كاربرد شاخص هرست در تعيين حافظه طولاني‌مدت سري‌هاي زماني بارش و دبي ايستگاه‌هاي منتخب استان اردبيل

Determining Long-term Memory using Hurst Index for Precipitation and Discharge Time Series of Selected Stations in Ardabil Province

زﻣﯿﻨﻪ و ﻫﺪف: آﺷﻔﺘﮕﯽ ﻫﺎي اﮐﻮﺳﯿﺴﺘﻢ ﻧﺎﺷﯽ از ﻋﻮاﻣﻞ اﺟﺘﻤﺎﻋﯽ ﺑﺮ ﺗﻐﯿﯿﺮات ﻣﺤﯿﻂ زﯾﺴﺖ، دﻣﺎ، ﺗﺒﺨﯿﺮ و ﺗﻌﺮق، ﺗﻮﻟﯿﺪ رواﻧﺎب و دﺑﯽ ﺟﺮﯾﺎن ﺗﺄﺛﯿﺮ ﻣﯽ ﮔﺬارﻧﺪ. در ﻫﻤﯿﻦ راﺳﺘﺎ، ﺷﺎﺧﺺ ﻫﺮﺳﺖ ﺑﺮاي ﺗﺤﻠﯿﻞ ﺗﻐﯿﯿﺮات ﻓﺮآﯾﻨﺪﻫﺎي ﻫﯿﺪروﻟﻮژي ﻧﺎﺷﯽ از ﻋﻮاﻣﻞ ﻣﺨﺘﻠﻒ ﺑﻪ ﮐﺎر ﺑﺮده ﺷﺪه اﺳﺖ. ﺷﺎﺧﺺ ﻫﺮﺳﺖ ﺑﻪ ﻋﻨﻮان ﯾﮏ وﯾﮋﮔﯽ ﻣﻬﻢ ﺑﺮاي ﺗﺤﻠﯿﻞ اﺛﺮات ﻫﯿﺪروﻟﻮژي ﺷﻨﺎﺧﺘﻪ ﺷﺪه اﺳﺖ. ﯾﮑﯽ از ﻣﻨﺎﺳﺐ ﺗﺮﯾﻦ آزﻣﻮن ﻫﺎ ﺑﺮاي ﺗﺸﺨﯿﺺ ﺣﺎﻓﻈﻪ ﺑﻠﻨﺪﻣﺪت، آزﻣﻮن داﻣﻨﻪ ﻣﻘﯿﺎس ﺑﻨﺪي ﺷﺪه )R/S( اﺳﺖ. آزﻣﻮن داﻣﻨﻪ ﻣﻘﯿﺎس ﺑﻨﺪي ﺷﺪه )R/S( اﻣﮑﺎن ﻣﺤﺎﺳﺒﻪ ﭘﺎراﻣﺘﺮ ﺧﻮد ﻫﻤﺎﻧﻨﺪي H ﻫﺮﺳﺖ( را اﯾﺠﺎد ﻣﯽ ﮐﻨﺪ ﮐﻪ ﺷﺪت واﺑﺴﺘﮕﯽ ﺑﻠﻨﺪﻣﺪت در ﯾﮏ ﺳﺮي زﻣﺎﻧﯽ را ﻣﯽ ﺳﻨﺠﺪ. ﺑﻨﺎﺑﺮاﯾﻦ ﭘﮋوﻫﺶ ﺣﺎﺿﺮ ﺑﺎ ﻫﺪف ﺗﻌﯿﯿﻦ ﺣﺎﻓﻈﻪ ﻃﻮﻻﻧﯽ ﻣﺪت ﺳﺮي ﻫﺎي زﻣﺎﻧﯽ ﺑﺎرش و دﺑﯽ اﯾﺴﺘﮕﺎه ﻫﺎي ﻣﻨﺘﺨﺐ اﺳﺘﺎن اردﺑﯿﻞ واﻗﻊ در ﺷﻤﺎل ﻏﺮب اﯾﺮان ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺ ﻫﺮﺳﺖ اﻧﺠﺎم ﺷﺪ. روش ﭘﮋوﻫﺶ: در ﭘﮋوﻫﺶ ﺣﺎﺿﺮ، ﺑﻪ ﺑﺮرﺳﯽ ﮐﺎرﺑﺮد ﺷﺎﺧﺺ ﻫﺮﺳﺖ در ﺗﻌﯿﯿﻦ ﺣﺎﻓﻈﻪ ﻃﻮﻻﻧﯽ ﻣﺪت ﺳﺮي ﻫﺎي زﻣﺎﻧﯽ ﺑﺎرش و دﺑﯽ اﯾﺴﺘﮕﺎه ﻫﺎي ﻣﻨﺘﺨﺐ اﺳﺘﺎن اردﺑﯿﻞ ﺣﺎﻓﻈﻪ ﺑﻠﻨﺪﻣﺪت در داده ﻫﺎي ﺑﺎرش و دﺑﯽ ﺳﺎﻻﻧﻪ )1370-92( در 17 اﯾﺴﺘﮕﺎه ﺑﺎران ﺳﻨﺠﯽ و 28 اﯾﺴﺘﮕﺎه آب ﺳﻨﺠﯽ اﺳﺘﺎن اردﺑﯿﻞ ﭘﺮداﺧﺘﻪ ﺷﺪ. ﻣﻘﺎدﯾﺮ ﻣﺤﺎﺳﺒﺎﺗﯽ ﺷﺎﺧﺺ ﻫﺮﺳﺖ از ﻟﺤﺎظ ﻣﯿﺰان واﺑﺴﺘﮕﯽ و ﻣﻘﯿﺎس ﻧﺎﭘﺎﯾﺪاري در ﺳﺮي زﻣﺎﻧﯽ ﺑﻪ ﭘﻨﺞ ﻃﺒﻘﻪ ﺧﯿﻠﯽ ﺿﻌﯿﻒ ﺗﺎ ﺧﯿﻠﯽ ﻗﻮي ﻃﺒﻘﻪ ﺑﻨﺪي ﺷﺪﻧﺪ. ﺗﺠﺰﯾﻪ و ﺗﺤﻠﯿﻞ ﻫﻤﺒﺴﺘﮕﯽ ﻣﮑﺎﻧﯽ ﺷﺎﺧﺺ ﻫﺮﺳﺖ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺ ﻣﻮران اﻧﺠﺎم ﺷﺪ. در اداﻣﻪ ﻣﻘﺎدﯾﺮ ﺷﺎﺧﺺ ﻫﺮﺳﺖ ﺑﻪ روش وزﻧﯽ ﻣﻌﮑﻮس ﻓﺎﺻﻠﻪ )IDW( در ﻣﺤﯿﻂ 10.8 ArcMap درون ﯾﺎﺑﯽ ﺷﺪ. ﯾﺎﻓﺘﻪ ﻫﺎ: در ﺣﺎﻟﺖ ﮐﻠﯽ از 17 اﯾﺴﺘﮕﺎه ﻣﻮرد ﺑﺮرﺳﯽ ﺑﻪ ﺗﺮﺗﯿﺐ 17/65 ،29/41 ،23/53 و 23/53 درﺻﺪ در ﻣﻘﯿﺎس ﭘﺎﯾﺪاري ﺧﯿﻠﯽ ﺿﻌﯿﻒ )0/55

Ecosystems disturbances induced from social factors affect the environmental changes, temperature, evapotranspiration, runoff production and flow rate. In this regard, Hurst index has been used to analyze changes in hydrological processes due to various factors. The Hurst index is known as an important feature for analyzing hydrological effects. One of the most appropriate tests for long-term memory detection is the rescaling range (R/S) test. The R/S test makes it possible to calculate the self-similarity parameter H (Hurst), which measures the severity of long-term dependence over a time series. Towards this, the present study was conducted to determine the long-term memory using Hurst index for precipitation and discharge time series throughout some selected stations in Ardabil Province, NW Iran. Method: In the present study, long-term memory for annual precipitation and discharge time series (1991-2013) in 17 rain gauges stations and28 river gauge stations in Ardabil Province was assessed. The Hurst index computational values were classified into five categories from very weak to very strong in terms of dependency and scale of instability in the time series. Spatial correlation analysis of Hurst index was performed using Moran index. The Hurst index values were then interpolated by the inverse weighted distance (IDW) method in Arc Map 10.8. Results: The results showed that the among 17 study stations, 23.53, 29.41, 17.65, and 23.53% respectively were classified in the stability scale of very weak (0.50