Uncertainty analysis of estuarine hydrodynamic models: an evaluation of input data uncertainty in the weeks bay estuary, alabama

Camacho, René A.; Martin, James L.; Díaz‐Ramirez, Jairo; McAnally, William H.; Rodriguez, Hugo; Suscy, Peter; Zhang, Song

doi:10.1016/j.apor.2014.04.004

Cited by 21 publications

(10 citation statements)

References 24 publications

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“…Fish River provides~73% of total freshwater input (~9 m 3 /s), while Magnolia River supplies the rest. Mean depth of Weeks Bay is about 1.5 m, with maximum local depths up to 7 m (see the deep area connecting Bon Secour Bay to Weeks Bay for example) [39]. The tidal regime is predominantly diurnal with tidal range of 0.4 m and currents exceeding 1 m/s at the mouth of the bay [40].…”

Section: Study Areamentioning

confidence: 99%

Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD

2019

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Emergent herbaceous wetlands are characterized by complex salt marsh ecosystems that play a key role in diverse coastal processes including carbon storage, nutrient cycling, flood attenuation and shoreline protection. Surface elevation characterization and spatiotemporal distribution of these ecosystems are commonly obtained from LiDAR measurements as this low-cost airborne technique has a wide range of applicability and usefulness in coastal environments. LiDAR techniques, despite significant advantages, show poor performance in generation of digital elevation models (DEMs) in tidal salt marshes due to large vertical errors. In this study, we present a methodology to (i) update emergent herbaceous wetlands (i.e., the ones delineated in the 2016 National Land Cover Database) to present-day conditions; and (ii) automate salt marsh elevation correction in estuarine systems. We integrate object-based image analysis and random forest technique with surface reflectance Landsat imagery to map three emergent U.S. wetlands in Weeks Bay, Alabama, Savannah Estuary, Georgia and Fire Island, New York. Conducting a hyperparameter tuning of random forest and following a hierarchical approach with three nomenclature levels for land cover classification, we are able to better map wetlands and improve overall accuracies in Weeks Bay (0.91), Savannah Estuary (0.97) and Fire Island (0.95). We then develop a tool in ArcGIS to automate salt marsh elevation correction. We use this ‘DEM-correction’ tool to modify an existing DEM (model input) with the calculated elevation correction over salt marsh regions. Our method and tool are validated with real-time kinematic elevation data and helps correct overestimated salt marsh elevation up to 0.50 m in the studied estuaries. The proposed tool can be easily adapted to different vegetation species in wetlands, and thus help provide accurate DEMs for flood inundation mapping in estuarine systems.

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Section: Study Areamentioning

confidence: 99%

Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD

2019

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“…A local sensitivity analysis was performed by evaluating the change in state variables following perturbation of each parameter from its original value (Soetaert and Petzoldt, 2010;Camacho et al, 2014a; RDCT (R Development Core Team), 2017). This approach is described as a First Order Variance Analysis that linearly propagates changes from model parameters to model predictions (Camacho et al, 2014a). Parameters were individually perturbed by a 50% increase of the original values (Table 1) and sensitivity S was estimated for each time step i given a change for parameter j as:…”

Section: Local Sensitivity Analysismentioning

confidence: 99%

Parameter sensitivity and identifiability for a biogeochemical model of hypoxia in the northern Gulf of Mexico

Beck

Lehrter

Lowe

et al. 2017

Ecological Modelling

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Local sensitivity analyses and identifiable parameter subsets were used to describe numerical constraints of a hypoxia model for bottom waters of the northern Gulf of Mexico. The sensitivity of state variables differed considerably with parameter changes, although most variables were responsive to changes in parameters that influenced planktonic growth rates and less sensitive to physical or chemical parameters. Variation in sensitivity had a direct correspondence with identifiability, such that only small subsets of the complete parameter set had unique effects on the model output. Selecting parameters by decreasing sensitivity demonstrated that only eight of 51 total parameters had a sufficiently unique effect on model output for accurate calibration. As a result, parameter selection heuristics were used to identify parameters for model calibration that depended on combined effects on output, relative sensitivity of each parameter, and ecological categories for the biogeochemical equations. The calibrated zero-dimensional (0-D) unit of the hypoxia model had improved fit to the observed data if sensitive phytoplankton parameters were included in an identifiable subset. Extension of results to a three-dimensional grid of the Gulf of Mexico showed that sensitive parameters for the 0-D model translated to non-trivial changes in the areal estimates of hypoxia.

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“…This, despite the important contributions of several researchers to identify adequate methods for these type of studies (e.g., Romanowicz et al ., ; Aronica et al ., ; Pappenberger et al ., ; Blumberg and Georgas, ; Rueda et al ., ; Warmink et al ., ). As an example, recent investigations on FOVA suggest the method can be used in estuarine hydrodynamic studies as an effective and less computer‐intensive alternative to traditional Monte Carlo Analysis, particularly if the models are subject to small errors in the input variables or parameters under analysis (Blumberg and Georgas, ; Camacho et al ., ). However, given that FOVA is based on linear assumptions regarding the response of the model to input uncertainties, it must be used with caution in highly nonlinear problems where these assumptions may be inappropriate (e.g., propagation of storm surges).…”

Section: Introductionmentioning

confidence: 97%

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

Camacho

Martin

McAnally

et al. 2015

J American Water Resour Assoc

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We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.

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Uncertainty analysis of estuarine hydrodynamic models: an evaluation of input data uncertainty in the weeks bay estuary, alabama

Cited by 21 publications

References 24 publications

Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD

Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD

Parameter sensitivity and identifiability for a biogeochemical model of hypoxia in the northern Gulf of Mexico

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

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