Projection of Precipitation and its Variability under the Climate Change Conditions in the Future Periods (Case Study: Tabriz)

In this research, projection of precipitation in the three distinct 20-years future periods (i.e. 2021-2040, 2041-2060, and 2061-2080) was performed using the LARS-WG6 statistical downscaling model. For this purpose, two scenarios of the IPCC fifth assessment report (namely RCP4.5 and RCP8.5) and MPI-ESM-MR general circulation model, which is known to be a CMIP5 coupled models were utilized. The precipitation trends were analyzed in the base and future periods using the Mann-Kendall method for both seasonal and annual time scales. Sen’s estimator method was used to estimate the slopes of trend lines. The results showed that winter precipitation will be increased during the three considered future periods. Moreover, in the spring and autumn, according to the RCP8.5 scenario, precipitation will be decreased in all future periods. The precipitation in December and January would be increased in all three future periods using both scenarios. March, July, September, and October will also experience a decline in precipitation in the three future periods, according to both scenarios. The mean annual precipitation in the future period of 2061-2080 would be decreased based on both scenarios. The highest reduction in precipitation would occur in the period 2061-2080 belonged to the RCP8.5 scenario in which the amount of reduction in mean annual precipitation is equal to 10.4%. In addition, based on the RCP4.5 scenario, the highest increase in the average annual precipitation during the future period of 2041-2060 was equal to 8.1%. There was no significant trend in precipitation series at 5% level. The slope of the trend lines in the base period and the future periods, based on the scenario RCP8.5, was found to be negative in all seasons. While in the future period, based on the RCP4.5 scenario, the trend line slope was positive for in all seasons

Testing for Stationarity and Nonlinearity of Daily Streamflow Time Series Based on Different Statistical Tests (Case Study: Upstream Basin Rivers of Zarrineh Roud Dam)

Introduction: Time series models are one of the most important tools for investigating and modeling hydrological processes in order to solve problems related to water resources management. Many hydrological time series shows nonstationary and nonlinear behaviors. One of the important hydrological modeling tasks is determining the existence of nonstationarity and the way through which we can access the stationarity accordingly. On the other hand, streamflow processes are usually considered as nonlinear mechanisms while in many studies linear time series models are used to model streamflow time series. However, it is not clear what kind of nonlinearity is acting underlying the streamflowprocesses and how intensive it is. Materials and Methods: Streamflow time series of 6 hydro-gauge stations located in the upstream basin rivers of ZarrinehRoud dam (located in the southern part of Urmia Lake basin) have been considered to investigate stationarity and nonlinearity. All data series used here to startfrom January 1, 1997, and end on December 31, 2011. In this study, stationarity is tested by ADF and KPSS tests and nonlinearity is tested by BDS, Keenan and TLRT tests. The stationarity test is carried out with two methods. Thefirst one method is the augmented Dickey-Fuller (ADF) unit root test first proposed by Dickey and Fuller (1979) and modified by Said and Dickey (1984), which examinsthe presence of unit roots in time series.The second onemethod is KPSS test, proposed by Kwiatkowski et al. (1992), which examinesthestationarity around a deterministic trend (trend stationarity) and the stationarity around a fixed level (level stationarity). The BDS test (Brock et al., 1996) is a nonparametric method for testing the serial independence and nonlinear structure in time series based on the correlation integral of the series. The null hypothesis is the time series sample comes from an independent identically distributed (i.i.d.) process. The alternative hypothesis arenot specified. Keenan test has also been proposed for assessing the linearity or nonlinearitybehavior of a time series in time series analysis. Keenan (1985) derived a test for nonlinearity analogous to Tukey’s degree of freedom for nonadditivity test. Keenan’s test is motivated by approximation a nonlinear stationary time series by a second-order Volterra expansion. While Keenan’s test for nonlinearity is designed for detecting quadratic nonlinearity, it may not be sensitive to threshold nonlinearity. Here, we applied the likelihood ratio test (TLRT) with the threshold model as the specific alternative.The null hypothesis of the TLRT approach for threshold nonlinearity is the fitted model to the series is an AR (p) model, and the alternative hypothesis is the fitted model to the series is a threshold autoregressive (TAR) model with autoregressive order p in each regime. Results and Discussion: Because both the ADF and KPSS tests are based on linear regression, which has the normal distribution assumption, logarithmization can convert exponential trend possibly present in the data into a linear trend. In the case of stationary analysis, the results showed the standardized daily streamflow time series of all stations are significantly stationary. According to KPSS stationary test, the daily standardized streamflow time series are stationary around a fixed level, but they are not stationary around a trend stationaryin low lag values. Based on the BDS test, the results showed the daily streamflowseries have strong nonlinear structure, but based on the Keenan test, it can be seen the linear structure in thembyusing logarithmization and deseasonalization operators, and it means the coefficients of the double sum part are zero. It should be considered the Keenan test is used to detect quadratic nonlinearity, and it cannot be adequatelyfor threshold autoregressive models since they are linear in each regime. Conclusion: Streamflow processes of main rivers at 6 stations located in the southern partof Urmia Lake basin were investigated for testingthenonstationarity and nonlinearity behaviors. In general, streamflowprocesses have been considered as nonlinear behaviors. But, the type and intensity of nonlinearity have not been detected at different time scale due to the existence of several evaluation tests. In this study, all daily streamflow series appear to be significantly stationary and have the nonlinearity behavior. Therefore, to model the daily streamflow time series, linear and nonlinear models can be used and their results can be evaluated

Modeling flood event characteristics using D-vine structures

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure