Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.     

Assessing the Potential Effects of Fungicides on Nontarget Gut Fungi (Trichomycetes) and Their Associated Larval Black Fly Hosts

Fungicides are moderately hydrophobic and have been detected in water and sediment, particularly in agricultural watersheds, but typically are not included in routine water quality monitoring efforts. This is despite their widespread use and frequent application to combat fungal pathogens. Although the efficacy of these compounds on fungal pathogens is well documented, little is known about their effects on nontarget fungi. This pilot study, a field survey in southwestern Idaho from April to December 2010 on four streams with varying pesticide inputs (two agricultural and two reference sites), was conducted to assess nontarget impact of fungicides on gut fungi, or trichomycetes. Tissues of larval black flies (Diptera: Simuliidae), hosts of gut fungi, were analyzed for pesticide accumulation. Fungicides were detected in hosts from streams within agricultural watersheds but were not detected in hosts from reference streams. Gut fungi from agricultural sites exhibited decreased percent infestation, density and sporulation within the gut, and black fly tissues had elevated pesticide concentrations. Differences observed between the sites demonstrate a potential effect on this symbiotic system. Future research is needed to parse out the details of the complex biotic and abiotic relationships; however, these preliminary results indicate that impacts to nontarget organisms could have far‐reaching consequences within aquatic ecosystems.     

Spatially explicit models of land abandonment in the Amazon

Given growing concerns about biodiversity loss and carbon emissions stemming from tropical deforestation, it is important to identify the factors associated with land abandonment as they might indicate areas that were deforested but are unsuitable for long-term cultivation. This article utilizes a high-resolution, Landsat-based data-set called TerraClass to relate land abandonment to precipitation, slope, farmgate price of beef, market access, soil quality, and land tenure characteristics in the Brazilian Amazon. The spatially explicit statistical models reveal that rates of land abandonment increase with higher rainfall and slope values, even when controlling for distance to roads, farmgate prices, type of settlement, and soil quality. Moreover, land adjacent to a major road is more likely to be consistently cultivated, but land near large rivers and far from highways is more likely to be abandoned. Land ownership type also influences the likelihood of land abandonment as settlements based on communal property rights have higher rates of land abandonment than settlements with private lots. These results suggest that policies aimed at occupying tropical forests such as the Amazon must consider these biophysical and socioeconomic factors in order to foster human development aligned with sustainability principles.     

Effects of in situ CO2 enrichment on the structural and chemical characteristics of the seagrass Thalassia testudinum

Seagrasses commonly display carbon-limited photosynthetic rates. Thus, increases in atmospheric pCO₂, and consequentially oceanic CO_2(aq) concentrations, may prove beneficial. While addressed in mesocosms, these hypotheses have not been tested in the field with manipulative experimentation. This study examines the effects of in situ CO_2(aq) enrichment on the structural and chemical characteristics of the tropical seagrass, Thalassia testudinum. CO_2(aq) availability was manipulated for 6 months in clear, open-top chambers within a shallow seagrass meadow in the Florida Keys (USA), reproducing forecasts for the year 2100. Structural characteristics (leaf area, leaf growth, shoot mass, and shoot density) were unresponsive to CO_2(aq) enrichment. However, leaf nitrogen and phosphorus content declined on average by 11 and 21 %, respectively. Belowground, non-structural carbohydrates increased by 29 %. These results indicate that increased CO_2(aq) availability may primarily alter the chemical composition of seagrasses, influencing both the nutrient status and resilience of these systems.     

Indoor and outdoor air PBDE levels in a Southwestern US city

Levels of polybrominated diphenyl ether (PBDE) flame retardants have been increasing in humans and the environment for the past few decades. Human levels are markedly higher in the US than Europe. Although food appears to be a significant route of intake, food PBDE levels are not substantially higher in the US than Europe. House and office dust appear to be major routes of exposure with air believed to usually provide a lesser route of intake. Because there are very few measurements of airborne PBDE that have been performed in relevant microenvironments in the US, increased efforts to assess airborne PBDE in the US as sources of exposure are needed. This study reports, for the first time from a Southwestern US city in Texas, the results of measurements of airborne PBDE in multiple locations, two outdoor and six indoor (residential and office) from active air sampling with collection of a combination of both vapor- and particulate-phase PBDE. Higher PBDE levels were measured in indoor than outdoor air, which confirms previous findings. Of 11 measured congeners including BDE 209, total PBDE levels in two outdoor air samples were 112 and 125 pg m^?3 and the indoor air levels ranged from 175 to 1232 pg m^?3 with a median of 572 pg m^?3. These findings suggest that sources of air contamination with PBDE may be similar in Texas as elsewhere in North America. However, more sampling is required to (1) better determine if this is the case and (2) attempt to characterize potential sources of PBDE contamination in both indoor and outdoor air by analysis of congener patterns.     

Mechanisms of moisture stress in a mid-latitude temperate forest: Implications for feedforward and feedback controls from an irrigation experiment

While it is well established that stomata close during moisture stress, strong correlations among environmental (e.g., vapor pressure deficit, soil moisture, air temperature, radiation) and internal (e.g., leaf water potential, sap flow, root-shoot signaling) variables obscure the identification of causal mechanisms from field experiments. Models of stomatal control fitted to field data therefore suffer from ambiguous parameter identification, with multiple acceptable (i.e., nearly optimal) model structures emphasizing different moisture status indicators and different processes. In an effort to minimize these correlations and improve parameter and process identification, we conducted an irrigation experiment on red maples (Acer rubrum L.) at Harvard Forest (summers of 2005 and 2006). Control and irrigated trees experienced similar radiative and boundary layer forcings, but different soil moisture status, and thus presumably different diurnal cycles of internal leaf water potential. Measured soil moisture and atmospheric forcing were used to drive a transient tree hydraulic model that incorporated a Jarvis-type leaf conductance in a Penman–Monteith framework with a Cowan-type (resistance and capacitance) tree hydraulic representation. The leaf conductance model included dependence on both leaf matric potential, Ψ_L (so-called feedback control) and on vapor pressure deficit, D (so-called feedforward control). Model parameters were estimated by minimizing the error between predicted and measured sap flow. The whole-tree irrigation treatment had the effect of elevating measured transpiration during summer dry-downs, demonstrating the limiting effect that subsurface resistance may have on transpiration during these times of moisture stress. From the best fitted model, we infer that during dry downs, moisture stress manifests itself in an increase of soil resistance with a resulting decrease in Ψ_L, leading to both feedforward and feedback controls in the control trees, but only feedforward control for the irrigated set. Increases in the sum-of-squares error when individual model components were disabled allow us to reject the following three null hypotheses: (1) the f(D) stress is statistically insignificant (p = 0.01); (2) the f(Ψ_L) stress is statistically insignificant (p = 0.07); and (3) plant storage capacitance is independent of moisture status (p = 0.07).     

Benchmark calculations from summarized data: an example

Benchmark calculations often are made from data extracted from publications. Such data may not be in a form most appropriate for benchmark analysis, and, as a result, suboptimal and/or non-standard benchmark analyses are often applied. This problem can be mitigated in some cases using Monte Carlo computational methods that allow the likelihood of the published data to be calculated while still using an appropriate benchmark dose (BMD) definition. Such an approach is illustrated herein using data from a study of workers exposed to styrene, in which a hybrid BMD calculation is implemented from dose response data reported only as means and standard deviations of ratios of scores on neuropsychological tests from exposed subjects to corresponding scores from matched controls. The likelihood of the data is computed using a combination of analytic and Monte Carlo integration methods.

Will climate change overwhelm fire management capacity?

Using anomalies calculated from General Circulation Model (GCM) climate predictions we developed scenarios of future fire weather, fuel moisture and fire occurrence and used these as the inputs to a fire growth and suppression simulation model for the province of Ontario, Canada. The goal of this study was to combine GCM predictions with the fire growth and suppression model to examine potential changes in area burned in Ontario due to climate change, while accounting for the large fire suppression activities of the Ontario Ministry of Natural Resources (OMNR). Results indicate a doubling of area burned in the Intensive and Measured fire management zones of Ontario by the decade of 2040 and an eightfold increase in area burned by the end of the 21st century in the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) A2 scenario; smaller increases were found for the A1b and B1 scenarios. These changes are driven by increased fire weather conducive to large fire growth, and increases in the number of fires escaping initial attack: for the Canadian GCM's business-as-usual (A2) scenario, escaped fire frequency increased by 34% by 2040 and 92% by the end of the 21st century. Incorporating more detail on large fire growth than previous studies, our model predicts higher area burned under climate change than do these previous studies, as large numbers of high-intensity fires overwhelm suppression capacity.