David F. Muñoz

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Verified

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• Ph.D.
• Assistant Professor at Virginia Tech

Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data‐rich sites with div...

Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in which concurrent or successive flood drivers synergize, producing larger impacts than those from individual drivers. However, CF modeling is subject to four main sources of uncertainty: (i) the initial condition, (ii) the forcing (or boundary) conditions, (iii)...

Compound flooding frequently threatens life and assets of people who live in low‐lying coastal regions. Co‐occurrence or sequence of extremes (e.g., high river discharge and extreme coastal water level) is of paramount importance as it may result in flood hazards with potential impacts larger than each extreme in isolation. Here, we use a coupled a...

Compound flood hazard assessment (CFHA) and modeling are prone to various sources of uncertainty including model structure, model parameters, input/forcing data, and those associated with nonlinear interactions among flood drivers. Data assimilation (DA) is an efficient method that helps quantify and reduce uncertainty in many hydrological applicat...

Compound flooding (CF), as a result of oceanic, hydrological, meteorological and anthropogenic drivers, is often studied with hydrodynamic models that combine either successive or concurrent processes to simulate inundation dynamics. In recent years, convolutional neural networks (CNNs) and data fusion (DF) techniques have emerged as effective alte...

In this study, we use feature engineering (FE) strategies to enhance the performance of machine learning (ML) models in forecasting runoff and peak runoff. We selected a 300-km² tropical Andean catchment, representative of rapid response systems where hourly runoff forecasting is particularly challenging. The selected FE strategies aim to integrate...

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relative...

Sea‐level rise (SLR) increasingly threatens coastal communities around the world. However, not all coastal communities are equally threatened, and realistic estimation of hazard is difficult. Understanding SLR impacts on extreme sea level is challenging due to interactions between multiple tidal and non‐tidal flood drivers. We here use global hourl...

Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in which concurrent or successive flood drivers synergize, producing larger impacts than those from individual drivers. CF modeling is yet subject to four main sources of uncertainty including (i) initial condition, (ii) forcing (or boundary) conditions, (iii) mod...

Introduction In complex mountain basins, hydrological forecasting poses a formidable challenge due to the intricacies of runoff generation processes and the limitations of available data. This study explores the enhancement of short-term runoff forecasting models through the utilization of long short-term memory (LSTM) networks. Methods To achieve...

Quantifying the role of sediment trapping by dams is important due to its control on fluvial and coastal geomorphology, aquatic ecology, water quality, and human water uses. Sediment trapping behind dams is a major source of bias in large‐scale hydrogeomorphic models, hindering robust analyses of anthropogenic influences on sediment fluxes in fresh...

Flooding is one of the most frequent and disastrous natural hazards triggered by extreme precipitation, high river runoff, hurricane storm surges, and compounding effects of various flood drivers. This study introduces a new multi-source remote sensing approach that leverages both multi-spectral optical imagery and the weather- and illumination-ind...

This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of Hydrometeorological and Hydrodynamic model simulations as well as their complex interactions and cascading effects (e.g., uncertainty propagation) in forecasting Compound Flooding (CF). Over t...

Maritime transportation is crucial to national economic development as it offers a low-cost, safe, and efficient alternative for movement of freight compared to its land or air counterparts. River and channel dredging protocols are often adopted in many ports and harbors of the world to meet the increasing demand for freight and ensure safe passage...

In the last decade, DEM-based classifiers based on height above nearest drainage (HAND) have been widely used for rapid flood hazard assessment, demonstrating satisfactory performance for inland floods. The main limitation is the high sensitivity of HAND to the topography, which degrades the accuracy of these methods in flat coastal regions. In add...

Deltas, estuaries, and wetlands are prone to frequent coastal flooding throughout the world. In addition, a large number of people in the United States have settled in these low-lying regions. Therefore, the ecological merit of wetlands for maintaining sustainable ecosystems highlights the importance of flood risk and hazard management in these reg...

Research Impact Statement: Delft3D-FM and 2D HEC-RAS can satisfactorily predict total water level depending on forcing conditions of extreme events. Simulations with Delft3D-FM can be faster than HEC-RAS by a factor of 6 to 10. ABSTRACT: Hydrodynamic models play a key role in simulating total water level (TWL), that is, a combination of river flow,...

Sea level rise and intense hurricane events make the East and Gulf Coasts of the United States increasingly vulnerable to flooding, which necessitates the development of computational models for accurate water level simulation in these areas to safeguard the coastal wellbeing. With this regard, a model framework for water level simulation over coas...

Current efforts on Deep Learning-based modeling are being put for solving real world problems with complex or even not-fully understood interactions between predictors and target variables. A special artificial neural network, the Long Short-Term Memory (LSTM) is a promising data-driven modeling approach for dynamic systems yet little has been expl...

Wetlands are endangered ecosystems that provide vital habitats for flora and fauna worldwide. They serve as water and carbon storage units regulating the global climate and water cycle, and act as natural barriers against storm-surge among other benefits. Long-term analyses are crucial to identify wetland cover change and support wetland protection...

Multi-criteria decision models have been recently implemented in many urban flood risk management schemes due to the increasing demand of low-cost and reliable solutions to prevent and control overflow from sewer systems. Based on specific targets, these decision models encompass multiple reward (or objective) functions according to socio-economic...

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...

National Water Center Innovators Program Summer Institute Report 2019