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Using Bayesian hierarchical models to better understand nitrate sources and sinks in agricultural watersheds

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dc.contributor.author Xia, Yongqiu en
dc.contributor.author Weller, Donald E. en
dc.contributor.author Williams, Meghan N. en
dc.contributor.author Jordan, Thomas E. en
dc.contributor.author Yan, Xiaoyuan en
dc.date.accessioned 2016-10-26T22:47:09Z
dc.date.available 2016-10-26T22:47:09Z
dc.date.issued 2016
dc.identifier.citation Xia, Yongqiu, Weller, Donald E., Williams, Meghan N., Jordan, Thomas E., and Yan, Xiaoyuan. 2016. "<a href="https://repository.si.edu/handle/10088/29649">Using Bayesian hierarchical models to better understand nitrate sources and sinks in agricultural watersheds</a>." <em>Water research</em>. 105:527&ndash;539. <a href="https://doi.org/10.1016/j.watres.2016.09.033">https://doi.org/10.1016/j.watres.2016.09.033</a> en
dc.identifier.issn 1879-2448
dc.identifier.uri https://hdl.handle.net/10088/29649
dc.description.abstract 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 (R(2) = 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. en
dc.relation.ispartof Water research en
dc.title Using Bayesian hierarchical models to better understand nitrate sources and sinks in agricultural watersheds en
dc.type Journal Article en
dc.identifier.srbnumber 140613
dc.identifier.doi 10.1016/j.watres.2016.09.033
rft.jtitle Water research
rft.volume 105
rft.spage 527
rft.epage 539
dc.description.SIUnit SERC en
dc.description.SIUnit Peer-reviewed en
dc.citation.spage 527
dc.citation.epage 539

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