL observations, we show how Holocene transgressions can be used to constrain the vertical viscosity structure of the mantle. Our findings suggest that a significant increase in viscosity with depth (1-2 orders of magnitude) likely exists below continents with nearfield transgressions.
Glacial lake outburst floods pose an increasing hazard to communities living downstream of glaciated areas. The Northern Patagonia Icefield has experienced catastrophic glacial lake outburst floods (GLOFs), and understanding past events will help to understand and prepare for future events. This region is vastly understudied, and much research remains to be completed to thoroughly understand such complex phenomena such as GLOFs. Satellite imagery, supplemented by high-resolution UAV imagery derived 3D models, are used to understand the hydrodynamics of a GLOF which took place March 16, 1989, in the Valle Soler of the Northern Patagonia Icefield. Using the 3D model and satellite-derived digital elevation model, flood parameters are calculated in order to describe the 1989 Soler GLOF. The results of this analysis are used to understand mass extraction and gradational fining of moraine material in Valle Soler. Blocks of rock, exceeding 10m, are found throughout the valley and a comparison of peak discharge and the necessary force to deposit the blocks are discussed. +
Glacially-derived debris often blankets alpine streams, yet few models have explicitly linked sediment supply and transport between glacial and fluvial systems. Here, we combine a 1-D river-incision model with a quarrying-dominated glacial erosion model. We link sediment production and supply between the two systems, and include a valley width variable that allows glaciers to widen valleys and temporarily store glacially-derived sediment within those valleys. A lateral erosion factor in the fluvial model re-incorporates this sediment, which is transported using a modified Meyer-Peter and Mueller equation and incorporated into bedrock erosion through a cover effect. We calibrated this model using the DAKOTA calibration software to Holocene glaciated alpine rivers in North America and are able to match observed topography within an acceptable Χ^2 fit of <2. +
Glaciers around the world are retreating in response to climate change, leaving behind tens of thousands of glacier-contact lakes in their wake. Some lake-terminating glaciers have been observed to flow, lose ice mass, and retreat at faster rates than land-terminating glaciers. However, these observations appear to contradict theory, which suggests that the cold, freshwater, and shallow conditions in these lakes should inhibit ice calving and melting—collectively “frontal ablation”—at the glacier terminus. To resolve this discrepancy between theory and observations, we must disentangle the relative influence of surface mass balance and frontal ablation on observed glacier retreat. In this study, we modeled three lake-terminating glaciers on the Juneau Icefield, Alaska (Gilkey, Meade, and Field) from 2005–2019 using a physics-informed ice flow model emulator—the Informed Glacier Model (IGM). We drove IGM with surface mass balance outputs from a COupled Snowpack and Ice surface energy and mass-balance (COSIPY) climate reanalysis model of the Juneau Icefield. These glacier simulations were then compared with LANDSAT-observed glacier terminus positions from 2005-2019 to assess modeled predictions of lake-terminating glacial retreat.
Preliminary results indicate that when the ice flow model is only forced by surface mass balance—as current theory suggests for lake-terminating glaciers—IGM underpredicts observed terminus retreat at all three glaciers. These results suggest that lake-terminating glaciers on the Juneau Icefield are experiencing an additional component of ice mass loss that significantly contributes to terminus retreat. Upcoming modeling work will aim to verify the quality of these preliminary results by performing similar model runs on land-terminating glaciers in the Juneau Icefield (e.g. Lemon Creek Glacier) as a control case. We will also perform field campaigns over 2025-2027 to the Gilkey, Meade, and Field glacier termini to quantify their in-situ ice mass loss rates.
Globally, more people are impacted by floods than all other forms of natural disasters combined. In global megacities, defined by the United Nations as cities with a population of over ten million, increased human exposure to flooding is both ubiquitous and extremely difficult to characterize. Over the past three decades, most of these cities have experienced a gradual or very rapid growth as the global population continues to urbanize. As both urban expansion and global climate change contribute to hydrologic intensification, and as globally more people live in urban areas than rural ones, the need to assess both the drivers and magnitude of flood risk associated with rapid growth in megacities is of critical humanitarian concern. Through a multitemporal analysis (2000, 2010, and 2020) of urban growth modes and urban landscape change detection using the Landsat dataset (ETM+, OLI), we estimate the growth rates and development patterns in ten global megacities (Guangzhou, Tokyo, Lagos, Jakarta, Delhi, Manila, Mumbai, Seoul, Mexico City, New York) representing different global climate zones using machine learning. Trends in runoff magnitudes over the time period are quantified and associated with urban expansion and non-stationarity in regional historical precipitation patterns. Preliminary results showed that the ten cities have experienced major flooding within the last ten years resulting mostly as a result of heavy rainfall. +
Globally, the occurrence of extreme hydrologic events such as flooding is known to be the widespread aftermath of torrential rain and the impacts are adverse and devastating in built areas with proximity to water bodies. An example is the 2012 and 2022 flooding along the Niger and Benue rivers in Nigeria. While Nigeria experiences seasonal flooding during the rainy season, the decadal interval between these two catastrophic flood events and the similarities between the natural and anthropogenic conditions responsible for their occurrence prompted this study. Additionally, some hydrologic characteristics and attributes of these flood events are yet to be evaluated. Hence, for the 2012 and 2022 floods, we estimated and compared the floodwater depths at different sections of the Niger and Benue Rivers using the Floodwater Depth Estimation Tool (FwDETv2.0 and FwDETv2.1) implemented in Google Earth Engine, Jupyter Notebook, and ArcGIS Pro. Since this algorithm requires minimal input (flood inundation map and Digital Elevation Model) which favors data-sparse regions such as Nigeria, the potential for the FwDET tool to automatically quantify flood water depths, an important variable in flood intensity estimation was assessed. This tool could be invaluable in flood management and mitigation studies along the rivers. +
Graphics Processing Units (GPUs) have been shown to be very successful in accelerating simulation in many fields. When they are used to accelerate simulation of earthquakes and tsunamis, a big challenge comes from the use of adaptive mesh refinement (AMR) in the code, often necessary for capturing dynamically evolving small-scale features without excessive resolution in other regions of the domain.
Clawpack is an open source library for solving general hyperbolic wave-propagation problems with AMR. It is the basis for the GeoClaw package used for modeling tsunamis, storm surge, and floods. It has also been used for coupled seismic-tsunami simulations. Recently, we have accelerated the library with GPUs and observe a speed-up of 2.5 in a benchmark problem using AMR on a NVIDIA K20 GPU. Many functions that facilitate the execution of computing kernels are added. Customized and CPU thread-safe memory managers are designed to manage GPU and CPU memory pools, which is essential in eliminating overhead of memory allocation and de-allocation. A global reduction is conducted on each AMR grid patch for dynamically adjusting the time step. To avoid copying back fluxes at cell edges from the GPU memory to the CPU memory, the conservation fixes required between patches on different levels are also conducted on the GPU. Some of these kernels are merged into bigger kernels, which greatly reduces the overhead of launching CUDA kernels. +
Gravel-bedded rivers are shaped during floods. Over time, certain floods do the most “geomorphic work” on river beds and banks by maximizing the product of sediment transport magnitude and frequency. The flood that does the most geomorphic work is known as the “formative flood” - it may not be the peak, but it reoccurs more often. However, it is unclear if this concept applies to freshet-dominated rivers in the Arctic. Gravel-bedded rivers in the Arctic continuous permafrost zone are occupied by river ice for 7-9 months each year. Freshet-dominated rivers in this region, like the Canning River, AK, receive a peak flood following snow melt while river ice can resist breakup for weeks, extending flood duration and magnitude. Still, the spring freshet occurs when hydraulic cross-sections are restricted by bedfast ice, limiting bed and bank exposure, but maximizing stage height. In contrast, summer floods generated by storm runoff occur when ice is absent, multiple times per year. Still, river bank and bed gravel is coarse and difficult to transport under low flows. We aim to investigate which flood maintains the Canning River’s hydraulic geometry, and more generally the hydraulic geometry of all ice-impacted, gravel-bedded rivers.
We explore if there is a formative flood for rivers that develop bedfast river ice with Basement V4. We focus on a 20 km reach of the Canning River in Arctic Alaska, where we monitored break-up period from 2021 through 2024. We use USGS data for realistic peak discharges, ArcticDEM for surface topography, and field measurements of river ice thickness to drive the model. We assess geomorphic significance of bedfast river ice with metrics for potential sediment transport along river bank toes - the main process eroding river banks in this setting.
Initial results suggest that even extreme floods are unlikely to fill the bankfull channel or erode river banks when the channel is free of ice. Conversely, we find that up to 1m of river ice during the spring freshet can induce bank erosion, under a constant discharge of 900 m3/s. For the Canning River, the spring freshet is likely the formative flood. Our findings also suggest that the recurrent development of bedfast river ice over winter leads to wider rivers.
Green Stormwater Infrastructure (GSI) plays a critical role in mitigating urban runoff, enhancing water quality, and promoting sustainable stormwater management. To ensure the long-term efficiency of these benefits, effective monitoring and maintenance of GSI is essential; however, current monitoring approaches are limited by costly and time-intensive in-person inspections. This study seeks to directly address these limitations through the integration of remote sensing and machine learning techniques to develop scalable, cost-effective monitoring solutions for GSI. To do so, we present a case study that utilizes high-resolution satellite (<30 cm) and drone imagery (2-4 cm) collected at GSI locations in Milwaukee, WI to extract key maintenance indicators such as vegetation health, sediment, and trash accumulation. Advanced machine learning (both supervised and unsupervised) algorithms, are employed to detect anomalies, assess performance, and automate condition assessment across GSI sites. The developed tools provide near-real-time insights for water resource managers, enabling proactive maintenance and data-driven decision making. This research demonstrates the potential of remote sensing and geospatial technologies to transform GSI monitoring practices and support resilient urban stormwater systems.
Keywords: Remote sensing, machine learning, near-real-time monitoring, green stormwater infrastructure +
Groundwater is a key water source, particularly in arid regions such as southern Africa, but direct monitoring is limited. Groundwater monitoring becomes increasingly valuable as rising water demand meets more frequent and severe droughts under climate change. This project explores the potential of vegetation indices, particularly NDVI, as a proxy for hydrological drought . We calculate monthly NDVI anomalies at a 250 m spatial resolution and 16 day composite temporal resolution from NASA’s MOD13Q1 dataset over a 2.5° × 2.5° area covering Cape Town in South Africa. These anomalies are plotted over time and compared with a recorded drought periods to assess vegetation response to water scarcity. The high spatiotemporal resolution of NDVI products and the satellite imagery from which they are derived makes them useful for understanding where hydrological drought may be occurring, even in the absence of groundwater wells. In ongoing work, we plan to simulate changing water table elevations at a monthly timescale using the Water Table Model (WTM) to determine how local and regional water table elevations respond to drought conditions. Further work will incorporate well data and lake surface elevations to validate model accuracy. This approach aims to develop an accessible method for groundwater monitoring and drought assessment in data-limited regions. +
Headwater catchments, primarily composed of hillslopes, valley heads, and colluvial valleys, are vital source areas that supply water, sediments, and nutrients to downstream river networks. Since their landscapes are generally characterized by steep hillslopes and confined, narrow valleys, mass movements dominate these landscapes. However, each catchment must transition at some point from hillslope-dominated to channel-dominated processes. While topographic relief has long been recognized as a key factor influencing these processes and explaining the positions of geomorphic units in headwater catchments, there remains a longstanding debate regarding how valley head locations, often approximated as channel head positions, vary with relief. Further, how does relief influence the lower boundary of headwater catchment, defined by the onset of the dominance of fluvial processes, remains understudied. To address these questions, we identified the valley head locations and the extents of headwater catchments in a drainage basin in South Korea, which spans a wide range of relief. We then quantified the sediment connectivity between valley heads and their upper hillslopes, as well as between headwater catchments and downstream channels, and examined their relationships with relief. Our results revealed an exponential relationship between relief and valley head location, indicating that valley heads shift downslope rapidly with increasing relief. Additionally, we found a positive, non-linear relationship between relief and the lower boundaries of colluvial channels, meaning that an increase in relief enlarges headwater catchment extent exponentially. Consequently, the headwater catchments in high-relief areas exhibit longer hillslopes, valley heads located farther downslope, extended colluvial valleys, and larger headwater catchments compared to those in low-relief areas. Moreover, the sediment connectivity between valley heads and upper hillslopes, as well as between headwater catchment and downstream channels, both were assessed and exhibited positive relationships with relief, respectively. Given the positive correlation of relief with valley head source area, this finding underscores how valley head infilling and subsequent valley head positioning are strongly affected by relief, which has not been fully captured by the stream power-based channel initiation theory that assumes a negative relationship between source area and slope. In addition, the positive relationship of relief with the connectivity of headwater catchment to downstream channels, along with the positive correlation of relief with the lower boundaries of colluvial channels, indicates that relief controls headwater catchment extent by influencing transport capacity-related attributes, including gradient and confinement of colluvial channels and, in turn, debris flow runout distance. Landscape evolution modeling experiments corroborate these findings. This study demonstrates how headwater catchment landscape scales with relief and highlights the fundamental role of topographic relief in controlling headwater catchment geomorphology. These findings advance our understanding of geomorphic processes in headwater catchment and provide practical guidance for managing mountainous environments across diverse topographic conditions.
High quality Digital Elevation Models (DEMs) do not exist in coastal wetlands prior to the widespread use of aerial LiDAR beginning in the early 2000's. This makes it difficult to develop models that capture the historical evolution of specific coastal marshes, creating a challenge in communications between the modeling community and wetland managers who seek to understand model outputs in the context of their experience, observations, history of management decisions, and perception of risk.
The project team is working with managers at four coastal wetlands to advance a method that will fill this data gap using historical remotely sensed imagery, historical in-situ observations, and machine learning. The team will compile Landsat imagery collected within one year of an existing high quality DEM. The suites of Landsat imagery will be processed to produce maps showing inundation frequency based on the Normalized Difference Water Index (NDWI), and these will be used as training data for a deep learning image segmentation model that relates inundation frequency with wetland elevation. The segmentation model will then be validated with observational data and applied to the period before DEMs are widely available but during which Landsat sensors are consistent with today’s standards (i.e. 1984 to the present). +
High-magnitude, sediment-charged flows can leave a lasting geomorphological legacy, but their short-term impacts remain poorly understood due to observational challenges in unstable landscapes. We combined satellite remote sensing and field observations with numerical hydro-geomorphic reconstruction using CAESAR-Lisflood to model the immediate fluvial response to the 7 February 2021 ice/rock avalanche-debris flow in Chamoli district, Uttarakhand. The event deposited 10.4 ± 1.6 Mm³ of sediment within the first 30 km, in places resetting the channel to a zero-state condition. Over the subsequent 12 months, 7.0 ± 1.5 Mm³ (~67%) of the initial deposit volume was removed, mostly by monsoon river flows, resulting in a median erosion rate of 2.3 ± 1.1 m a⁻¹. Our modelling implies the presence of erosion-aggradation sediment waves moving at 0.1 to 0.3 km day⁻¹ in this initial adjustment period. Thereafter we model an exponential decline in sediment evacuation over the next decade (assuming no additional, major system shocks) and analyse CL outputs to consider the potential for sediment waves to reactivate annually. The work highlights the rapid relaxation of a high-mountain fluvial system following extreme geomorphic disturbance, with implications for water quality and downstream hydropower infrastructure. +
Hourly precipitation for one historical (1991-2000) and two future periods (2031-2040 and 2071-2079) were generated using the Weather Research and Forecasting (WRF) Regional Climate Model (RCM). The climate simulations were conducted for the Southwest region of the United States using an hourly temporal and 10 km spatial resolution grid. The boundary forcing for the WRF model was developed by the Hadley Centre for Climate Prediction and Research/Met Office’s HadCM3 model with A2 emission scenario. The precipitation from the RCM-WRF model was bias-corrected using the observed data, and then used to quantify the impact of climate change on the magnitude and frequency of flood flow in the upper Santa Cruz River watershed (USCRW) in southern Arizona. The Computational Hydraulics and River Engineering two-dimensional (CHRE2D) model, a two-dimensional hydrodynamic and sediment transport model, was adapted for surface flow routing. The CHRE2D model was first calibrated using a storm event on July 15th, 1999, and then applied to the watershed for three selected periods. The simulated annual maximum discharges in two future periods were added to the historical records to obtain the flood frequency curve. Results indicate the peak discharges of 100-year, 200-year, and 500-year flood only increased slightly, and the increase is within the 90% confidence interval limits. Therefore, the flood magnitude and frequency curve will not change with the inclusion of projected future climate data for the study watershed. +
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Hurricanes are one of the most costly natural disasters impacting US coastal areas. Recent studies point towards an increase in damages caused by hurricanes, resulting from sea-level rise (SLR), possible hurricane intensification due to a warmer climate and increasing coastal populations. The SLR is one of the most significant factors of climate change that will impact coastal areas. Besides geometrical changes in coastal bays (i.e., deeper water depth and larger surface area), SLR is also expected to have substantial impacts on the patterns and process of coastal wetlands, thereby affecting surge generation and propagation inside the bays. We analyzed the impacts of SLR on hurricane storm surges, structural building damage, and population and businesses affected for coastal bays located on the Texas central coast. To evaluate the effects of SLR on surges, we considered its impacts on changes in land cover and bay geometry caused by SLR. The analyses were conducted using the hydrodynamic model ADCIRC and a wind and pressure field model (PBL) representing the physical properties of historical hurricane Bret and hypothetical storms. The effects of land cover change were represented within ADCIRC by the changes in the frictional drag at the sea bottom and changes in momentum transfer from the wind to the water column caused by vegetation losses. Simulations were performed using a high-resolution unstructured numerical mesh to study surge response in communities along the coastal bays of Texas. First, we evaluated the impacts of land cover changes due to SLR on the surge response. Second, we evaluated the impacts of neglecting land cover changes due to SLR on the surge response. Finally, we evaluated the overall effect of SLR on the mean maximum surge and the consequent extent of the flooded areas.
Although the overall impacts of SLR on surge (i.e.: water elevation above mean water level) are highly dependent on storm conditions and specific locations within the study area, we showed that the mean maximum surge (spatial average within each bay) increases with SLR. The overall mean maximum surge within the study area increased on average approximately 0.1 m (SLR of 0.5 m) and 0.7 m (SLR of 2.0 m). Simulations neglecting land cover changes due to SLR did significantly underestimate the expected structural damage for buildings. This difference increased with SLR and was affected by the storm meteorological conditions. Stronger and faster storms were associated with higher underestimation. Although considering land cover changes resulted in an overall damage increase, for SLR below 0.5 m, this increase was almost negligible. As a result, the land cover changes arising from SLR are important for damage estimation considering SLR scenarios over at least 0.5 m. For example, when considering a SLR of 0.6 m, based on the Intergovernmental Panel on Climate Change’s (2007) high emission scenario, we demonstrated a 10% increase in building structural damage. The assimilation of land cover changes is especially important when calculating expected damages from high SLR scenarios. If a SLR of 2.0 m is assumed, a 35% increase in the expected structural damage to buildings is estimated. In summary, the changes in coastal bay geometry and land cover caused by SLR play an important role in the resulting surge response. The variability of the surge response is also greatly affected by location and the characteristics of the storm.
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Hydrodynamic models that include a morphology component, such as Delft3D, are used widely for understanding the evolution of coastal systems, and they inform strategies for coastal protection and ecosystem preservation. Vegetation growth is often an important factor in coastal morphology, and even though some models may include vegetation via a roughness parameter, they do not have built-in capabilities for modeling dynamic vegetation processes. In recent years, a number of biophysical models with coupled dynamic vegetation processes have been developed, but few are open-source and compatible with non-proprietary software, hindering community development. This work presents a simple, open-source Python model that has been coupled dynamically with Delft3D FM to represent colonization, growth, and mortality of multiple species of vegetation in coastal environments. The model includes detailed vegetation processes with colonization and mortality as functions of hydromorphodynamic conditions and species-specific growth curves, resulting in spatial and temporal updates of friction effects in Delft3D FM. The code is designed to prioritize accessibility for all user levels and allow for smooth implementation of additional structures and processes. We present validation of the vegetation cover and density as compared to field data and highlight key differences between various techniques for converting vegetation stem heights to roughness parameters in the hydromorphodynamic model. +
Hydrologic connectivity can change as climate changes, seasonally, or even after a single rain event. Here, I assess the depression structure of the topography of the United States and determine its capacity to hold surface water in lakes. I provide results from a simulation indicating the pre-industrial water level in these depressions and the resulting degree of hydrologic connectivity. I then share results from a series of experimental simulations to modify water levels and the resulting hydrologic connectivity across the country. +
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IEDA (Integrated Earth Data Applications, www.iedadata.org) is a data facility funded through a contract with the US National Science Foundation to operate data systems and data services for solid earth geoscience data. There are many similarities between IEDA and its community of data producers and users and CSDMS and its community of model creators and users. IEDA has developed a comprehensive suite of data services that are designed to address the concerns and needs of investigators, especially researchers working in the 'Long Tail of Science' (Heidorn 2008). IEDA provides a data publication service, registering data sources (including models) with DOI to ensure their proper citation and attribution. IEDA works with publishers on advanced linkages between datasets in the IEDA repository and scientific online articles to facilitate access to the data, enhance their visibility, and augment their use and citation. IEDA also developed a comprehensive investigator support that includes tools, tutorials, and virtual or face-to-face workshops that guide and assist investigators with data management planning, data submission, and data documentation. A relationship between IEDA and CSDMS benefits the scientists from both communities by providing them with a broader range of tools and data services. +
