The midwestern United States offers some of the most productive agricultural soils in the world. Given the cool humid climate, much of the region would not be able to support agriculture without subsurface (tile) drainage because high water tables may damage crops and prevent machinery usage in fields at critical times. Although drainage is designed to remove excess soil water as quickly as possible, it can also rapidly transport agrochemicals, including phosphorus (P). This paper illustrates the potential importance of tile drainage for P transport throughout the midwestern United States. Surface runoff and tile drainage from fields in the St. Joseph River Watershed in northeastern Indiana have been monitored since 2008. Although the traditional concept of tile drainage has been that it slowly removes soil matrix flow, peak tile discharge occurred at the same time as peak surface runoff, which demonstrates a strong surface connection through macropore flow. On our research fields, 49% of soluble P and 48% of total P losses occurred via tile discharge. Edge-of-field soluble P and total P areal loads often exceeded watershed-scale areal loadings from the Maumee River, the primary source of nutrients to the western basin of Lake Erie, where algal blooms have been a pervasive problem for the last 10 yr. As farmers, researchers, and policymakers search for treatments to reduce P loading to surface waters, the present work demonstrates that treating only surface runoff may not be sufficient to reach the goal of 41% reduction in P loading for the Lake Erie Basin.

Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P losses can occur in drainage water from artificially drained fields. In this review, we assess the applicability of nine models to predict this type of P loss. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P into runoff, macropore flow, and matrix flow. Within the soil profile, sorption and desorption of dissolved P and filtering of particulate P will be important. Eight models are reviewed (ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAMDB, PLEASE, and SWAT) along with P Indexes. Few of the models are designed to address P loss in drainage waters. Although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. ADAPT, HSPF, and most P Indexes do not simulate flow to tiles or drains. DRAINMOD simulates drains but does not simulate P. The ICECREAMDB model from Sweden is an exception in that it is designed specifically for P losses in drainage water. This model seems to be a promising, parsimonious approach in simulating critical processes, but it needs to be tested. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.

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Conservation practices are implemented on farm fields in the USA through Farm Bill programs; however, there is a need for greater verification that these practices provide environmental benefits (e.g., water quality). This study was conducted to assess the impact of Farm Bill eligible conservation practices on soluble P (SP) and total P (TP) losses from four fields that were monitored between 2004 and 2013. No-tillage doubled SP loading compared to rotational tillage (e.g., tilled only before planting corn); however, no-tillage decreased TP loading by 69 % compared to rotational tillage. Similarly, grassed waterways were shown to increase SP loads, but not TP loads. A corn–soybean–wheat–oat rotation reduced SP loads by 85 % and TP loads by 83 % compared to the standard corn–soybean rotation in the region. We can potentially attain TP water quality goals using these Farm Bill practices; however, additional strategies must be employed to meet these goals for SP.

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Open surface inlets that connect to subsurface tile drainage systems provide a direct pathway for movement of sediment, nutrients, and agrochemicals to surface waters. This study was conducted to determine the reduction in drainage effluent total suspended sediment (TSS) and phosphorus (P) concentrations and loads when open surface inlets were replaced with blind (in gravel capped with 30 cm of soil) or gravel (in very coarse sand/fine gravel) inlets. In Indiana, a pair of closed depressions in adjacent fields was fitted with open inlet tile risers and blind inlets in 2005 and monitored for flow and water chemistry. Paired comparisons on a storm event basis during the growing season for years 2006 to 2013 showed that TSS loads were 40.4 and 14.4 kg ha event for tile risers and blind inlets, respectively. Total P (TP) and soluble reactive P (SRP) loads were 66 and 50% less for the blind inlets, respectively. In Minnesota, TSS and SRP concentrations were monitored for 3 yr before and after modification of 24 open inlets to gravel inlets in an unreplicated large-field on-farm study. Median TSS concentrations were 97 and 8.3 mg L and median SRP concentrations were 0.099 and 0.064 mg L for the open inlet and gravel inlet periods, respectively. Median TSS and SRP concentrations were elevated for snowmelt vs. non-snowmelt seasons for open and gravel inlets. Both replacement designs reduced suspended sediment and P concentrations and loads. The Indiana study suggests blind inlets will be effective beyond a 10-yr service life.

Microbial contamination of surface waters, a substantial public health concern throughout the world, is typically identified by fecal indicator bacteria such as E. coli. Thus, monitoring E. coli concentrations is critical to evaluate current conditions, determine restoration effectiveness, and inform model development and calibration. An often overlooked component of these monitoring and modeling activities is understanding the inherent random and systematic uncertainty present in measured data. In this research, a review and subsequent analysis was performed to identify, document, and analyze measurement uncertainty of E. coli data collected in stream flow and stormwater runoff as individual discrete samples or throughout a single runoff event. Data on the uncertainty contributed by sample collection, sample preservation/storage, and laboratory analysis in measured E. coli concentrations were compiled and analyzed, and differences in sampling method and data quality scenarios were compared. The analysis showed that: 1) manual integrated sampling produced the lowest random and systematic uncertainty in individual samples, but automated sampling typically produced the lowest uncertainty when sampling throughout runoff events; 2) sample collection procedures often contributed the highest amount of uncertainty, although laboratory analysis introduced substantial random uncertainty and preservation/storage introduced substantial systematic uncertainty under some scenarios; and 3) the uncertainty in measured E. coli concentrations was greater than that of sediment and nutrients, but the difference was not as great as may be assumed. This comprehensive analysis of uncertainty in E. coli concentrations measured in streamflow and runoff should provide valuable insight for designing E. coli monitoring projects, reducing uncertainty in quality assurance efforts, regulatory and policy decision making, and fate and transport modeling.