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Phosphorus (P) is the limiting nutrient for primary production in most freshwater ecosystems. The magnitude of P leaching from agricultural soils is therefore critical. Preferential flow has been proposed as a major cause for high P losses in structured clay soils. Undisturbed soil of two texturally different soils, that is, a day soil in which preferential flow was expected to be the main mode of water transport and a sandy soil where piston flow is the dominant process, were used in this study. Use of labeled P made it possible to determine the origin of leached P. An equivalent of 100 kg P ha−1, labeled with 33P, was added to the soil surface of each lysimeter. Water equivalents to 100 mm were added on five occasions with 7 d between each watering event. Ponded flow conditions were established during periods when water was added, to trigger preferential Bow behavior. Phosphorus leaching loads from day columns were much higher than P loads from sand columns. The average P leaching load for the five day columns was 4.0 kg ha−1, compared to only 56 g ha−1 for the three sand columns. The main part of leached P was in dissolved (PO4‐P) form. The recently added P prevailed in leachate from the clay soil indicating rapid transport of added P from the soil surface through the profile via macropores.

Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000‐km2 watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15‐yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change. Core Ideas At the midpoint of the Chesapeake TMDL, dissolved P is increasing in some tributaries. Lingering concerns include legacy P, artificial drainage, animal heavy use areas. Extreme events represent an acute risk to water quality.

Although the U.S. Congress established the Total Maximum Daily Load (TMDL) program in the original Clean Water Act of 1972, Section 303(d), it did not receive attention until the 1990s. Currently, two methods are available for tracking pollution in the environment and assessing the effectiveness of the TMDL process on improving the quality of impaired water bodies: field monitoring and mathematical/computer modeling. Field monitoring may be the most appropriate method, but its use is limited due to high costs and extreme spatial and temporal ecosystem variability. Mathematical models provide an alternative to field monitoring that can potentially save time, reduce cost, and minimize the need for testing management alternatives. However, the uncertainty of the model results is a major concern. Uncertainty is defined as the estimated amount by which an observed or calculated value may depart from the true value, and it has important policy, regulatory, and management implications. The source and magnitude of uncertainty and its impact on TMDL assessment has not been studied in depth. This article describes the collective experience of scientists and engineers in the assessment of uncertainty associated with TMDL models. It reviews sources of uncertainty (e.g., input variability, model algorithms, model calibration data, and scale), methods of uncertainty evaluation (e.g., first-order approximation, mean value first-order reliability method, Monte Carlo, Latin hypercube sampling with constrained Monte Carlo, and generalized likelihood uncertainty estimation), and strategies for communicating uncertainty in TMDL models to users. Four case studies are presented to highlight uncertainty quantification in TMDL models. Results indicate that uncertainty in TMDL models is a real issue and should be taken into consideration not only during the TMDL assessment phase, but also in the design of BMPs during the TMDL implementation phase. First-order error (FOE) analysis and Monte Carlo simulation (MCS) or any modified versions of these two basic methods may be used to assess uncertainty. This collective study concludes that a more scientific method to account for uncertainty would be to develop uncertainty probability distribution functions and transfer such uncertainties to TMDL load allocation through the margin of safety component, which is selected arbitrarily at the present time. It is proposed that explicit quantification of uncertainty be made an integral part of the TMDL process. This will benefit private industry, the scientific community, regulatory agencies, and action agencies involved with TMDL development and implementation.

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