Location: Watershed Management Research2012 Annual Report
1a. Objectives (from AD-416):
1. Evaluate how snow deposition, energy balance and melt rates vary with site conditions such as elevation, topographic structure and vegetation cover. 2. Use high resolution remote sensing of terrain and vegetation structure to improve characterization of landscape features and condition. 3. Continure testing, evaluation, validation and improvement of meterologic, hydrologic and snoe models. 4. develop a modeling strategy for extending point and catchment-scale results to larger areas and regions.
1b. Approach (from AD-416):
This project will involve collaboration between ARS and University of Idaho scientists to: a. extend the surface energy balance and flux measurement efforts to evaluate growing season conditions; b. continue software testing and development for meteorological, hydrologic and snow modleing; and c. analysis of recently acquired OWLX LiDAR data, including development of specific products, and modification of simulation model structures.
3. Progress Report:
Completion of post-doctoral efforts on processing, development and assessment of datasets from Reynolds Creek Experimental Watershed (RCEW) resulted in four publications, one presenting a 25-year model forcing and validation data set, one on an initial 25-year simulation of snow deposition and melt, one on interannual variability of snow deposition and melt, and one on rangeland fluxes of water and energy. Final Light Detection and Ranging (LiDAR) data processing for RCEW has been completed, and development of base DEM and vegetation structure layers has been initiated. Software engineering provided final refinements to the snow and climate software system, and modified the NRCS-developed DTK utility so that hourly precipitation surfaces could be generated. This agreement was established in support of objective 3 of the in-house project, the goal being to expand integrated snow hydrology modeling to larger scales, coupling to belowground processes, including wind effects on precipitation input, and helping to incorporate snow-related processes into ARS watershed and management simulation models (e.g., SWAT, AnnAgNPS, KINEROS, AgES, AGWA, RHEM, ISNOBAL, PIHM, etc).