[1] An optimization algorithm linked with a nonpoint source (NPS) pollution model can be used to optimize NPS pollution control strategies on a field-by-field basis in a watershed by maximizing NPS pollution reduction and net monetary return. In this paper a methodology is described which integrated a genetic algorithm (GA) (an optimization algorithm) with a continuous simulation, watershed-scale, NPS pollution model, Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to optimize the selection of best management practices (BMP) on a field-by-field basis for an entire watershed. To test the methodology, optimization analysis was performed for a U.S. Department of Agriculture experimental watershed in Pennsylvania to identify BMPs that minimized long-term (over a 4-year period) water quality degradation and maximized net farm return on an annual basis. Results indicate that the GA was able to identify BMP schemes that reduced pollutant load by as much as 56% and increased net annual return by 109%.

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available.

Mine reclamation with biosolids increases revegetation success but nutrient addition well in excess of vegetation requirements has the potential to increase leaching of NO3 and other biosolids constituents. A 3-yr water quality monitoring study was conducted on a Pennsylvania mine site reclaimed with biosolids applied at the maximum permitted and standard loading rate of 134 Mg ha(-1). Zero-tension lysimeters were installed at 1-m depth 1 yr before reclamation: three in the biosolids application area, one in a control area (no biosolids). Before reclamation, all water samples had pH in the range 4.7 to 6.2, acidity < 20 mg L(-1), and very low levels of all other measured parameters. Following reclamation, percolate water in the biosolids-treated area had lower pH and greater acidity than the control area. Acidity was greatest during the first winter following biosolids application, decreased during the spring, and showed a similar pattern but with much smaller concentrations the second year. Maximum first- year leachate NO3 concentrations were approximately 300 mg L(-1) and half as large the second year. Estimated inorganic N leaching loss during the first 2 yr after biosolids application was 2327 kg N ha(-1). Aluminum, Mn, Cu, Ni, Pb, and Zn followed similar leaching patterns as did acidity, and their mobilization appeared to be the result of the increased acidity. These results indicate that large applications of low-C/N-ratio biosolids could negatively impact area water quality and that biosolids reclamation practices should be modified to reduce this possibility.

Soil solution was collected with passive capillary samplers of sampling is unknown and may change with varying (PCAPS) and zero-tension samplers (ZTS) from A horizons of forested soils. The volume and chemistry of collected solutions were moisture conditions or suction applied. In addition, sucmeasured weekly during discrete seasonal collection periods. Acidtion must be applied manually to the sampler several washed PCAPS increased alkalinity (3-fold), pH (1-3 units), and hours or days prior to solution collection. This is probconcentrations of Ca (2-fold), Na (8-fold), and Si (4-fold), relative to lematic because the magnitude of tension exerted on ZTS solutions. Aluminum concentrations were dramatically reduced soil water gradually decreases over time. Lastly, because in PCAPS compared with ZTS samples. Differences in solution chemof the small cross-sectional area of the cup, multiple istry were attributed to leaching and weathering of fiberglass wicking samplers are required to represent adequately soil varimaterial utilized in the PCAPS. In addition, PCAPS collected greater ability (Barbee and Brown, 1986). volumes (normalized by sampler area) of solution relative to ZTS in Brown et al. (1986) first introduced passive capillary weak-structured sandy loam soil due to a preponderance of matrix flow. The results indicate that the PCAPS used in this study are not samplers (PCAPS) to provide an alternative means of suitable for aqueous geochemical studies of dilute soil solutions. sampling soil water in the field. As a result of their high conductivity and the tension exerted, PCAPS collect matrix and macropore flow under both saturated and

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.

It is widely known that a close relationship exists between crop production and water stress. In this study, field‐measured data were used to test the performance of AquaCrop and its ability to capture this relationship for rainfed maize (Zea mays L.) in Pennsylvania. The objectives were to evaluate AquaCrop’s ability to simulate the progression of cumulative biomass and grain yield with time, final biomass and harvestable yield, and volumetric water content at six depths. Two years of data from a study conducted in Rock Springs, PA, were used to validate AquaCrop’s ability to accurately simulate the progression of cumulative biomass and grain yield with time, as well as final biomass and harvestable yield. Data collected from January 2004 to March 2006 from a study conducted near Landisville, PA, were used to assess AquaCrop’s ability to effectively simulate soil moisture content at six depths. In addition, the 2004 and 2005 seasonal final biomass measurements obtained from the Landisville location were compared with the model’s simulated values. The results indicated that AquaCrop was able to accurately simulate the progression of cumulative biomass and grain yield with time, with index of agreement values ranging from 0.96 to 0.99. Comparisons between simulated and measured final biomass and final harvestable yield produced biomass deviations ranging from 2.4 to 20.7% and yield deviations of 2.9 and 15.3%. The water balance evaluation indicated that, averaged across all depths, the results were consistent with other validation studies of soil water balance models, with RMSE ranging from 1.5 to 9.8% (v/v).

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

In order to resolve the spatial component of the design of a water quality monitoring network, a methodology has been developed to identify the critical sampling locations within a watershed. This methodology, called Critical Sampling Points (CSP), focuses on the contaminant total phosphorus (TP), and is applicable to small, predominantly agricultural-forested watersheds. The CSP methodology was translated into a model, called Water Quality Monitoring Station Analysis (WQMSA). It incorporates a geographic information system (GIS) for spatial analysis and data manipulation purposes, a hydrologic/water quality simulation model for estimating TP loads, and an artificial intelligence technology for improved input data representation. The model input data include a number of hydrologic, topographic, soils, vegetative, and land use factors. The model also includes an economic and logistics component. The validity of the CSP methodology was tested on a small experimental Pennsylvanian watershed, for which TP data from a number of single storm events were available for various sampling points within the watershed. A comparison of the ratios of observed to predicted TP loads between sampling points revealed that the model's results were promising.