HR: 14:55h
AN: H13M-05 [Abstracts]
TI: An Adaptive Multi-Scale Watershed Characterization Approach Utilizing Geoinformatics and Self-Organizing Maps
AU: * Coleman, A M
EM: Andre.Coleman@pnl.gov
AF: Pacific Northwest National Laboratory, P.O. Box 999
MSIN K9-33, Richland, WA 99352, United States
AU: Vail, L W
EM: Lance.Vail@pnl.gov
AF: Pacific Northwest National Laboratory, P.O. Box 999
MSIN K9-33, Richland, WA 99352, United States
AB:
Environmental management and research within heterogeneous watersheds provides challenges for consistent
evaluation and understanding of system functions. Assessing, mitigating, and managing diverse systems can
be difficult due to varying natural characteristics, large geographic areas, domestic and international political
boundaries, and varying degrees of spatial, temporal, and empirical data availability and quality. These
characteristics often allow only specific geographic areas and research/monitoring topics to be realized. Through
the development of data relationships and patterning, existing geographically specific studies and data can be
used to infer responses of other areas which have limited available data, but exhibit similar landscape and
watershed characteristics. The discussed approach aims to identify patterns from various data sources at a
variety of spatial and temporal scales, including terrain morphometry, hydrology, vegetation, land use, soils, and
climate and apply this data to active functions in the system such as hydrograph response. Automatic data
collection methods have dramatically increased with advances in technology over the past two decades. Despite
these advancements, it still remains difficult and expensive to monitor and understand all aspects of a system.
This method looks to utilize available and known information at a various watershed scales and apply these
observed values to other basins with less resolute or available data. The use of advanced geospatial analysis
and Artificial Neural Network (ANN) processes, particularly Self-Organizing Maps (SOMs), is proposed as a
method to discover landscape and watershed function patterns and similarities between areas in a watershed
that are not only spatially disjointed, but dissimilar in their available data. An adaptive and evolutionary capability
is presented, in which varying types of data can be fused to evaluate different management needs such as water
quality, aquatic habitat, groundwater recharge, land use, and what-if scenarios.
DE: 1813 Eco-hydrology
DE: 1819 Geographic Information Systems (GIS)
DE: 1839 Hydrologic scaling
DE: 1879 Watershed
SC: Hydrology [H]
MN: 2007 Fall Meeting