Long-term changes in precipitation and temperature have already

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Environmental Modeling

Long-term changes in precipitation and temperature have already impacted nitrogen loading Tristan Ballard, Eva Sinha, and Anna M Michalak Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06898 • Publication Date (Web): 12 Apr 2019 Downloaded from http://pubs.acs.org on April 14, 2019

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Environmental Science & Technology

Long-Term Changes in Precipitation and Temperature Have Already Impacted Nitrogen Loading Tristan C. Ballard*,†,‡, Eva Sinha†,‡,*, Anna M. Michalak†,‡ † Department of Earth System Science, Stanford University, Stanford, California, USA. ‡ Department of Global Ecology, Carnegie Institution for Science, Stanford, California, USA. * Now at: Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA.

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ABSTRACT. Increases in nitrogen loading over the past several decades have led to widespread

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water quality impairments across the U.S. Elevated awareness of the influence of climate

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variability on nitrogen loading has led to several studies investigating future climate change

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impacts on water quality. However, the question remains whether long-term climate impacts can

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already be observed in the historical record. Here, we quantify long-term trends in total nitrogen

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loading over the period 1987-2012 across the contiguous U.S. and attribute these trends to long-

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term changes in nitrogen inputs and climatic variables. We find that annual precipitation, extreme

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springtime precipitation, and springtime temperature are key drivers of historical loading trends in

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most regions. These decadal climate trends have led to both amplification and offsetting of loading

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trends expected from nitrogen inputs alone. We also find that rising temperatures have been

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insufficient to offset precipitation-induced loading increases, suggesting future increases in

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temperature under climate change may have limited potential to counteract loading increases

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expected as a result of anticipated changes in precipitation. This work demonstrates the important

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role of decadal climate variability in long-term nitrogen loading, emphasizing the need to consider

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climate change risks when designing and monitoring nutrient reduction programs.

ACS Paragon Plus Environment

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1. INTRODUCTION Nitrogen inputs to freshwater and coastal marine environments in the United States (U.S.) have

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increased markedly over the 21st century, resulting in widespread ecosystem and water quality

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impacts. For example, elevated nitrogen levels have dramatically increased primary productivity

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in coastal ecosystems, leading to massive ‘dead zones’ of low dissolved oxygen. Eutrophication

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in aquatic systems also drives harmful algal blooms capable of producing compounds toxic to

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both marine life and humans. Elevated awareness of the numerous water quality threats posed by

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increased nutrient loading has motivated the development and implementation of statewide and

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regional nutrient reduction programs across the U.S. targeting nitrogen and phosphorus loads

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over the next several decades.

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1

2

3

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Understanding how various factors affect nitrogen loading from multiyear to decadal

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timescales is therefore critical to designing effective nutrient reduction programs and estimating

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potential water quality impacts in the future. In addition to direct riverine input of nitrogen from

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anthropogenic and natural sources, previous work has demonstrated numerous factors driving

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nitrogen loading, such as land use, soil immobilization, and biological activity.

5,6

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A growing body of research has also recognized the important impact of climate, in the form of

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precipitation and temperature changes, on nitrogen loading and eutrophication. Precipitation

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increases can decrease the residence times of nitrogen within a watershed and therefore decrease

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losses from denitrification, storage, and crop utilization. Extreme precipitation events can also

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lead to higher runoff and extreme loading events. Higher temperatures can impact a variety of

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biotic and abiotic mechanisms related to the nitrogen cycle, for example by increasing

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denitrification rates, enhancing soil microbial activity and associated rates of nitrogen

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mineralization, and reducing discharge and soil moisture through increased evapotranspiration,

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9–11


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among others.

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contributing additional variability to loading.

8,12–15

Precipitation and temperature changes can also affect biological activity, 16,17

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While climate variability is known to affect interannual nitrogen loading variability, the

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impact of decadal climate variability on long-term loading trends remains largely unknown.

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Several studies have investigated how simulated future climate, particularly changing

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precipitation patterns and rising temperatures, would impact hydrology and overall nutrient

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loading at local, regional, and global scales. However, the question remains whether such

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changes can be observed in the historical record, with the potential to inform future modeling

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and management efforts. There is emerging evidence of decadal climate variability impacts on

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loading, with a recent study of catchments in France finding a link between historical loading

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and the North Atlantic Oscillation.

18,19

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21,22

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Here we explore the question of how decadal climate variability and nitrogen input trends have

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combined to influence nitrogen loading for the contiguous U.S. (CONUS). We do so through the

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use of a parsimonious empirical model for estimating and attributing long-term regional loading

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trends for the period 1987-2012 to their component climatic and nitrogen input drivers.

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Regional-scale estimates are particularly relevant to nutrient reduction programs, which often set

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targets at the statewide level. The modeling framework implemented here leverages earlier work

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by Sinha and Michalak that focused on spatial and interannual variability over a shorter period,

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but here we focus on long-term trends, incorporate newer calibration data with improved spatial

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and temporal coverage to develop an updated model, and attribute long-term trends to drivers

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related to climate variability and trends in nitrogen inputs. We use the trend attribution to assess

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how climatological factors and anthropogenic nitrogen inputs have combined to influence

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historical loading trends on decadal timescales. The results and modeling framework presented

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here are important for identifying drivers of historical loading trends for the CONUS, relating

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those drivers to future climate change impacts on water quality, and developing realistic

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timelines for nutrient reduction programs.

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2. METHODS

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We build on the modeling framework outlined in Sinha and Michalak to develop an empirical 18

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model for 1987-2012 annual total nitrogen (TN) flux, where TN refers to the sum of nitrate,

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nitrite, organic nitrogen, and ammonia. We use the same model selection procedure and

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covariates (Table S1), but extended to 2012. We also update the observational dataset used to fit

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the model by incorporating recently released 2012 nitrogen inputs data. This expanded dataset

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provides an opportunity to refine the Sinha and Michalak parameter estimates and conceivably

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support a more complex model.

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2.1 Observed TN Loading

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18

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We select catchments for the observational dataset from the USGS Geospatial Attributes of

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Gages for Evaluating Streamflow Version II (GAGES-II) database, a collection of catchments

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within the CONUS with stream gages maintained by the USGS. Annual riverine TN load [kg-N

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yr ] estimates are derived from daily discharge and TN measurements from the USGS National

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Water Information System using the weighted regressions of concentration on time, discharge,

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and season (WRTDS) method. Loads are then divided by catchment area to obtain annual TN

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flux estimates [kg-N km yr ]. We then use the natural-log-transform of TN flux to fit the model;

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for ease of interpretation, results presented herein are shown as TN flux estimates transformed

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into original units [kg-N km yr ].

83 84

26

-1

27

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-2

-1

-2

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We apply a variety of constraints to catchment observations to eliminate locations with limited data availability. First, to provide sufficient data for the WRTDS model, we restrict the analysis


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to GAGES-II catchments with at least 20 years of continuous, complete daily discharge estimates

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and at least 200 daily TN measurements over the 1987-2012 study period, resulting in 125

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catchments. For gages on the Cuyahoga, Maumee, Raisin, and Sandusky rivers, we substitute TN

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and discharge values with those from the Heidelberg University National Center for Water

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Quality Research (NCWQR) due to their more extensive data records. We further restrict the

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resulting WRTDS loads by removing yearly estimates based on fewer than six TN observations.

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For the remaining years, a median of 12 TN samples are available for developing annual TN flux

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estimates for the model build. We fit the model only to data in 1987, 1992, 1997, 2002, 2007,

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and 2012 to correspond with covariate data availability. We also eliminate two stations in

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Arizona from the analysis due to extreme covariate values with large influence on model

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estimates, resulting in 123 catchments (see SI). Finally, while we expect some level of spatial

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autocorrelation between nearby catchments, we did not remove catchments with overlapping or

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nested basin boundaries; applying the modeling framework to a subset of catchments without

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basin overlap and nesting led to similar model selection (see SI).

29

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The Sinha and Michalak model was built for the period 1987-2007 with 242 TN flux

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estimates across 70 catchments, and the addition of 2012 data almost doubles the available

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calibration data, with a total of 440 available annual TN flux estimates across 123 catchments

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(Figure 1). This represents an 82% increase in sample size and a 76% increase in sampling

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locations, allowing us to both reassess the Sinha and Michalak model and potentially support a

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more complex model. Moreover, these 123 catchments span an even wider variety of land use

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and climate regimes (Figure 1), reinforcing the generalizability of the resulting model.

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2.2. Model Covariates We include the same candidate model covariates as in Sinha and Michalak to represent the 18

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impact of climate, nitrogen inputs, and land use on TN loading. We obtain values of all

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covariates at the catchment scale by averaging values over the drainage basin extent. Here we

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provide a brief description of covariates considered in the modeling framework with a more

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thorough discussion available in Sinha and Michalak.

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We consider multiple covariates related to annual precipitation and temperature from 1987 to

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2012 based on 4km resolution daily data provided by the PRISM group. For precipitation, we

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consider covariates for total annual precipitation, total springtime precipitation, and thirteen

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covariates related to extreme precipitation (Table S1). For temperature, we consider average

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annual temperature and average springtime temperature.

117

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Annual nitrogen inputs to the land surface from 1987 to 2012 are represented by net

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anthropogenic nitrogen inputs (NANI), defined as the sum of fertilizer inputs, atmospheric

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deposition, agricultural nitrogen fixation, net food and feed import, and non-food crop export.

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Annual fertilizer data for both farm and non-farm uses is based on commercial fertilizer sales

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data. Annual atmospheric deposition data were obtained from the National Atmospheric

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Deposition Program (NADP) and aggregated following the methods of Ruddy et al. The

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remaining three terms were accessed through the NANI toolbox version 3.1, which estimates

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nitrogen inputs based on agricultural census data released every five years by the U.S.

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Department of Agriculture. Additionally, to estimate NANI in non-agricultural census years

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(e.g., 1988), we combine observed fertilizer and atmospheric deposition data with linearly-

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interpolated estimates of nitrogen fixation, net food and feed import, and non-food crop export,

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as in Sinha and Michalak. Both NANI and its inverse hyperbolic sine transform (f ) are

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33

34

35,36

18

15

NANI


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considered as candidate covariates. This transformation of NANI yields similar values to the

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natural-log-transform but is defined for both negative and positive values.

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Thirty-one covariates are defined based on land use, though the model selection procedure

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significantly reduces the set of potential covariate combinations. All land use covariates are time-

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invariant, in part because land use change over the CONUS has been relatively minimal during

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the study period. These variables therefore represent spatial variability in loading rather than

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spatiotemporal variability. One covariate indicates the percent catchment area with tile drainage,

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based on county-level tile drainage estimates. The remaining covariates are based on 30m

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resolution land use terms from the National Land Cover Database 2006 indicating the percent of

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catchment area categorized as either agriculture, urban, forest, wetlands, or shrubland and

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herbaceous (Table S2). Thirty combinations of these five land use categories are considered

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under the constraint that each land use category can appear in a combination at most once;

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consequently, a maximum of four combinations can be included in the model (Table S1).

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2.3 Model Selection and Application

37,38

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We identify a final model from a set of candidate linear regression models following the same

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model selection procedure as described in Sinha and Michalak. The model selection procedure

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employs an optimization algorithm over constrained sets of models, seeking the model in each

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set that minimizes the Bayesian Information Criterion (BIC). BIC is a metric for inter-model

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comparison that accounts for the tradeoff between model fit and model complexity, favoring

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more complex models only when there is a large relative gain in performance. We consider a

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BIC difference between models greater than two as evidence for a difference in model

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performance. To further reduce the likelihood of overfitting, we impose the constraint that

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candidate models may contain at most one covariate from each set of covariates related to

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extreme precipitation, temperature, and nitrogen inputs (Table S1).

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We apply the final model to HUC8 watersheds spanning the CONUS (Figure 1) to estimate

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annual TN fluxes in all years from 1987 to 2012. HUC8 watersheds delineated by the USGS are

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comparable in size to the GAGES-II catchments used in this analysis (Table S3).

156

2.4 Regional Trend Detection and Attribution

42

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Because the focus of this study is on regional changes, we aggregate annual HUC8 watershed

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loading estimates to the HUC2 scale (Figure 1) and then calculate linear trends over the period

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1987-2012. We aggregate HUC8 load estimates rather than applying the model directly to

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HUC2-averaged covariate inputs because the model is built on watersheds more comparable in

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size to HUC8 watersheds (Table S3). We note that the focus here is on trends in TN flux, and not

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on trends in TN concentration or specific forms of nitrogen, which may be different.

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Qualitatively, however, we expect TN trends to be indicative of trends in inorganic nitrogen flux

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during the study period, but less so for organic or particulate nitrogen. For example, while

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nitrate export in the Mississippi River Basin has increased dramatically in the past century,

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driving increases in TN export, dissolved organic nitrogen export has remained relatively stable,

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and particulate organic nitrogen export has decreased by approximately 50%.

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43–45

46

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In addition to estimating long-term trends in TN flux, we are also interested in understanding

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their primary drivers. For the CONUS, nitrogen inputs have been shown to be the primary

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drivers of spatial variability in U.S. loading, while precipitation primarily controls interannual

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variability. However, the relative roles of climatic versus land management drivers for long-

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term trends, rather than interannual variability, are not well understood.

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Several approaches for trend attribution have been applied in hydrologic settings (e.g. ), often 47,48

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customized to the dataset and model, and the general approach is to compare observed trends

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with trends simulated through removing—as best one can—the influence of individual variables

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or sets of variables on long-term trends. It is also generally preferable to maintain the influence

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of these variables on interannual variability to avoid producing artificially low estimates of

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natural variability in the simulated data.

179

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Here we approach trend attribution by detrending (subtracting the linear least squares trend)

180

selected covariates at the HUC8 scale and applying the model to this modified data to obtain

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simulated TN flux estimates. A similar method has been used to examine the effects of long-term

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precipitation and temperature trends on loading in a Finnish catchment. We then aggregate the

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simulated TN fluxes to the HUC2 scale as before and compare simulated and observed trends.

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This approach removes long-term trends in selected covariates but maintains their interannual

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variability, as desired. This enables us to compare the relative influence of individual covariates

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and groupings of covariates on overall TN flux trends.

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We do not formally attempt to attribute precipitation and temperature trends to anthropogenic

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climate change. This is, in part, due to the long period of record required to distinguish

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anthropogenic signals from natural decadal variability. Previous work has found evidence for an

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anthropogenic influence on increased temperatures across North America and on increased

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precipitation extremes in CONUS regions with already high levels of precipitation extremes.

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However, the attribution of regional precipitation trends to climate change remains an active area

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of research.

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3. RESULTS AND DISCUSSION

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3.1 Total Nitrogen Loading Model

196 197

The final selected model for representing annual TN flux (Q [kg-N km yr ]) includes six covariates: -2

-1

TN

ln (𝑄&' ) = 2.58 + 0.00160×𝑃344536 + 0.00247×𝑃9:9,.?@ + 0.466×𝑓':'B − 0.0585×𝑇9:9 − 0.0438×𝐿𝑈H − 0.0229×𝐿𝑈J,KL + ε (1) 198

where P

199

precipitation defined as total precipitation above the historical 95th percentile, where the

200

percentile distribution is defined for each month using days with at least 1mm precipitation for

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1981-2010, f

202

to May average temperature, LU is percent coverage of wetlands, and LU is percent coverage

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of forest, shrubland, and herbaceous. The error term ε is assumed to be normally distributed with

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a mean of zero and an estimated standard deviation of 0.67 [ln(kg-N km yr )]. Although a model

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with seven covariates has a slightly lower BIC score, we opt for the six-covariate model because

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the increase in explanatory power with the more complex model is minimal (Section 2.3).

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The selected covariates are identical to those selected in Sinha and Michalak, with the

annual

[mm] is total annual precipitation, P

MAM,p>0.95

NANI

[mm] is total March to May extreme

[sinh (kg-N km yr )] is the inverse hyperbolic sine of NANI, T -1

-2

-1

15

W

MAM

[°C] is March

F,SH

-2

-1

41

18

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exception of the addition of springtime temperature (T ). Overall, modeled patterns of spatial

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and temporal TN flux variability are similar to those in Sinha and Michalak, with the primary

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difference being a moderate decrease in estimated fluxes over the southern U.S. and an increase

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in the Souris-Red-Rainy region (Figure S1). The positive coefficient on nitrogen inputs (f )

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indicates the positive association between nitrogen inputs and loading, and as in Sinha and

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Michalak, nitrogen inputs are the dominant control of spatial variability in loading (Figure S1).

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The positive coefficients on annual precipitation (P ) and springtime extreme precipitation

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(P

MAM

18

NANI

18

annual

) reflect the positive association with surface runoff and flushing of nitrogen from soils.

7,52–54

MAM,p>0.95


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For example, a study of sixteen watersheds in the northeastern U.S. found that a larger fraction

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of nitrogen inputs were exported in years with high precipitation. The negative coefficient on

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springtime temperature (T ) is consistent with the hypothesis that rising temperatures reduce

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loading through a variety of mechanisms, for example via enhanced denitrification and reduction

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of discharge from increases in evapotranspiration, among others.

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influenced by rising temperatures may lead to both increasing or decreasing nitrogen loads, the

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observed net negative effect of rising temperatures on loading is consistent with previous work

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documenting higher rates of nitrogen removal at lower latitudes. Rising springtime temperatures

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may also increase crop nitrogen uptake and lengthen the growing season; however, we expect

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this mechanism to be accounted for by reductions in the net food and feed import component of

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NANI. The negative coefficients on the two land use terms (LU , LU ) suggest that higher

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proportions of wetlands, forest, shrubland, and herbaceous land cover are associated with lower

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overall loading, all else being equal.

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MAM

8,12–15

While individual processes

12

W

F,SH

The model explains a considerable amount (74%) of the variability in ln (𝑄&' ) (Figure S2a,b).

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The model also performs well on a test set consisting of observations at the same GAGES-II

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catchments but for non-agricultural census years, explaining 73% of the variability in ln (𝑄&' )

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(Figure S2c,d). In the test set case, NANI is estimated with a combination of observed and

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interpolated components (Section 2.3). Additionally, analysis of residuals suggests no major

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violations of linear model assumptions aside from the tendency of the model to overestimate

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ln (𝑄&' ) for very low values of ln (𝑄&' ) and conversely underestimate ln (𝑄&' ) for very high

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values of ln (𝑄&' ) (Figures S2 and S3). This common behavior in the tails of the distribution is

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not overly concerning since it suggests the model is conservative in its estimates of extremes,

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and the focus of this study is on long-term changes in average, rather than extreme, TN flux.


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The GAGES-II catchments data used to build the model is representative of environmental

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conditions across the majority of the contiguous U.S., supporting application of the model at the

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HUC8 scale (Figure S4). The primary exception is in the southwest U.S., where average NANI

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or average annual precipitation in some HUC8 watersheds is lower than what is observed in the

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GAGES-II training dataset (Figure S4). While TN flux estimates in these areas may therefore be

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less robust, this uncertainty is concentrated in HUC8 watersheds with low mean estimated TN

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flux (Figure S1) and is therefore of lesser concern for water quality impacts.

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3.2 Regional Long-Term TN Flux Trends

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Aggregation of modeled loads to the HUC2 scale enables us to estimate multidecadal regional

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TN flux trends across the entire CONUS. While there is variation in trends at the HUC8 level

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within each HUC2 region (Figure 2), aggregating loads to the HUC2 level averages out some of

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this local variability and provides a clearer picture of large-scale trends and their drivers (Figure

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3a, S5). Given the relatively limited timespan of the available observations (26 years) and large

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interannual variability (Figure S5), we focus on mapping the observed increase or decrease in

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loading onto observed variability in precipitation, temperature, and NANI, rather than on the

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statistical significance of the trend. We therefore make no statements about whether the observed

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trends are likely to be representative of longer-term underlying shifts in the system.

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Additionally, while we present least squares trends here, we also estimated trends using the

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nonparametric Theil-Sen trend estimator, which requires fewer assumptions about the underlying

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data, and find similar overall patterns of regional change (Figure S6a).

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We find minimal long-term changes throughout much of the southwest U.S. during the study

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period and focus the remainder of our discussion on HUC2 regions with larger magnitude trends.


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We find that TN fluxes have increased over the northern and western U.S. over the 1987-2012

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study period. Areas with the most pronounced increases (Figure 2) coincide with HUC8

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watersheds with high mean TN flux (Figure S1a), suggesting an exacerbation of historical

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conditions. Though some of the regional trends are relatively small in magnitude relative to

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interannual variability (Figure S5), the extent of regional TN flux increases highlights the

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potential for water quality impairment across the country and demonstrates the scale of the

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problem faced by watershed managers.

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We further find that TN fluxes have decreased across the southeastern U.S., in particular the

269

Texas-Gulf, Lower Mississippi, Tennessee, and South Atlantic Gulf HUC2 regions (Figure 3a).

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Large-scale decreases in nitrogen loading have rarely been observed historically due to nitrogen

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inputs primarily increasing across the world and to variable lag times in response to nutrient

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reductions. Interestingly, the Mississippi-Atchafalaya River Basin, which drains into the Gulf of

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Mexico and causes an annual widespread hypoxic zone, consists of HUC2 regions with both

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increasing and decreasing TN flux trends. Also, while the Arkansas-White-Red HUC2 region has

275

only a small overall trend, closer inspection reveals HUC8 sub-regions with larger increasing and

276

decreasing trends that offset one another (Figures 2, 3a).

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We evaluate the performance of HUC8-aggregated loads by also estimating TN flux trends at

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ten large CONUS watersheds from the National Stream Quality Accounting Network

279

(NASQAN) comparable in size to HUC2 regions (Table S3). These TN flux estimates are then

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compared to observed, WRTDS-derived fluxes at the primary watershed outlet (Figure 4). In

281

short, at all ten NASQAN watersheds there is no statistically significant difference between

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modeled and observed trend estimates based on an analysis of covariance procedure, and model

283

estimates correspond well with observed interannual variability (Figure 4). This strengthens our

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confidence in using the model to estimate long-term trends at the HUC2 scale. Moreover, several

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of the NASQAN watersheds span HUC2 regions that have sparse spatial coverage from the

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GAGES-II stations used to build the model, providing support for extending the model to all

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eighteen HUC2 regions.

288

One important consideration for the regional aggregation procedure is that we generally expect

289

aggregated TN flux estimates to exceed observed loading at the watershed outlet because the model

290

application aims to represent fluxes within the larger regions, rather than loading out of them.

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Although the model implicitly accounts for much of the processing occurring at the catchment

292

scale, by design it does not account for riverine nitrogen reductions that occur between export from

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local HUC8 outlets and export from the regional outlet. This nitrogen may be either lost, primarily

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through denitrification, or stored in soil, groundwater, or vegetation. The proportion of nitrogen

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removed during transport is highly variable and dependent on factors such as residence times, river

296

geomorphology, and land cover, among others.

297

between 37% and 76% of nitrogen inputs to large river networks in the northeast U.S. are removed

298

during transport. There is also evidence that the fraction of nitrogen inputs ultimately exported

299

from a watershed increases for watersheds with high levels of nitrogen inputs. Indeed, aggregated

300

HUC8 estimates tend to exceed observed NASQAN loads, as expected; however, this does not

301

appear to limit the model’s ability to reproduce long-term trends and interannual variability at the

302

regional scale (Figure 4).

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3.3 Trend Attribution

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3.3.1 Climate

5

5,12,61,62

For example, Seitzinger et al. estimated that 61

19

305

By selectively detrending individual covariates or combinations of related covariates, we can

306

shed light on potential drivers of long-term loading trends. We begin by investigating the impact


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of climate variability as a whole and discuss contributions from individual climate covariates in

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Section 3.3.3. While we focus here on the U.S. HUC2 regions, the trend attribution results may

309

be generalizable to other regions globally with similar magnitudes of change in annual

310

precipitation, extreme springtime precipitation, springtime temperature, and NANI. Rivers

311

dominated by point source nitrogen inputs, rather than nonpoint sources as in much of the U.S.,

312

however, may exhibit lower sensitivities to climate variability and other factors such as geology

313

would also play a role.

63

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We find that climate—in the form of annual precipitation, extreme springtime precipitation,

315

and springtime temperature—is a key driver of historical loading trends for most HUC2 regions.

316

That is, there is a strong correspondence between estimated TN flux trends (Figure 3a) and

317

corresponding trends simulated from observed climate trends alone (Figure 3b).

318

The spatial pattern of increased TN fluxes in the northern and western U.S. and decreased

319

fluxes in the southeastern U.S. appears to result primarily from changes in climate rather than

320

changes in nitrogen inputs for most HUC2 regions (Figure 3a-c). Similarly, the relative

321

magnitudes of HUC2 trends correspond well with the climate-attributable signal. Together, this

322

emphasizes the ability of precipitation and temperature to drive long-term trends in loading at

323

large spatial scales. This is consistent with the finding that annual and extreme springtime

324

precipitation control interannual variability at the local watershed level across the country. The

325

links between climate and TN loading should therefore be considered not just in the context of

326

future climate change but also in retrospective analyses of historical loading.

327

3.3.2 Net Anthropogenic Nitrogen Inputs

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64

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While recent trends in nitrogen inputs have generally contributed to moderate increases in TN

329

fluxes across the U.S., this effect varies in magnitude (Figures 3c, 5). Large increases in nitrogen


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inputs have impacted loading in several regions, including the Souris-Red-Rainy and Missouri

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regions of the northern Great Plains. Increases in these regions have been driven primarily by

332

enhanced agricultural activity in the form of increased fertilizer application and nitrogen fixation

333

(Figure 5), and the increases appear to be largely concentrated in areas with high mean TN flux

334

(Figure S1). However, a large component of these increases is offset by export of nitrogen within

335

agricultural products (Figure 5). Continued and potentially expanded agricultural activity in these

336

regions would exacerbate increasing loading trends unless improvements in fertilizer efficiency

337

and runoff management are implemented.

338

4

In contrast, some HUC2 regions have seen decreases in nitrogen inputs with an associated

339

reduction in TN fluxes (Figures 3c, 5). For example, in the Great Lakes region decreases in

340

atmospheric deposition and enhanced export of nitrogen embedded in agricultural products have

341

led to an overall decrease in nitrogen inputs (Figure 5). Indeed, decreases in atmospheric

342

deposition across several HUC2 regions particularly in the northeast U.S. have partially offset

343

increases in nitrogen inputs from other sources (Figure 5). The atmospheric deposition decreases

344

correspond with nitrate reductions following regulations on vehicle and industrial combustion

345

processes. These positive spillover effects of air quality regulations on water quality point to the

346

potential for coupling nutrient reduction plans with other climate change and pollution mitigation

347

efforts. These findings also reinforce the argument for ongoing, coordinated monitoring of

348

atmospheric deposition and water quality parameters.

349

3.3.3 Interactive Effects on Loading Trends

350

65

66

67

Because climate trends have played such a large role in recent U.S. loading dynamics, we also

351

estimate the relative loading impacts of precipitation and temperature separately through

352

additional trend simulation experiments (Figure 3d,e). Overall, the majority of the climate-driven


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353

signal can be attributed to changes in precipitation rather than changes in temperature, especially

354

in the northeast U.S. where precipitation increases have led to increased TN fluxes. Meanwhile,

355

temperature trends across the U.S. have generally led to either a moderate reduction in TN fluxes

356

or little to no change. The precipitation signal also appears to be largely controlled by annual

357

precipitation rather than springtime extreme precipitation, although the positive precipitation

358

signal in the Midwest and northeast is driven by increases in both precipitation indicators (Figure

359

3e-g). One explanation for the controlling influence of annual, rather than springtime extreme,

360

precipitation at the HUC2 scale is that the trends in annual precipitation exhibit more spatial

361

coherence (Figure S7).

362

If we consider the precipitation and temperature trend simulations (Figure 3d,e) in relation to

363

the overall climate-driven signal (Figure 3b), we see that in regions with large precipitation-

364

induced increases in TN fluxes, the effect of rising temperatures has been insufficient to offset

365

precipitation-induced flux increases. For example, the simulations suggest that in the

366

northeastern U.S., large increases in TN fluxes are attributable primarily to precipitation trends,

367

despite the partially offsetting impact of increases in temperature (Figure 3). This area has been

368

identified previously as being particularly susceptible to loading increases in the future as a result

369

of anticipated increases in annual and extreme precipitation. Similarly, simulations for a large

370

agricultural watershed in the California basin suggest that the effects of precipitation on nitrogen

371

export far outweigh those of temperature. On the other hand, in the northwest, temperatures

372

have actually decreased during the study period (Figure S7), but this did not translate into a large

373

effect on TN fluxes (Figure 3d).

374 375

23

68

These results suggest that future increases in temperature expected from further climate change may have limited offsetting potential for nitrogen loading if the historical relationship between


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376

precipitation and temperature continues. This is in contrast to the model results of Alam et al.,

377

which project widespread reductions in CONUS loading by the end of the century due to

378

increasing temperatures, despite projected increases in precipitation for much of the northern

379

CONUS Therefore, the modeled sensitivities of loading to temperature in Alam et al. may be

380

overestimated, or conversely the sensitivity of loading to precipitation in that study may be

381

underestimated. A separate study on the upper Mississippi River Basin highlighted the

382

importance of spatial variability in precipitation and temperature relationships, projecting that

383

temperature will play an important role in reducing loads in Iowa but that in Illinois the effects of

384

increased precipitation will far exceed those of temperature. While we consider only the effects

385

of temperature on TN loading itself, an additional consideration for downstream water quality

386

impacts is the ecological response to temperature, with cyanobacteria abundance in harmful algal

387

blooms projected to increase across the U.S. due to increased water temperatures and heightened

388

biological activity.

69

21

21

70

71

389

We also find that decreases in precipitation coupled with increasing temperatures have acted to

390

amplify reductions in TN fluxes in the Lower Mississippi and Tennessee regions of the southeast

391

U.S. (Figure 3). These climate trends are generally projected for much of the southern U.S. in the

392

future due to climate change, suggesting the potential for loading reductions under similar land

393

use and nitrogen input conditions. For example, a study of a semiarid Arizona watershed in the

394

southern CONUS projected reductions in future loading due to the combined effects of decreased

395

precipitation and increased temperature on streamflow and mineralization rates. On the other

396

hand, Johnson et al. projected decreases in TN loading in the southwest U.S. but increases in the

397

southeast.

21

72

73


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398


Also interesting is the finding that climate trends have led to both amplification and offsetting

399

of TN flux trends expected from nitrogen inputs alone. For example, in the Souris-Red-Rainy

400

region, climate conditions favorable to increasing TN fluxes compounded an already large flux

401

increase associated with significant additions of nitrogen inputs (Figures 3b,c and 5).

402

Conversely, in the Great Lakes region, climate trends offset decreases in nitrogen inputs to such

403

an extent that the overall estimated TN flux trend for the region is positive rather than negative.

404

Observations from 2002-2012 at 12 sites in the Great Lakes region suggest predominantly

405

decreasing trends in TN flux, albeit for a TN load metric that removes the influence of discharge

406

variability and is therefore less dependent on precipitation and more indicative of watershed

407

responses to changes in nitrogen inputs, which we expect to be negative for the overall region

408

(Figure 3c). In the South Atlantic Gulf region, estimated decreases in TN fluxes are attributable

409

primarily to climate variability (Figure 3), in part because the large reductions in fertilizer use

410

have been offset by increases in food and feed import (Figure 5), leading to a minimal relative

411

impact of net nitrogen input changes on the observed loading trend (Figures 3c, S6c). Assuming

412

that observed changes in loading correspond directly to changes in nitrogen inputs can therefore

413

yield false conclusions. In the future, as impacts of climate change become more prevalent,

414

discerning the efficacy of nutrient reduction programs may become even more challenging, and

415

it may be necessary to further reduce nitrogen inputs to offset loading increases associated with

416

precipitation.

46

23,74

417

Together, these results indicate that while changes in nitrogen inputs are clearly important for

418

TN loading, long-term changes in precipitation and temperature have also played a large role in

419

influencing historical CONUS loading trends. Climate trends can either amplify or offset loading

420

changes expected from changes to nitrogen inputs alone and can therefore complicate the


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421

evaluation of long-term nutrient reduction programs across the country. Further, because we do

422

not explicitly attempt to attribute observed climate variability over the study period to long-term

423

climate change, observed climate trends are not necessarily indicative of variability in the

424

coming years and decades. Watershed managers stand to benefit from identifying the relative

425

roles of local climate variability, nitrogen inputs, and land use on historical loading, especially in

426

light of the altered regimes expected to coincide with anthropogenic climate change.

427

ASSOCIATED CONTENT

428

Supporting Information. Removal of two GAGES-II catchments; use of overlapping or nested

429

GAGES-II catchments; Table S1, covariates evaluated; Table S2, NLCD land cover classes;

430

Table S3, basin characteristics; Table S4, TN flux trend values associated with Figure 3; Figure

431

S1, TN flux mean and standard deviation; Figure S2, Observed and estimated TN fluxes; Figure

432

S3, relationship between covariates and response variable; Figure S4, maps of HUC8 mean

433

covariate values relative to training data limits; Figure S5, time series of HUC2 TN flux

434

estimates; Figure S6, HUC2 TN flux nonparametric evaluation of trends; Figure S7, maps of

435

HUC8 covariate trends

436

AUTHOR INFORMATION

437

Corresponding Author

438

*[email protected]


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439

Notes

440

We use the R 3.4.3 computing language for analysis, and re relevant code and data is archived

441

in the following GitHub repository: https://github.com/tristanballard/N_trend_attribution. The

442

authors declare no competing financial interest.

443

ACKNOWLEDGMENT

444

The authors thank the USGS, NCWQR, and NADP for ongoing efforts collecting and

445

distributing environmental data. We also thank Dennis Swaney and Bongghi Han for assistance

446

with the NANI toolbox and Nina Randazzo for comments on figures.

447

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448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469

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Figure 1. Newly available data make it possible to expand the number of GAGES-II catchments with observational data to include 53 new catchments (green) in addition to the 70 used in Sinha and Michalak (blue). TN measurements from these catchments are used to build a model for annual TN flux that is then applied to estimate fluxes at all HUC8 watersheds (grey). HUC8 loading estimates are ultimately aggregated to the HUC2 regional scale (black outline and text). 18


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Figure 2. TN flux trends for HUC8 watersheds, with annual TN fluxes estimated using the final selected model (eq 1). HUC2 regions are outlined in black.


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Figure 3. Substantial TN flux trends are apparent at the HUC2 regional scale (a) and are attributable primarily to historical trends in climatic variables (b). HUC2 regional TN flux trends (a) are attributed to various covariates (c,d,f,g), including nitrogen inputs (f , c), springtime temperature (T , d), total annual precipitation (P , f), extreme springtime precipitation (P , g), and combinations thereof (b,e). Trends are simulated by detrending certain covariates at the HUC8 scale and applying the TN flux model (eq 1) to the modified data. Due to the nonlinearity of the model, covariate effects are not strictly additive. Raw values are presented in Table S4. NANI

MAM

annual

MAM,p>0.95


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Figure 4. We evaluate the HUC8 to HUC2 aggregation procedure by examining aggregated estimates for a set of ten large NASQAN watersheds not used in the model build. R values indicate the fraction of variability in observed TN fluxes explained by the modeled fluxes. Overall, modeled TN fluxes correspond well with both observed interannual variability (high R values) and long-term trends (dashed lines). We expect modeled TN fluxes to exceed observed fluxes at regional scales, because the modeled fluxes represent TN export at the HUC8 scale and do not include nitrogen losses within the larger region. Gaps indicate years with missing or insufficient observational data. 2

2

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Figure 5. Attribution of NANI trends (red square) for individual HUC2 regions (Figure 1) to trends in each of its five components for 1987-2012 (colored bars). Positive (negative) trends in any component contribute additively to increases (decreases) in NANI. Increases in fertilizer use, nitrogen fixation, and food and feed export are generally associated with increased agricultural activity. Decreases in atmospheric deposition have offset increases in other components in several regions. Average total NANI over the period 1987-2012 is shown for context (black dots).


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For Table of Contents Only:


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Long-term changes in precipitation and temperature have already

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