Goal
The goal of the Desktop Watersheds (DW) Integrated Project (IP) is to discover and advance the fundamental relations needed to predict landscape evolution and to model the coupling of ecosystem, landscape, and land-use dynamics. We pursue this goal through mechanistic field and laboratory studies, scaling analysis, and numerical modeling.

One central theme in DW is to exploit high resolution digital topographic data to advance hypotheses, guide field work, and test theories. Combining digital environmental data (topography, vegetation, precipitation, runoff, etc.) with research showing how local properties are controlled by drainage basin structure, we propose, for example, that such data can be used to make spatially explicit predictions about resource attributes (landslide locations, river bed grain size, algae abundance, food web interactions, etc.). Such predictions can then become null hypotheses to guide fieldwork, transforming it from simple data gathering or monitoring to hypothesis-testing.

Digital topographic data also offer the possibility of building watershed-scale numerical models of real landscapes to explore problems ranging from long time-scale controls on landscape evolution to short time-scale response of aquatic ecosystems to land-use change. Such modeling efforts are inhibited, however, by a lack of knowledge and quantitative expressions for many of the fundamental geomorphic and biotic processes. NCED Principal Investigators (PIs) and collaborators are closing this knowledge gap and introducing new theories and approaches, leading to discoveries about landscape evolution and to the construction of practical numerical models that will revolutionize land-use management and environmental forecasting. NCED’s unique breadth of researchers, experimental facilities, and field programs has enabled us to assume this leadership role.

Approach
High-resolution digital topography provides the common template for DW research. To unlock the potential of digital topography, we introduce new theories, propose new analytical approaches, conduct innovative experimental studies, and perform intensive field studies to discover, parameterize, and evaluate the fundamental driving equations. Our findings are made available to others to improve the watershed-scale numerical modeling being developed across the community. We use our current digital-terrain based models (prototype DW) to guide prioritization of research and to maintain a tight coupling between modeling and observation. In their simplest form, in which the topography is used to estimate such features as biological productivity, probable landslide location, channel morphology, or bed grain size, DW models can provide a relatively parameter-free prediction of landscape attributes useful in guiding field work and in applications such as planning timber harvests and stream restoration projects. The advances from the new research will lead to the ability to model cumulative watershed effects, controls on total maximum daily load levels of sediment, and to “game” management scenarios in order to optimize land-use activities for ecosystem protection and restoration.

Eight Major Accomplishments

1.  Development of new analytic methods for feature extraction, network controls on local particle size distributions, and nonlocal controls on sediment flux.

2.  New data, theory, and digital terrain based predictive methods for key physical processes: a) deep and shallow landsliding and b) bedrock incision by rivers, debris flows, and propagating waterfalls.

3.  First quantitative, comprehensive analysis of the influence of life on topography.

4.  Development of a “predictive mapping” approach for river ecosystems in which the distribution and abundance of organisms are explicitly linked to a channel network framework. Drainage area thresholds in several “ecological regimes” have been discovered.

5.  Identification of processes leading to co-organization of vegetation, topography, and hydrology.

6.  Discovery that species interactions can reverse expected trends in vegetation response to climate change, but that soil microbial abundance and diversity is surprisingly stable relative to above ground changes.

7.  Development and launch of our first DW model—Ripple—that estimates Coho salmon populations throughout a river network based on digital terrain data.

8.  Creation of a key environmental observatory at the Angelo Coast Range Reserve where collaborations by ecologists, hydrologists, engineers, atmospheric scientists, geologists, and others to exploit a powerful wireless backbone, an adaptable sensor network, high resolution light detection and ranging (LIDAR) data, and extensive ecological monitoring, leading to high levels of leveraged funding.