Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.     

Emerging Tools for Continuous Nutrient Monitoring Networks: Sensors Advancing Science and Water Resources Protection

Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions. In particular, nutrient sensors are of interest because of the well‐known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health. Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment. Using nitrate sensors as the primary example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real‐time nutrient data. The concurrent emergence of new tools to integrate, manage, and share large datasets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous monitoring to rapidly move forward. We highlight several near‐term opportunities for federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large‐scale, integrated monitoring network. Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation's water resources.     

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.     

Applying Statistical Causal Analyses to Agricultural Conservation: A Case Study Examining P Loss Impacts

Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by factors such as differing crop types and management practices. As we may not have the full knowledge of these confounding factors, conventional statistical meta‐analysis methods can be misleading. We discuss the use of two statistical causal analysis methods for quantifying the effects of water and soil conservation practices in reducing P loss from agricultural fields. With the propensity score method, a subset of data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling method, data were stratified based on important confounding factors, and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect (total P load reduction avg. ~70%). In addition, both methods show evidence of conservation practices reducing the incremental increase in total P export per unit increase in fertilizer application. These results are presented as examples of the types of outcomes provided by statistical causal analyses, not to provide definitive estimates of P loss reduction. The enhanced meta‐analysis methods presented within are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed‐scale modeling.     

Improving Pro-environmental Behaviours in Spain. The Role of Attitudes and Socio-demographic and Political Factors

Over the last 30 years, Spain has witnessed great economic growth. Nevertheless, in spite of an increasing degree of environmental concern, environmentally friendly attitudes and behaviours are still clearly below the European average. In this context, this paper analyses the impact of personal control and attitudes, and of certain socio-demographic, economic and political variables in the development of two environmentally friendly behaviours in Spain: consumption and purchase. The data were provided by a survey carried out in 2007 by the Centro de Investigaciones Sociológicas (Centre for Sociological Research) in Spain. The results show the importance of personal control, level of environmental information, political ideology, age and educational level variables to explaining the development of pro-environmental-related consumption behaviours. Personal attitudes, work situation, female gender and level of environmental information influence pro-environmental purchase behaviour in a positive way. Taking into account that personal attitudes are more determining of pro-environmental purchase than consumption behaviour, policy measures such as informal education, tax measures, social and individual norms and interpersonal and institutional trust are discussed to increase the development of pro-environmental behaviours in Spain.     

Reducing the risk of invasive forest pests and pathogens: Combining legislation,targeted management and public awareness

Intensifying global trade will result in increased numbers of plant pest and pathogen species inadvertently being transported along with cargo. This paper examines current mechanisms for prevention and management of potential introductions of forest insect pests and pathogens in the European Union (EU). Current European legislation has not been found sufficient in preventing invasion, establishment and spread of pest and pathogen species within the EU. Costs associated with future invasions are difficult to estimate but past invasions have led to negative economic impacts in the invaded country. The challenge is combining free trade and free movement of products (within the EU) with protection against invasive pests and pathogens. Public awareness may mobilise the public for prevention and detection of potential invasions and, simultaneously, increase support for eradication and control measures. We recommend focus on commodities in addition to pathways, an approach within the EU using a centralised response unit and, critically, to engage the general public in the battle against establishment and spread of these harmful pests and pathogens.     

The in vitro effect of selected essential oils on the growth and mycotoxin production of Aspergillus species

The aim of the present study was to assess the antifungal and anti-toxinogenic activity of 15 essential oils (EOs) against three fungi of the genus Aspergillus (A. parasiticus KMi-227-LR, A. parasiticus KMi-220-LR and A. flavus KMi-202-LR). The minimum inhibitory doses (MIDs) of the tested essential oils and their antifungal activity were determined using the micro-atmosphere method. The original commercial essential oil samples of Jasminum officinale L., Thymus vulgaris L., Syzygium aromaticum (L.) Merrill &; Perry, Rosmarinus officinalis L., Ocimum basilicum L., Eucalyptus globulus Labill., Salvia officinalis L., Citrus limon (L.) Burm, Origanum vulgare L., Lavandula angustifolia Mill., Carum carvi L., Citrus sinensis (L.) Osbeck., Zingiber officinalis Rosc., Mentha piperita L. and Cinnamomum zeylanicum Nees. (C. verum J.S.Presl.) were produced in Slovakia (Calendula a.s., Nová ?ubovňa, Slovakia). All essential oils exhibited activity against all tested strains of fungi. After 14 days of incubation, A. flavus (KMi-202-LR) showed the highest susceptibility with a growth inhibition percentage (GIP) of 18.70% to C. limon and 5.92% to C. sinensis, while A. parasiticus (KMi-220-LR) exhibited a GIP of 20.56% to J. officinale. The minimum inhibitory doses (MIDs) of EOs with the most significant activity were recorded. The best antifungal activity, using the micro-atmosphere method was found in S. aromaticum with an MID of 62.5 μL L^?1 air, T. vulgaris (MID of 62.5 μL L^?1 air) and O. vulgare (MID of 31.5 μL L^?1 air) against all tested strains. Mycotoxin production of the tested strains was evaluated by the thin layer chromatography (TLC) method. Mycotoxin production of AFB₁ and AFG₁ was inhibited following all treatments with C. carvi, R. officinale and S. officinale, Eucalyptus globulus L. and O. basilicum L. Essential oils exhibited a potential inhibition activity against toxic fungi, although, these affected only the production of AFB₁.     

Metal contents of marine turtle eggs (Chelonia mydas; Lepidochelys olivacea) from the tropical eastern pacific and the implications for human health

Concentrations of eight elements were measured in Chelonia mydas and Lepidochelys olivacea eggs collected along the Pacific coast of Panama. Manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and mercury (Hg) concentrations were similar to previous reports of these species from around the world, while lead (Pb) was lower than previous reports. Cd posed the highest health risk to people who regularly eat the eggs, with average consumption rates leading to target hazard quotients (THQ) of up to 0.35 ± 0.15. Our conclusions indicate that current turtle egg consumption in isolated, coastal Pacific communities may pose a health concern for young children, and that youth and young adults should limit their consumption of turtle eggs to reduce their total intake of nonessential metals.