Ellen Cooter

Sensitivity of the National Oceanic and Atmospheric Administration multilayer model to instrument error and parameterization uncertainty

The response of the National Oceanic and Atmospheric Administration multilayer inferential dry deposition velocity model (NOAA-MLM) to error in meteorological inputs and model parameterization is reported. Monte Carlo simulations were performed to assess the uncertainty in NOAA-MLM deposition velocity V d estimates for ozone (O 3 ), sulfur dioxide (SO 2 ), and nitric acid (HNO 3 ) associated with measurements of meteorological variables (including temperature, humidity, radiation, wind speed, wind direction, and leaf area index). Summer daylight scenarios for grass, corn, soybean, oak, and pine were considered. Model sensitivity to uncertainty in the leaf area index (LAI), minimum stomatal resistance, and soil moisture parameterizations was explored. For SO 2 and HNO 3 , instrument error associated with the measurement of wind speed and direction resulted in the greatest V d error. Depending on vegetation type, the most important source of uncertainty due to instrument error for the V d of O 3 was LAI. Of the model parameterizations studied, accurate estimation of temporal aspects of the annual LAI profile and the characterization of soil moisture supply and demand are most important to model-estimated V d uncertainty. Considered individually, these factors can result in SO 2 and HNO 3 V d estimate uncertainty of ±25% and O 3 estimate uncertainty greater than 60%. For single plant species settings, reductions in estimate uncertainty should be possible with minor algorithmic modification, inclusion of more species-appropriate LAI profiles, and careful application of remote sensing technology.

The role of the atmosphere in the provision of ecosystem services.

Solving the environmental problems that we are facing today requires holistic approaches to analysis and decision making that include social and economic aspects. The concept of ecosystem services, defined as the benefits people obtain from ecosystems, is one potential tool to perform such assessments. The objective of this paper is to demonstrate the need for an integrated approach that explicitly includes the contribution of atmospheric processes and functions to the quantification of air-ecosystem services. First, final and intermediate air-ecosystem services are defined. Next, an ecological production function for clean and clear air is described, and its numerical counterpart (the Community Multiscale Air Quality model) is introduced. An illustrative numerical example is developed that simulates potential changes in air-ecosystem services associated with the conversion of evergreen forest land in Mississippi, Alabama and Georgia to commercial crop land. This one-atmosphere approach captures a broad range of service increases and decreases. Results for the forest to cropland conversion scenario suggest that although such change could lead to increased biomass (food) production services, there could also be coincident, seasonally variable decreases in clean and clear air-ecosystem services (i.e., increased levels of ozone and particulate matter) associated with increased fertilizer application. Metrics that support the quantification of these regional air-ecosystem changes require regional ecosystem production functions that fully integrate biotic as well as abiotic components of terrestrial ecosystems, and do so on finer temporal scales than are used for the assessment of most ecosystem services.

Comparison of Measured and Modeled Surface Fluxes of Heat, Moisture, and Chemical Dry Deposition

Realistic air quality modeling requires accurate simulation of both meteorological and chemical processes within the planetary boundary layer (PBL). Surface energy and moisture fluxes control the temperature and humidity profiles. Similarly, chemical fluxes (dry deposition) have an important influence on PBL concentrations of trace chemical species. Therefore, accurate and consistent methods for simulation of both meteorological and chemical surface exchange processes are critical for realistic modeling of boundary layer atmospheric chemistry.

The Impacts of Different Meteorology Data Sets on Nitrogen Fate and Transport in the SWAT Watershed Model

In this study, we investigated how different meteorology data sets impacts nitrogen fate and transport responses in the Soil and Water Assessment Tool (SWAT) model. We used two meteorology data sets: National Climatic Data Center (observed) and Mesoscale Model 5/Weather Research and Forecasting (simulated). The SWAT model was applied to two 10-digit hydrologic unit code watersheds in the Coastal Plain and Piedmont zones of North Carolina. Nitrogen cycling and loading response to these meteorological data were investigated by exploring 19 SWAT nitrogen outputs relating to landscape delivery, biogeochemical assimilation, and atmospheric deposition. The largest difference in model output using both meteorology data sets was for large loads/fluxes. Landscape delivery outputs (e.g., NO−3 watershed discharge, groundwater NO−3 flux, soil NO−3 percolation) showed the largest difference across all values. Use of the two weather data sources resulted in a nearly twofold difference in NO−3 watershed discharge and groundwater NO−3 flux. Differences for many nitrogen outputs were greater than those for sub-basin flow. Nitrogen outputs showed the greatest difference for agricultural land covers and there was no flow-related pattern in output differences across sub-basins or over time (years). In general, nitrogen parameter models that had a greater number of nitrate concentration, flow, and temperature terms (equation variables) in each transport model showed the greatest difference between both meteorology applications.

Potential Impact of Clean Air Act Regulations on Nitrogen Fate and Transport in the Neuse River Basin: a Modeling Investigation Using CMAQ and SWAT

There has been extensive analysis of Clean Air Act Amendment (CAAA) regulation impacts to changes in atmospheric nitrogen deposition; however, few studies have focused on watershed nitrogen transfer particularly regarding long-term predictions. In this study, we investigated impacts of CAAA NOx emissions on the fate and transport of nitrogen for two watersheds in the Neuse River Basin. We applied the Soil and Water Assessment Tool (SWAT) using simulated deposition rates from the Community Multiscale Air Quality (CMAQ) model. Two scenarios were investigated: one that considered CAAA emission controls in CMAQ simulation (with) and a second that did not (without). By 2020, results showed a 70 % drop in nitrogen discharge for the Little River watershed and a 50 % drop for the Nahunta watershed from 1990 levels under the with-CAAA scenario. Denitrification and plant nitrogen uptake played important roles in nitrogen discharge from each watershed. Nitrogen watershed response time to a change in atmospheric nitrogen deposition was 4 years for Nahunta and 2 years for Little River. We attribute these differences in nitrogen response time to contrasts in agricultural land use and diversity of crop types. Soybean, hay, and corn land covers had comparatively longer response times to changes in atmospheric deposition. The studied watersheds demonstrate relatively large nitrogen retention: ≥80 % of all delivered nitrogen.

Development of an Agricultural Fertilizer Modeling System for Bi-Directional Ammonia Fluxes in the CMAQ Model

Atmospheric ammonia (NH3) plays an important role in fine-mode aerosol formation. Accurate estimates of ammonia from both human and natural emissions can reduce uncertainties in air quality modeling. The majority of ammonia anthropogenic emissions come from the agricultural practices, such as animal operations and fertilizer applications. The current emission estimates at the U.S. Environmental Protection Agency (U.S. EPA) are based on the annual National Emission Inventory (NEI). However, accurate estimation of ammonia emissions in space and time has been a challenge. For instance, fertilizer applications vary in the date of application and amount by crop types and geographical area. With the support of the U.S. EPA, we have responded by an agricultural fertilizer modeling system for use with a newly developed ammonia bi-directional flux algorithm in the Community Multiscale Air Quality (CMAQ) model. This modeling system will simulate NH3 emissions from fertilizer applications on agricultural lands rather than from emission estimates based on pre-defined emission factors. The goal for this paper is to demonstrate how this agricultural fertilizer modeling system is developed for a continental U.S. CMAQ 12-km modeling domain and the tools we developed in this system.

Development and Evaluation of an Ammonia Bi-Directional Flux Model for Air Quality Models

Ammonia is an important contributor to particulate matter in the atmosphere and can significantly impact terrestrial and aquatic ecosystems. Surface exchange between the atmosphere and biosphere is a key part of the ammonia cycle. Agriculture, in particular, is a large source of ammonia emitted to the atmosphere, mostly from animal operations and fertilized crops, while dry and wet deposition are the primary sinks of atmospheric ammonia. Although, current air quality models consider all of these source and sink processes, algorithms for emissions from fertilized crops and dry deposition are too simplistic to provide accurate accounting of the net surface fluxes. New modeling techniques are being developed that replace current ammonia emission from fertilized crops and ammonia dry deposition with a bi-directional surface flux model. Comparisons of the ammonia bi-direction flux algorithm to field experiments involving both lightly fertilized soybeans and heavily fertilized corn are presented and discussed. Initial tests and evaluation of CMAQ modeling results for a full year (2002) at 12 km grid resolution including implementation of a soil nitrification model and the ammonia bi-directional flux algorithm result in improved NHxwet deposition.

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA

Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult to develop as a result of interspecies competition, age and spatial distribution. Satellite-based LAI estimates hold promise for retrospective analyses, but we must continue to rely on numerical models for alternative management analysis. Our objective for this study is to calculate and validate LAI estimates generated from the USDA Environmental Policy Impact Climate (EPIC) model (a widely used, field-scale, biogeochemical model) on four forest complexes spanning three physiographic provinces in Virginia and North Carolina. Measurements of forest composition (species and number), LAI, tree diameter, basal area, and canopy height were recorded at each site during the 2002 field season. Calibrated EPIC results show stand-level temporally resolved LAI estimates with R2 values ranging from 0.69 to 0.96, and stand maximum height estimates within 20% of observation. This relatively high level of performance is attributable to EPIC’s approach to the characterization of forest stand biogeochemical budgets, stand history, interspecies competition and species-specific response to local weather conditions. We close by illustrating the extension of this site-level approach to scales that could support regional air quality model simulations.

Examining the impacts of increased corn production on groundwater quality using a coupled modeling system.

This study demonstrates the value of a coupled chemical transport modeling system for investigating groundwater nitrate contamination responses associated with nitrogen (N) fertilizer application and increased corn production. The coupled Community Multiscale Air Quality Bidirectional and Environmental Policy Integrated Climate modeling system incorporates agricultural management practices and N exchange processes between the soil and atmosphere to estimate levels of N that may volatilize into the atmosphere, re-deposit, and seep or flow into surface and groundwater. Simulated values from this modeling system were used in a land-use regression model to examine associations between groundwater nitrate-N measurements and a suite of factors related to N fertilizer and groundwater nitrate contamination. Multi-variable modeling analysis revealed that the N-fertilizer rate (versus total) applied to irrigated (versus rainfed) grain corn (versus other crops) was the strongest N-related predictor variable of groundwater nitrate-N concentrations. Application of this multi-variable model considered groundwater nitrate-N concentration responses under two corn production scenarios. Findings suggest that increased corn production between 2002 and 2022 could result in 56% to 79% increase in areas vulnerable to groundwater nitrate-N concentrations ≥ 5 mg/L. These above-threshold areas occur on soils with a hydraulic conductivity 13% higher than the rest of the domain. Additionally, the average number of animal feeding operations (AFOs) for these areas was nearly 5 times higher, and the mean N-fertilizer rate was 4 times higher. Finally, we found that areas prone to high groundwater nitrate-N concentrations attributable to the expansion scenario did not occur in new grid cells of irrigated grain-corn croplands, but were clustered around areas of existing corn crops. This application demonstrates the value of the coupled modeling system in developing spatially refined multi-variable models to provide information for geographic locations lacking complete observational data; and in projecting possible groundwater nitrate-N concentration outcomes under alternative future crop production scenarios.

Using a Coupled Modelling System to Examine the Impacts of Increased Corn Production on Groundwater Quality and Human Health

Attributing nitrogen (N) in the environment to emissions from agricultural management practices is difficult because of the complex and inter-related chemical and biological reactions associated with N and its cascading effects across land, air and water. Such analyses are critical, however, in understanding the benefits and disbenefits associated with environmental management options. Coupled physical models present new opportunities to understand relationships among environmental variables across multiple sources, pathways and scenarios. Because they trace the environmental fate of pollutant concentrations found in the environment through first-principle physical and chemical processes, they shed new light on these complex interactions and how they will respond under various management scenarios. In this study, we use a coupled modeling system to holistically assess the impacts of increased corn production on groundwater and air quality. In particular, we show how the models provide new information on the drivers for contamination in groundwater and air, and then relate pollutant concentration changes attributed to potential changes in corn production between 2002 and 2022 to health and cost outcomes.