Skip to main content
ARS Home » Southeast Area » Oxford, Mississippi » National Sedimentation Laboratory » Watershed Physical Processes Research » Research » Publications at this Location » Publication #196628

Title: ANNAGNPS: ACCOUNTING FOR SNOWPACK, SNOWMELT, FREEZING AND THAWING OF SOIL

Author
item MOORE, DANIEL - USDA, NRCS
item Bingner, Ronald - Ron
item THEURER, FRED - USDA, NRCS

Submitted to: Federal Interagency Sedimentation Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 4/4/2006
Publication Date: 4/4/2006
Citation: Moore, D.S., Bingner, R.L., Theurer, F. 2006. ANNAGNPS: Accounting for snowpack, snowmelt, freezing and thawing of soil. In: Proceedings of the 8th Federal Interagency Sedimentation Conference, April 2-6, 2006, Reno, Nevada. CDROM.

Interpretive Summary: Significant erosion can occur from conditions that experience freezing and thawing cycles within soils. Evaluating the impact of these cycles can be very difficult without extensive monitoring. Simulation models can be applied to address the impact management practices may have on controlling erosion from these conditions. This paper describes the development of freezing and thawing soils within a watershed model. The purpose of modeling the freeze/thaw process down into the soil profile, rather than merely at the surface, is that many watersheds, for example the Palouse area in the state of Washington, experience significant soil loss when a moderate rainfall event occurs on a thin layer of unfrozen soil, overlaying a deeper frozen layer. There are many improvements into watershed assessments that including these capabilities into watershed models will provide. First, the model will better account for the lag in runoff from precipitation held for months in snowpack. Second, the model will more accurately predict the movement of water through the soil layers. And third, the model will better account for the increase in sheet and rill erosion due to runoff over soil layers that have experienced the freeze thaw process. This technology will provide a more robust watershed evaluation of conservation practices by NRCS when freezing and thawing conditions are present.

Technical Abstract: The watershed model, AnnAGNPS (Annualized AGricultural Non-Point Source Pollution model) has been enhanced by incorporating winter climate algorithms that account for frozen soil conditions. The model includes snowpack accumulation and melt, and the freeze/thaw process in the soil. Three major improvements can be expected for watersheds with significant winter climates. First, the model will better account for the lag in runoff from precipitation held for months in snowpack. Second, the model will more accurately predict the movement of water through the soil layers. And third, the model will better account for the increase in sheet and rill erosion due to runoff over soil layers that have experienced the freeze thaw process. These model improvements synthesize the science of the SHAW model (Simultaneous Heat and Water) by Gerald Flerchinger, Agricultural Research Service, Boise Idaho. SHAW, however, is a research model, while AnnAGNPS is a watershed model used by engineers and other practitioners for practical applications. In adapting SHAW, several modifications in computational procedures were made. For example, while the AnnAGNPS heat flux algorithm retains a simultaneous matrix solution of the temperature profile in soil and snow layers, the default timesteps and solution tolerances are larger than in SHAW. In addition, the first release of winter-enhanced AnnAGNPS will not include a full simultaneous matrix solution of soil moisture in thermal layers, as is done in SHAW. AnnAGNPS may adopt this in the future, but presently computes soil moisture in a more simplified manner. Also, SHAW includes a thermal layer for surface residue. This highly desired model component will be incorporated into AnnAGNPS in a future release. The AnnAGNPS winter enhancements improve modeling capability for many more geographic locations and will result in better sediment yield and pollutant loading estimates for water quality improvement in natural resources planning.