A Network-Based Approach for Modeling Water, Sediment, and Nutrient Dynamics: Guiding Watershed Management Through a Systems Perspective

Abstract:

River networks form the arteries of a landscape, efficiently conveying water, sediment, and nutrients through a small fraction of a watershed. Yet this small fraction, which comprises the river network, dictates the watershed-scale response. Thus, by incorporating the dominant transport, storage, and transformation processes of a given flux into a river-network context, one can reveal the large-scale system functioning and the emergence of vulnerabilities and “hotspots” of change. We apply such a network-modeling framework by (1) decomposing the landscape into a connected network of elements including river channels, lakes, wetlands, etc., (2) spatially and temporally distributing inputs of water, sediment, and nutrients, and (3) tracking these inputs through individual landscape elements using process-based time delays and transformations. We suggest that landscapes are too complex to be modeled with fully distributed deterministic models that consider all the small-scale physics and interactions, due to large and unavoidable uncertainties. Besides, changes in climate, land use, and water management impose non-stationary conditions, and also nonlinearities in the system make it sensitive to small perturbations. Instead, the aim of this framework is to combine the system connectivity and most important processes in an effort to guide watershed-management decisions in a simple, physically-based way. We will describe the application of this framework to bed-material sediment, nitrogen, and streamflow. Specifically, we will use the framework to identify channel-migration and nitrate hotspots, and explore various management strategies for hotspot reduction. Also, we will show, through the network perspective offered by this framework, how simple questions about where to manage for peak-flow reduction can be answered. This framework offers a simple approach for gaining systems-level understanding that can be applied in route to more complex watershed modeling.