Modeling the effect of land cover land use change on estuarine environmental flows

Environmental flows are important to maintain the ecological integrity of the estuary. In a watershed, it is influenced by land use land cover (LULC) change, climate variability, and water regulations. San Antonio, Texas, the 8th largest city in the US, is likely to affect environmental flows to the San Antonio Bay/Guadalupe Estuary, due to rapid urbanization. Time series analysis was conducted at several stream gauging stations to assess trends in hydrologic variables. A bootstrapping method was employed to estimate the critical value for global significance. Results suggested a greater number of trends are observed than are expected to occur by chance. Stream gauging stations present in lower half of the watershed experienced increasing trend, whereas upper half experienced decreasing trends. A similar spatial pattern was not observed for rainfall. Winter season observed maximum number of trends. Wavelet analysis on hydrologic variables, suggested presence of multi-scale temporal variability; dominant frequencies in 10 to 15 year scale was observed in some of the hydrologic variables, with a decadal cycle. Dominant frequencies were also observed in 17 to 23 year scale with repeatability in 20 to 30 years. It is therefore important to understand various ecological processes that are dominant in this scale and quantify possible linkages among them. Genetic algorithm (GA) was used for calibration of the Hydrologic Simulation Program in FORTRAN (HSPF) model. Although, GA is computationally demanding, it is better than manual calibration. Parameter values obtained for the calibrated model had physical representation and were well within the ranges suggested in the literature. Information from LANDSAT images for the years 1987, 1999, and 2003 were introduced to HSPF to quantify the impact of LULC change on environmental flows. Modeling studies indicated, with increase in impervious surface, peak flows increased over the years. Wavelet analysis pointed, that urbanization also impacted storage. Modeling studies quantified, on average about 50% of variability in freshwater inflows could be attributed to variation in precipitation, and approximately 10% of variation in freshwater inflows could be attributed to LULC change. This study will help ecologist, engineers, scientist, and politicians in policy making pertinent to water resources management

Autocalibration of HSPF for Simulation of Streamflow Using a Genetic Algorithm

Hydrologic models are essential to watershed planning and management, particularly in the San Antonio River watershed where competition for scarce water resources is a challenge. As a result, the calibration and validation of hydrologic models are essential steps for their successful application. In this study, we examined the use of a loosely coupled genetic algorithm (GA) as an autocalibration tool for optimization of model parameters for the Hydrologic Simulation Program - Fortran (HSPF), a model frequently used in surface hydrology and water quality modeling. The GA-HSPF model is a more objective and less time-consuming alternative to traditional trial-and-error methods. The objective function was optimized by minimizing the mean absolute error (MAE) between corresponding simulated and observed average daily streamflow in the San Antonio River watershed. The MAE was used to evaluate the fitness of the parameter set in the GA. The calibrated model parameters (LZSN, INFILT, AGWRC, UZSN, DEEPFR, LZETP, and INTFW) were selected based on a sensitivity analysis from a previous study. Goodness-of-fit of the GA calibrated model was evaluated using the Nash-Sutcliffe coefficient of efficiency, MAE, root mean square error, flow duration curves, wavelet analysis, and total volume error. Overall simulation time with 2000 model simulations was 11 days, which can be improved significantly under parallel computing, as GA-HSPF simulations are highly independent. The objective function ceased improvement after approximately 250 simulations, with a minimized MAE of 25.8 m3/s. With the exception of DEEPFR, all optimized model parameter values were within the range cited in the literature. Nash-Sutcliffe coefficients in all simulations were above 0.5, suggesting that the simulated flows were in good agreement with the observed flows. Visual comparison between observed and simulated stream flow using time series and flow duration curves showed that the GA calibrated model was unable to simulate peak flow events accurately, particularly in the 0% to 10% exceedence range. It is hypothesized that the storage-based routing scheme in HSPF limits its ability to predict peak flows in this watershed. Comparison between observed and simulated flows in the wavelet domain indicated that the GA calibrated model was not able to preserve the scale and location of some high frequencies, but the scale and location of lower frequencies were preserved. The cyclic nature of the streamflow in this watershed was more prominent in lower frequencies. While overall flow rates were adequately predicted using a GA-HSPF approach, future work in this watershed needs to focus on multi-objective optimization that optimizes both volumes and peak flows. The GA-HSPF model offers an objective and efficient method for calibration and validation, a useful tool in watershed planning efforts

Chlorophyll a Predictions in a Piedmont Lake in Upstate South Carolina Using Machine-Learning Approaches

Freshwater systems are often breeding grounds for harmful algal blooms (HABs), although they are more dominant in ponds and lakes due to the prevailing conditions in those bodies of water. Therefore, the monitoring, modeling, and management of HABs requires knowledge of the complex interrelationship between factors that influence HABs and their detrimental effect on the ecosystem. High concentrations of chlorophyll a are often used to measure algal blooms in bodies of water. Generally, water samples are collected from the field and the concentration of chlorophyll a is measured in a laboratory and compared to water quality standards in order to indicate the potential presence or absence of an algal bloom. While numerical water quality models can help answer some of the critical environmental conditions that affect HABs and their effective management, numerous model inputs, the uncertainty in model predictions, and the complexity of HABs ecosystems encourage the application of newly rising data-driven models. The current study utilized high-frequency water quality data and investigated machine-learning algorithms (random forest (RF) and artificial neural network (ANN)) to predict chlorophyll a concentrations in Boyd Millpond, a lake in Upstate South Carolina. The model performances were compared using root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient. The water quality parameters used as inputs were pH, specific conductivity, dissolved oxygen, saturated dissolved oxygen, temperature, oxidation-reduction potential (ORP), and turbidity, while chlorophyll a was selected as the target variable. The results from this study showed that RF performed better than ANN. The error metrics observed using all parameters as input were RMSE, R2, and correlation with values 0.00013, 0.86, and 0.93, respectively, when testing the RF model and 0.00025, 0.74, and 0.86, respectively, during the testing stage of the ANN model. The Least Absolute Shrinkage and Selection Operator (LASSO) was used for variable selection and identified pH and specific conductivity as essential parameters. The broader outcome of this research upon further field validation will enable the timely detection of HABs with chlorophyll a as a signal to instigate further tests and early warning for recreational activities and livestock protection and initiate countermeasures to safeguard the lives of aquatic organisms

Copper mediated decarboxylative direct C-H arylation of heteroarenes with benzoic acids

Decarboxylative coupling reactions to date require a stoichiometric oxidant (such as copper and silver salts) for decarboxylation purposes along with a metal catalyst (e.g. palladium) for cross-coupling. In this communication, an economic and sustainable approach by using a simple copper salt was developed in the presence of molecular oxygen as the sole oxidant. A wide range of 5-membered heteroarenes undergo aryl–heteroaryl cross-coupling with electron deficient aryl carboxylic acids

Stakeholder Voice in Water Resource Planning

Stakeholder engagement for natural resource management at the state and local levels has become an important governance practice. This study examines the association of individual traits (aggressive communication, comfort with technology, and argumentativeness) with stakeholder participant voice in a water basin planning virtual meeting setting. Individual participants of the Edisto River Basin Council (RBC) meetings are the subject of the study. South Carolina decentralized water planning to the river basin level, creating RBCs and appointing interested and relevant stakeholders as members. While the river basin planning process did not envisage virtual (Zoom) meetings for the regular meetings of the RBC, the COVID pandemic required this to begin the planning process. Moreover, meeting participants possess diverse interests, powers, and individual traits that may affect the use of voice and engagement. There is well-established literature on stakeholder participation in resource planning. However, there are gaps in the literature regarding use of voice in virtual meeting settings in water resources planning, especially in settings like water-abundant areas in the Southeastern United States. Using the Edisto RBC as a pilot basin and quantitative surveys, preliminary results found that while RBC participants were on average comfortable with technology, they generally avoided conflict, they exhibited average communication apprehension in a meeting environment, and virtual meetings appear to limit participant’s use of voice. Consequently, meeting planners must recognize that not all participants express themselves optimally in virtual meeting settings. In this vein, planners must work to develop opportunities for as much active engagement and sharing as possible

Harmful algal blooms : prevention, response, and avoidance manual

This manual contains information about the prevention, response, and avoidance of harmful algal blooms

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

Peer reviewe

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe