Simultaneous estimation of shape factor and depth of subsurface cavities from residual gravity anomalies using feed-forward back-propagation neural networks

We develop a new method of using feed-forward back-propagation (FFBP) neural networks to simultaneously estimate shape factor and depth of gravity anomalies. The advantages compared to neural network methods are the following: no pre-assumptions are made on source shape, the FFBP neural network estimates both depth and shape factor of source bodies and, once trained, works well for any new data in the training space, without repeating the initial calculations.     

An investigation of trends in precipitation volume for the last three decades in different regions of Fars province, Iran

Under condition of climate changes as global warming, monitoring and detecting trend of precipitation volume is essential and will be useful for agricultural sections. Considering the fact that there were not enough research related to precipitation volume, this study aimed to determine trends in precipitation volume, monthly and annually in different regions of Fars province for the last three decades (33?years period; 1978–2010). Fars province is located in arid and semi-arid regions of Iran, and it plays an important role in agricultural production. Inverse distance weighting interpolation method was used to provide precipitation data for all regions. To analyze the trends of precipitation volume, Mann–Kendall test, Sen’s slope estimator, and 10-year moving average low-pass filter (within time series) were used. The negative trends were identified by the Sen’s slope estimator as well as Mann–Kendall test. However, all the trends were insignificant at the surveyed confidence level (95%). With regards to the application of 10-year moving average low-pass filter, a considerable decreasing trend was observed after around year 1994. Since one of the most important restrictions in agricultural development of the Fars province is lack of sufficient water resources, any changes onward to lack of sufficient precipitation impose impressive pressure and stress on valuable resources and subsequently agricultural production.     

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann?CKendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature (T _dew) time series at ten coastal weather stations in Iran during 1966?C2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28?%/decade at the northern and southern coastal regions of the country, respectively, while annual T _dew increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the T _dew series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and T _dew time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution.     

Comparison of Speed-Up Over Hills Derived from Wind-Tunnel Experiments,Wind-Loading Standards,and Numerical Modelling

We evaluate the accuracy of the speed-up provided in several wind-loading standards by comparison with wind-tunnel measurements and numerical predictions, which are carried out at a nominal scale of 1:500 and full-scale, respectively. Airflow over two- and three-dimensional bell-shaped hills is numerically modelled using the Reynolds-averaged Navier–Stokes method with a pressure-driven atmospheric boundary layer and three different turbulence models. Investigated in detail are the effects of grid size on the speed-up and flow separation, as well as the resulting uncertainties in the numerical simulations. Good agreement is obtained between the numerical prediction of speed-up, as well as the wake region size and location, with that according to large-eddy simulations and the wind-tunnel results. The numerical results demonstrate the ability to predict the airflow over a hill with good accuracy with considerably less computational time than for large-eddy simulation. Numerical simulations for a three-dimensional hill show that the speed-up and the wake region decrease significantly when compared with the flow over two-dimensional hills due to the secondary flow around three-dimensional hills. Different hill slopes and shapes are simulated numerically to investigate the effect of hill profile on the speed-up. In comparison with more peaked hill crests, flat-topped hills have a lower speed-up at the crest up to heights of about half the hill height, for which none of the standards gives entirely satisfactory values of speed-up. Overall, the latest versions of the National Building Code of Canada and the Australian and New Zealand Standard give the best predictions of wind speed over isolated hills.     

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.     

An investigation of near surface wind speed trends in arid and semiarid regions of Iran

Wind plays an important role on the ecosystems and hydrological cycles besides other meteorological parameters such as temperature, precipitation, sunshine, and relative humidity. It strongly affects evapotranspiration, especially in arid and semiarid regions where there are serious problems in regard to water resource management. Evaluating the wind speed trend can provide good information for future agricultural planning. This study was conducted in order to investigate the wind speed trends over 24 synoptic meteorological stations located in arid and semiarid regions of Iran from 1975 to 2005. Near-surface wind speed was trended by nonparametric Mann–Kendall test spatially and temporally in three time scales (annual, seasonal, and monthly). Then, Sen’s slope estimator was used to determine the amount of the changes; furthermore, 10-year moving average low-pass filter was applied to show general trends. Finally, the smoothed time series derived from the mentioned filter were classified in three clusters for each time series and then mapped to show their spatial distribution pattern. Results showed insignificant and significant, increasing and decreasing trends during the surveyed time. Wind speeds in less than 50 % of stations changed statistically in all time scales, and in most cases, the frequency of the upward trends was more than that of downward ones. The spatial distribution of significant wind speed showed that the increase mostly occurred in eastern part. Clustering gave us the turning point around 1990. Clearly, when clusters were mapped, they indicated the same pattern as the Z value maps derived from Mann–Kendall test which meant that the outputs of the mentioned method confirmed the other one. As the wind speed trends in different stations likely to follow the previous evapotranspiration (ET₀) trend results in Iran, it confirms that wind speed was an effective parameter on ET₀, even though other parameters should be considered too.     

Sediment deposition from flow at low gradients into a buffer strip—a critical test of re-entrainment theory

The spatial and size distribution of sediment deposited from short periods of overland flow due to the effect of a simulated grass buffer strip was measured for low slopes of 1.6, 3.4 and 5.1%. These data were analysed so as to critically evaluate two alternative models of the process of re-entrainment of recently deposited sediment. A model of re-entrainment, previously thought to be appropriate only for a steady-state or equilibrium situation, was found to give better agreement with experiments than did a model previously used in the literature on this subject.     

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.