Reza Modarres

Rainfall trends analysis of Iran in the last half of the twentieth century

[1] The present study performs the spatial and temporal trend analysis of the annual and 24-hr maximum rainfall of a set of 145 precipitation gauging stations of Iran. The study shows that the annual rainfall is decreasing at 67% of the stations while the 24-hr maximum rainfall is increasing at 50% of the stations. The negative trends of annual rainfall are mostly observed in northern and northwestern regions, whereas positive trends of 24-hr maximum rainfall are mostly located in arid and semiarid regions of Iran. However, the Kolmogorov-Smirnov test for Mann-Kendall (MK) statistics show that the regional trend of annual rainfall is significant, but it is not significant for 24-hr maximum rainfall. On the other hand, the sequential MK test reveals that the trends of annual rainfall and 24-hr maximum rainfall began since 1970s for most of the stations. The negative trend of the rainfall for most of the country may show the initial stages of climate change in Iran, but further infonnation and analysis is required for future studies.

Daily air pollution time series analysis of Isfahan City

Different time series analysis of daily air pollution of Isfahan city were performed in this study. Descriptive analysis showed different long-term variation of daily air pollution. High persistence in daily air pollution time series were identified using autocorrelation function except for SO2 which seemed to be short memory. Standardized air pollution index (SAPI) time series were also calculated to compare fluctuation of different time series with different levels. SAPI time series indicated that NO and NO2, CH4 and non-CH4 have similar time fluctuations. The effects of weather condition and vehicle accumulation in Isfahan city in cold and warm seasons are also distinguished in SAPI plots.

Frequency Distribution of Extreme Hydrologic Drought of Southeastern Semiarid Region, Iran

Hydrologic drought is a type of drought which directly affects the water supply of a region. Long streamflow dry spells or streamflow under a specific threshold are usually considered as hydrologic drought. The annual extreme hydrologic dry spell length (AEHDSL) data of the Halilrud basin in the southeastern semiarid region of Iran were considered to estimate the return period of hydrologic drought and the associated risk in this region. The method of L -moments was applied to check discordant stations and test the homogeneity of the region which consists of 15 gauging watersheds. One discordant station was found and the region was homogeneous according to the homogeneity measure after removing the discordant station. The three-parameter lognormal distribution was found to be representative of the regional distribution for the entire region based on the goodness-of-fit test. For prediction in ungauged basins, the AEHDSL regional regression was developed for the region. The regression model indicates that ...

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.

Modeling seasonal variation of hip fracture in Montreal, Canada

The investigation of the association of the climate variables with hip fracture incidences is important in social health issues. This study examined and modeled the seasonal variation of monthly population based hip fracture rate (HFr) time series. The seasonal ARIMA time series modeling approach is used to model monthly HFr incidences time series of female and male patients of the ages 40-74 and 75+ of Montreal, Québec province, Canada, in the period of 1993-2004. The correlation coefficients between meteorological variables such as temperature, snow depth, rainfall depth and day length and HFr are significant. The nonparametric Mann-Kendall test for trend assessment and the nonparametric Levenes test and Wilcoxons test for checking the difference of HFr before and after change point are also used. The seasonality in HFr indicated sharp difference between winter and summer time. The trend assessment showed decreasing trends in HFr of female and male groups. The nonparametric test also indicated a significant change of the mean HFr. A seasonal ARIMA model was applied for HFr time series without trend and a time trend ARIMA model (TT-ARIMA) was developed and fitted to HFr time series with a significant trend. The multi criteria evaluation showed the adequacy of SARIMA and TT-ARIMA models for modeling seasonal hip fracture time series with and without significant trend. In the time series analysis of HFr of the Montreal region, the effects of the seasonal variation of climate variables on hip fracture are clear. The Seasonal ARIMA model is useful for modeling HFr time series without trend. However, for time series with significant trend, the TT-ARIMA model should be applied for modeling HFr time series.

Modelling heteroscedasticty of streamflow times series

Abstract Time series modelling approaches are useful tools for simulating and forecasting hydrological variables and their change through time. Although linear time series models are common in hydrology, the nonlinear time series model, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, has rarely been used in hydrology and water resources engineering. The GARCH model considers the conditional variance remaining in the residuals of the linear time series models, such as an ARMA or an ARIMA model. In the present study, the advantages of a GARCH model against a linear ARIMA model are investigated using three classes of the GARCH approach, namely Power GARCH, Threshold GARCH and Exponential GARCH models. A daily streamflow time series of the Matapedia River, Quebec, Canada, is selected for this study. It is shown that the ARIMA (13,1,4) model is adequate for modelling streamflow time series of Matapedia River, but the Engle test shows the existence of heteroscedasticity in the residuals of the ARIMA model. Therefore, an ARIMA (13,1,4)-GARCH (3,1) error model is fitted to the data. The residuals of this model are examined for the existence of heteroscedasticity. The Engle test indicates that the GARCH model has considerably reduced the heteroscedasticity of the residuals. However, the Exponential GARCH model seems to completely remove the heteroscedasticity from the residuals. The multi-criteria evaluation for model performance also proves that the Exponential GARCH model is the best model among ARIMA and GARCH models. Therefore, the application of a GARCH model is strongly suggested for hydrological time series modelling as the conditional variance of the residuals of the linear models can be removed and the efficiency of the model will be improved. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Modarres, R. and Ouarda, T.B.M.J., 2013. Modelling heteroscedasticty of streamflow times series. Hydrological Sciences Journal, 58 (1), 1–11.

Assessing Multi-site Drought Connections in Iran Using Empirical Copula

Drought is a multi-dimensional natural hazard with stochastic characteristics usually related to each other. Separate univariate statistical models cannot capture the important relationships among drought characteristics, that is, severity and duration. In this study, an empirical copula is employed to construct a bivariate model of droughts, where droughts are defined as continuously negative standardized precipitation index (SPI) periods with one SPI value reaching −1 or less. Bivariate frequency analyses in terms of recurrence intervals are performed using the established empirical copula-based bivariate drought model. The inter-connection among different regions of droughts is explored by a lower tail dependence coefficient. A nonparametric estimation based on an empirical copula is employed pairwisely to calculate the lower tail dependence coefficient among stations. The proposed method is applied to six rainfall gauge stations in Iran to explore drought properties of single sites as well as the inter-connection among multi-sites. The results show that greater mean drought severity and duration are associated with the least arrival rate of drought events, which occurs at the Ahwaz station. The tail dependence analysis reveals that distance between stations is not a key parameter. Generally, the Ahwaz and Isfahan stations have the highest probability of simultaneous droughts among the six stations.

Modeling the relationship between climate oscillations and drought by a multivariate GARCH model

Typical multivariate time series models may exhibit comovement in mean but not in variance of hydrologic and climatic variables. This paper introduces multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models to capture the comovement of the variance or the conditional covariance between two hydroclimatic time series. The diagonal vectorized and Baba-Engle-Kroft-Kroner models are developed to evaluate the covariance between drought and two atmospheric circulations, Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) time series during 1954-2000. The univariate generalized autoregressive conditional heteroscedasticity model indicates a strong persistency level in conditional variance for NAO and a moderate persistency level for SOI. The conditional variance of short-term drought index indicates low level of persistency, while the long-term index drought indicates high level of persistency in conditional variance. The estimated conditional covariance between drought and atmospheric indices is shown to be weak and negative. It is also observed that the covariance between drought and atmospheric indices is largely dependent on short-run variance of atmospheric indices rather than their long-run variance. The nonlinearity and stationarity tests show that the conditional covariances are nonlinear but stationary. However, the degree of nonlinearity is higher for the covariance between long-term drought and atmospheric indices. It is also observed that the nonlinearity of NAO is higher than that for SOI, in contrast to the stationarity which is stronger for SOI time series. Key Points Multivariate heteroscedastic models are developed for drought analysis Conditional covariance between drought, SOI, and NAO is not strong Time-varying correlations between drought and atmospheric indices are estimated.

Testing and Modelling the Volatility Change in ENSO

Abstract The El Niño–Southern Oscillation (ENSO) is by far the most energetic climate signal. Any change in ENSO characteristics will have serious consequences for the global climate system. This work suggests a different view at the change in ENSO volatility in addition to change in its descriptive statistics. The volatility or the conditional variance of ENSO is tested and modelled using both the Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) error model and the GARCH model, to investigate the change in the short-run and long-run persistency of the second-order moment of ENSO before and after a change point detected by a Bayesian change point analysis. Nonparametric tests revealed a significant change in descriptive statistical characteristics such as the mean, the (unconditional) variance, and the probability distribution of ENSO after a change point in 1975. An Engles test did not show heteroscedasticity in the random process (residuals) of the Southern Oscillation Index (SOI) time series before 1975 although heteroscedasticity increased and appeared after 1975. The GARCH model indicates an increasing short-run persistency after 1975 and decreasing long-run persistency. A seasonal shift in extreme heteroscedasticity is observed from summer to winter. In addition, the non-linearity and nonstationarity of the SOI volatility have increased in recent decades. This may be caused by an increase in frequency and magnitude of extreme volatilities after 1975. The results of this study indicate that ENSO has become more dynamic and uncertain in recent decades. The increase in the frequency of extreme events together with extreme conditional variance after 1975 may increase the prediction uncertainty of ENSO-driven climate phenomena.

Low Flow Scaling with Respect to Drainage Area and Precipitation in Northern Iran

Low flow spatial scaling relationships have been defined by log-log linearity between 7-, 15-, 30-, and 60-day low flow probability weighted moments (PWMs) and drainage area size in north of Iran. The PWMs are used to avoid the influence of outliers. Across the entire region, the regression relationship is not significant which is believed to be due to climate heterogeneity of the region. Dividing the region into two humid and semiarid regions, the log-log relationship is found to be significant for the humid subdivision while it is not significant in semiarid region. This implies that in a heterogeneous climate regime, scaling alone is a poor method for extending low flow at-site probabilistic behavior to a region. However, for the semiarid subdivision, the relationship between mean annual rainfall and PWMs verges on significant which suggests to the hydrologist that other alternatives to drainage area size should be examined for scaling low flows in such regions.