Property:CSDMS meeting abstract
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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.
Delivery of large blocks of rock from steepened hillslopes to incising river channels inhibits river incision and strongly influences the river longitudinal profile. We use a model of bedrock channel reach evolution to explore the implications of hillslope block delivery for erosion rate-slope scaling. We show that incorporating hillslope block delivery results in steeper channels at most erosion rates, but that blocks are ineffective at steepening channels with very high erosion rates because their residence time in the channel is too short. Our results indicate that the complex processes of block delivery, transport, degradation, and erosion inhibition may be parameterized in the simple shear stress/stream power framework with simple erosion-rate-dependent threshold rules. Finally, we investigate the effects of blocks on channel evolution for different scenarios of hydrologic variability, and compare and contrast our results with those of more common stochastic-threshold channel incision models. We show that hillslope-derived blocks have a different signature in erosion rate-slope space than the effects of constant erosion thresholds, and propose characteristic scaling that could be observed in the field to provide evidence for the influence of hillslope-channel coupling on landscape form. +
Delta environments, on which over half a billion people live worldwide, are sustained by sediment delivery. Factors such as subsidence and sea level rise cause deltas to sink relative to sea level if adequate sediment is not delivered to and retained on their surfaces, resulting in flooding, land degradation and loss, which endangers anthropogenic activities and populations. The future of fluvial sediment fluxes, a key mechanism for sediment delivery to deltas, is uncertain due to complex environmental changes which are predicted to occur during the coming decades. Fluvial sediment fluxes under environmental changes were investigated to assess the global sustainability of delta environments under potential future scenarios up to 2100. Climate change, reservoir construction, and population and GDP (as proxies for other anthropogenic influences) change datasets were used to drive the catchment numerical model WBMsed, which was used to investigate the effects of these environmental changes on fluvial sediment delivery. This method produced fluvial sediment fluxes under 12 scenarios of climate and socioeconomic change which are used to assess the future sustainability of 47 deltas, although the approach can be applied to deltas, rivers, and coastal systems worldwide. The results suggest that fluvial sediment delivery to most deltas will decrease throughout the 21st century, primarily due to anthropogenic activities. These deltas will likely become unsustainable environments, if they are not already, unless catchment management plans are drastically altered. +
Delta integrity is a function of adequate fluvial sediment supply since the form at the shoreline is the result interaction between fluvial and basinal processes. Globally, sediment supply to river deltas has been on the decline. Specifically, present sediment supply to the Niger Delta is less than what is required for a sustained growth. Anthropogenic intervention in the lower Niger Basin and within the delta is the main control of the decrease in sediment supply. Changes in shore form is a main consequence of shifting volume of sediment supply in the Niger Delta region. This study attempts a morphodynamic analysis of shoreline changes along the Niger Delta using recent high resolution remote sensing techniques within the Google Earth Engine Platform. Attempt will also be made to characterise the spatial or temporal variability in shoreline dynamics along the Niger Delta with a view to establish the drivers of change. The study will also attempt to model the future evolution of the Niger Delta given present forcing scenarios. The research is within the overall framework of ensuring a sustainable development within the Niger Delta coastal zone in order to preserve its huge economic and ecological potentials for future generation. +
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Delta morphology is traditionally explained by differences in fluvial energy and wave and tidal energy. However, deltas influenced by similar ratios of river to marine energy can display strikingly different morphologies. Other variables, such as grain size of the sediment load delivered to the delta, influence delta morphology, but these models are largely qualitative leaving many questions unanswered. To better understand how grain size modifies deltaic processes and morphologies we conducted 33 numerical modeling experiments using the morphodynamic physics-based model Delft3D and quantified the effects produced by different grain sizes. In these 33 runs we change the median (0.01 – 1 mm), standard deviation (0.1 – 3 φ), and skewness (-0.7 – 0.7) of the incoming grain-size distribution. The model setup includes a river carrying constant discharge entering a standing body of water devoid of tides, waves, and sea-level change. The results show that delta morphology undergoes a transition as median grain size and standard deviation increase while changing skewness has little effect. At low median grain size and standard deviation, deltas have elongate planform morphologies with sinuous shorelines characterized by shallow topset gradients ranging from 1 x 10<sup>-4</sup> to 3 x 10<sup>-4</sup>, and 1 - 8 stable active channels. At high median grain size and standard deviation, deltas transition to semi-circular planform morphologies with smooth shorelines characterized by steeper topset gradients ranging from 1 x 10<sup>-3</sup> to 2 x 10<sup>-3</sup>, and 14 - 16 mobile channels. The change in delta morphology can be morphodynamically linked to changes in grain size. As grain size increases delta morphology transitions from elongate to semi-circular because the average topset gradient increases. For a given set of flow conditions, larger grain sizes require a steeper topset gradient to mobilize and transport. The average topset gradient reaches a dynamic equilibrium through time. This requires that, per unit length of seaward progradation, deltas with steeper gradients have higher vertical sedimentation rates. Higher sedimentation rates, in turn, perch the channel above the surrounding floodplain (so-called ‘super-elevation’) resulting in unstable channels that frequently avulse and create periods of overbank flow. That overbank flow is more erosive because the steeper gradient causes higher shear stresses on the floodplain, which creates more channels. More channels reduce the average water and sediment discharge at a given channel mouth, which creates time scales for mouth bar formation in coarse-grained deltas that are longer than the avulsion time scale. This effectively suppresses the process of bifurcation around river mouth bars in coarse-grained deltas, which in turn creates semi-circular morphologies with smooth shorelines as channels avulse across the topset. On the other hand, finest-grained (i.e. mud) deltas have low topset gradients and fewer channels. The high water and sediment discharge per channel, coupled with the slow settling velocity of mud, advects the sediment far from channel mouths, which in turn creates mouth bar growth and avulsion time scales that are longer than the delta life. This creates an elongate delta as stable channels prograde basinward. Deltas with intermediate grain sizes have nearly equal avulsion and bifurcation time scales, creating roughly semi-circular shapes but with significant shoreline roughness where mouth bars form.
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Delta shoreline structure has long been hypothesized to encode information on the relative influence of fluvial, wave, and tidal processes on delta formation and evolution. However analyses and comparisons of deltaic shorelines have typically been qualitative or utilized relatively coarse quantitative metrics. We ask whether robust quantification of shoreline structure would enable mapping of deltas to a physically-based space in which the relative influence of the different processes could be compared, as has recently been done using a sediment flux budget approach. To explore this question, we analyze Landsat-derived shorelines from more than 50 deltas across the globe. Since the shorelines exhibit variability on scales ranging from tens of meters to tens of kilometers, we propose a multiscale characterization of shoreline structure by mapping the shorelines to a univariate series, through a macro-scale convexity-informed framework, and using localized multi-resolution analysis via wavelets to quantify shoreline variability across a range of spatial scales within and across deltas. Specifically, we focus on the relative energy contributed by meso-scale features (river mouths) and small-scale (less than 1 km scale features). We find that distinct classes of deltas naturally emerge in that metric space, which we attribute to the different processes driving the sources and sinks of sediment in these systems. The analysis suggests the potential towards a quantitative, process-based classification of delta morphology via multi-scale analysis of shoreline structure. +
Deltaic, estuarine, and barrier coasts are experiencing unprecedentedly fast rates of morphological changes, which constitute a threat to people, infrastructures, and economies. Predicting these changes in the future could help to develop cost-efficient mitigation and adaptation plans. Here I present recent progresses in simulating large scale and long term coastal evolution using a new morphodynamic-oriented model. Through opportune simplifications the model simulates tides, surges (hurricanes), wind waves, swells, sand/mud/organic sediment, stratigraphy, and vegetation in a numerically-efficient way. The model reproduces the self-organization of barrier islands and the formation of marshes in the backbarrier/estuarine region. The model emphasizes how mud supply is a major driver for the long-term retreat of marshes. The model also simulates how riverine inputs into backbarrier basins – for example through man-made river diversions – can reduce both marsh edge erosion and barrier island retreat. +
Deltas are home to approximately 7% of global population and play a crucial role in regional food security owing to the favorable conditions for agriculture. As a result, these areas are often heavily irrigated as humans strive to use the local water resource to maximise production. This study aims to incorporate irrigation practices into the LISFLOOD-FP hydrodynamic model to determine the impact of irrigation on the flood dynamics of the Mekong Delta, one of the most intensively irrigated deltas. Irrigation data is based on global databases of irrigation area, crop type and crop calendars, supplemented with local information allowing for this approach to be used across irrigated areas around the world. This study therefore builds upon the localized estimates of flood storage capacity of paddy fields through the region and generates a new estimate across a wider area that is subsequently used to assess the impact on the hydrodynamics and flood inundation pattern. It is envisaged this approach can be used for future analysis of the impact of the changing irrigation practices of the Mekong Delta. +
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Deltas are the important interface between continents and oceans, providing home to over half a billion people. The unique environment supports a wide variety of diverse ecosystems and is highly susceptible to a broad spectrum of interacting forces. Therefore it is critical to understand its current and future changes, especially against the background of climate change and human impact, something that could be explored by studying its historical evolution process. Delta evolution is mainly governed by: a) sediment load supply from its contributing river, and 2) ocean dynamics (e.g. waves, tides). Fluvial sediment supply to a delta fluctuates over time either e.g. due to shifts in climate or, on shorter time scales, due to human interference (e.g. deforestation which could increase sediment supply or the emplacement of dams and reservoirs that reduces the sediment supply). How does this affect the morphology of a delta? Waves interact on deltas by dispersing fluvial sediment, reshaping its shoreline, how will it be illustrated in delta’s shape and morphology?
To study this, we explored hypothetical delta evolution scenarios given the following boundary conditions: a medium size upstream drainage basin (~80,000km2) with, as base case, a typical Mediterranean climate. The analysis is done through coupling two numerical models, HydroTrend and CEM. HydroTrend, a climate-driven hydrological transport model, is applied to replicate freshwater and sediment flux to the delta, and subsequently a coastline evolution model (CEM) is applied to simulate the according changes in the delta’s coastline morphology. A component-modeling tool (CMT) developed by CSDMS, is used to couple the models for this study. Several scenarios are considered that take into account: 1) stepwise increasing fluvial sediment supply, to the delta and 2) the release time of these stepwise sediment increases by changing the storm intensity for periods of time.
Preliminary model experiments will be presented demonstrating: 1) the capability of the CMT to couple models that represent different process domains and were developed and designed independently (i.e. without the intentions of such coupling), 2) the impact of changes in fluvial sediment on deltas.
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Deltas are threatened not only by climate and environmental changes (sea level rise, soil salinization, water shortages and erosion), but also by socioeconomic factors (high population density, intensive land use). These processes threaten people’s livelihoods and wellbeing, and as a result, there is a growing concern that significant environmental change induced migration might occur from deltaic areas. Migration, however, is already happening for economic, education and other reasons (e.g. livelihood change, marriage, planned relocation, etc.). Migration has multiple, interlinked drivers and depending on the perspective, can be considered as a positive or negative phenomenon.
The DECCMA project (Deltas, Vulnerability & Climate Change: Migration & Adaptation) studies migration as part of a suite of adaptation options available to the coastal populations in the Ganges delta in Bangladesh, the Mahanadi delta in India and the Volta delta in Ghana. It aims to develop a holistic framework of analysis that assesses the impact of climate and environmental change, economics and governance on the migration patterns of these areas. The project will test plausible future scenarios and evaluate them by considering a range of perspectives.
The dynamic Bayesian Network integrated model of the DECCMA project formally brings together the project elements in fully coupled, quantitative assessment framework. The presentation introduces the overall integration concept and describes the household decision-making component in detail. This component is based on a detailed household survey from delta migrant sending and receiving areas. We describe the model structure, and contrast the model setup and sensitivities across the three study areas. In doing so we illustrate some key causal relationships between changes in the environment, livelihoods and migration decision. The outputs of the integrative modelling is used to objectively evaluate the simulated environmental, social and economic changes for decision makers including the benefits and disadvantages of migration as an adaptation option.
Deltas exhibit spatially and temporally variable subsidence due, in part, to faulting that lowers the land surface over time, thereby converting subaerial land to open water. In light of expected billion-dollar investments globally to redirect sediment via channel diversions and thus restore delta land, it is crucial to understand whether discrete faulting-induced subsidence events drive distributary channel networks to reorganize. Here, we take inspiration from examples from two deltas of faulting with documented surface expression and with distinct flux-to-shoreline symmetries: the symmetric-flux Selenga River delta (Russia) and the asymmetric-flux Mississippi River delta (Louisiana, USA). Using simulations with the DeltaRCM numerical model resembling these deltaic landscapes, we examine distributary network reorganization to faulting-induced subsidence over a range of surface area and slip displacement. Our findings indicate that in a symmetric-flux delta system, the duration of fault surface expression is strongly and non-linearly related to displacement, because slip above a threshold length-scale drives wholesale channel network reorganization, whereas smaller displacement does not. In contrast, displacement is only weakly related to network reorganization in the asymmetric-flux simulations. In this environment, faults located in areas of the delta not maintaining a surface-water connection to the main channel at the time of the subsidence event do not instigate network reorganization. Moreover, for the range of surface area and slip displacement we examined, areas of faulting also do not significantly influence the distributary network at later times. Nevertheless, all faulting events in simulated deltas, with both symmetric and asymmetric flux, create accommodation space and so inhibit the construction of subaerial land to some degree. +
