A Tool for Modeling the Winter Oxygen Depletion Rate in Arctic Lakes1

Abstract: Many arctic lakes freeze completely in winter. The few that retain unfrozen water for the entire winter period serve as overwintering fish habitat. In addition to serving as fish habitat, water in arctic lakes is needed for industrial and domestic use. Permits for water extraction seek to maximize water use without impacting dissolved oxygen (DO) levels and endangering fish habitat. The relationship between lake volume, winter DO budget, and extraction of water through pumping has historically not been well understood. A management model that could estimate end‐of‐winter DO would improve our understanding of the potential impacts of different management strategies. Using under‐ice DO measurements (November to April) taken from two natural lakes and one flooded gravel mine on the North Slope of Alaska, a physically based model was developed to predict end‐of‐winter DO concentration, water‐column DO profiles, and winter oxygen depletion rate in arctic lakes during periods of ice cover. Comparisons between the measured and model‐predicted oxygen profiles in the three study lakes suggest that the depth‐based DO modeling tool presented herein can be used to adequately predict the amount of DO available in arctic lakes throughout winter.     

Nutrient and sediment losses under simulated rainfall following manure incorporation by different methods

Incorporation of manure into cultivated soils is generally recommended to minimize nutrient losses. A 3-yr study was conducted to evaluate sediment and nutrient losses with different tillage methods (moldboard plow, heavy-duty cultivator, double disk, and no-incorporation) for incorporation of beef cattle manure in a silage barley (Hordeum vulgare L.) cropping system. Runoff depths, sediment losses, and surface and subsurface nutrient transfers were determined from manured and unmanured field plots at Lethbridge, Alberta, Canada. A Guelph rainfall simulator was used to generate 30 min of runoff. Sediment losses among our tillage treatments (137.4-203.6 kg ha(-1)) were not significantly different due to compensating differences in runoff depths. Mass losses of total phosphorus (TP) and total nitrogen (TN) in surface runoff were greatest from the no-incorporation (NI) treatments, with reductions in TP loads of 14% for double disk (DD), 43% for cultivator (CU), and 79% for moldboard plow (MP) treatments. Total N load reductions in 2002 were 26% for DD, 70% for CU, and 95% for MP treatments compared to the NI treatments. Nutrient losses following incorporation of manure with the DD or CU methods were not significantly different from the NI treatments. Manure treatments generally had lower runoff depths and sediment losses, and higher phosphorus and nitrogen losses than the control treatments. Subsurface concentrations of NH4-N, NO3-N, and TN were greatest from the MP treatments, whereas subsurface phosphorus concentrations were not affected by tillage method. Tillage with a cultivator or double disk minimized combined surface and subsurface nutrient losses immediately after annual manure applications.     

WATER QUALITY MODEL CALIBRATION: A COMPARISON OF INPUT AND OUTPUT ERROR CRITERIA1

ABSTRACT: A common framework for the analysis of water resources systems is the input-parameter-output representation. The system, described by its parameters, is driven by inputs and responds with outputs. To calibrate (estimate the parameters) models of these systems requires data on both inputs and outputs, both of which are subject to random errors. When one is uncertain as to whether the predominant source of error is associated with inputs or outputs, uncertainty also exists as to the correct specification of a calibration criterion. This paper develops and analyzes two alternative least squares criteria for calibrating a numerical water quality model. The first criterion assumes that errors are associated with inputs while the second assumes output errors. Statistical properties of the resulting estimators are examined under conditions of pure input or output error and mixed error conditions from a theoretical perspective and then using simulated results from a series of Monte Carlo experiments.