Advances in the understanding of physical, chemical, and biological processes influencing water quality, coupled with improvements in the collection and analysis of hydrologic data, provide opportunities for significant innovations in the manner and level with which watershed-scale processes may be explored and modeled. This paper provides a review of current trends in watershed modeling, including use of stochastic-based methods, distributed versus lumped parameter techniques, influence of data resolution and scalar issues, and the utilization of artificial intelligence (AI) as part of a data-driven approach to assist in watershed modeling efforts. Important findings and observed trends from this work include (i) use of AI techniques artificial neural networks (ANN), fuzzy logic (FL), and genetic algorithms (GA) to improve upon or replace traditional physically-based techniques which tend to be computationally expensive; (ii) limitations in scale-up of hydrological processes for watershed modeling; and (iii) the impacts of data resolution on watershed modeling capabilities. In addition, detailed discussions of individual watershed models and modeling systems with their features, limitations, and example applications are presented to demonstrate the wide variety of systems currently available for watershed management at multiple scales. A summary of these discussions is presented in tabular format for use by water resource managers and decision makers as a screening tool for selecting a watershed model for a specific purpose.

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Two distinctive, independently developed technologies, geographic information systems (GIS) and predictive water resource models, are being interfaced with varying degrees of sophistication in efforts to simultaneously examine spatial and temporal phenomena. Neither technology was initially developed to interact with the other, and as a result, multiple approaches to interface GIS with water resource models exist. Additionally, continued model enhancements and the development of graphical user interfaces (GUIs) have encouraged the development of application "suites" for evaluation and visualization of engineering problems. Currently, disparities in spatial scales, data accessibility, modeling software preferences, and computer resources availability prevent application of a universal interfacing approach. This paper provides a state-of-the-art critical review of current trends in interfacing GIS with predictive water resource models. Emphasis is placed on discussing limitations to efficient interfacing and potential future directions, including recommendations for overcoming many current challenges.(KEY TERMS: geographic information systems (GIS); modeling; surface water hydrology; water quality; water resources; simulation.)

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With increasing attention being given to vulnerable U.S. transportation system infrastructure, several different types of risk assessments are being performed. As this applies to marine transportation, one area of risk analysis is safe transport on the inland waterway network. Along the approximately 10,000 miles of navigable waterway in the United States reside major bridges, locks and dams, and population centers. Not only could an incident affect human health, property, and the ecology, but were it to make the network impassable, this could also cause a ripple effect throughout the U.S. economy. This article explores how advanced information technology can be used for identification and visualization of hazardous locations along U.S. navigable waterways. The initial intent of this research was to identify these locations using Geographic Information Systems technology. However, it soon became apparent that there were significant quality issues with the U.S. Coast Guard accident data. Consequently, visualization using satellite imagery (in programs such as Google Earth) proved valuable in validating accident locations and understanding how characteristics of each location may have contributed to accident causation and consequence. This article therefore also discusses how cost-effective technologies can be meaningfully applied to marine casualty data analysis and validation.

Historically, inland marine transportation has been one of the most efficient and reliable modes of commercial freight transportation. However, hazardous material shipments have become more common on inland waterways, creating concerns about the dissemination of shipment information to land-based emergency responders immediately following an incident. Marine transportation accidents have demonstrated that a spill involving hazardous materials near a major urban area can have devastating consequences. For these reasons a prototypical decision support system for inland marine transportation risk management has been developed. The system is designed to support real-time response as well as planning decisions such as risk resource allocation and evaluation of potential response strategies. It can be applied to barge accidents as well as counterterrorism planning activities. The system is based on the integration of geographic information systems (GIS), database management systems, Global Positioning Systems, and the Internet. In the event of an incident, this system enables en route responders to view incident details via an Internet GIS map service. The map service contains data that describe land use, population, and dispersion results for air and surface water at downstream intakes. To illustrate system capability, a recent accident is re-created to show how the response could have been improved with system access. A case study showing planning applications is also provided.

Data from archived automatic identification systems in the Paducah, Kentucky, region were analyzed to produce reliable trip data for inland waterway vessels. Because of confidentiality concerns, few options for finding such trip data exist; this lack of data affects the quality of risk calculations. A combination of geographic information systems, relational databases, custom programming, and data visualization tools was applied to extract meaningful vessel traffic information and to detect events occurring within ports and waterways. The geographic configuration of the Paducah port area made the generation of trip data more difficult. However, this problem was overcome by the categorization of all trips into general river movements and the calculation of the total number of towboat trips transiting in the area through river movements or engagement in fleeting, docking, or lockage operations. The data from automatic identification systems were discovered to be of high quality and capable of supporting many analyses. These analyses included waterway and port congestion, hotspot identification, accident reconstruction (and near-miss investigation), and the impact of extreme weather on port and waterway traffic.

The U.S. Maritime Administration made a strong commitment to short-sea shipping in 2010 in America's Marine Highway Program. There are few statistics about coastal vessel traffic, however, and even less is known about casualty rates in those waters because of the absence of trip data and the relatively poor quality of casualty data. Geographic information systems (GIS) are unique tools that enable greater visualization and understanding of complex problems. A methodology was used to adapt a GIS-based highway planning traffic assignment model for use in maritime risk assessment. The planning model routed 12 years of vessel entrance and clearance data through an international waterway network to estimate the number of trips traversing network links by any number of metrics, including year, ship type, flag of registry, and draft. The risk methodology deployed a 100-mi2 mesh (10 mi × 10 mi) over the entire United States and coastal waters to estimate the highest casualty rate (casualties per million vessel trips) and casualty frequency locations.