Estimates of critical acid loads and exceedances for forest soils across the conterminous United States

Concern regarding the impacts of continued nitrogen and sulfur deposition on ecosystem health has prompted the development of critical acid load assessments for forest soils. A critical acid load is a quantitative estimate of exposure to one or more pollutants at or above which harmful acidification-related effects on sensitive elements of the environment occur. A pollutant load in excess of a critical acid load is termed exceedance. This study combined a simple mass balance equation with national-scale databases to estimate critical acid load and exceedance for forest soils at a 1-km(2) spatial resolution across the conterminous US. This study estimated that about 15% of US forest soils are in exceedance of their critical acid load by more than 250eqha(-1)yr(-1), including much of New England and West Virginia. Very few areas of exceedance were predicted in the western US.     

An uncertainty analysis of mean flow velocity measurements used to quantify emissions from stationary sources

Point velocity measurements conducted by traversing a Pitot tube across the cross section of a flow conduit continue to be the standard practice for evaluating the accuracy of continuous flow-monitoring devices. Such velocity traverses were conducted in the exhaust duct of a reduced-scale analog of a stationary source, and mean flow velocity was computed using several common integration techniques. Sources of random and systematic measurement uncertainty were identified and applied in the uncertainty analysis. When applicable, the minimum requirements of the standard test methods were used to estimate measurement uncertainty due to random sources. Estimates of the systematic measurement uncertainty due to discretized measurements of the asymmetric flow field were determined by simulating point velocity traverse measurements in a flow distribution generated using computational fluid dynamics. For the evaluated flow system, estimates of relative expanded uncertainty for the mean flow velocity ranged from ±1.4% to ±9.3% and depended on the number of measurement locations and the method of integration.

Implications:Accurate flow measurements in smokestacks are critical for quantifying the levels of greenhouse gas emissions from fossil-fuel-burning power plants, the largest emitters of carbon dioxide. A systematic uncertainty analysis is necessary to evaluate the accuracy of these measurements. This study demonstrates such an analysis and its application to identify specific measurement components and procedures needing focused attention to improve the accuracy of mean flow velocity measurements in smokestacks.     

Instream Restoration to Improve the Ecohydrologic Function of a Subalpine Meadow: Pre‐implementation Modeling with HEC‐RAS

Vegetation in subalpine meadows in the Sierra Nevada Mountains is particularly vulnerable to lowering of groundwater levels because wet meadow vegetation is reliant upon shallow groundwater during the dry summer growing season. These ecosystems are especially vulnerable to channel incision as meadow aquifers are hydrologically connected to tributaries, and many have not yet recovered from previous anthropogenic influences. While instream restoration projects have become a common approach, lack of postrestoration monitoring and communication often result in a trial‐and‐error approach. In this study we demonstrate that preimplementation modeling of possible instream restoration solutions, chosen to raise stream stage and subsequently groundwater levels, is a useful tool for evaluating and comparing potential channel modifications. Modeling allows us to identify strategic locations and specific methods. Results show additional sediment depth and roughness on tributaries along with introduced woody debris (simulated by high roughness) on the Tuolumne River are the most effective means of raising stream stage. Results demonstrate that restoration efforts are most efficient in tributary streams. Managers and planners can more efficiently direct resources while minimizing the potential for negative impacts or failed restoration projects by modeling the possible effects of multiple restoration scenarios before implementation.     

Repeatability,Sensitivity, and Uncertainty Analyses of the BANCS Model Developed to Predict Annual Streambank Erosion Rates

Accelerated streambank erosion caused by channel instability can be the leading cause of sediment impairment of streams. Obtaining accurate streambank erosion rates for sediment budgeting and prioritizing mitigation efforts can be difficult and costly. One approach to quantifying streambank erosion rates is through the development and implementation of an empirically derived “Bank Assessment for Non‐point Source Consequences of Sediment” (BANCS) model. This study aims to improve the BANCS model application by evaluating repeatability between users and identifying sensitive and/or uncertain model inputs. Statistical analysis of streambank evaluations conducted by 10 different individuals suggests the implementation of the BANCS model may not be repeatable. This finding may be due to sensitive model inputs, such as streambank height and near‐bank stress level prediction method selection, and/or uncertain model inputs, such as bank material identification and the associated adjustment of erosion potential. Furthermore, it was found assessing streambanks as a group by obtaining a measure of central tendency from individual evaluations, as well as obtaining a higher level of training, may improve model implementation precision. Application of these suggestions may result in improved prediction of streambank erosion rates utilizing the BANCS model methodology.     

Comparison study and parameter identification of three battery models for an off-grid photovoltaic system

There is growing interest in solar batteries, especially for photovoltaic (PV) applications. Therefore, an accurate battery model is required for the PV system because of its influence on system efficiency. Several mathematical models of batteries have been described in the scientific literature. However, this paper reviews three electrochemical models most commonly used for PV systems, such as Shepherd, Manegon and Coppetti, in order to define the most appropriate model for PV systems. This paper discusses an application of the pattern search optimization technique to extract the parameters of three battery models derived from experimental test results obtained from sealed gelled lead acid batteries for both charge and discharge modes. A comparative case and regression analysis based on statistical tests and a quantitative method were conducted to demonstrate the effectiveness and accuracy of the updated model from the three aforementioned. The simulation results and tests performed on the battery charge and discharge modes lead us as well to approve the algorithm’s accuracy regarding the updated model.