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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 of larger vessels. However, such protocols may have unintended adverse consequences on flood risks and functioning of coastal ecosystems and thereby compromising the valuable services they provide to society and the environment. This study analyzes the compound effects of dredging protocols under a range of terrestrial and coastal flood drivers, including the effects of sea level rise (SLR) on compound flood risk, vessel navigability, and coastal wetland inundation dynamics in Mobile Bay (MB), Alabama. We develop a set of hydrodynamic simulation scenarios for a range of river flow and coastal water level regimes, SLR projections, and dredging protocols designed by the U.S. Army Corps of Engineers. We show that channel dredging helps increase bottom (‘underkeel’) clearances by a factor of 3.33 under current mean sea level and from 4.20 to 4.60 under SLR projections. We find that both low and high water surface elevations (WSEs)more »could be detrimental, with low WSE (< -1.22 m) hindering safe navigation whereas high WSE (> 0.87 m) triggering minor to major flooding in the surrounding urban and wetland areas. Likewise, we identify complex inundation patterns emerging from nonlinear interactions of SLR, flood drivers, and dredging protocols, and additionally estimate probability density functions (PDFs) of wetland inundation. We show that changes in mean sea level due to SLR diminish any effects of channel dredging on wetland inundation dynamics and shift the PDFs beyond pre-established thresholds for moderate and major flooding. In light of our results, we recommend the need for integrated analyses that account for compound effects on vessel navigation and wetland inundation, and provide insights into environmental-friendly solutions for increasing cargo transportation.« less

Rates of poverty and economic inequality in rural Alabama are among the nation's highest and increasing agricultural productivity can provide a needed boost to these communities. The transition from rain-fed to irrigation-fed (RFtoIF) agriculture has significantly increased farm productivity and profitability elsewhere in the United States. Despite this potential to enhance stability and resilience in rural economies, irrigated cropland accounts for only 5% of Alabama's total cropland as numerous barriers remain to irrigation adoption. To encourage RFtoIF transition, it is imperative to identify the challenges faced by individual farmers at farm, community, and state levels. This study presents a multi-level mixed effects survival analysis to identify the physiographic, socioecological, and economic factors that influence the location and timing of irrigation adoption. We integrate spatiotemporal cropland and climatological data with field-verified locations of center-pivot irrigation systems, local physiographic characteristics, and parcel-level surface water access and average well depth. Access to surface water, costs to access groundwater, and soil characteristics were generally important influences in all regions, but regions were differentiated by the extent to which new irrigation was more responsive to social influences vs. precipitation and price trends. Our findings also highlighted the diversity of farming conditions across the state, whichmore »suggested that diverse policy tools are needed that acknowledge the varying motivations and constraints faced by Alabama's farmers.« less

The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodologicalmore »innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain.« less