Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia.     

B and V photometry and analysis of the eclipsing binary RZ Cas

Photoelectric light curves of the eclipsing binary RZ Cas are presented forB andV filters. The light curves are analysed for light and geometrical elements, starting with a previously suggested preliminary method. The approximate results thus obtained are then optimised through the Wilson-Devinney computer programs.     

Seismic risk assessment considering cumulative damage due to aftershocks

Calculating the limit state (LS) exceedance probability for a structure considering the main seismic event and the triggered aftershocks (AS) is complicated both by the time‐dependent rate of aftershock occurrence and also by the cumulative damage caused by the sequence of events. Taking advantage of a methodology developed previously by the authors for post‐mainshock (MS) risk assessment, the LS probability due to a sequence of mainshock and the triggered aftershocks is calculated for a given aftershock forecasting time window. The proposed formulation takes into account both the time‐dependent rate of aftershock occurrence and also the damage accumulation due to the triggered aftershocks. It is demonstrated that an existing reinforced concrete moment‐resisting frame with infills subjected to the main event and the triggered sequence exceeds the near‐collapse LS. On the other hand, the structure does not reach the onset of near‐collapse LS when the effect of triggered aftershocks is not considered. It is shown, based on simplifying assumptions, that the derived formulation yields asymptotically to the same Poisson‐type functional form used when the cumulative damage is not being considered. This leads to a range of approximate solutions by substituting the fragilities calculated for intact, MS‐damaged, and MS‐plus‐one‐AS‐damaged structures in the asymptotic simplified formulation. The latter two approximate solutions provide good agreement with the derived formulation. Even when the fragility of intact structure is employed, the approximate solution (considering only the time‐dependent rate of aftershock occurrence) leads to higher risk estimates compared with those obtained based on only the mainshock. Copyright © 2016 John Wiley & Sons, Ltd.     

A decision support system for post-earthquake reliability assessment of structures subjected to aftershocks: an application to L��Aquila earthquake, 2009

The post-earthquake assessment of existing structures can be further complicated by the progressive damage induced by the occurrence of a sequence of aftershocks. This work presents a simple methodology for the calculation of the probability of exceeding a certain limit state in a given interval of time. The time-decaying mean daily rate of occurrence of significant aftershock events is modeled by employing a site-specific aftershock model for the L??Aquila 2009 aftershock sequence (central Italy). The number of aftershock events occurring in a given interval of time elapsed after the main event is modeled using a non-homogenous Poisson model. An equivalent single-degree of freedom structure with cyclic stiffness degradation is used in order to evaluate the progressive damage caused by a sequence of aftershock events. Given the time history of the main-shock and the residual damage caused by it, the probability of exceeding a set of discrete limit states in a given interval of time is calculated. Of particular importance is the time-variant probability of exceeding the limit state in a 24-h (a day) interval of time which can be used as a proxy for the life-safety considerations regarding the re-occupancy of the structure and to complement the results of visual inspections for prioritizing the emergency operations. The method presented herein can also be used in an adaptive manner, progressively conditioned on the time-histories of aftershock events following the main-shock and on the corresponding residual damage caused by them.     

Determining the groundwater potential recharge zone and karst springs catchment area: Saldoran region,western Iran

Assessing the groundwater recharge potential zone and differentiation of the spring catchment area are extremely important to effective management of groundwater systems and protection of water quality. The study area is located in the Saldoran karstic region, western Iran. It is characterized by a high rate of precipitation and recharge via highly permeable fractured karstic formations. Pire-Ghar, Sarabe-Babaheydar and Baghe-rostam are three major karstic springs which drain the Saldoran anticline. The mean discharge rate and electrical conductivity values for these springs were 3, 1.9 and 0.98 m³/s, and 475, 438 and 347 μS/cm, respectively. Geology, hydrogeology and geographical information system (GIS) methods were used to define the catchment areas of the major karstic springs and to map recharge zones in the Saldoran anticline. Seven major influencing factors on groundwater recharge rates (lithology, slope value and aspect, drainage, precipitation, fracture density and karstic domains) were integrated using GIS. Geology maps and field verification were used to determine the weights of factors. The final map was produced to reveal major zones of recharge potential. More than 80 % of the study area is terrain that has a recharge rate of 55–70 % (average 63 %). Evaluating the water budget of Saldoran Mountain showed that the total volume of karst water emerging from the Saldoran karst springs is equal to the total annual recharge on the anticline. Therefore, based on the geological and hydrogeological investigations, the catchment area of the mentioned karst springs includes the whole Saldoran anticline.     

Origin of brine in the Kangan gasfield: isotopic and hydrogeochemical approaches

The Kangan Permo-Triassic brine aquifer and the overlying gas reservoir in the southern Iran are located in Kangan and Dalan Formations, consisting dominantly of limestone, dolomite, and to a lesser extent, shale and anhydrite. The gasfield, 2,900 m in depth and is exploited by 36 wells, some of which produce high salinity water. The produced water gradually changed from fresh to saline, causing severe corrosion in the pipelines and well head facilities. The present research aims to identify the origin of this saline water (brine), as a vital step to manage saline water issues. The major and minor ions, as well as δ²H, δ¹⁸O and δ³⁷Cl isotopes were measured in the Kangan aquifer water and/or the saline produced waters. The potential processes causing salinity can be halite dissolution, membrane filtration, and evaporation of water. The potential sources of water may be meteoric, present or paleo-seawater. The Na/Cl and I/Cl ratios versus Cl^? concentration preclude halite dissolution. Concentrations of Cl, Na, and total dissolved solid were compared with Br concentration, indicating that the evaporated ancient seawater trapped in the structure is the cause of salinization. δ¹⁸O isotope enrichment in the Kangan aquifer water is due to both seawater evaporation and interaction with carbonate rocks. The δ³⁷Cl isotope content also supports the idea of evaporated ancient seawater as the origin of salinity. Membrane filtration is rejected as a possible source of salinity based on the hydrochemistry data, the δ¹⁸O value, and incapability of this process to dramatically enhance salinity up to the observed value of 330,000 mg/L. The overlaying impermeable formations, high pressure in the gas reservoir, and the presence of a cap rock above the Kangan gasfield, all prevent the downward flow of meteoric and Persian Gulf waters into the Kangan aquifer. The evaporated ancient seawater is autochthonous, because the Kangan brine aquifer was formed by entrapment of brine seawater during the deposition of carbonates, gypsum, and minor clastic rocks in a lagoon and sabkha environment. The reliability of determining the source of salinity in a deep complicated inaccessible high-pressure aquifer can be improved by combining various methods of hydrochemistry, isotope, hydrodynamics, hydrogeology and geological settings.     

Mapping of the soil texture using geostatistical method (a case study of the Shahrekord plain, central Iran)

Soil texture is a key variable that reflect a number of soil properties such as soil permeability, water holding capacity, nutrient storage and availability, and soil erosion. The main objective of this study was to produce the kriged maps of soils of the Shahrekord region, central Iran. One hundred four soil samples were collected on a 375-m² sampling grid from the depths of 0–30, 30–60, and 60–100 centimeter, and their particle sizes were determined using hydrometer method. The results showed a moderately spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, increasing clay and therewith observation of heavier soil textures is evident from surface to subsurface layers of the soils in the studied area due to rainfall and/or irrigation agriculture. These findings indicated that study of the soil texture variation with depth can be used as a clue for site-specific management and precision agriculture. Moreover, we suggest further analysis by using other data layers like topographical parameters, land use, parent material, soil erosion, and any other information which might influence the spatial distribution of soil texture.     

Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R ²), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS–ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS–ICA model with R ² of 0.942 can forecast PPV better than the USBM with R ² of 0.634, Ambraseys–Hendron with R ² of 0.638, Langefors–Kihlstrom with R ² of 0.637 and Indian Standard with R ² of 0.519.