Evaluation of Gauge‐Radar Merging Methods Using a Semi‐Distributed Hydrological Model in the Upper Thames River Basin,Canada

Gauge‐radar merging methods combine rainfall estimates from rain gauges and radar to capitalize on the strengths of the individual instruments. The performance of four well‐known gauge‐radar merging methods, including mean field bias correction, Brandes spatial adjustment, local bias correction using kriging, and conditional merging, are examined using Environment Canada radar and the Upper Thames River Basin in southwestern Ontario, Canada, as a case study. The analysis assesses the effect of gauge‐radar merging methods on: (1) the accuracy of predicted rainfall accumulations; and (2) the accuracy of predicted streamflows using a semi‐distributed hydrological model. In addition, several influencing factors (i.e., gauge density, storm type, basin type, proximity to the radar tower, and time‐step of adjustment) are analyzed to determine their effect on the performance of the rainfall estimation techniques. Confirming results of previous studies, the merging methods provide an increase in the accuracy of both rainfall accumulation estimations and predicted streamflows. The results also indicate specific factors such as gauge density, rainfall intensity, and time‐step of adjustment can reduce the accuracy of merging methods and play a key role in the examination of its use for operational purposes. Results provide guidance for hydrologists and engineers assessing how best to apply corrected radar products to improve rainfall estimation and hydrological modeling accuracy.     

Shine a light: Under-ice light and its ecological implications in a changing Arctic Ocean

The Arctic marine ecosystem is shaped by the seasonality of the solar cycle, spanning from 24-h light at the sea surface in summer to 24-h darkness in winter. The amount of light available for under-ice ecosystems is the result of different physical and biological processes that affect its path through atmosphere, snow, sea ice and water. In this article, we review the present state of knowledge of the abiotic (clouds, sea ice, snow, suspended matter) and biotic (sea ice algae and phytoplankton) controls on the underwater light field. We focus on how the available light affects the seasonal cycle of primary production (sympagic and pelagic) and discuss the sensitivity of ecosystems to changes in the light field based on model simulations. Lastly, we discuss predicted future changes in under-ice light as a consequence of climate change and their potential ecological implications, with the aim of providing a guide for future research.     

Comparison of field-scale herbicide runoff and volatilization losses: an eight-year field investigation

An 8-yr study was conducted to better understand factors influencing year-to-year variability in field-scale herbicide volatilization and surface runoff losses. The 21-ha research site is located at the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD. Site location, herbicide formulations, and agricultural management practices remained unchanged throughout the duration of the study. Metolachlor [2-chloro--(2-ethyl-6-methylphenyl)--(2-methoxy-1-methylethyl) acetamide] and atrazine [6-chloro--ethyl--(1-methylethyl)-1,3,5-triazine-2,4-diamine] were coapplied as a surface broadcast spray. Herbicide runoff was monitored from a month before application through harvest. A flux gradient technique was used to compute volatilization fluxes for the first 5 d after application using herbicide concentration profiles and turbulent fluxes of heat and water vapor as determined from eddy covariance measurements. Results demonstrated that volatilization losses for these two herbicides were significantly greater than runoff losses ( < 0.007), even though both have relatively low vapor pressures. The largest annual runoff loss for metolachlor never exceeded 2.5%, whereas atrazine runoff never exceeded 3% of that applied. On the other hand, herbicide cumulative volatilization losses after 5 d ranged from about 5 to 63% of that applied for metolachlor and about 2 to 12% of that applied for atrazine. Additionally, daytime herbicide volatilization losses were significantly greater than nighttime vapor losses ( < 0.05). This research confirmed that vapor losses for some commonly used herbicides frequently exceeds runoff losses and herbicide vapor losses on the same site and with the same management practices can vary significantly year to year depending on local environmental conditions.     

Wet deposition of mercury within the vicinity of a cement plant before and during cement plant maintenance

Hg species (total mercury, methylmercury, reactive mercury) in precipitation were investigated in the vicinity of the Lehigh Hanson Permanente Cement Plant in the San Francisco Bay Area, CA., USA. Precipitation was collected weekly between November 29, 2007 and March 20, 2008, which included the period in February and March 2008 when cement production was minimized during annual plant maintenance. When the cement plant was operational, the volume weighted mean (VWM) and wet depositional flux for total Hg (Hg_T) were 6.7 and 5.8 times higher, respectively, compared to a control site located 3.5 km east of the cement plant. In February and March, when cement plant operations were minimized, levels were approximately equal at both sites (the ratio for both parameters was 1.1). Due to the close proximity between the two sites, meteorological conditions (e.g., precipitation levels, wind direction) were similar, and therefore higher VWM Hg_T levels and Hg_T deposition likely reflected increased Hg emissions from the cement plant. Methylmercury (MeHg) and reactive Hg (Hg(II)) were also measured; compared to the control site, the VWM for MeHg was lower at the cement plant (the ratio = 0.75) and the VWM for Hg(II) was slightly higher (ratio = 1.2), which indicated the cement plant was not likely a significant source of these Hg species to the watershed.     

Mangrove recruitment after forest disturbance is facilitated by herbaceous species in the Caribbean.

Plant communities along tropical coastlines are often affected by natural and human disturbances, but little is known about factors influencing recovery. We focused on mangrove forests, which are among the most threatened ecosystems globally, to examine how facilitation by herbaceous vegetation might improve forest restoration after disturbance. We specifically investigated whether recovery of mangrove forests in harsh environments is accelerated by nurse plants and whether the beneficial effects are species-specific. Quantification of standardized effects allowed comparisons across performance parameters and over time for: (1) net effect of each herbaceous species on mangrove survival and growth, (2) effects of pre- and post-establishment factors associated with each herbaceous species, and (3) need for artificial planting to enhance growth or survival of mangrove seedlings. Mangrove recruitment in a clear-cut forest in Belize was accelerated by the presence of Sesuvium portulacastrum (succulent forb) and Distichlis spicata (grass), two coastal species common throughout the Caribbean region. The net effect of herbaceous vegetation was positive, but the magnitude of effects on mangrove survival and growth differed by species. Because of differences in their vegetative structure and other features, species effects on mangroves also varied by mechanism: (1) trapping of dispersing propagules (both species), (2) structural support of the seedling (Distichlis), and/or (3) promotion of survival (Sesuviumn) or growth (Distichlis) through amelioration of soil conditions (temperature, aeration). Artificial planting had a stronger positive effect on mangrove survival than did edaphic conditions, but planting enhanced mangrove growth more in Sesuvium than in Distichlis patches. Our study indicates that beneficial species might be selected based on features that provide multiple positive effects and that species comparisons may be improved using standardized effects. Our findings are not only relevant to the coastal environments found in the Caribbean region, but our assessment methods may be useful for developing site-specific information to restore disturbed mangrove forests worldwide, especially given the large pool of mangrove associates (>45 genera) available for screening.     

Texas Regulation Requires Control of Opacity Using Instrumental Measurements

For over four years, a Texas air pollution control regulation has been in force which permits use of a stack mounted transmissometer instrument to measure visible emissions, thus avoiding the necessity for visual observations. Many types of industrial plants have installed instruments, and extensive experience has been obtained with this method of measurement and control. Plant operators and enforcement officials have expressed a strong preference for this method, both to demonstrate compliance and to provide data to aid in operating control equipment. Some operational problems have been encountered, but these usually can be overcome with proper care. Because of the advantages obtained with this regulation a new regulation has been adopted which makes the instrumental method mandatory for large sources of visible emissions.     

Vegetation and climate

Over the last two centuries, man's activities have caused a 30% increase in the atmospheric concentration of CO₂, with continued increases seeming inevitable. This change in CO₂ concentration will act on vegetation, both directly and indirectly through global climatic change. It is well established that, on a global scale, patterns of vegetation and climate are closely correlated. Such correlations indicate that climatic change will cause the distribution of vegetation to change. However, the use of correlations for predicting vegetation responses to climatic change is fundamentally unreliable because correlations have no mechanistic underpinning of causation. This paper outlines a mechanistic model for predicting the equilibrium state between current climate and vegetation. It is also used to indicate the sensitivity of global vegetation to the changed climate associated with a doubled CO₂—greenhouse scenario. The interpretation of this static model is discussed in terms of rates and patterns of vegetation change.     

Soil Moisture Data Assimilation Using Support Vector Machines and Ensemble Kalman Filter1

Abstract: A hybrid data assimilation (DA) methodology that combines two state‐of‐the‐art techniques, support vector machines (SVMs) and ensemble Kalman filter (EnKF), is applied for soil moisture DA in this work. The SVM methodology provides a statistically sound and robust approach to solving the inverse problem, and thus to building statistical models. EnKF is an extension of the Kalman Filter (KF), a well‐known tool in prediction updating. In the present research, ground measurements were used to build a SVM‐type soil moisture predictor. Subsequent observations and their statistics were assimilated to update predictions from the SVM model by coupling it with EnKF. In this way, both model predictions and ground data, as well as their statistics, are fused thus minimizing the prediction error and making the predictions and observations statistically consistent. The results are shown for two approaches; one in which update is done at every time step and the other which assumes that data is only available at alternate time steps (in window of 10 time steps) and hence update is performed at those occasions. The SVM‐EnKF coupling is shown to improve soil moisture forecasts in an example using data from the Soil Climate Analysis Network site at Ames, Iowa.