Characterization of Land Cover for Estimating Nitrogen and Salt Losses to Groundwater
Water quality parameters influence the usability of water supplies, with uses generally becoming more restricted as concentrations increase. Concentrations are determined by watershed characteristics such as land and water uses, hydrography, hydrology, and water treatment and discharge. Leaching of constituents like salt and nitrate through surface soil, and the influence of these losses on groundwater quality, is a focus of interest for producers and regulators in California?s Central Valley. Various models can be used to predict groundwater quality and to assess risk of salt and nitrate pollution. Fate and transport of constituents emanating from widely distributed sources are extremely sensitive to the character of these sources and their location in the landscape. The more detailed models therefore depend to some extent on accurate characterization and mapping of land cover, land management, and soils to estimate load and concentration of salt and nitrate moving through the soil. Land cover units were developed such that diverse uses generally similar in their influence on salt and nitrate budgets were grouped together, resulting in 32 distinct land cover classes to characterize the Central Valley landscape. Natural and managed parameters, such as percent impervious surfaces, plant communities, as well as fertilization, amendment, irrigation, and nitrogen removal rates were characterized for each land cover class. These classes were intended to capture source and sink characteristics of urban, industrial, commercial, agricultural, and undeveloped land. Existing land cover mapping by the California Department of Water Resources and the U.S. Geological Survey were employed and re-coded into the 32 classes. Supplementary information collected under the auspices of a developing dairy waste management regulatory program (dairy locations, herd sizes, waste loads, and land application practices), by the California Department of Food and Agriculture (fertilizer and amendment use), and by county agricultural commissioners (annual production reports) were used to refine the land cover mapping and to refine management parameters for each class. The resulting data set greatly enhances the level of detail relative to land cover mapping previously employed in similar modeling efforts. Further refinements will be prioritized based on results of sensitivity analysis. These include validation of surface parameters for land cover classes, and characterization of other livestock land cover classes at a level of detail similar to current dairy classes. Incorporating more detailed soils data based on SSURGO (NRCS) mapping data is also being evaluated.