جستجوی مقالات مرتبط با کلیدواژه "autocorrelation" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Drought is one of the most important climate phenomena which occurs in all climate conditions and most of the regions on the earth and brings in a lot of injuries to human and natural ecosystems. Therefore, it is important to investigate drought‛s characteristics including intensity, duration and its frequency and to find out their regional risk and forecasting, too. For this reason, some of rain gauge stations in Kheirabad watershed in which their data had been recorded until the end of water years 2011-2012, were selected. Drought conditions for each station were investigated for four different periods including three monthly and one annual periods, using Standardized Prediction Index. Then, drought prediction was performed using SPI values modeling using ARIMA and SARIMA time series. The results showed that in most of the studied time periods, the droughts in the Khyrababad basin are often of medium drought and wet years are of the same type. Study of the persistence of dry and wet periods in selected stations indicates that drought and wet years are more likely to be 1 and 2 years old and have more frequency than other dry and wet periods but the continuity of the normal situation, even for 5 years or more, has been observed in some stations, including the Boyeri and the Nazmakan. The results of drought prediction using time series show that the ARIMA model can better predict SPI values and increase the time interval from 12 months to 48 months more appropriate prediction. Also, the results of the evaluation of the models show that by increasing the time scale from 12 months to 48 months, the slope of the regression line and the MAE and RMSE values are lower.

Understanding the behavior of phenomena requires attention to all its dimensions, and one way to understand their complexities is modeling. Soil surface moisture is a key variable for describing drought, water, and energy exchanges between Korea and the air, as well as assessing crop conditions. Soil moisture is affected by both environmental variables and many environmental variables such as runoff, soil erosion, and crop production, but is highly variable due to unstable spatial and temporal conditions. The purpose of this paper is to investigate, extract and evaluate the spatial model of soil moisture dispersal after more than normal rainfall in 1979-98 in Kurdistan province. In this regard, after analyzing soil moisture dispersion as dependent variable and precipitation variables, snow water equivalent, topographic moisture index and vegetation index were selected as independent variables. Then, using a general regression model (OLS) and geographically weighted regression (GWR), spatial modeling was performed. Based on the evaluation criteria, the results showed that the GWR model with R2 = 0.74 has better explanatory power and better estimation than the general regression model with R2 = 0.68. According to the results of the GWR model, snow water equivalent variable in the northern mountainous regions had the highest effect on evapotranspiration and the least effect on soil moisture. The obtained spatial model can identify low or moist soil areas in order to identify environmental potentials and improve decision making, allocation and spatial distribution of agricultural services.

Considering the importance of Lake Urmia, the purpose of this research is to survive the human activates on the trend of surface water and groundwater changes in the Urmia plain. For this purpose, the changes in input and output discharge, aquifer drainage and land use changes in the Urmia Plain were studied on the basis of nine hydrometric stations data (during 1973-1903) and information of 74 piezometers and using satellite images. The test used for trend analysis was Mann-Kendall nonparametric test with considering the effect of autocorrelation. According to the results, the trend of input discharge from the Nazlou-Chay, Rouzh-Chay and Barandoz-Chay sub-basins to the Urmia Plain was decreasing and the negative trend of outlet discharge related to the Rouzh-Chay, Shahr-Chay and Barandoz-Chay sub-basins was significant at 95% confidence level. Comparison of the results the difference between discharge average during after and before 1995 (the year which in it the lake begins to dry) showed that at all stations except Ghassemlou, there is a significant difference between average discharge during these two periods. The value of the statistic (Z = -2.18) indicates a significant decreasing trend at 95% confidence level for the drainage volume of the aquifer reservoir. The results of this study showed that discharge to Lake Urmia in all seasons had a decreasing trend during the studied period and serious actions should be done to manage water resources in the region.

Hydrologic drought in the sense of deficient river flow is defined as the periods that river flow does not meet the needs of planned programs for system management. Drought is generally considered as periods with insignificant precipitation, soil moisture and water resources for sustaining and supplying the socioeconomic activities of a region. Thus, it is difficult to give a universal definition of drought. The most well-known classification of droughts is based on the nature of the water deficit: (a) the meteorological drought, (b) the hydrological drought, (c) the agricultural drought, (d) the socio-economic drought. Perhaps the most widely used model is the ARIMA model for predicting drought. The two general forms of ARIMA models are non-seasonal ARIMA (p, d, q) and multiplicative seasonal ARIMA (p, d, q) × (P, D, Q) in which p and q are non-seasonal autoregressive and moving average, P and Q are seasonal autoregressive and moving average parameters, respectively. The other two parameters, d and D, are required differencing used to make the series stationary. The differencing operator that is usually used in the case of non-stationary time series.
The aim of the study is to predict hydrological drought using time series analysis in the small forest watershed.

MATERIAL AND METHODS: Monthly discharge of Nahrkhoran hydrometric station (53 58 02E, 36 43 02N) during 1980-1981 to 2010-2011 located in Gharasoo watershed, Iran were collected from Company of Water Resources Management of Iran.
The present study was carried out using Box and Jenkins (1976) modeling approach. This approach involves the following three steps:Step 1-Model identification
In this step, the model that seems to represent the behavior of the series is searched, by the means of autocorrelation function (ACF) and partial autocorrelation function (PACF), for further investigation and parameter estimation. The behavior of ACF and PACF, is to see whether the series is stationary or not, seasonal or non-seasonal. Differencing is done to make non-stationary time series to stationary time series.
Step 2-Parameter estimation
After identifying models, we need to obtain efficient estimates of the parameters. These parameters should satisfy two conditions namely stationary and invertibility for autoregressive and moving average models, respectively. The parameters should also be tested whether they are statistically significant or not. Associated with parameters value are standard errors of estimate and related t-values.
Step 3-Goodness-of-fit test
Goodness-of-fit tests verify the validity of the model by some tools. The residuals of the model are usually considered to be time-independent and normally distributed over time. The most common tests applied to test time-independence and normality are the Mann-Kendall of test, the non-parametric Kolmogorov–Smirnov test.
Model calibration
In order to evaluate the accuracy of the streamflow forecasts obtained by applying the fitted model, Nash-Sutcliff (NS) coefficient of efficiency, root mean square error (RMSE), P-value of Wilcox and determination coefficient (R2) were used.
Drought definitions and thresholds
A drought is defined as an uninterrupted sequence of streamflow below an arbitrary level. Thus the mean and median value of streamflow time series is selected as the first truncation level. In the present study, as the monthly streamflow time series is applied for drought forecasting, the monthly mean and median values are also applied as the truncation level for each month. The two above truncation levels, we apply two other drought indices called standardized streamflow index (SSFI) and a probabilistic index which is based on hydrologic drought return periods. The SSFI for a given period is defined as the difference of streamflow from mean divided to standard deviation. A multiplicative seasonal autoregressive integrated moving average (SARIMA) model was applied to the monthly streamflow forecasting of the Naharkhoran River. In the first step of model identification, the ACF and PACF of the actual data and nonparametric Mann-Kendall test indicate the need of differencing. The Q–Q plot of the main series does not show normality. Thus, the logarithmic transformation was applied. The transformed Q–Q plot shows that the new series is normal. Based on Autocorrelation (ACF) and Partial Autocorrelation Functions (PACF) and results of Mann-Kendall test, SARIMA (1, 1, 1) * (0, 1, 1) 12 was selected. For testing the validity of SARIMA (1, 1, 1) * (0, 1, 1) 12 model for forecasting, the model is used for forecasting 10-, 9-, 5-, 3-, 2- and 1-year monthly streamflow. The present study result demonstrates the performance of time series models for 5-year period forecasting during October 2005 to September 2010.
The selected SARIMA model was then used to forecast streamflow from October, 1980 to September, 2011. The forecasted and observed flow rates are compared first with three truncation levels, which are SSFI, time series mean and median. The results showed that when SSFI is as truncation level, the selected model has not the ability to forecast drought. But when time series mean and median are as truncation level, the ability of the selected model is clear to forecast drought. Hydrologic drought frequency analysis was applied as an alternative truncation level for drought forecasting. Different frequency distributions were fitted to monthly streamflow and the flow rate for hydrologic drought in different 2-, 5-, 10- and 20-year return periods were estimated using maximum likelihood method of quantile estimation. The SARIMA model predict drought and humid periods as well. Due to the important role of drought forecasting in water resources management, a multiplicative seasonal autoregressive integrated moving average (SARIMA) model was applied to the monthly streamflow forecasting of the Naharkhoran River located in Gharasoo watershed, Iran. After normality examination of streamflow data, nonparametric Mann-Kendall test was used to detect trend analysis of data at confidence level of 95%. Based on ACF and PACF and results of Mann-Kendall test, SARIMA (1, 1, 1) * (0, 1, 1) 12 was selected. Mean and median streamflow as constant threshold and SSFI and drought frequency analysis in different return periods as variable threshold were used for determining drought periods. The present study result demonstrates the performance of time series models for 5-year period forecasting during October 2005 to September 2010. These models can be applied in water resources management.

Trend analysis and understanding of the historical volatility in the climate system, is one of the most important requirements in research on climate change. In order to study the trend, there are different methods covering parametric and non-parametric approaches. The non-parametric Mann - Kendall methods are widely used to study the hydrological series. But the autocorrelation in the hydrological series increases probability occurrence of type I error. In this study in order to evaluate the effects of autocorrelation, the time series trend without excluding the effect of autocorrelation and then by applying TFPW method and excluding the effect of autocorrelation were analyzed. The results showed that by removing the effect of autocorrelation, number of stations with significant negative trend was decreased. Therefore, in annual and monthly scales, none of rainfall data series in 22 rain gauge stations were significant at the 95 percentage confidence level. Therefore, it revealed that in evaluating the hydro-climatological data, the effects of autocorrelation must be removed from time series to provide accurate and reliable results.

Within the climate change debate and its probable impacts on water resources systems, design and operation of management plans based on the assumption of stationary hydrology may cause serious challenge to accurately predict future supplies. Therefore this case study is trying to assess trend in hydroclimatic variables of Karkheh Rivers upstream by applying modified Mann-Kendall trend test on long term daily time series of temperature, precipitation and discharge. Temperature variables are mostly showing meaningful increasing trends but observed changes in assessed stations were not spatially uniform for precipitation. Streamflow variables depict a decreasing trend, though more noticeable in base flows. Decreasing trend is meaningful for annual discharge median in Holailan at 90% confidence level. Total yearly precipitation, number of precipitation days and number of days with precipitation equal to, or greater than, 10 mm/d show the most correlation with stream flow variables. Comparing monthly discharge with temperature and precipitation variables in the studied gages indicates a time-delay in system response to inputs. This may related to snowmelt contributions or contributions of water into streams after passing through different hydrological pathways such as groundwater. Some parts of streamflow changes, especially about base flows, is not completely verified by precipitation changes and can be attributed to changes in temperature or another factors such as groundwater overexploitation.

In this study، the mean monthly air temperature data from Shah-Mokhtar hydrometric station in Kohgiloyeh and Boyer Ahmad province for a period of 39 years between 1970 and 2009 was investigated. Using different graphical EDA techniques such as spectral، autocorrelation and partial autocorrelation plots، i) existence of seasonal part and ii) suitability of using time series analysis to model the residuals were determined. The lag plot and autocorrelation plot of the original data showed that a sinusoidal model was appropriate to model the seasonal effect. So using sinusoidal model and determining its parameters precisely، the seasonal effect was modeled properly. Time series analysis was also used to model the residuals using ARIMA models. Among different models، ARIMA (0،1،2) model was selected as the best model using Normalized Bayesian Information Criterion (NBIC). Finally، null hypotheses for Kolmogorov-Smirnov and Ljung-Box tests were not to be rejected at 5% level for the obtained model which confirms the adequacy of the model.