Effectiveness of two conventional methods for seismic retrofit of steel and RC moment resisting frames based on damage control criteria

This study investigates the efficiency of two types of rehabilitation methods based on economic justification that can lead to logical decision making between the retrofitting schemes. Among various rehabilitation methods, concentric chevron bracing (CCB) and cylindrical friction damper (CFD) were selected. The performance assessment procedure of the frames is divided into two distinct phases. First, the limit state probabilities of the structures before and after rehabilitation are investigated. In the second phase, the seismic risk of structures in terms of life safety and financial losses (decision variables) using the recently published FEMA P58 methodology is evaluated. The results show that the proposed retrofitting methods improve the serviceability and life safety performance levels of steel and RC structures at different rates when subjected to earthquake loads. Moreover, these procedures reveal that financial losses are greatly decreased, and were more tangible by the application of CFD rather than using CCB. Although using both retrofitting methods reduced damage state probabilities, incorporation of a site-specific seismic hazard curve to evaluate mean annual occurrence frequency at the collapse prevention limit state caused unexpected results to be obtained. Contrary to CFD, the collapse probability of the structures retrofitted with CCB increased when compared with the primary structures.     

Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems.Recently,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public use.This study evaluated the ensemble forecasts of three C3S models over the period 1993-2017 in Iran’s eight classified precipitation clusters for one-to three-month lead times.Probabilistic and non-probabilistic criteria were used for evaluation.Furthermore,the skill of selected models was analyzed in dry and wet periods in different precipitation clusters.The results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill scores.All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons.Moreover,with increasing lead time,the forecast skill of the models worsened.In terms of forecasting in dry and wet years,the forecasts of the models were generally close to observations,albeit they underestimated several severe dry periods and overestimated a few wet periods.Moreover,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models.In general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran.For the clusters considered in Iran and for the long-range system versions considered,the Météo France model had lower skill than the other models.     

Recognition of shallow karst water resources and cave potentials using thermal infrared image and terrain characteristics in semi-arid regions of Iran

Shallow karst water resources and caves may influence land surface temperatures due to cold transfer property of rocks and evaporation from buried karst. The objective of this research was to develop a method for recognition of karst areas based on evaluating the surface characteristics that manifest itself by low land surface temperature in the satellite images. Investigation of thermal ETM⁺ image of the study region in Iran showed that parts of carbonate rocks that bear karst water are relatively cooler compared to areas with similar terrain conditions. Relational modeling provided useful information on spatial distribution of areas that have the potential to hold karst water resources and/or caves. Further inspection of ASTER images, along with geotechnical, geophysical and geological field surveys verified the approach. Significant correlation was found between electrical resistivity and thermal band values. The method may be used as a primary exploratory tool for shallow karst water explorations in similar areas.     

Monte Carlo analysis of the effect of spatial distribution of storms on prioritization of flood source areas

Implementation of structural and non-structural flood control measures in flood-prone watersheds is on increasing demand. Different watershed areas are not necessarily hydrologically similar and impose variable effects on the outlet flow hydrograph. Meanwhile, prioritization of watershed areas in terms of flood generation is essential for economic flood control planning. Previous works have focused on the definition of a flood index that quantifies the contribution of each subwatershed unit or grid cell to the outlet flood hydrograph through the application of unit flood response (UFR) approach. In the present research, for the first time, the effect of spatial pattern of storm events on the flood index variation was assessed via a Monte Carlo uncertainty analysis. To do so, the UFR approach was carried out for a large number of randomly generated rainfall spatial pattern. The proposed methodology was adopted to the Tangrah watershed in northern Iran. The watershed is frequently hit by floods that have historically caused loss of life and properties. The results indicated that for the more frequent flood events, the flood index is quite sensitive to the spatial distribution of rainfall such that for the highest ranked subwatershed (SW6), the standardized variation of the flood index values (i.e., the uncertainty range) decreases from 1.0 to 0.5 when the rainfall depth increases from 20 to 150 mm, respectively. The results further revealed that increasing the rainfall depth from 20 to 150 mm would cause the effect of rainfall spatial distribution on subwatersheds’ flood indices to diminish. The implications are that if flood control measures are designed for more frequent floods with lower return periods, an uncertainty analysis is required to identify the range of flood index variations.     

A dynamic prediction model for time-to-peak

A simplified empirical equation is developed for widespread prediction of dynamic catchment response time. This model allows for time-to-peak prediction to evolve from static, lumped models, thereby providing a single value for any storm within a given catchment, using a single set of input parameters, that can be applied to a dynamic model, thus accounting for the variability between storm sizes and catchment moisture conditions. These dynamic prediction methods are translated to North America for the first time. This allows the concepts and prediction methods for catchment response time prediction previously established for the United Kingdom (UK), to be translated to a simple empirical equation for use in North America, through the use of selected study areas in Canada and the United States. This reconfigured model is statistically successful in both the UK and North America and allows for a straightforward implementation of dynamic time-to-peak prediction. Further, the reconfigured model introduces the use of a runoff coefficient (R_c) to encompass historical catchment wetness, increasing the ease of incorporating antecedent moisture condition into predictions.     

A new daily weather generator to preserve extremes and low-frequency variability

This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series.     

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

ABSTRACT

Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow.     

K- and Si-Metasomatism, Mineral Transformation and Formation of Granitoids from Basic Rocks in Qooshchi Area, NW Iran. A Mineralogical Context

The Qooshchi area lies to northwest of Orumieh Lake in western Azerbaijan, NW Iran. A basement metamorphic complex, consisting of Precambrian schists and gneisses, has been intruded by gabbres and diorites. Granitolds are grouped into five suites according to their mineralogy, texture and exposed features. The main body, pink Qooshchi granite, and apophyse-like, myrmekite-bearing granit-oids are discussed in this paper. On the basis of field observations and microscopic studies, an intensive metasonmtism has overprinted the country rocks, especially gabbros, transforming them into a more felsic composition. A prior event of intensive deformation and cataclasis preceded the metasonmtism, al-lowing the introduction of hydrothermal fluids. K-metasonmtism converted plagiuclase into K-feldspar (microdme), myrmekite, and sodic plagioclase as Si-metasomatism replaced the ferromagnesian silicates by quartz. Apophyse-like bodies within gabbros, called leucometasomatites, are formed during this process.     

Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.