River–groundwater connectivity in a karst system,Wellington, New South Wales,Australia

The characterization of river–aquifer connectivity in karst environments is difficult due to the presence of conduits and caves. This work demonstrates how geophysical imaging combined with hydrogeological data can improve the conceptualization of surface-water and groundwater interactions in karst terrains. The objective of this study is to understand the association between the Bell River and karst-alluvial aquifer at Wellington, Australia. River and groundwater levels were continuously monitored, and electrical resistivity imaging and water quality surveys conducted. Two-dimensional resistivity imaging mapped the transition between the alluvium and karst. This is important for highlighting the proximity of the saturated alluvial sediments to the water-filled caves and conduits. In the unsaturated zone the resistivity imaging differentiated between air- and sediment-filled karst features, and in the saturated zone it mapped the location of possible water- and sediment-filled caves. Groundwater levels are dynamic and respond quickly to changes in the river stage, implying that there is a strong hydraulic connection, and that the river is losing and recharging the adjacent aquifer. Groundwater extractions (1,370 ML, megalitres, annually) from the alluvial aquifer can cause the groundwater level to fall by as much as 1.5 m in a year. However, when the Bell River flows after significant rainfall in the upper catchment, river-leakage rapidly recharges the alluvial and karst aquifers. This work demonstrates that in complex hydrogeological settings, the combined use of geophysical imaging, hydrograph analysis and geochemical measurements provide insights on the local karst hydrology and groundwater processes, which will enable better water-resource and karst management.     

Trends and climate evolution: Statistical approach for very high temperatures in France

The existence of an increasing trend in average temperatures during the last 50 years is widely acknowledged. Furthermore, there is compelling evidence of the variability of extremes, and rapid strides are made in studies of these events. Indeed, by extending the results of the “extreme value theory” (EVT) to the non-stationary case, analyses can examine the presence of trends in extreme values of stochastic processes. Definition of extreme events, their statistical significance as well as their interpretations have to be handled with great care when used for environmental concerns and public safety. Thus, we will discuss the validity of the hypothesis allowing the use of mathematical theories for these problems. To answer safety requirements, respect installation norms and reduce public risk, return levels are a major operational goal, obtained with the EVT. In this paper, we give quantitative results for observations of high temperatures over the 1950–2003 period in 47 stations in France. We examined the validity of the non-stationary EVT and introduced the notion of return levels (RL) in a time-varying context. Our analysis puts particular accent on the difference between methods used to describe extremes, to perform advanced fits and tests (climatic science), and those estimating the probability of rare future events (security problems in an evolving climate). After enouncing the method used for trend identification of extremes in term of easily interpretable parameters of distribution laws, we apply the procedure to long series of temperature measurements and check the influence of data length on trend estimation. We also address the problem of choosing the part of observations allowing appropriate extrapolation. In our analysis, we determined the influence of the 2003 heat wave on trend and return-level estimation comparing it to the RL in a stationary context. The application of the procedure to 47 stations spread over France is a first step for a refined spatial analysis. Working on the behavior of distribution parameters while assessing trend identification is a primary tool in order to classify climatic change with respect to the location of the station and open a systematic work using the same methodology for other variables and multivariate studies.     

Gaseous air pollution background estimation in urban,suburban, and rural environments

There is a great demand for estimating the ambient air pollutant background concentrations in order to assess the effectiveness of different emission control strategies. In this paper, the background concentrations of four pollutants, namely sulfur dioxide (SO₂), nitrogen oxides (NOx), carbon monoxide (CO), and ozone (O₃) pollutants in urban, suburban, and rural environments were investigated using Kolmogorov–Zurbenko (KZ) filter technique. Air quality data from monitoring stations over a period of 4 years (2007–2010) was analyzed for three locations in Kuwait, namely urban, suburban, and rural. The spatial and temporal (daily, weekly, and monthly) variations of the four pollutants were analyzed. The results show that the levels of ambient air pollutant background concentrations were high in the urban site compared to suburban and rural area. The diurnal variation of SO₂ concentration showed an early morning peak, while the diurnal variation of NOx concentration constituted has two peaks, one was in the early morning hours (5 to 8 a.m.) and the second was in nighttime hours (8 to 11 p.m.). These two peaks were observed at all three locations. The monthly background NOx concentration reached a maximum in winter and minimum in summer. Diurnal variation of CO concentration showed a similar trend to SO₂ concentrations in all three locations. Because of the photochemical reactions that occur in the atmosphere, the background concentration of O₃ showed an inverse relation with respect to background concentration of NOx.     

The inclusive and simplified forms of Bayesian interpolation for general and monotonic models using Gaussian and Generalized Beta distributions with application to Monte Carlo simulations

A recently developed Bayesian interpolation method (BI) and its application to safety assessment of a flood defense structure are described in this paper. We use a one-dimensional Bayesian Monte Carlo method (BMC) that has been proposed in (Rajabalinejad 2009) to develop a weighted logical dependence between neighboring points. The concept of global uncertainty is adequately explained and different uncertainty association models (UAMs) are presented for linking the local and global uncertainty. Based on the global uncertainty, a simplified approach is introduced. By applying the global uncertainty, we apply the Guassian error estimation to general models and the Generalized Beta (GB) distribution to monotonic models. Our main objective in this research is to simplify the newly developed BMC method and demonstrate that it can dramatically improve the simulation efficiency by using prior information from outcomes of the preceding simulations. We provide theory and numerical algorithms for the BI method geared to multi-dimensional problems, integrate it with a probabilistic finite element model, and apply the coupled models to the reliability assessment of a flood defense for the 17th Street Flood Wall system in New Orleans.     

Continuous measurement of aerodynamic and alfalfa canopy resistances using the Bowen ratio-energy balance and Penman-Monteith methods

To investigate the alfalfa crop response to environmental factors, a Bowen ratio-energy balance method was used to evaluate short-term alfalfa canopy resistance. Continuous evapotranspiration (ET _a ) and the aerodynamic resistance (r _a ) for an alfalfa crop in each 20-min interval were calculated. Using the calculated ET _a and r _a and the Penman-Monteith approach, the bulk stomatal or actual canopy resistance (r _c ) was evaluated. The continuous 20-min resistances were computed for clear and partially cloudy sky conditions, and different average crop heights. The results show that this technique can satisfactorily be used to study the manner in which the aerodynamic and canopy resistances respond to short-term variations in weather elements such as photosynthetically active radiation (PAR), wind speed and atmospheric saturation vapor deficit.Research Assistant Professor and Assistant Utah State Climatologist, Research Associate Professor and Research Assistant, respectively.