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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-39
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
13 Feb 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
A Bayesian Approach to Infer Nitrogen Loading Rates from Crop and Landuse Types Surrounding Private Wells in the Central Valley, California
Katherine M. Ransom1, Andrew M. Bell2, Quinn E. Barber3, George Kourakos1, and Thomas Harter1 1Department of Land, Air, and Water Resources, University of California, Davis, USA
2Center for Watershed Sciences, University of California, Davis, USA
3Department of Renewable Resources, University of Alberta, Edmonton, Canada
Abstract. Nitrate contamination of alluvial aquifers in agricultural areas is a typical and major problem around the world. Nitrogen applied to crops, in the form of synthetic fertilizers or manure, in excess of plant uptake, largely leaches to groundwater in the form of nitrate, which is stable and highly mobile in oxygen-rich groundwaters. Increased awareness of the impact that excess nitrogen has had on groundwater and major health concerns about nitrate are prompting new regulations for farmers, e.g., in Europe and California, USA. This study is focused in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Though nitrogen loading rates for several crop and landuse types in the Central Valley have been estimated or measured in a handful of studies, nitrogen loading rates for specific crop or landuse types and their impact to groundwater quality remain largely unknown. Knowledge of crop or other landuse specific groundwater nitrate impact may aid future regulatory decisions. Nitrogen loading rates for specific crop or landuse types are expected to vary depending on individual landuse practices; and interactions with hydrogeologic parameters that may promote or inhibit nitrate leaching. In this study, we developed a novel Bayesian regression model that allowed us to estimate crop or other landuse-specific groundwater nitrogen loading rate probability distributions from surveys of private wells, each of which is likely impacted by more than one landuse. We used recent nitrate measurements from 2149 wells in the Central Valley. We estimated nitrogen loading rate distributions for 15 crop and landuse groups. These were shown to compare favorably with prior mass-balance estimates of loading rates based on agronomic estimates of nitrogen loading.

Citation: Ransom, K. M., Bell, A. M., Barber, Q. E., Kourakos, G., and Harter, T.: A Bayesian Approach to Infer Nitrogen Loading Rates from Crop and Landuse Types Surrounding Private Wells in the Central Valley, California, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-39, in review, 2017.
Katherine M. Ransom et al.
Katherine M. Ransom et al.
Katherine M. Ransom et al.

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We estimated a probability distribution of nitrogen loading rates for crop groups and compared our results to previous measurements and estimates. Most compared favorably, though mass balance estimates for several crop groups were higher than our model estimates. We attributed this to dilution with old groundwater or river water and denitrification. The information can provide a better assessment of landuse impacts to water quality absent information on farm nutrient management.
We estimated a probability distribution of nitrogen loading rates for crop groups and compared...
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