Using Bayesian hierarchical models to better understand nitrate sources and sinks in agricultural watersheds.
Export coefficient models (ECMs) are often used to predict nutrient sources and sinks in watersheds because ECMs can flexibly incorporate processes and have minimal data requirements. However, ECMs do not quantify uncertainties in model structure, parameters, or predictions; nor do they account for spatial and temporal variability in land characteristics, weather, and management practices. We applied Bayesian hierarchical methods to address these problems in ECMs used to predict nitrate concentration in streams. We compared four model formulations, a basic ECM and three models with additional terms to represent competing hypotheses about the sources of error in ECMs and about spatial and temporal variability of coefficients: an ADditive Error Model (ADEM), a SpatioTemporal Parameter Model (STPM), and a Dynamic Parameter Model (DPM). The DPM incorporates a first-order random walk to represent spatial correlation among parameters and a dynamic linear model to accommodate temporal correlation. We tested the modeling approach in a proof of concept using watershed characteristics and nitrate export measurements from watersheds in the Coastal Plain physiographic province of the Chesapeake Bay drainage. Among the four models, the DPM was the best--it had the lowest mean error, explained the most variability (R2= 0.99), had the narrowest prediction intervals, and provided the most effective tradeoff between fit complexity (its deviance information criterion, DIC, was 45.6 units lower than any other model, indicating overwhelming support for the DPM). The superiority of the DPM supports its underlying hypothesis that the main source of error in ECMs is their failure to account for parameter variability rather than structural error. Analysis of the fitted DPM coefficients for cropland export and instream retention revealed some of the factors controlling nitrate concentration: cropland nitrate exports were positively related to stream flow and watershed average slope, while instream nitrate retention was positively correlated with nitrate concentration. By quantifying spatial and temporal variability in sources and sinks, the DPM provides new information to better target management actions to the most effective times and places. Given the wide use of ECMs as research and management tools, our approach can be broadly applied in other watersheds and to other materials.
Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China; Smithsonian Environmental Research Center, P.O. Box 28, 647 Contees Wharf Road, Edgewater, MD 21037-0028, USA. Electronic address: yqxia@issas.ac.cn.
Yongqiu
Y
Xia
Smithsonian Environmental Research Center, P.O. Box 28, 647 Contees Wharf Road, Edgewater, MD 21037-0028, USA.
Donald E
DE
Weller
Smithsonian Environmental Research Center, P.O. Box 28, 647 Contees Wharf Road, Edgewater, MD 21037-0028, USA.
Meghan N
MN
Williams
Smithsonian Environmental Research Center, P.O. Box 28, 647 Contees Wharf Road, Edgewater, MD 21037-0028, USA.
Thomas E
TE
Jordan
Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
Xiaoyuan
X
Yan
D000383:Agriculture; D001499:Bayes Theorem; D004784:Environmental Monitoring; D008962:Models, Theoretical; D009566:Nitrates / Q000737:chemistry
D009566:Nitrates
Bayesian hierarchical models; Export coefficient model; Nutrient sources and sinks; Temporal and spatial variation; Uncertainty