Regional analysis of streamflow drought: a case study in southwestern Iran

Droughts are complex natural hazards that, to a varying degree, affect some parts of the world every year. The range of drought impacts is related to drought occurring in different stages of the hydrological cycle and usually different types of droughts such as meteorological, agricultural, hydrological, and socio-economical are the most distinguished types. Hydrological drought includes streamflow and groundwater droughts. In this paper, streamflow drought was analyzed using the method of truncation level (at 70 % level) by daily discharges at 54 stations in southwestern Iran. Frequency analysis was carried out for annual maximum series of drought deficit volume and duration. 35 factors such as physiographic, climatic, geologic and vegetation were studied to carry out the regional analysis. According to conclusions of factor analysis, the six most effective factors include watershed area, the sum rain from December to February, the percentage of area with NDVI <0.1, the percentage of convex area, drainage density and the minimum of watershed elevation, explained 89.2 % of variance. The homogenous regions were determined by cluster analysis and discriminate function analysis. The suitable multivariate regression models were ascertained and evaluated for hydrological drought deficit volume with 2 years return period. The significance level of models was 0.01. The conclusion showed that the watershed area is the most effective factor that has a high correlation with drought deficit volume. Moreover, drought duration was not a suitable index for regional analysis.     

Mineralogical and geochemical studies on the Gowd-e-Morad (Ni,Co, As–Cu) mineral deposit,Anarak (central Iran)

The Ni, Co, As, and Cu deposit of Gowd-e-Morad is located 20 km northwest of Anarak in Central Iran. In this hydrothermal deposit, mineralization occurs as veins in a fault breccia zone hosted by the Chahgorbeh (schist and metabasite) complex. The main ores are made up of Ni, Co, and Cu arsenides. Petrologic studies and results obtained from geochemical analyses have indicated that the Ni, Co, As, and Cu are derived from ultramafic rocks while Pb and Zn are likely to be derived from schist. Based on the geochemical evidence, particularly the high correlation between Ni, Co, and As, it is proposed that this deposit be categorized as a “five elements” mineral deposit. Fluid inclusion studies have shown homogenization temperatures (T_H) in the range 113?206 ?C and salinity 3?13.5 % wt eq. NaCl. Therefore this “five elements” mineral deposit has been determined as a low temperature, epithermal deposit type. It is proposed that the low fluid temperatures are a result of an environment of formation which was distal to a volcanogenic source systems and the major influence of meteoric waters in the hydrothermal system.     

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth??s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.