WATER RESOURCES RESEARCH GRANT PROPOSAL
Project ID: 2004NV67B
Title: Development of a Classification System for Natural Impervious Cover in the Lake Tahoe Basin
Project Type: Research
Focus Categories: Models, Water Quality, Geomorphological Processes
Keywords: Impervious cover, classification system, remote sensing, transport, loading, sediment, erosion
Start Date: 03/01/2004
End Date: 02/28/2005
Federal Funds: $19,126
Non-Federal Matching Funds: $38,145
Congressional District: Nevada 02
Desert Research Institute
Impervious surfaces are believed to be a major factor contributing to the
reduction of water quality/clarity of Lake Tahoe. Specifically, anthropogenic
impervious cover has received attention in the past two years and DRI has
just completed a basin-wide analysis of impervious surface (i.e., urban development).
Preliminary and as yet unreleased estimates of impervious surface basin-wide
indicate that very little of the basin actually has been developed. It could
be argued that the estimates of impervious cover are so low that development
could not be the sole or most significant contributor to Lake Tahoe's water
quality and clarity issues. There exists a tremendous amount of natural impervious
surface in the basin that is yet undocumented but which likely contributes
to "background" sediment and nutrient loads into the lake. The physical
attributes of natural impervious cover that determine its actual level of
permeability or attenuation vary with environmental conditions and geographic
location. Unlike anthropogenic impervious surface, natural impervious surfaces
occur in ranges, or degrees, of imperviousness. Weather and other forces act
upon these surfaces differently and as such similarly defined geologic surfaces
may be characterized differently from an impervious perspective. The nature
of this research is to investigate and offer a solution to a potentially important
piece of information, in the form of a classification scheme directly translatable
into a data set that may advance or refine existing means for assessing water
quality issues at Lake Tahoe. Knowledge of natural impervious surfaces will
assist in developing and calibrating TMDL models and to establish "background"
levels of runoff into the lake.
The idea of "soft impervious cover" has been put forth by TRPA. "Soft" cover, a subset of natural impervious cover, would include compacted areas that may not be entirely impermeable, but does not retain its natural permeability. Examples of soft cover would include gravel roads, gravel parking lots, compacted road base, and dirt roads. These cover types are comprised of a variety of materials, have different erosion potential, experience varying degrees of compaction, and also may or may not be or become completely impervious. These cover types are also different from hard natural rocks, loose DG over hard surfaces, or other surfaces that exist in the basin but are not used in the same manner as the above mentioned soft cover types. Nonetheless, both natural and soft impervious cover types clearly do not fall into the current classification employed in the basin. It is for this reason that a classification system, rather than a simple binary impervious/non-impervious schema, would be very useful.
The objectives of this research are to develop a classification system for natural impervious cover and determine the feasibility of generating a subsequent classified data set. This classification would be developed to be useful with existing models such as TMDL or other runoff process models in use or under development. The current classification system for impervious cover employed by TRPA is straightforward and includes only anthropogenic impermeable surfaces, not natural impervious surfaces such as granite or other rock surfaces, or compacted dirt or gravel surfaces. It is a binary classification. This project will accomplish two tasks. The first task is to develop a classification system for natural impervious cover for the Lake Tahoe Basin. The second task will be to determine the feasibility of delineating those categories spatially using existing 2002 Ikonos imagery. The result would be a classification system that can be used in TMDL modeling or other surface runoff process models and derived in part or in whole from existing high spatial resolution satellite imagery. The benefits of this data layer extend to all parties involved in research, management, and regulation in the basin.