Rebecca D. Marjerison

A Phosphorus Index transport factor based on variable source area hydrology for New York State

The Phosphorus (P) Index concept is used in many states to help develop nutrient management plans for livestock agriculture to protect water quality. Although many P indices conceptually incorporate variable source area (VSA) runoff processes, they generally assume proximity to a water course is an adequate proxy of runoff risk. Here we propose a VSA-based transport factor that uses the topographic index concept to indicate runoff risk. We compared both transport factors based on the current New York State P Index and our VSA-based P Index to field measures of runoff probability across an abandoned agricultural site in upstate New York. We also compared transport factors and P indices using the current and VSA-based approaches on a farm in the Catskill Mountains of New York to evaluate differences at whole-field and farm scales. Field runoff-risk measurements were better correlated with VSA-based transport factor (r2 = 0.63, α = 0.05) than with the current dissolved-P transport factor (r2 = 0.40, α = 0.05). Although these point-scale differences in transport factor values translated into field-scale differences in transport factor, the net differences at the farm scale and in P Index were not very large. On a field-by-field basis, 12 out of 21 fields had different transport factor categories with the VSA method. However, the total land area classified as high risk changed very little between the two methods. There was more land classified as moderate risk using the VSA-based approach than using the current methods, due to some low risk areas being classified as higher risk and some high-risk areas being classified as lower risk. The VSA approach allows managers and producers to more easily manage farm units (e.g., fields) at finer resolutions both spatially and temporally, which will increase the options for managing nutrients on fields. These types of more rigorous links between management tools and physical hydrology provide valuable, more scientifically defensible information for improving our ability to control nonpoint source pollution.

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over-apply N fertilizers in maize ( L.) production environments, often resulting in large environmental N losses. Static Stanford-type N recommendation tools are typically promoted in the United States, but new dynamic model-based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt-N, a dynamic simulation tool that combines soil, crop, and management information with real-time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N-rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower-estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site-specific conditions, the Adapt-N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt-N can help reduce environmental impacts while sustaining farm economic viability.

Improving risk estimates of runoff producing areas: formulating variable source areas as a bivariate process.

Predicting runoff producing areas and their corresponding risks of generating storm runoff is important for developing watershed management strategies to mitigate non-point source pollution. However, few methods for making these predictions have been proposed, especially operational approaches that would be useful in areas where variable source area (VSA) hydrology dominates storm runoff. The objective of this study is to develop a simple approach to estimate spatially-distributed risks of runoff production. By considering the development of overland flow as a bivariate process, we incorporated both rainfall and antecedent soil moisture conditions into a method for predicting VSAs based on the Natural Resource Conservation Service-Curve Number equation. We used base-flow immediately preceding storm events as an index of antecedent soil wetness status. Using nine sub-basins of the Upper Susquehanna River Basin, we demonstrated that our estimated runoff volumes and extent of VSAs agreed with observations. We further demonstrated a method for mapping these areas in a Geographic Information System using a Soil Topographic Index. The proposed methodology provides a new tool for watershed planners for quantifying runoff risks across watersheds, which can be used to target water quality protection strategies.

Drainage and Nitrate Leaching from Artificially Drained Maize Fields Simulated by the Precision Nitrogen Management Model

Environmental nitrogen (N) losses (e.g., nitrate leaching, denitrification, and ammonia volatilization) frequently occur in maize ( L.) agroecosystems. Decision support systems, designed to optimize the application of N fertilizer in these systems, have been developed using physically based models such as the Precision Nitrogen Management (PNM) model of soil and crop processes, which is an integral component of Adapt-N, a decision support tool providing N fertilizer recommendations for maize production. Such models can also be used to estimate N losses associated with particular management practices and over a range of current climates and future climate projections. The objectives of this study were to update the PNM model to include an option for simulating soil-water processes in artificially drained soils, and to calibrate the revised PNM model and test it against multiyear field studies in New York and Minnesota with different soils and management practices. Minimal calibration was required for the model. Denitrification rate constants were calibrated by minimizing the error between simulated and observed nitrate leaching for each study site. The normalized root mean squared error of cumulative daily drainage for the validation sets ranged from 10 to 23%. For cumulative daily nitrate leaching, the normalized root mean squared error ranged from 11 to 28% for the validation sets. The minimal calibration required and relatively simple data inputs make the PNM model a broadly applicable tool for simulating water and N flows in maize systems.

Dynamic model-based recommendations increase the precision and sustainability of N fertilization in midwestern US maize production

Abstract The US Midwest encompasses one of the largest intensive maize (Zea mays L.) production environments in the world. Managing these lands in a more sustainable way is essential to reducing environmental stresses. This study explores the potential of Adapt-N, a dynamic biogeochemical model, to more precisely manage N inputs compared to a static N management approach, the Maximum Return to N (MRTN). Data from 16 multiple N rate trials conducted over two years (2013–2014) in three Midwest states were used to reconstruct two yield response functions: quadratic (QD) and linear-plateau (LP), allowing estimation of the Economic Optimal N Rate (EONR), and yields resulting from Adapt-N and MRTN recommendations. Model-based N rates were better correlated with the EONR based on the LP function, and were similar based on the QD function. Applying a dynamic approach to N recommendations allowed a significant reduction in applied N (averaging 28 kg ha−1; 13%) without compromising yield, thereby maintaining farmer’s profits while reducing simulated environmental N losses. Longer-term simulations showed that the largest reductions in N rates by Adapt-N compared to the MRTN occurred in dry seasons when early season N losses were small. This study shows that model-based N recommendations can have both economic and environmental benefits compared to a static N management approach.

Does Population Affect the Location of Flash Flood Reports

AbstractFlash floods cause more fatalities than any other weather-related natural hazard and cause significant damage to property and infrastructure. It is important to understand the underlying processes that lead to these infrequent but high-consequence events. Accurately determining the locations of flash flood events can be difficult, which impedes comprehensive research of the phenomena. While some flash floods can be detected by automated means (e.g., streamflow gauges), flash floods (and other severe weather events) are generally based on human observations and may not reflect the actual distribution of event locations. The Storm Data–Storm Events Database, which is produced from National Weather Service reports, was used to locate reported flash floods within the forecast area of the Binghamton, New York, Weather Forecast Office between 2007 and 2013. The distribution of those reports was analyzed as a function of environmental variables associated with flood generation including slope, impervious...