Compound flood modeling framework for surface–subsurface water interactions

Abstract

Abstract. Compound floods are an active area of research in which the complex interaction between pluvial, fluvial, coastal and groundwater flooding are analyzed. A number of studies have simulated the compound flooding impacts of precipitation, river discharge and storm surge variables with different numerical models and linking techniques. However, groundwater flooding is often neglected in flood risk assessments due to its sporadic frequency (as most regions have water tables sufficiently low that do not exacerbate flooding conditions), isolated impacts and considerably lower severity with respect to other types of flooding. This paper presents a physics-based, loosely coupled modeling framework using FLO-2D and MODFLOW-2005 that is capable of simulating surface–subsurface water interactions. FLO-2D, responsible for the surface hydrology and infiltration processes, transfers the infiltration volume as recharge to MODFLOW-2005 until the soil absorption capacity is exceeded, while MODFLOW-2005 returns exchange flow to the surface when the groundwater heads are higher than the surface depth. Three events characterized by short-duration intense precipitation, average tide levels and unusually high water table levels are used to assess the relevance of groundwater flooding in the Arch Creek Basin, a locality in North Miami particularly prone to flooding conditions. Due to limitations in water level observations, the model was calibrated based on properties that have experienced repetitive flooding losses and validated using image-based volunteer geographic information (VGI). Results suggest that groundwater-induced flooding is localized, and high groundwater heads influence pluvial flooding as the shallow water table undermines the soil infiltration capacity. Understanding groundwater flood risk is of particular interest to low-elevation coastal karst environments as the sudden emergence of the water table at ground surface can result in social disruption, adverse effects to essential services and damage to infrastructure. Further research should assess the exacerbated impacts of high tides and sea level rise on water tables under current and future climate projections.

Compound flooding from storm surges, rivers, and groundwater - Hydrodynamic modelling in a coastal catchment

“…The evaluation, however, suggested that the Ribe Flood model was able to replicate the flooded areas in most instances. There is, however, a need for more data to validate flooding events like this, especially ground truth data as e.g., drone images (Iqbal et al, 2023), citizen science e.g, photos (Peña et al, 2022) and piezometers (Neri-Flores et al, 2019). Additionally, future modelling efforts assessing flooding may seek to include the impact of cascading events like dam breaks or sediment deposits in drainage systems, though it would require information on location and timing of such events, which may not be readily available.…”

“…The study found that groundwater flooding was frequent on the floodplain. Peña et al (2022) simulated three flooding events with a coupled FLO-2D and MODFLOW model in Florida, USA. The events had high rainfall, water table and normal tide conditions to investigate the groundwater contribution.…”

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

“…Another advantage of process-based modeling is the ability to simulate complex WL dynamics such as backwater effects, tidal propagation, and overtopping in estuarine environments and urban settings that are usually ignored in point-based statistical analyses (Gallien et al, 2018;Kumbier et al, 2018;Leijnse et al, 2021). Also, processbased models can simulate complex CF dynamics in coastal to inland transition zones where hydrological and coastal processes determine flood extent, duration, and inundation depth (Bilskie et al, 2021;Jafarzadegan et al, 2023;Peña et al, 2022). Nevertheless, CF modeling is subject to uncertainties that interact and cascade in series throughout the modeling chain if they are not treated appropriately (Beven et al, 2005;Hasan Tanim and Goharian, 2021;Meresa et al, 2021).…”