Dale M. Robertson

Influence of various water quality sampling strategies on load estimates for small streams

Extensive streamflow and water quality data from eight small streams were systematically subsampled to represent various water-quality sampling strategies. The subsampled data were then used to determine the accuracy and precision of annual load estimates generated by means of a regression approach (typically used for big rivers) and to determine the most effective sampling strategy for small streams. Estimation of annual loads by regression was imprecise regardless of the sampling strategy used; for the most effective strategy, median absolute errors were ∼30% based on the load estimated with an integration method and all available data, if a regression approach is used with daily average streamflow. The most effective sampling strategy depends on the length of the study. For 1-year studies, fixed-period monthly sampling supplemented by storm chasing was the most effective strategy. For studies of 2 or more years, fixed-period semimonthly sampling resulted in not only the least biased but also the most precise loads. Additional high-flow samples, typically collected to help define the relation between high streamflow and high loads, result in imprecise, overestimated annual loads if these samples are consistently collected early in high-flow events.

Lake ice records used to detect historical and future climatic changes

Historical ice records, such as freeze and breakup dates and the total duration of ice cover, can be used as a quantitative indicator of climatic change if long homogeneous records exist and if the records can be calibrated in terms of climatic changes. Lake Mendota, Wisconsin, has the longest uninterrupted ice records available for any lake in North America dating back to 1855. These records extend back prior to any reliable air temperature data in the midwestern region of the U.S. and demonstrate significant warming of approximately 1.5 °C in fall and early winter temperatures and 2.5 °C in winter and spring temperatures during the past 135 years. These changes are not completely monotonie, but rather appear as two shorter periods of climatic change in the longer record. The first change was between 1875 and 1890, when fall, winter, and spring air temperatures increased by approximately 1.5 °C. The second change, earlier ice breakup dates since 1979, was caused by a significant increase in winter and early spring air temperatures of approximately 1.3 °C. This change may be indicative of shifts in regional climatic patterns associated with global warming, possibly associated with the ‘Greenhouse Effect’.With the relationships between air temperature and freeze and break up dates, we can project how the ice cover of Lake Mendota should respond to future climatic changes. If warming occurs, the ice cover for Lake Mendota should decrease approximately 11 days per 1 °C increase. With a warming of 4 to 5 °C, years with no ice cover should occur in approximately 1 out of 15 to 30 years.

Changes in the thermal structure of moderate to large sized lakes in response to changes in air temperature

Interannual variability in the thermal structure of lakes is driven by interannual differences in meteorological conditions. Dynamic or mechanistic models and empirical or statistical methods have been used to integrate the physical processes in lakes enabling the response of the thermal structure to changes in air temperature to be determined. Water temperature records for Lake Mendota, WI., are possibly the most extensive for any dimictic lake in the world and allowed both approaches to be used. Results from both techniques suggest the mixed layer temperature increases with increasing air temperature. Results from the empirical approach suggested epilimnion temperatures increase 0.5 to 1.0°C per 1.0°C increase in air temperature compared to 0.4 to 0.85°C estimated from a dynamical model (DYRESM). Increased air temperatures are related to significant warming in deep water temperatures in the absence of stratification; however, mid summer hypolimnion temperatures are expected to change very little or increase only slightly in response to climatic warming. Both approaches suggest increases in air temperatures increase the length of summer stratification; results from the dynamic model suggest an increase of approximately 5 days per 1°C increase in air temperature. Longer stratification is reflected in shallower late summer thermocline depths. With these quantitative relationships and forecast increases in air temperature for the 2 × CO2 climatic scenario (Greenhouse Effect) from three General Circulation Models, projections are made describing the changes in the future mean thermal structure of moderate to large sized lakes.

INCORPORATING UNCERTAINTY INTO THE RANKING OF SPARROW MODEL NUTRIENT YIELDS FROM MISSISSIPPI/ATCHAFALAYA RIVER BASIN WATERSHEDS

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.

Regionalized Loads of Sediment and Phosphorus to Lakes Michigan and Superior—High Flow and Long-term Average

Daily loads of suspended sediment and total phosphorus for the 10-year, 1-day design high flow and average of the 16-year period (1975 to 1990) were computed for 18 well-monitored tributaries to Lake Michigan and Lake Superior by use of constituent-transport models. The loads from these 18 reference tributaries were used to estimate the loads from all the United States tributaries (with drainage basins greater than 325 km2) to Lake Michigan and Lake Superior by selection of a reference tributary with the most similar physical characteristics and use of a unit-area yield. Statistical comparisons between computed yields and environmental factors were used to determine the physical characteristics that were most influential in selecting a reference tributary. Suspended sediment yields were affected primarily by river gradient and secondarily by the texture of surficial deposits, whereas total phosphorus yields were affected primarily by the texture of surficial deposits and secondarily by river gradient. Average total phosphorus loads were greatest in rivers entering the middle to southern part of Lake Michigan, especially those draining clayey surficial deposits in agricultural areas. During high flow, loads of phosphorus and suspended sediment from tributaries entering the southwestern part of Lake Superior dominate the total input of these constituents because of the steep gradients of the rivers and the clayey surficial deposits that they drain. These loads were used to compute regional loads and to rank the tributaries on the basis of their respective loads during a specified high flow and over extended periods.

A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models.

Abstract Stream-loading information was compiled from federal, state, and local agencies, and selected universities as part of an effort to develop regional SPAtially Referenced Regressions On Watershed attributes (SPARROW) models to help describe the distribution, sources, and transport of nutrients in streams throughout much of the United States. After screening, 2,739 sites, sampled by 73 agencies, were identified as having suitable data for calculating long-term mean annual nutrient loads required for SPARROW model calibration. These sites had a wide range in nutrient concentrations, loads, and yields, and environmental characteristics in their basins. An analysis of the accuracy in load estimates relative to site attributes indicated that accuracy in loads improve with increases in the number of observations, the proportion of uncensored data, and the variability in flow on observation days, whereas accuracy declines with increases in the root mean square error of the water-quality model, the flow-bias ratio, the number of days between samples, the variability in daily streamflow for the prediction period, and if the load estimate has been detrended. Based on compiled data, all areas of the country had recent declines in the number of sites with sufficient water-quality data to compute accurate annual loads and support regional modeling analyses. These declines were caused by decreases in the number of sites being sampled and data not being entered in readily accessible databases.

SPARROW Models Used to Understand Nutrient Sources in the Mississippi/Atchafalaya River Basin

Nitrogen (N) and phosphorus (P) loading from the Mississippi/Atchafalaya River Basin (MARB) has been linked to hypoxia in the Gulf of Mexico. To describe where and from what sources those loads originate, SPAtially Referenced Regression On Watershed attributes (SPARROW) models were constructed for the MARB using geospatial datasets for 2002, including inputs from wastewater treatment plants (WWTPs), and calibration sites throughout the MARB. Previous studies found that highest N and P yields were from the north-central part of the MARB (Corn Belt). Based on the MARB SPARROW models, highest N yields were still from the Corn Belt but centered over Iowa and Indiana, and highest P yields were widely distributed throughout the center of the MARB. Similar to that found in other studies, agricultural inputs were found to be the largest N and P sources throughout most of the MARB: farm fertilizers were the largest N source, whereas farm fertilizers, manure, and urban inputs were dominant P sources. The MARB models enable individual N and P sources to be defined at scales ranging from SPARROW catchments (∼50 km) to the entire area of the MARB. Inputs of P from WWTPs and urban areas were more important than found in most other studies. Information from this study will help to reduce nutrient loading from the MARB by providing managers with a description of where each of the sources of N and P are most important, thus providing a basis for prioritizing management actions and ultimately reducing the extent of Gulf hypoxia.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the models predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

Regional Effects of Agricultural Conservation Practices on Nutrient Transport in the Upper Mississippi River Basin.

Despite progress in the implementation of conservation practices, related improvements in water quality have been challenging to measure in larger river systems. In this paper we quantify these downstream effects by applying the empirical U.S. Geological Survey water-quality model SPARROW to investigate whether spatial differences in conservation intensity were statistically correlated with variations in nutrient loads. In contrast to other forms of water quality data analysis, the application of SPARROW controls for confounding factors such as hydrologic variability, multiple sources and environmental processes. A measure of conservation intensity was derived from the USDA-CEAP regional assessment of the Upper Mississippi River and used as an explanatory variable in a model of the Upper Midwest. The spatial pattern of conservation intensity was negatively correlated (p = 0.003) with the total nitrogen loads in streams in the basin. Total phosphorus loads were weakly negatively correlated with conservation (p = 0.25). Regional nitrogen reductions were estimated to range from 5 to 34% and phosphorus reductions from 1 to 10% in major river basins of the Upper Mississippi region. The statistical associations between conservation and nutrient loads are consistent with hydrological and biogeochemical processes such as denitrification. The results provide empirical evidence at the regional scale that conservation practices have had a larger statistically detectable effect on nitrogen than on phosphorus loadings in streams and rivers of the Upper Mississippi Basin.

A linked hydrodynamic and water quality model for the Salton Sea

A linked hydrodynamic and water quality model was developed and applied to the Salton Sea. The hydrodynamic component is based on the one-dimensional numerical model, DLM. The water quality model is based on a new conceptual model for nutrient cycling in the Sea, and simulates temperature, total suspended sediment concentration, nutrient concentrations, including