This section is composed of three basic subareas: combined sewer overflows (CSOs), sanitary sewer overflows (SSOs), and stormwater discharges. Much of the literature cited came from documents covering noteworthy global conferences (Bathala, 1996; Engineering Foundation, 1996; Hallam et al., 1996; Maxwell et al., 1996; Sieker and Verworn [Eds.], 1996; Society of Environmental Toxicology and Chemistry, 1996; U.S. EPA 1996a; Water Environment Federation, 1996a,b,c). In addition, the U.S. Environmental Protection Agency (U.S. EPA) published guidance documents (U.S. EPA, 1996,c,d,e), which are discussed in more detail in the subsection Regulatory Policies and Financial Aspects.

https://www.jstor.org/stable/pdfplus/29763265.pdf

Urban wet-weather flows

This paper presents key issues associated with uncertainty in flood inundation mapping. Currently flood inundation extent is represented as a deterministic map without consideration to the inherent uncertainties associated with various uncertain variables pre- cipitation, stream flow, topographic representation, modeling parameters and techniques, and geospatial operations that are used to produce it. Therefore, it is unknown how the uncertainties associated with topographic representation, flow prediction, hydraulic model, and inundation mapping techniques are transferred to the flood inundation map. In addition, the propagation of these individual uncer- tainties and how they affect the overall uncertainty in the final flood inundation map is not well understood. By using a sample data set for Strouds Creek, N.C., this paper highlights key uncertainties associated with flood inundation mapping. In addition, the idea of a probabilistic flood inundation map is articulated, and an integrated framework approach that will connect data, models, and uncertainty analysis techniques in producing probabilistic flood inundation maps is presented.

Uncertainty in Flood Inundation Mapping: Current Issues and Future Directions

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.

/pdf/self-organizing-linear-output-map-solo-an-artificial-neural-3jopwbse3h.pdf

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

A review of virus removal in wastewater treatment pond systems.

The Amerasian Arctic, covering northern Siberia (Russia), Arctic Alaska (USA), and the Canadian Arctic, extends over seven coastal Large Marine Ecosystems and makes up the seasonally ice-free part of FAO Statistical Area 18 (Arctic Sea). Historically, the harsh climate has limited marine fisheries (here excluding marine mammal hunting) to small-scale operations conducted mainly in estuaries and river deltas. Most of the catches have traditionally not been reported to FAO, with the result that total catch estimated here from 1950 to 2006 is 75 times higher than the sum of the catches reported for FAO 18 (Arctic). Catches were reconstructed from data on fishing communities in Canada and Alaska, and from various government and non-government sources for Siberia. Based on national data supplied to FAO since 1950, catches have been reported as 12,700 t in toto (reported on behalf of the former Soviet Union). This compares with our reconstructed catches of over 950,000 t, i.e., 770,000, 89,000, and 94,000 t by Russia, USA, and Canada, respectively for the same time period. The reconstructed catch (mainly whitefishes in Siberia, various salmonids in Alaska, and Arctic char in Canada) was 24,100 t year−1 in 1950, but declined to 10,200 t year−1 by the mid-2000s. Reasons for these trends are discussed by country, along with sources of uncertainty (particularly large for Siberia). Catches were allocated to Large Marine Ecosystems to present ecosystem-relevant baselines for the impact of fisheries on the Arctic, which can be expected to increase, as industrial fisheries move into a warming Arctic following the invasion of boreal species, unless countries apply precautionary ecosystem-based management approaches.

Arctic fisheries catches in Russia, USA, and Canada: baselines for neglected ecosystems

Proceedings of the 2001 World Water and Environmental Resources Congress, held in Orlando, Florida, May 20-24, 2001. Sponsored by Environmental and Water Resources Institute of ASCE. Cooperating organizations: American Public Works Association; American Water Resources Association; Elsevier Science Ltd.; Florida Water Environment Association; Interstate Council on Water Policy; National Water Resources Association; Universities Council on Water Resources; Water Environmental Federation; U.S. Army Corps of Engineers; U.S. Bureau of Reclamation; U.S. Environmental Protection Agency; U.S. Geological Survey. This collection contains 502 papers presenting the state of the practice in environmental and water resource issues. Topics include: environmental modeling; rivers and basins; dams and reservoirs; watersheds and wetlands; water and wastewater; scour and sedimentation; southeastern U.S. water issues; stormwater and best management practice; water distribution systems analysis; water treatment and remediation; flood management; water resource management; and water policies and regulations.

Don Phelps

Papers

Bridging the Gap : Meeting the World's Water and Environmental Resources Challenges