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.     

Production of soybean ethanol-based biodiesel using CaO heterogeneous catalysts promoted by Zn,K and Mg

This article focuses on the optimization of the production of fatty acid ethyl esters from soybean oil using CaO-based heterogeneous catalysts. Three different catalytic promoters were evaluated: Magnesium, zinc, and potassium. The reaction has evaluated the promoter content (promoter to calcium molar ratio), catalyst load, alcohol to oil molar ratio, and temperature. Response surface methodology (RSM) was used to evaluate the influence of each variable on the yield of biodiesel. The addition of K₂O or MgO in the catalyst has enhanced the yield in fatty acid ethyl esters, while the use of ZnO as a promoter was not successful.     

Clam shell catalyst for continuous production of biodiesel

Continuous flow transesterification of waste frying oil (WFO) with methanol for the biodiesel production was tested in a laboratory scale jacketed reactive distillation (RD) unit packed with clam shell based CaO as solid catalyst. The physiochemical properties of the clam shell catalysts were characterized by X-ray Diffraction (XRD), Brunauer–Emmett–Teller (BET), Scanning Electron Microscopy (SEM), and Energy Dispersive Atomic X-ray Spectrometry (EDAX). The effects of the reactant flow rate, methanol-to-oil ratio, and catalyst bed height were studied to obtain the maximum methyl ester conversion. Reboiler temperature of 65°C was maintained throughout the process for product purification and the system reached the steady state at 7 hr. The experimental results revealed that the jacketed RD system packed with clam shell based CaO showed high catalytic activity for continuous production of biodiesel and a maximum methyl ester conversion of 94.41% was obtained at a reactant flow rate of 0.2 mL/min, methanol/oil ratio of 6:1, and catalyst bed height of 180 mm.     

Biogas production from pistachio (Pistaciavera L.) de-hulling waste

Pistachio processing wastes create significant waste management problems unless properly managed. However, there are not well-established methods to manage the waste generated during the processing of pistachios. Anaerobic digestion can be an attractive option not only for the management of pistachio processing wastes but also producing renewable energy in the form of biogas. This study investigated anaerobic digestibility and biogas production potential of pistachio de-hulling waste from wet de-hulling process. Best to our knowledge, this is the first report on biogas production from pistachio de-hulling waste. The results indicated that (1) anaerobic digestion of pistachio de-hulling wastewater, solid waste, and their mixtures in different ratios is possible with varying levels of performance; (2) 1 L of de-hulling wastewater (chemical oxygen demand concentration of 30 g/L) produced 0.7 L of methane; (3) 1 L of de-hulling wastewater and 20 g of pistachio de-hulling solid waste produced 1.25 L of methane; and (4) 1 g of de-hulling solid waste produced 62.6 mL of methane (or 134 mL of biogas).     

Improving invasive species management by integrating priorities and contributions of scientists and decision makers

Managing invasive species is a major challenge for society. In the case of newly established invaders, rapid action is key for a successful management. Here, we develop, describe and recommend a three-step transdisciplinary process (the “butterfly model”) to rapidly initiate action for invasion management. In the framing of a case study, we present results from the first of these steps: assessing priorities and contributions of both scientists and decision makers. Both scientists and decision makers prioritise research on prevention. The available scientific knowledge contributions, however, are publications on impacts rather than prevention of the invasive species. The contribution of scientific knowledge does thus not reflect scientists’ perception of what is essentially needed. We argue that a more objective assessment and transparent communication of not only decision makers’ but also scientists’ priorities is an essential basis for a successful cooperation. Our three-step model can help achieve objectivity via transdisciplinary communication.