Total phosphorus export from Iowa agricultural watersheds: Quantifying the scope and scale of a regional condition

Journal of Hydrology 581 (2020) 124397

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Research papers

Total phosphorus export from Iowa agricultural watersheds: Quantifying the scope and scale of a regional condition

T

Keith E. Schillinga, , Matthew T. Streetera, Anthony Seemanb, Christopher S. Jonesc, Calvin F. Wolterd ⁎

a

Iowa Geological Survey, University of Iowa, Iowa City, IA, United States Iowa Soybean Association, Ankeny, IA, United States c IIHR Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States d Iowa Department of Natural Resources, Des Moines, IA, United States b

ARTICLE INFO

ABSTRACT

This manuscript was handled by Huaming Guo, Editor-in-Chief

Total phosphorus (TP) export in rivers contributes to development of hypoxic conditions in receiving waterbodies and is typically sourced to agricultural nonpoint sources. Systematic quantification of TP loads and yields at a regional scale is not often reported but TP yields are known to vary considerably. In this study, TP yields from 46 Iowa watersheds were evaluated for the period from 2000 to 2017 to evaluate long-term patterns of TP export and quantify the contribution of Iowa-sourced TP loads to the Gulf of Mexico. Using two common load estimation programs, annual mean and median TP yields ranged from 0.44 to 7.71 kg/ha and 0.32 to 3.75 kg/ha, respectively. Highest median yields were found in western and southern Iowa. The weighted average TP yield for Iowa was 1.70 kg/ha and, on average, Iowa’s TP export contributes approximately 15% of the TP load to the Gulf of Mexico. Reducing TP export will require improved quantification and differentiation of sources of TP loads in Midwestern rivers and a commitment to find strategies that reduce both TP and NO3-N loss from agricultural lands in a complementary and additive approach.

Keywords: Phosphorus Nutrients Iowa Hypoxia Agriculture

1. Introduction Riverine nutrients, consisting principally of nitrogen (N; predominantly in the form of NO3-N) and phosphorus (P) are major contributors to development of hypoxic conditions in the Gulf of Mexico (Turner et al., 2008) and around the world (Diaz, 2001). Although N has received more attention (Jones et al., 2018), P export has been viewed by some researchers as the limiting nutrient in phytoplankton growth in freshwater ecosystems and coastal waters (Sylvan et al, 2006; Hecky and Kilham, 1988). Part of the focus placed on N export has been the relative ease of monitoring of N compared to P. Quantifying N loads in rivers is relatively straightforward since concentrations can be adequately characterized by regular grab sampling (Jiang et al., 2014; Tiemeyer et al., 2010) or with the use of NO3-N sensors (Davis et al., 2014; Feng et al., 2013), whereas total phosphorus (TP) concentrations and loads are difficult to measure with much certainty. Sources of TP (sum of particulate and dissolved P) vary in watersheds, but nonpoint sources of P are considered dominant in agricultural regions (Jacobson et al., 2011; Jarvie et al., 2013). Farm fertilizers, manure and urban inputs comprise the largest TP sources

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(Jacobson et al., 2011; Robertson and Saad, 2013). P losses to the environment can occur through highly episodic, event-driven transport or through more continuous groundwater or tile drainage discharge (Stamm et al., 2013). Episodic P delivery is dominated by surface runoff and erosion of sediment-bound P from agricultural fields (Sharpley and Withers, 1994), whereas groundwater or tile drainage sources contribute to soluble P losses (Gentry et al., 2007; King et al., 2014, 2015; Smith et al., 2015). The proportion of soluble P to TP concentrations and loads in agricultural watersheds can vary greatly (Schilling et al., 2017c; Gentry et al., 2007). Schilling et al. (2017b) found that orthophosphorus (OP) contributions from croplands were greater in watersheds characterized by widespread tile drainage and well-drained soils, whereas TP export was dominated by particulate P in dissected till plains with poorly drained soils. Overall, TP export from agricultural watersheds reflects contributions from both particulate and dissolved P sources integrated across various spatial and temporal scales. Systematic quantification of TP loads and yields at a regional scale is not often reported and when done, it is usually the output of watershed models (i.e., Robertson et al., 2009; Jacobson et al., 2011; Robertson and Saad, 2013; White et al., 2014). The United States Geological

Corresponding author at: State Geologist and Director, Iowa Geological Survey, 300 Trowbridge Hall, University of Iowa, Iowa City, IA 52242, United States. E-mail address: [email protected] (K.E. Schilling).

https://doi.org/10.1016/j.jhydrol.2019.124397 Received 25 January 2019; Received in revised form 18 November 2019; Accepted 22 November 2019 Available online 27 November 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.

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Survey (USGS) developed a SPARROW (spatially referenced regression on watershed attributes) model to estimate TP yields and annual loads (Robertson et al., 2009) in the Mississippi/Atchafalaya Basin (MARB). TP yields were found to exceed 1.3 kg/ha in many regions of the Cornbelt. Likewise, Jacobson et al. (2011) reported spatial patterns of TP yields across the MARB for a January-July period (1997–2006) based on estimates derived from regression models and SPARROW. They estimated annual TP loads ranging from < 0.1 to 8.5 kg/ha in MARB watersheds. White et al. (2014) used a modeling framework of APEX and SWAT models to evaluate P loading patterns and source in the MARB and reported that average annual TP yields across watersheds in the MARB ranged from < 0.2 to > 6 kg/ha. The diversity of annual TP yields in the MARB is not surprising given the range of hydrologic flow pathways across many agricultural regions. Schilling et al. (2015) proposed the idea of “agro-hydrologic” regions to characterize cropland areas in the Upper Mississippi and Ohio river basins using soil and slope categories. In regions dominated by high relief (slopes greater than or equal to 5%) and poorly drained soils, surface runoff will dominate TP export. In recently glaciated areas, the landscape is flat and poorly drained, so TP losses will primarily occur via artificial drainage. The State of Iowa located in the heart of the U.S. Cornbelt contains a cross-section of all agro-hydrologic regions found throughout the Upper Mississippi and Ohio river basins and the diversity of annual TP yields reported for Iowa is typical for the entire region. In a small subset of 12 Iowa watersheds, Schilling et al. (2017c) reported mean TP yields ranging from 0.8 to 4.4 kg/ha. Quantification of annual export of TP from Iowa agricultural watersheds would be expected to represent the range and magnitude of TP losses likely encountered from all cropland areas in the MARB. The goal of this study was to estimate and report on variations in annual TP yields from 46 Iowa watersheds for the period from 2000 to 2017 (18 years). Our specific objectives were to 1) evaluate long-term statistics of TP export at the watershed and regional (state) scale; 2) quantify the contribution of Iowa-sourced TP loads to the Gulf of Mexico and 3) investigate the correlation of median watershed-scale TP yields to hydrologic and land cover characteristics. Study results are also intended to serve as a benchmark for other TP loading studies conducted around the world where annual yields are quantified. Given Iowa’s agricultural intensity and landscape diversity, a comparison of annual TP yields to Iowa-based watersheds provides an important comparison for TP temporal and spatial loading patterns.

and cold and relatively dry winters. Average annual precipitation and temperature range from approximately 700 to 900 mm and 6 to 12C across a gradient from northwest to southeast Iowa (INRS, 2015). Corn and soybean yields average approximately 10 and 3.3 Mg/ha, respectively. Typical annual P application to corn, soybean and hay areas in Iowa is approximately 54 kg/ha from fertilizer and manure sources (INRS, 2015). 2.2. Monitoring data TP concentrations were measured at an approximate monthly frequency at 46 ambient river monitoring sites across Iowa. The watershed areas for the 46 sites ranged from 20,155 km2 (Cedar River near Conesville) to 88 km2 (Bloody Run) (Fig. 1; Table 1). Monthly measurements are not ideal to characterize TP concentrations, but this sampling frequency was established by the Iowa Department of Natural Resources (IDNR) for their ambient river monitoring program. All the ambient monitoring sites evaluated in this study were specifically located to be beyond the extent of urban areas when the statewide ambient program was established (IDNR, 2000). There were occasional months of missing data, but the sample size ranged from approximately 180 to nearly 220 for the 18 years between 2000 and 2015. All surface water samples were collected as unfiltered grab samples from mid-stream bridge locations at fixed monitoring sites following an EPA-approved Quality Assurance Project Plan. TP was quantified by digesting samples followed by the molybdenum blue procedure with Lachat QuickChem method LAC 10-115-10-1D. Sample collection methods and laboratory analytical procedures were unchanged during the monitoring period. TP concentration data were obtained from the IDNRSTORET/WQX Water Quality Database (https://programs. iowadnr.gov/iastoret/). Stream discharge data were obtained from USGS gages that are collocated with the Iowa DNR monitoring sites. Annual discharge and TP concentrations from the MississippiAtchafalaya rivers were obtained from USGS reports (USGS, 2017). 2.3. Load estimation We used two different USGS software programs (Load Estimator or LOADEST and Weighted Regressions on Time, Discharge and Season or WRTDS) to estimate annual TP loads at the 46 monitoring sites. LOADEST (Cohn et al., 1992; Runkel et al., 2004) uses a rating curve approach to estimate daily TP loads using discrete samples and continuous streamflow data. We used the LOADEST program to estimate daily TP loads in the selected rivers using the seven-parameter load prediction model: ln(L) = β0 + β1ln(Q) + β2[ln(Q)]2 + β3t + β4t2 + β5sin (2πt) + β6cos(2πt) + ε where L = CQ is the load or flux, C is concentration, Q is discharge, t is time in decimal years, β0, β1,…, β6 are regression coefficients, and ε is assumed to be an independent and normally distributed error with zero mean and constant variance. Although the model can perform poorly to estimate NO3-N loads, Stenback et al. (2011) found no consistent positive or negative bias for TP load estimation. The LOADEST model has been used to estimate TP loads in many studies (e.g., Goolsby et al., 1999; Hooper et al., 2001; Aulenbach and Hooper, 2006; Robertson et al., 2009; White et al., 2014). TP loads were also estimated using the WRTDS method from the USGS EGRET R-package (Hirsch and De Cicco, 2015). WRTDS is a smoothing method to interpret the behavior of the water quality analyte of interest on the basis of four components: the relationship to discharge, seasonality, long-term trend, and a random component (Hirsch et al., 2010). The method was designed for data sets with two hundred or more measured concentration values that span a period of a decade or more. WRTDS estimates a gridded surface of concentration for the length of the record and over all discharges by using a regression where the actual measurements are weighted by their distance from the

2. Methods and materials 2.1. Regional setting The State of Iowa is the largest producer of corn (Zea mays L.) and soybeans (Glycine max [L.] Merr.) in the U.S., as well as the leading producer of eggs and pork and the fourth largest producer of feeder cattle (USDA, 2017). The success of the agricultural industry in the state can be sourced to the highly productive glacial-derived soils and favorable climate for rain-fed crop production. The surficial geology of Iowa is dominated by Pleistocene glacial deposits consisting of finetextured glacial till and loess (predominantly silt to silt loam) of varying ages (Prior and Iowa, 1991). The Wisconsin-age Des Moines Lobe represents the most recent glacial advance into Iowa with maximum extent around 12,000 years ago (Fig. 1). This region is poorly drained with little topographic relief and is dominated by artificial drainage. In western and southern Iowa, the rolling hillslope landscape is comprised of a varying thickness of fine-textured silt (loess) overlying older (preIllinoian) glacial till. Northeast Iowa is largely devoid of glacial deposits and characterized by shallow carbonate bedrock, often with karst topography, and incised river valleys. Together, the landscape diversity of Iowa mirrors the diversity of agro-hydrologic regions found throughout the Upper Mississippi and Ohio river basins (Schilling et al., 2015). Iowa has a humid continental climate with hot and humid summers 2

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Fig. 1. Location of TP sampling sites and load calculations in Iowa. Site names for the numbered sites are provided in Table 1.

estimated point in time, season and discharge within an adjustable window (Hirsch and De Cicco, 2014), and thus allows the relationship between concentration and discharge to change over time. The grid of estimated concentration is used to estimate flux by using the actual discharge to compute a load for each day of the record. WRTDS has been used to estimate river loads in many studies (e.g., Sprague et al., 2011; Hirsch, 2012) It is recognized that different load estimation methods can produce different results (Schilling et al., 2017a; Hirsch, 2014) and variations in TP loads can occur based on the load estimation method selected. We compared the annual estimated TP yields using the two models for all monitoring sites and years (n = 798) and found close agreement between the two methods at low TP yields (< 2 kg/ha based on LOADEST) but divergence occurred when TP yields were higher (Fig. 2). In particular the WRTDS/ LOADEST ratio was close to one at low TP yields (1.01), but the ratio was 0.48 when LOADEST estimated loads exceeded 2 kg/ha. At higher TP yields, the LOADEST method appeared to produce exceptionally high annual TP yield estimates relative to WRTDS. For this reason, we have chosen to report TP loads and yields using the WRTDS methodology in this paper. The annual output from the LOADEST method is provided in the Supplementary materials. The flux bias statistic (Bm) is a measure of the potential bias of the TP load estimate and it quantifies how well the estimated WRTDS load matches the measured load on the sampling days (Hirsch, 2014). Hirsch (2014) developed a non-linear relation between Bm and the true error in the model output in percent (Em) and indicated that Bm values between −0.1 to +0.1 likely have a bias within 10%, whereas for values near 0.2, the true bias is likely between 5 and 25%. Values near 0.6 may have a bias on the order of 100–125%. The flux bias statistics and diagnostic outputs for the 46 river load datasets analyzed in this study are provided in the Supplementary materials. On a percentile basis, Bm for most TP monitoring sites ranged between ± 0.2, implying a bias of

approximately 20% (Fig. 3). A few sites showed positive or negative Bm with potential bias of ± 100%. However, there was no systematic pattern of Bm across the statewide monitoring sites, nor were there any statistically significant relations between Bm and TP yield statistics or watershed area. Hence, the TP loads estimated using the WRTDS model appear to provide reasonable estimates of annual TP loads for Iowa rivers. 2.4. Ancillary data We compiled additional ancillary factors for the watersheds to assess their influence on TP yields. Precipitation data for a climate station located closest to the centroid of the watershed was obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/). Hydrograph separation into baseflow and runoff components was performed on the USGS discharge data collected at the 46 sites using the local minimum method (Sloto and Crouse, 1996). The hydrograph separation was conducted using the on-line program WHAT: Web-based Hydrograph Analysis Tool hosted by Purdue University (https:// engineering.purdue.edu/mapserve/WHAT/). Average annual water yield and baseflow were normalized to watershed area and reported in mm of discharge. Land cover information on the percent row crop area (combined corn and soybean acreage) was obtained using the 2009 High Resolution Land Cover database provided by the IDNR as accessed through the Iowa Water Quality Information System web site (http:// iwqis.iowawis.org/). Other variables (slope, percent manure, percent tiled soils) were summarized for their watershed areas by Nielson (2017) based on compilation from IDNR digital GIS databases. Percent manure represents the potential amount of land in a watershed (expressed as a percent of the total watershed area) needed to utilize the amount of nitrogen in manure generated by the number of animals by 3

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Table 1 Annual TP loads estimated using WRTDS for Iowa rivers for the period 2000–2017. Annual TP Yields in kg ha−1 No.

Watershed

Area (ha)

mean

stdev

median

min

max

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

Beaver Creek near Cedar Falls* Beaver Creek near Grimes* Black Hawk Creek at Waterloo* Bloody Run Creek near Marquette* Boone River at Stratford* Boyer River near Missouri Valley^ Cedar Creek near Bussey* Cedar Creek near Oakland Mills* Cedar River at Janesville* Cedar River near Charles City* Cedar River near Conesville* East Nishnabotna River near Shenandoah^ English River at Riverside* Floyd River near Sioux City^ Indian Creek Near Colfax* Iowa River Downstream of Marshalltown* Iowa River near Lone Tree* Little Sioux River at Larrabee^ Little Sioux River near Smithland^ Maple River at Mapleton^ Middle River near Indianola* North Fork Maquoketa River at Hurstville* North Raccoon River near Sac City* North River near Norwalk* North Skunk River near Sigourney* Old Mans Creek near Iowa City* Raccoon River upstream of Des Moines* Rock River near Hawarden^ Shell Rock River at Shell Rock* Soldier River at Pisgah^ South Raccoon River at Redfield* South River near Ackworth* South Skunk River Downstream of Ames* South Skunk River near Oskaloosa* Thompson Fork-Grand River at Davis City^ Turkey River near Garber* Upper Iowa River at Dorchester* Volga River near Elkport* Wapsipinicon River at Independence* Wapsipinicon River near DeWitt* West Fork Cedar River at Finchford* West Fork Des Moines near Humbolt* West Fork Ditch at Hornick^ West Nodaway River near Shambaugh Wolf Creek at Laporte City* Yellow River Near Ion*

102,124 95,697 84,495 8,882 229,924 235,669 96,319 138,063 432,904 283,988 2,015,491 264,546 162,363 229,558 102,561 423,327 1,110,394 480,082 694,384 166,851 126,680 152,719 183,689 90,477 164,973 52,173 886,898 436,857 448,351 105,750 253,854 122,859 151,359 424,789 180,080 402,267 198,820 104,251 238,279 604,720 220,277 601,746 104,243 204,658 84,411 56,647

0.87 1.12 0.99 0.44 1.01 6.29 5.34 2.97 0.80 0.82 1.18 3.99 2.60 1.50 1.06 1.00 1.01 0.52 0.95 2.25 7.13 2.59 1.03 2.56 2.08 2.54 1.49 0.86 0.70 5.53 4.49 7.71 1.31 1.26 4.60 3.03 1.09 2.92 0.72 0.91 0.65 0.44 1.84 6.30 1.49 1.34

0.62 0.97 0.82 0.43 0.80 10.05 4.78 2.79 0.41 0.39 0.60 4.72 2.22 1.14 0.86 0.53 0.63 0.34 0.71 1.99 8.74 2.83 0.62 2.56 1.69 2.29 1.15 0.69 0.36 10.55 5.17 8.86 1.04 0.77 4.75 4.26 0.81 4.05 0.39 0.38 0.31 0.23 1.36 7.47 1.64 1.13

0.75 0.76 0.78 0.32 0.74 2.97 3.47 1.89 0.81 0.77 1.12 2.28 1.73 1.16 0.77 0.92 0.96 0.47 0.83 1.80 3.75 1.80 0.86 1.68 1.50 1.54 1.30 0.69 0.63 2.11 2.17 3.34 0.91 1.01 3.30 1.78 0.94 1.47 0.70 0.92 0.58 0.42 2.02 3.06 0.97 1.02

0.10 0.08 0.03 0.09 0.07 0.28 0.04 0.14 0.16 0.18 0.38 0.14 0.14 0.28 0.10 0.21 0.20 0.09 0.09 0.12 0.13 0.32 0.24 0.11 0.30 0.15 0.08 0.11 0.11 0.14 0.11 0.24 0.18 0.16 0.05 0.19 0.10 0.17 0.16 0.31 0.09 0.10 0.18 0.14 0.11 0.17

2.57 3.46 3.16 1.89 3.35 42.15 15.72 10.01 1.59 1.45 2.58 16.53 7.09 4.92 3.30 2.04 2.67 1.20 3.09 6.71 35.03 11.02 2.87 10.24 5.99 7.60 4.50 2.28 1.48 45.04 15.57 33.05 4.21 3.14 17.55 15.04 3.09 13.72 1.64 1.58 1.25 0.92 4.57 22.01 6.88 3.83

*Mississippi River basin ^Missouri River basin.

Fig. 2. Left: The relation of annual TP yields estimated using LOADEST and WRTDS models when LOADEST model predicted yield < 2 kg/ha. Right: Same relation when LOADEST model predicted yields greater than 2 kg/ha. 4

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these basins. Overall, the weighted average TP yield for Iowa was 1.70 kg/ha (median 1.40 kg/ha), ranging from 0.37 to 4.95 kg/ha (Table 2). Total TP export from Iowa ranged from 5,377 to 72,182 Mg and averaged 24,842 Mg. Comparing the annual Iowa TP export to the TP load exported from the Mississippi River suggests that Iowa’s TP export contributes approximately 15% of the TP load to the Gulf of Mexico (Table 2). Iowa’s TP contribution to the Gulf ranged from approximately 5 to 34%. 3.2. Temporal patterns TP yields fluctuated during the 18-year period as shown by seven representative watersheds (Fig. 5). TP yields from the Boyer and Middle rivers in western and southern Iowa were clearly much higher than the other Iowa watersheds, whereas the English and Turkey rivers were more intermediate and TP yields from Boone, Beaver and Floyd rivers were much lower. The temporal pattern of TP yields in the Boyer River were most pronounced, with values increasing from approximately 1 kg/ha in 2005–2006 to 42.15 kg/ha in 2008 (Fig. 5). Likewise, TP yields from the Middle River in southern Iowa also fluctuated greatly, ranging from annual TP yields of 0.13 kg/ha during a drought year to 35.03 kg/ha during a wet year. In contrast, TP yields from the Boone River and Beaver Creek fluctuated within a much narrower range during the 18-year period, with annual values ranging from 0.07 to 2.10 kg/ha and 0.10 to 2.57 kg/ha, respectively. TP yields were correlated with annual precipitation at all sites but the level of correlation varied (Fig. 5). Correlation was less in the Boyer and Turkey rivers where pulses of annual TP loads were evident but TP variations were highly correlated with variations in precipitation at most sites (p < 0.01). Overall, the annual TP yield data clearly show that while TP export fluctuates in Iowa rivers, the range of fluctuations was much higher in western and southern Iowa.

Fig. 3. Percentile of the WRTDS-calculated flux bias statistics for the 46 monitoring sites. The flux bias statistic is not a linear measure of model performance (see Hirsch, 2014) but values ranging between ± 0.2 are considered to be accurate within 20% of the true measured load.

confined animal feeding operations (CAFOs) assuming an N application rate of 90 kg/ha to corn-soybean areas. Similarly, present tiled soils represents the percent of land in a watershed with hydric soils under corn-soybean rotation. TP load data for individual watersheds were compiled and summarized as annual totals (in kg) and divided by watershed area (ha) for yields. Summary statistics of annual yields (mean, median, standard deviation, minimum and maximum) were computed. Pearson correlation was used to investigate the relation of hydrologic and land cover variables to watershed-scale median TP yields at the 0.05 level of significance. Statistical analysis was performed using Minitab, Release 18 software (Mintab, Inc. State College, PA).

3.3. Correlation of median TP yields to other factors

3. Results

TP yields from 45 watersheds were correlated with watershed hydrologic and land characteristics (Table 3). Bloody Run results were excluded from the analyses because of its extremely small size and its very different hydrology (karst hydrology) compared to the other sites. TP yields were found to be significantly positively correlated (p < 0.01) with mean annual precipitation, mean basin slope and mean annual stormflow. Row crop (%), manure application area (%), tiled soils (%) and mean annual baseflow (mm) were negatively correlated at the 0.01 probability level. TP yields were not significantly correlated to precipitation and discharge but were negatively correlated to baseflow and positively correlated to stormflow fractions (Table 3). Among the ancillary variables, row crop, manure applications and tile soils were positively correlated with one another and negatively correlated with slope, whereas the hydrologic variables (water yield, baseflow, stormflow and Q/P ratio) were positively correlated with each other.

3.1. TP yields from Iowa watersheds Mean annual TP yields from Iowa watersheds varied considerably over an 18-year period of monitoring ranging from 0.44 kg/ha at Bloody Run Creek in northeast Iowa to 7.71 kg/ha at Middle River in southern Iowa (Table 1). Mean values were clearly influenced by maximum annual TP loads which exceeded more than 10 kg/ha in 14 different watersheds. Two western Iowa rivers draining to the Missouri River (Boyer River and Soldier River) had maximum annual TP yields of 42.15 and 45.04 kg/ha, respectively. Due to large standard deviations and skewness of annual TP yields at most sites, long-term TP yields in Iowa rivers are better represented by median values. Median annual TP yields ranged from 0.32 to 3.75 kg/ha (Table 1), and, as a population, were best fit with a log-normal distribution. The majority of annual TP yields cluster near 1 kg/ha but a tail of higher TP yields greater than 2 kg/ha was also evident (Fig. 2). Highest median TP yields are found in western and southern Iowa basins (Fig. 4). Five watersheds draining this region of the state had long-term median TP yields > 3 kg/ha, and five other watershed areas were greater than 2 kg/ha. In contrast, TP yields in north-central and northeast-central Iowa were < 1 kg/ha. Area-weighted average TP yields were calculated for watersheds draining to the Mississippi River (represented by 12 rivers) and Missouri River (10 rivers). Watersheds draining to the Mississippi River averaged approximately 1.30 kg/ha of TP loss, whereas watersheds draining to the Missouri River were approximately double (2.73 kg/ha). Median values were less but showed similar differences (1.26 vs. 1.63 kg/ha, respectively). For the State of Iowa, a weighted average TP yield was calculated by summing the loads from the largest basins in Iowa (i.e., did not include nested watersheds) and dividing by the watershed area represented by

4. Discussion 4.1. Regional patterns The magnitude and range of annual TP yields exported from Iowa watersheds are striking and among the highest reported in the literature. Richards and Baker (2002) reported TP export of 1.35 and 1.40 kg/ha from the Maumee and Sandusky watersheds in Ohio, whereas David and Gentry (2000) reported that average annual export from six Illinois watersheds varied from 0.7 to 1.1 kg/ha. Likewise, Royer et al. (2006) found similar average annual yields in the Embarrass (0.83 kg/ha) and Kaskaskia (0.67 kg/ha) rivers. Goolsby et al. (1999) quantified much higher yields for the Illinois River at Marseilles (1.9 kg/ha), but this river receives very large point source discharges 5

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Fig. 4. A) Median annual TP yield in Iowa watersheds, 2000–2017. B) Mean annual TP yield in Iowa watersheds, 2000–2017. C) Percentage of the watershed with row crop land cover. D) Average slope of the watershed. The outline of the recently glaciated Des Moines Lobe landform region is shown on all maps with a solid red line.

from the City of Chicago metropolitan area. Other higher TP export rivers included the Grand River in Missouri (1.83 kg/ha) and the Big Black River in MS (1.45 kg/ha). Mean annual TP loads from edge-offield monitoring in the eastern Corn Belt averaged 0.72 kg/ha and 0.51 kg/ha in subsurface and surface runoff, respectively (Pease et al., 2018). Kalkhoff et al. (2016) reported mean TP yields of 0.32 to 0.82 kg/ha in two smaller Iowa and Minnesota watersheds. Many Iowa rivers had median annual TP yields similar to those reported in the Midwestern Corn Belt (approximately 1 kg/ha; Fig. 4), but it is clearly evident that long-term mean values for many watersheds are considerably higher. However, both mean and median annual TP loads for several western and southern Iowa watersheds greatly exceeded these regional values. Patterns of TP yields in Iowa and throughout the Midwestern Corn Belt reflect the landscape and agricultural history of the region. In Iowa, annual TP yields fluctuating around 1 kg/ha were found in the recently glaciated Des Moines Lobe region, or in flat, northern Iowa regions on either side of the Lobe (Fig. 4). These areas are heavily cropped (> 70%) and many areas are intensely drained by subsurface drainage tiles. Much of the TP exported from this region likely consists of dissolved P delivered via subsurface tiles (Smith et al., 2015; King et al., 2014). Schilling et al. (2019) examined the ratio of dissolved P to TP in north central Iowa and reported that dissolved P accounted for > 60% of the TP load. These northern Iowa regions are similar to much of the

northern and eastern Cornbelt of the US (Schilling et al., 2015) where the dominant agro-hydrologic region is characterized by poorly-drained soils with < 2% slope. Median annual TP loads exported from this agrohydrologic region would be expected to range from approximately 0.5 to 1 kg/ha, which is consistent with TP loads reported from other Midwestern states (see above). In contrast, median annual TP yields in western and southern Iowa exceeded 2 to 3 kg/ha and mean values for some rivers exceeded 5 kg/ ha. Maximum annual TP yields were 20 to > 40 kg/ha in some areas (Table 1). The geology of western Iowa is dominated by thick deposits of highly erodible, wind-blown silt (loess) that is highly vulnerable to gully and streambank erosion (Bradford and Piest, 1977; Thomas et al., 2004). As the thick loess thins across southern Iowa, the region becomes characterized by rolling hills of thinner loess overlying older weathered glacial deposits. Although the cropping intensity is less in the region (particularly in southern Iowa), widespread sediment erosion from channel sources is contributing to high TP yields. In particular, recent studies are showing that streambank erosion is a major contributor to watershed sediment and TP loads. Channel derived sediments totaled between roughly 50 and 80 percent of total sediment load in five Midwestern watersheds (Wilson et al., 2008). Likewise, Schilling et al. (2011) and Palmer et al. (2014) estimated that approximately 0 to 68% of the annual suspended sediment load in a south central Iowa watershed could be explained by streambank erosion. Streambank 6

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contributions to TP loads have also been documented (Thoma et al., 2005; Kronvang et al., 2012; Walling, 2008; Beck et al., 2018). In the recent study by Beck et al. (2018), as much as 37.5% of the annual TP load was attributable to streambank erosion in a southern Iowa watershed. Land slopes are also contributing to greater TP loads in western and southern Iowa (Fig. 4). Indeed, this was reflected in the correlation analysis which indicated that median TP yields were significantly positively correlated with mean basin slope (r = 0.735). Related to this, was the significant positive relation of annual stormflow runoff on TP yields (r = 0.480) in Iowa watersheds (Table 3). In contrast, TP yields were inversely correlated with many attributes indicative of intensive production agriculture in Iowa, such as row crop area, manure applications, and tiled soils. This counter-intuitive result is a function of intensive agricultural production occurring in flat, recently glaciated areas of northern Iowa where soil erosion is less and TP yields are lower (Gelder et al., 2018). Further, several explanatory watershed variables are correlated through the row crop variable. For example, livestock manure is typically generated in Iowa watersheds with greater row crop intensity (Schilling et al., 2017c). In western and southern Iowa, agricultural intensity may be less but soil erosion and TP yields are much higher due to overland runoff and stream bed and bank erosion. Overall, there are many factors contributing to spatial patterns of TP yields in Iowa and many of the factors themselves are highly correlated. Future work will consider using more advanced modeling techniques such as multivariate autoregressive state space (MARSS) models (e.g., Ward et al., 2010) to disentangle effects of multiple co-variates.

Table 2 Iowa’s contribution to TP export from the Mississippi River to the Gulf of Mexico. Year

Iowa TP load (Mg)

Iowa mean TP Yield (kg/ha)

Miss. river water yield (mm)

Miss. river load (Mg)

Miss. river TP yield (kg/ha)

Iowa contribution to MR (%)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Mean

7128 30,701 9232 10,744 29,065 9936 7576 51,211 72,182 19,415 50,929 19,015 5377 21,368 30,900 27,354 27,573 17,444 24,842

0.49 2.11 0.63 0.74 1.99 0.68 0.52 3.51 4.95 1.33 3.49 1.30 0.37 1.47 2.12 1.88 1.89 1.20 1.70

134 198 211 195 231 174 137 180 255 256 226 243 139 208 189 244 238 175 202

94,349 161,482 166,925 136,080 158,760 149,688 73,483 175,997 212,285 150,595 195,048 157,853 114,307 159,667 161,482 183,254 197,770 142,430 155,081

0.29 0.5 0.52 0.42 0.49 0.46 0.23 0.55 0.66 0.47 0.61 0.49 0.35 0.50 0.50 0.57 0.61 0.44 0.48

7.6% 19.0% 5.5% 7.9% 18.3% 6.6% 10.3% 29.1% 34.0% 12.9% 26.1% 12.0% 4.7% 13.4% 19.1% 14.9% 13.9% 12.2% 14.9%

Fig. 5. Annual variations in TP yields in kg/ha (solid line; left axis) and precipitation in mm (dashed line, right axis) at seven representative watersheds in Iowa. Correlation and statistical significance of TP yields in relation to precipitation is provided below each graph. 7

Journal of Hydrology 581 (2020) 124397

0.578** −0.005 0.760**

It is interesting to note that the correlation results from 45 Iowa watersheds are different compared to results from a regional assessment. Jacobson et al. (2011) reported that TP yields in 101 Mississippi River basin watersheds were positively correlated with row crop area (0.45), tile drainage (0.45) and manure P (0.36) and negatively correlated with slope (−0.39). Since the study by Jacobson et al. (2011) included basins in Missouri River basin, including some in Montana and Wyoming, along with basins in West Virginia, Kentucky and eastern Tennessee, some differences in correlations may be due to differences in geography and climate. However, this also indicates the challenges involved with relying on simple bivariate correlations to characterize complex processes influenced by multiple factors. 4.2. Iowa contribution of TP to the Gulf of Mexico On a statewide basis, long-term TP yields from Iowa averaged 1.7 kg/ha, and Iowa’s TP contribution to the Gulf of Mexico averaged 14.9%. Over a similar 17-year period (1999–2016), Iowa was found to contribute 5.9% of the water to the Mississippi-Atchafalaya River Basin while occupying 4.5% of the basin (Jones et al., 2018). Hence, Iowa contributes approximately 2.5 times more TP load to the Gulf of Mexico than the amount of discharge contributed from the state. It is interesting to compare Iowa’s TP contribution to that of nitrogen. Jones et al. (2018) found Iowa’s long-term (1999–2016) NO3-N load contribution to be 29% of the total delivered to the Mississippi-Atchafalaya outlet, making Iowa’s phosphorus contribution reported here (14.9%) about half that for NO3-N. This is likely an indication that Iowa’s intensely tile-drained landscape is disproportionately losing nitrogen relative to the Upper Mississippi Basin as a whole, and that Iowa’s phosphorus loss, largely linked to overland surface runoff, is more typical of the Upper Mississippi Basin as a whole. Interestingly, when a five-year running annual average of Iowa TP loading is calculated, loads were 43.5% higher in 2017 compared to 2004, the first year that this metric can be calculated with our dataset. This is very similar to the NO3-N increase (40%) reported by Jones et al. (2018) from 2003 to 2016. Thus it appears that the land use and climatic drivers of nutrient loss in Iowa are affecting nitrogen and phosphorus similarly. In light of that, statewide P yield data was available for the neighboring state of Illinois, which is very similar to Iowa in terms of cropping systems and climate. David and Gentry (2000) reported TP yields of 1.0 kg/ha for the period 1980–1997, somewhat lower than the Iowa yields reported here (1.7 kg/ha). They also reported statewide contribution to Mississippi River discharge to be 9.6% of the total, whereas Jones et al. (2018) found Iowa’s discharge contribution to be substantially less at 5.9%, indicating that the P concentrations in Iowa streams are likely 2–3 times higher than Illinois streams on average. Recently the Illinois Nutrient Loss Reduction Strategy (INLRS, 2015) estimated statewide P yields to be 1.2 kg/ha for the 1997–2011 water years, still 0.5 kg/ha less than the mean TP loss from Iowa despite the larger water yield from that state.

−0.771** −0.479** −0.899** 0.049 −0.190 0.259* −0.113 0.320** −0.488** −0.713** −0.209 0.849** 0.553** 0.639** 0.677**

0.571** 0.627** −0.269* 0.162 −0.541** −0.054

0.364* −0.515* −0.266 −0.461* −0.370*

0.060 0.289* −0.204 0.218

0.705** 0.705** 0.948**

Mean annual stormflow (mm) Mean annual baseflow (mm) Mean annual water yield (mm) Tiled soils (%) Manure applications (%) Row crop (%) Mean slope (%) Mean P (mm)

4.3. Sources of TP 0.234 0.744** −0.732** −0.398** −0.736** −0.069 −0.437** 0.340** −0.274*

In this study, our focus was on annual TP loads and yields from 46 Iowa watersheds and was not directed towards quantifying various TP sources. The Iowa Nutrient Reduction Strategy (INRS) indicated that 79% of the TP loads in Iowa rivers was from nonpoint source pollution and 21% was due to point sources (mainly wastewater treatment plants or WWTP). Contributions from WWTPs are incorporated into the TP loads reported herein, but we did not account for their specific impacts. Point sources are known to increase TP loads downstream of major urban centers (Haggard, 2010), although improvements in some WWTPs are reducing P loads delivered to rivers (Grizzetti et al., 2012; Scott et al., 2011). In Iowa, several new WWTP plants were built during the 1998–2014 study timeframe, but most were built to comply with more stringent ammonia and E. coli limits and may be only incidentally

Mean P (mm) Mean slope (%) Row crop (%) Manure applications (%) Tiled soils (%) Mean annual water yield (mm) Mean annual baseflow (mm) Mean annual stormflow (mm) Q/P ratio

Median annual TP yield (kg/ha)

Table 3 Pearson correlation of median TP yields at 45 Iowa watersheds to hydrologic and land management variables (** = significant at p < 0.05, * = significant at p < 0.1).

K.E. Schilling, et al.

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better at removing P. Nonetheless, we cannot rule out the possible influence of WWTP discharges as a contributor for TP loads in Iowa rivers. Overall, additional monitoring and modeling is needed to accurately differentiate sources of TP loads in Iowa rivers. Of the nonpoint source load sources, most of the TP load in Iowa rivers can be attributed to agricultural runoff and sediment erosion (Jacobson et al., 2011; Mallarino and Wittry, 2010), although dissolved P delivered via subsurface tiles can be an important nonpoint source in some areas (Smith et al., 2015; King et al., 2014). Dissolved P likely dominates the TP load in north central Iowa, whereas in western and southern Iowa, particulate P from sediment erosion dominates the high TP loading areas in Iowa. 4.4. Source of error in TP loads Estimation of nutrient loads in rivers is a long-standing challenge subject to many potential sources of error and uncertainty (Guo and Jia, 2012; Stenback et al., 2011; Hirsch, 2014; Schilling et al., 2017a). Concentrations at many sites are measured on an intermittent frequency (i.e., monthly in this study) and there is a need to estimate concentrations during periods when no data was available. The estimated concentrations are typically paired with continuously monitored discharge to derive estimates of daily constituent load. Due to the challenge to estimating daily concentrations, there is often no true “measured” load to report, only the best estimated value based on statistical modeling. Two common rating curve methods (LOADEST, WRTDS) were used in this study to estimate annual TP loads in Iowa rivers and while there was good agreement between the two estimation methods at lower TP yields (< 2 kg/ha), LOADEST produced much higher loading estimates at higher TP yields (Fig. 2). These study results point to the complexity of estimating TP loads in Iowa rivers, but it is important to state that our study goal was not focused on evaluating model performance as this is discussed in more detail elsewhere (e.g., Hirsch, 2014; Stenback et al., 2011). Specific to Iowa, Stenback et al. (2011) examined LOADEST TP estimation for 44 Iowa rivers and reported that while there was no bias in the load prediction, 15% of the 44 cases either underestimated or overestimated loads by more than 25%. Although no consistent positive or negative bias was found in LOADEST results (Stenback et al., 2011), study results reported herein showed that LOADEST produced much higher estimates of annual TP export for some years compared to WRTDS. For this reason we focused on WRTDS results in this paper but provide LOADEST results in the Supplementary materials. Approximately 80% of the WRTDS flux bias statistics (Bm) for the studied watersheds were less than ± 0.2, implying that TP bias in annual loads is on the order of ± 20%. This level of potential bias in the estimated TP loads was consistent with Stenback et al. (2011) and is considered acceptable herein because the monthly TP concentration monitoring record available for analysis is not ideal for estimating TP loads, particularly at high discharge events (Jones and Schilling, 2011). The diagnostic output from the WRTDS model indicates that for the poorly performing models, the observed discharge was considerably higher than the discharge on sampling days (Fig. 6), implying that insufficient sampling during high discharge events was mainly responsible for model performance. For this reason, we emphasized median TP values more than mean values in this study as a long-term measure of the central tendency of TP loading patterns in Iowa. It should be noted that both LOADEST and WRTDS regression models struggled with capturing TP loads at high discharge events. Although sensor technology has not been developed yet to measure continuous TP concentrations in rivers, turbidity has shown promise as a surrogate for TP concentrations in watersheds (Grayson et al., 1996; Kronvang et al., 1997; Jones and Schilling, 2011; Schilling et al., 2017b; Stubblefield et al., 2007). Schilling et al. (2017b) indicated that TP concentrations in many Iowa rivers can be estimated very well using

Fig. 6. Number of samples collected during the observed discharge in the North Fork Maquoketa River compared to the discharge measured during the entire period of record.

turbidity with correlation coefficients between turbidity and TP concentrations averaging 0.78 and exceeding 0.9 in 14 of 43 watersheds analyzed. However, the researchers also noted that in heavily tiled areas where contributions of TP are dominated by dissolved OP, the TPturbidity relation is poor and OP should be explicitly added to any regression model estimating TP loads (Schilling et al., 2017b). 5. Conclusions Annual TP yields were estimated for the period from 2000 to 2017 (18 years) for 46 Iowa watersheds. Mean and median TP yields varied considerably, ranging from 0.44 to 7.71 kg/ha and 0.32 to 3.75 kg/ha, respectively. Mean values were clearly influenced by maximum annual TP loads which exceeded 10 kg/ha in 14 different watersheds. Highest median TP yields were found in western and southern Iowa, with yield from many watersheds exceeding 2 to 3 kg/ha, whereas in the flatter and more recently glaciated regions of north-central Iowa, median TP yields were often < 1 kg/ha. Overall, the weighted average TP yield for Iowa was 1.70 kg/ha (median 1.40 kg/ha), ranging from 0.37 to 4.95 kg/ha and Iowa’s TP export contributes approximately 15% of the TP load to the Gulf of Mexico. Median annual TP yields in Iowa were positively correlated to precipitation, basin slope and stormflow and negatively correlated with row crop, manure application areas, tiled soils and baseflow. This pattern reflects the geologic and land use history of Iowa where runoff on steeply sloping croplands along with significant contributions from severe streambank erosion produces soil erosion and high TP yields in western and southern Iowa that dwarf the TP contributions from intense agricultural activity (row crop, manure, tile) on flatter landscapes in northern Iowa. Finally, study results point out the challenges involved with evaluating and reducing TP loads from the agricultural Midwest. We used two different USGS load estimation programs in this study to estimate annual TP loads but additional monitoring and modeling is needed to better capture high TP loading events and improve load quantification. Reducing TP loads will require improved quantification and differentiation of sources of TP loads in Midwestern rivers and it will be important to find nutrient reduction strategies that reduce both TP and NO3-N loss from agricultural lands in a complementary and additive approach. 9

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CRediT authorship contribution statement

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Keith E. Schilling: Conceptualization, Supervision. Matthew T. Streeter: Methodology, Investigation. Anthony Seeman: Methodology, Investigation. Christopher S. Jones: Methodology, Investigation. Calvin F. Wolter: Conceptualization. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement Funding for the project was provided, in part, by the Iowa Nutrient Research Center under grant INRC 2017-01. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jhydrol.2019.124397. References Aulenbach, B.T., Hooper, R.P., 2006. The composite method: an improved method for stream-water solute load estimation. Hydrol. Process. Int. J. 20, 3029–3047. Beck, W., Isenhart, T., Moore, P., Schilling, K., Schultz, R., Tomer, M., 2018. Streambank alluvial unit contributions to suspended sediment and total phosphorus loads, Walnut Creek, Iowa, USA. Water 10, 111. Bradford, J., Piest, R., 1977. Gully wall stability in loess-derived alluvium 1. Soil Sci. Soc. Am. J. 41, 115–122. Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., Summers, R.M., 1992. The validity of a simple statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads entering Chesapeake Bay. Water Resour. Res. 28, 2353–2363. David, M.B., Gentry, L.E., 2000. Anthropogenic inputs of nitrogen and phosphorus and riverine export for Illinois, USA. J. Environ. Qual. 29, 494–508. Davis, C.A., Ward, A.S., Burgin, A.J., Loecke, T.D., Riveros-Iregui, D.A., Schnoebelen, D.J., Just, C.L., Thomas, S.A., Weber, L.J., St Clair, M.A., 2014. Antecedent moisture controls on stream nitrate flux in an agricultural watershed. J. Environ. Qual. 43, 1494–1503. Diaz, R.J., 2001. Overview of hypoxia around the world. J. Environ. Qual. 30, 275–281. Feng, Z., Schilling, K.E., Chan, K.-S., 2013. Dynamic regression modeling of daily nitratenitrogen concentrations in a large agricultural watershed. Environ. Monit. Assess. 185, 4605–4617. Gelder, B., Sklenar, T., James, D., Herzmann, D., Cruse, R., Gesch, K., Laflen, J., 2018. The Daily Erosion Project–daily estimates of water runoff, soil detachment, and erosion. Earth Surf. Process. Land 43, 1105–1117. Gentry, L., David, M., Royer, T., Mitchell, C., Starks, K., 2007. Phosphorus transport pathways to streams in tile-drained agricultural watersheds. J. Environ. Qual. 36, 408–415. Goolsby, D. A., W. A. Battaglin, G. B. Lawrence, R. S. Artz, B. T. Aulenbach, R. P. Hooper, D. R. Keeney, and G. J. Stensland. 1999. Flux and sources of nutrients in the Mississippi-Atchafalaya River Basin. National Oceanic and Atmospheric Administration National Ocean Service …. Grayson, R.B., Finlayson, B.L., Gippel, C.J., Hart, B.T., 1996. The potential of field turbidity measurements for the computation of total phosphorus and suspended solids loads. J. Environ. Manage. 47 (3), 257–267. Grizzetti, B., Bouraoui, F., Aloe, A., 2012. Changes of nitrogen and phosphorus loads to E uropean seas. Glob. Change Biol. 18, 769–782. Guo, Y., Jia, H., 2012. An approach to calculating allowable watershed pollutant loads. Front. Environ. Sci. Eng. 6, 658–671. Haggard, B.E., 2010. Phosphorus concentrations, loads, and sources within the Illinois River drainage area, northwest Arkansas, 1997–2008. J. Environ. Qual. 39, 2113–2120. Hecky, R.E., Kilham, P., 1988. Nutrient limitation of phytoplankton in freshwater and marine environments: a review of recent evidence on the effects of enrichment 1. Limnol. Oceanogr. 33, 796–822. Hirsch, R.M., 2012. Flux of nitrogen, phosphorus, and suspended sediment from the Susquehanna River basin to the Chesapeake Bay during Tropical Storm Lee, September 2011, as an indicator of the effects of reservoir sedimentation on water quality. US Department of the Interior, US Geological Survey. Hirsch, R.M., 2014. Large biases in regression-based constituent flux estimates: causes and diagnostic tools. JAWRA J. Am. Water Resour. Assoc. 50, 1401–1424. Hirsch, R.M., and L.A. De Cicco, 2015, User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data (version 2.0, February 2015): U.S. Geological Survey Techniques and Methods book 4, chap. A10,

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