State Water Resources Research Institute Program


Project ID: 2011MI187B
Title: Developing Advanced Modeling Tools for Red Cedar Watershed Planning
Project Type: Research
Start Date: 3/01/2011
End Date: 2/28/2012
Congressional District: 8
Focus Categories: Hydrology, Models, Nutrients
Keywords: watershed, integrated decision support system, water quality, and water quantity
Principal Investigators: Shi, Yi (Michigan State University); Mantha, Phanikumar S
Federal Funds: $ 15,000
Non-Federal Matching Funds: $ 37,989
Abstract:
Problem
The Red Cedar Watershed (HUC 04040004) is approximately 294,496 acres (461 sq. miles). The Red Cedar River starts in eastern Livingston County, in the south-central portion of the Lower Peninsula; it flows west across Ingham County. The river meets the Grand River near the Ingham and Eaton County border in downtown Lansing. The Red Cedar Watershed has diverse land use types: 59% agriculture/bare; 14 % residential/commercial/industrial (intensity developed); 13% forest/range; 14% wetland/water. The watershed has several TMDLs listed for E.coli and warm water fishery. It is also impaired due to Mecury and PCBs in both the water column and fish tissue. Based on water quality and land use data in the area, sediment and nutrients are additional potential pollutants of concern in the watershed. Potential sources include: agricultural practices, animal waste from cattle/pasture land, fertilizers, pesticides, stream channelization and failing septic systems.

Methods
(1) A mechanistic watershed model will be used to describe the flow of water in different hydrologic units within the Red Cedar watershed (rivers/streams, overland flow, etc.) taking into account the soils, geology, geomorphology, climate and land use. The PAWS (Process-based Adaptive Watershed Simulator) model is a well-tested model that was applied to several watersheds in Michigan (including the Grand River, Saginaw Bay and Clinton watersheds). The model is grid-based (as opposed to the HRU-based framework used in SWAT); therefore, it is well-suited for bacterial transport modeling, especially in situations when additional details are needed around sources or stream reaches where impairments are occurring. The PAWS model will be used to simulate the hydrology of the Red Cedar River watershed using a variety of data including stream flows, unsteady groundwater heads, soil moisture, soil temperature etc. This level of detailed comparisons with observed data is not usually attempted (only stream flows are used) in watershed modeling since many subsurface processes are complex and are tightly coupled to the 3D Richards equation for the vadose zone. Vegetation dynamics (e.g., extraction of water by plant roots in different land use settings) plays another important role in hydrology. All the above processes will be modeled before developing a bacterial transport model. E. coli modeling will be completed in the future if sufficient funds are available to support a graduate student. The additional information can be easily obtained by refining the spatial step sizes in areas of interest. For this research, we will apply the PAWS model to the Red Cedar River watershed to describe the observed streamflow data (USGS) collected as part of the monitoring study in all sub-watersheds of interest (12-digit HUC code) and identify priority subwatersheds using a combination of watershed modeling and monitoring data available. This work is based on Dr. Phanikumar Mantha's previous research on PAWS and E. Coli (Shen & Mantha, 2010, Liu & Mantha et. al., 2006) (2) A new DEM-based flow direction algorithm and associated surface water flow visualization routines will be developed to assist users visually identify potential pollutant sources and their movement over the landscape. This new algorithm will overcome the problems associated with traditional DEM-based D8 algorithm and reflect water flow on the topography more realistically. This work is based on Dr. Yi Shi's doctoral research (Shi, 2008).

Objectives
(1) Establish PAWS model for Red Cedar Watershed taking into account soil moisture states, vegetation dynamics, groundwater and soil temperature (2) Identify priority subwatersheds within Red Cedar Watershed using a combination of watershed modeling and monitoring data available, (3) Develop new DEM-based flow direction algorithm and associated visualization routine, (4) Apply new flow direction algorithm and its visualization routine to Red Cedar Watershed.

Progress/Completion Report, 2011, PDF

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