Spatial and temporal trends of dust storms across desert regions of Iran

Dust storms are among natural and anthropogenic hazards for socioeconomic resources, especially in desert regions. In recent years, dust storms have become a serious problem, especially in desert regions of Iran. This study investigates temporal and spatial variation of dust storm frequency in desert regions of Iran. The number of dusty days (NDD) are collected from 22 stations across the region. The statistical analysis of NDD time series is carried out to show both spatial and seasonal pattern of dust storm occurrence in the region. The regional map of statistical characteristics indicates a north to south increasing dust storm frequency. The spatial map also reveals higher year-to-year variation in south eastern Iran. The seasonality of NDD shows the highest frequency for summer followed by the spring and autumn seasons. The popular Mann–Kendall and the bootstrap MK test to consider serial correlation are then applied for Trend assessment. Results showed both negative (across the north and northwestern regions) and positive trend (across south and south eastern regions) in the annual and seasonal NDD time series. This north-to-south gradient in the spatial and temporal frequency NDD may arise from harsh dry and gusty winds as well as intense land use change in the south eastern territories of Iran. However, more careful and detailed studies are required to connect environmental conditions to change in NDD frequency.     

Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series

The existence of time‐dependent variance or conditional variance, commonly called heteroscedasticity, in hydrologic time series has not been thoroughly investigated. This paper deals with modelling the heteroscedasticity in the residuals of the seasonal autoregressive integrated moving average (SARIMA) model using a generalized autoregressive conditional heteroscedasticity (GARCH) model. The model is applied to two monthly rainfall time series from humid and arid regions. The effect of Box–Cox transformation and seasonal differencing on the remaining seasonal heteroscedasticity in the residuals of the SARIMA model is also investigated. It is shown that the seasonal heteroscedasticity in the residuals of the SARIMA model can be removed using Box–Cox transformation along with seasonal differencing for the humid region rainfall. On the other hand, transformation and seasonal differencing could not remove heteroscedasticity from the residuals of the SARIMA model fitted to rainfall data in the arid region. Therefore, the GARCH modelling approach is necessary to capture the heteroscedasticity remaining in the residuals of a SARIMA model. However, the evaluation criteria do not necessarily show that the GARCH model improves the performance of the SARIMA model. Copyright © 2012 John Wiley & Sons, Ltd.     

Data‐based analysis of bivariate copula tail dependence for drought duration and severity

In recent decades, copula functions have been applied in bivariate drought duration and severity frequency analysis. Among several potential copulas, Clayton has been mostly used in drought analysis. In this research, we studied the influence of the tail shape of various copula functions (i.e. Gumbel, Frank, Clayton and Gaussian) on drought bivariate frequency analysis. The appropriateness of Clayton copula for the characterization of drought characteristics is also investigated. Drought data are extracted from standardized precipitation index time series for four stations in Canada (La Tuque and Grande Prairie) and Iran (Anzali and Zahedan). Both duration and severity data sets are positively skewed. Different marginal distributions were first fitted to drought duration and severity data. The gamma and exponential distributions were selected for drought duration and severity, respectively, according to the positive skewness and Kolmogorov–Smirnov test. The results of copula modelling show that the Clayton copula function is not an appropriate choice for the used data sets in the current study and does not give more drought risk information than an independent model for which the duration and severity dependence is not significant. The reason is that the dependence of two variables in the upper tail of Clayton copula is very weak and similar to the independent case, whereas the observed data in the transformed domain of cumulative density function show high association in the upper tail. Instead, the Frank and Gumbel copula functions show better performance than Clayton function for drought bivariate frequency analysis. Copyright © 2012 John Wiley & Sons, Ltd.     

Streamflow drought time series forecasting

Drought is considered to be an extreme climatic event causing significant damage both in the natural environment and in human lives. Due to the important role of drought forecasting in water resources planning and management and the stochastic behavior of drought, a multiplicative seasonal autoregressive integrated moving average (SARIMA) model is applied to the monthly streamflow forecasting of the Zayandehrud River in western Isfahan province, Iran. After forecasting 12 leading month streamflow, four drought thresholds including streamflow mean, monthly streamflow mean, 2-, 5-, 10- and 20-year return period monthly drought and standardized streamflow index were chosen. Both observed and forecasted streamflow showed a drought period with different severity in the lead-time. This study also demonstrates the usefulness of SARIMA models in forecasting, water resources planning and management.     

The First Light Curve Solutions and Period Study of BQ Ari

Astronomy Letters - The first analysis of the photometric observation in BVR filters of a W UMa type binary system BQ Ari was performed. Light curve analysis was performed using...     

Evaluating the effect of ocean-atmospheric indices on drought in Iran

Theoretical and Applied Climatology - This study investigated the relationship between ocean-atmospheric indices and drought in Iran. Using > 30-year precipitation data from 37...     

Spatial patterns and temporal trends of daily precipitation indices in Iran

Spatial patterns of daily precipitation indices and their temporal trends over Iran are investigated using the APHRODITE gridded daily precipitation dataset for the period 1961–2004. The performance and limitations of the gridded dataset are checked against observations at ten rain-gauge stations that are representative of different climates in Iran. Results suggest that the spatial patterns of the indices reflect the role of orography and sea neighborhoods in differentiating central-southern arid and semi-arid regions from northern and western mountainous humid areas. It is also found that western Iran is impacted by the most extreme daily precipitation events occurring in the country, though the number of rainy days has its maximum in the Caspian Sea region. The time series of precipitation indices is checked for long-term trends using the least squares method and Mann-Kendall test. The maximum daily precipitation per year shows upward trends in most of Iran, though being statistically significant only in western regions. In the same regions, upward trends are also observed in the number of wet days and in the accumulated precipitation and intensity during wet days. Conversely, the contribution of precipitation events below the 75th percentile to the annual total precipitation is decreasing with time, suggesting that extreme events are responsible for the upward trend observed in the total annual precipitation and in the other indices. This tendency towards more severe/extreme precipitation events, if confirmed by other datasets and further analyses with longer records, would require the implementation of adequate water resources management plans in western Iran aimed at mitigating the increasing risk of intense precipitation and associated flash floods and soil erosion.     

Regional scale rainfall–runoff modeling using VARX–MGARCH approach

Multivariate time series modeling approaches are known as useful tools for describing, simulating, and forecasting hydrologic variables as well as their changes over the time. These approaches also have temporal and cross-sectional spatial dependence in multiple measurements. Although the application of multivariate linear and nonlinear time series approaches such as vector autoregressive with eXogenous variables (VARX) and multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models are commonly used in financial and economic sciences, these approaches have not been extensively used in hydrology and water resources engineering. This study employed VARX and VARX–MGARCH approaches in modeling mean and conditional heteroscedasticity of daily rainfall and runoff records in the basin of Zarrineh Rood Dam, Iran. Bivariate diagonal VECH (DVECH) model, as a main type of MGARCH, shows how the conditional variance–covariance and conditional correlation structure vary over the time between residuals series of the fitted VARX. For this purpose, five model fits, which consider different combinations of twofold rainfall and runoff, including both upstream and downstream stations, have been investigated in the present study. The VARX model, with different orders, was applied to the daily rainfall–runoff process of the study area in each of these model fits. The Portmanteau test revealed the existence of conditional heteroscedasticity in the twofold residuals of fitted VARX models. Therefore, the VARX–DVECH model is proposed to capture the heteroscedasticity existing in the daily rainfall–runoff process. The bivariate DVECH model indicated both short-run and long-run persistency in the conditional variance–covariance matrix related to the twofold innovations of rainfall–runoff processes. Furthermore, the evaluation criteria for the VARX–DVECH model revealed the improvement of VARX model performance.