Temporal structures of the North Atlantic Oscillation and its impact on the regional climate variability

In this study, the temporal structure of the variation of North Atlantic Oscillation （NAO） and its impact on regional climate variability are analyzed using various datasets. The results show that blocking formations in the Atlantic region are sensitive to the phase of the NAO. Sixty-seven percent more winter blocking days are observed during the negative phase compared to the positive phase of the NAO. The average length of blocking during the negative phase is about 11 days, which is nearly twice as long as the 6-day length observed during the positive phase of the NAO. The NAO-related differences in blocking frequency and persistence are associated with changes in the distribution of the surface air temperature anomaly, which, to a large extent, is determined by the phase of the NAO. The distribution of regional cloud amount is also sensitive to the phase of the NAO. For the negative phase, the cloud amounts are significant, positive anomalies in the convective zone in the Tropics and much less cloudiness in the mid latitudes. But for the positive phase of the NAO, the cloud amount is much higher in the mid-latitude storm track region. In the whole Atlantic region, the cloud amount shows a decrease with the increase of surface air temperature. These results suggest that there may be a negative feedback between the cloud amount and the surface air t.emperature in the Atlantic region.     

The impact of el Nino‐Southern oscillation on the temperature field over Canada: Research note

Abstract

The impact of the two phases of El Niño‐Southern Oscillation (ENSO), namely El Niño and La Niña, on the surface and lower tropospheric temperature fields over Canada is documented. Gridded surface temperature data for 91 years (1900–1990) and 500–1000 hPa thickness data for 49 years (1946–1994) have been analyzed statistically in the context of El Niño, La Niña and normal years.

Using a composite analysis, the present study conclusively demonstrates that significant positive surface temperature anomalies spread eastward from the west coast of Canada to the Labrador coast from the late fall to early spring (November through May) following the onset of El Niño episodes. The accompanying temperatures in the lower troposphere show a transition from the Pacific/North American (PNA) pattern to the Tropical/Northern Hemisphere (TNH) pattern over the North American sector during the same period. Conversely, significant negative surface temperature anomalies spread southeastward from the Yukon and extend into the upper Great Lakes region by the winter season following the onset of La Niña episodes. Furthermore, the lower tropospheric temperatures show a negatively‐phased PNA‐like pattern in early winter which weakens considerably by May of the following year. Thus, while western Canadian surface temperatures are influenced during both phases of ENSO, eastern Canadian surface temperature effects are found during the El Niño phase only. The impact of ENSO on the Canadian surface temperatures is the strongest during the winter season and nearly disappears by spring (April and May). The largest positive (negative) anomalies are found to be centred over two separate regions, one over the Yukon and the other just west of Hudson Bay in the El Niño (La Niña) years. Over western Canada, mean wintertime temperature distribution of the El Niño (La Niña) years is found to be shifted towards warmer (colder) values relative to the distribution of the normal years.

This study suggests the possibility of developing a long‐range forecasting technique for Canada using ENSO related indices.     

Forward induced seismic hazard assessment: application to a synthetic seismicity catalogue from hydraulic stimulation modelling

The M _w 3.2-induced seismic event in 2006 due to fluid injection at the Basel geothermal site in Switzerland was the starting point for an ongoing discussion in Europe on the potential risk of hydraulic stimulation in general. In particular, further development of mitigation strategies of induced seismic events of economic concern became a hot topic in geosciences and geoengineering. Here, we present a workflow to assess the hazard of induced seismicity in terms of occurrence rate of induced seismic events. The workflow is called Forward Induced Seismic Hazard Assessment (FISHA) as it combines the results of forward hydromechanical-numerical models with methods of time-dependent probabilistic seismic hazard assessment. To exemplify FISHA, we use simulations of four different fluid injection types with various injection parameters, i.e. injection rate, duration and style of injection. The hydromechanical-numerical model applied in this study represents a geothermal reservoir with preexisting fractures where a routine of viscous fluid flow in porous media is implemented from which flow and pressure driven failures of rock matrix and preexisting fractures are simulated, and corresponding seismic moment magnitudes are computed. The resulting synthetic catalogues of induced seismicity, including event location, occurrence time and magnitude, are used to calibrate the magnitude completeness M _c and the parameters a and b of the frequency-magnitude relation. These are used to estimate the time-dependent occurrence rate of induced seismic events for each fluid injection scenario. In contrast to other mitigation strategies that rely on real-time data or already obtained catalogues, we can perform various synthetic experiments with the same initial conditions. Thus, the advantage of FISHA is that it can quantify hazard from numerical experiments and recommend a priori a stimulation type that lowers the occurrence rate of induced seismic events. The FISHA workflow is rather general and not limited to the hydromechanical-numerical model used in this study and can therefore be applied to other fluid injection models.     

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model.     

Limit states and failure mechanisms of viscous dampers and the implications for large earthquakes

Fluid viscous dampers are used to control story drifts and member forces in structures during earthquake events. These elements provide satisfactory performance at the design‐level or maximum considered earthquake. However, buildings using fluid viscous dampers have not been subjected to very large earthquakes with intensities greater than the design and maximum considered events. Furthermore, an extensive database of viscous damper performance during large seismic events does not exist. To address these issues, a comprehensive analytical and experimental investigation was conducted to determine the performance of damped structures subjected to large earthquakes. A critical component of this research was the development and verification of a detailed viscous damper mathematical model that incorporates limit states. The development of this model and the laboratory and simulation results conclude good correlation with the new model and the damper limit states and provide superior results compared with the typical damper model when considering near collapse evaluation of structures. Copyright © 2010 John Wiley & Sons, Ltd.     

Artificial recharge efficiency assessment by soil water balance and modelling approaches in a multi-layered vadose zone in a dry region

To assess recharge through floodwater spreading, three wells, approx. 30 m deep, were dug in a 35-year-old basin in southern Iran. Hydraulic parameters of the layers were measured. One well was equipped with pre-calibrated time domain reflectometry (TDR) sensors. The soil moisture was measured continuously before and after events. Rainfall, ponding depth and the duration of the flooding events were also measured. Recharge was assessed by the soil water balance method, and by calibrated (inverse solution) HYDRUS-1D. The results show that the 15 wetting front was interrupted at a layer with fine soil accumulation over a coarse layer at the depth of approx. 4 m. This seemed to occur due to fingering flow. Estimation of recharge by the soil water balance and modelling approaches showed a downward water flux of 55 and 57% of impounded floodwater, respectively.     

Assessment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters

The complex stream bank profiles in alluvial channels and rivers that are formed after reaching equilibrium has been a popular topic of research for many geomorphologists and river engineers. The entropy theory has recently been successfully applied to this problem. However, the existing methods restrict the further application of the entropy parameter to determine the cross-section slope of the river banks. To solve this limitation, we introduce a novel approach in the extraction of the equation based on the calculation of the entropy parameter (λ) and the transverse slope of the bank profile at threshold channel conditions. The effects of different hydraulic and geometric parameters are evaluated on a variation of the entropy parameter. Sensitivity analysis on the parameters affecting the entropy parameter shows that the most effective parameter on the λ-slope multiplier is the maximum slope of the bank profile and the dimensionless lateral distance of the river banks.     

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.     

Latitudinal and Longitudinal Variations of Earth’s Magnetic Force on Electrified Hydrometeors

Geomagnetism and Aeronomy - In this study, a hypothesis is proposed about the possible effect of Geomagnetic field (GMF) on the charge structure of a thundercloud based on Lorentz force equation...