Quantifying the Effects of Data Integration Algorithms on the Outcomes of a Subsurface - Land Surface Processes Model

TitleQuantifying the Effects of Data Integration Algorithms on the Outcomes of a Subsurface - Land Surface Processes Model
Publication TypeJournal Article
Year of Publication2014
AuthorsShen, Chaopeng, Niu Jie, and Fang Kuai
JournalEnvironmental Modelling & Software
Pages146-161
Date Published09/2014
Abstract / Summary

Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in the integration of various data sources on outcomes of the model Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, the represented land use percentages are much closer to the raw dataset, and lead to a 26% difference in carbon flux from that of the traditional dominant classes method. River bed elevations extracted using a novel algorithm agree well with the groundwater table and significantly increase baseflow contribution to streams relative to a coarse-DEM-based model. The inclusion of additional information in the soil pedotransfer functions drastically shifts ET, Net Primary Production and recharge. These results indicate that judicious treatment of input data  has strong impacts on hydrologic and ecosystem fluxes. We emphasize the need to report details of data integration procedures.

URLhttp://www.sciencedirect.com/science/article/pii/S1364815214001376
DOI10.1016/j.envsoft.2014.05.006
Journal: Environmental Modelling & Software
Year of Publication: 2014
Pages: 146-161
Date Published: 09/2014

Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in the integration of various data sources on outcomes of the model Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, the represented land use percentages are much closer to the raw dataset, and lead to a 26% difference in carbon flux from that of the traditional dominant classes method. River bed elevations extracted using a novel algorithm agree well with the groundwater table and significantly increase baseflow contribution to streams relative to a coarse-DEM-based model. The inclusion of additional information in the soil pedotransfer functions drastically shifts ET, Net Primary Production and recharge. These results indicate that judicious treatment of input data  has strong impacts on hydrologic and ecosystem fluxes. We emphasize the need to report details of data integration procedures.

DOI: 10.1016/j.envsoft.2014.05.006
Citation:
Shen, C, J Niu, and K Fang.  2014.  "Quantifying the Effects of Data Integration Algorithms on the Outcomes of a Subsurface - Land Surface Processes Model."  Environmental Modelling & Software 146-161.  https://doi.org/10.1016/j.envsoft.2014.05.006.