چکیده:
آنالیز و پایش خشکسالی یکی از اصول مهم در مدیریت خشکسالی و ریسک، بویژه در مناطق در معرض خطر خشکسالی است. سیستمهای پایش در تدوین طرحهای مقابله با خشکسالی و مدیریت آن از اهمیت زیادی برخوردار میباشند. با این حال، مطالعات انجام شده در رابطه با این پدیده بر اساس روشهای مناسب بسیار کم است، بررسی ویژگیهای خشکسالی و پیشبینی آن میتواند در کاهش خسارات حاصل از آن موثر باشد بدین منظور، در این پژوهش به بررسی خشکسالی و ارزیابی امکان پیشبینی آن برای ایستگاههای حوضه دریاچه ارومیه پرداخته شد. دادههای مورد استفاده در این پژوهش، مقدار بارندگی به صورت ماهانه در دوره آماری 29 ساله از سال 1985 تا 2014 میباشد. شاخص SEPI در مقیاس زمانی 6 و 12 ماهه برای بررسی ویژگی خشکسالی و مدل سیستم استنتاج عصبی–فازی تطبیقی برای پیشبینی خشکسالی استفاده میشود با توجه به یافتههای حاصل در این پژوهش، درصد فراوانی وقوع خشکسالی در حوضه دریاچه ارومیه در ایستگاههای ارومیه و سقز و مراغه در مقیاس 6 ماهه بیشتر از مقیاس 12 ماهه است اما در ایستگاههای تبریز و مهاباد شرایط بر عکس میباشد. و روند خشکسالی در حوضه دریاچه ارومیه افزایشی است و روند دما با شدت بیشتری روند افزایشی دارد. بیشترین درصد وقوع خشکسالی در ایستگاه ارومیه و کمترین آن در مهاباد مشاهده شد. نتایج حاصل از پیشبینی شاخص با مدل ANFIS نشان داد در رابطه کد نویسی بیشترین میانگین خطای آموزشی 51/0 درصد در ایستگاه تبریز در مقیاس 12 ماهه و کمترین میانگین خطای آموزشی 36/0 درصد در ایستگاه مراغه در مقیاس 12 ماهه میباشد. در مدلسازی دادههای اعتبارسنجی، میانگین خطای مدلسازی طبیعتا بیشتر از میانگین خطای آموزشی میباشد.
Drought is a concept that is generally understood on a basic level, but is difficult to quantify. Palmer defined a drought as a meteorological phenomenon that is characterized by prolonged and abnormal moisture deficiency. A drought can alternatively be broadly defined as a temporary, recurring reduction in the precipitation in an area.
Aridity and drought are not synonymous. Aridity is a measure of long-term average climatic conditions. Both humid and arid regions experience droughts. However, the inter-year variation in precipitation is greater in arid regions and there is a greater probability of below average precipitation in any particular year. Arid regions are thus more prone to droughts and may experience more severe impacts from droughts.
In this research was used temperature and precipitation monthly data of Urmia, Tabriz, saghez, Maragheh, and Mahabad station in statistically period 1985-2014. Run test was used to study the homogeneity of data. Randomness and homogeneity of data was approved.at a confidence level of %95. SEPI Index and ANFIS model was used for determining and forecasting drought in Urmia lake basin. SEPI index is more complete than SPI. Results of SEPI were used in ANFIS model.
Fuzzy index SEPI[1]: Standardized precipitation index and evapotranspiration (SEPI) to address some of the disadvantages of SPI index is provided. Evapotranspiration and precipitation index SPI index and SEI standardized integration is achieved. The index is the result of drought monitoring phase of architectural models using fuzzy logic in a fuzzy inference system is designed. How to design this model and determine SEPI is described below.
Fuzzy architecture drought monitoring: for derivatization indices SPI and SEI using Fuzzy Inference System, Due to the structure of fuzzy models were considered.
SPI index[2]: Standardized Precipitation Index is an indicator widely used in Drought Monitoring. This index is one of the few indicators drought monitoring and could even say the only indicator that the time scale is considered. Depending on the time scale to determine the effect of different sources of agricultural drought, hydrological and so determined. Time scale can be determined from one month to several years. SPI index is used to calculate the only element rainy climate. Monthly precipitation amounts for each station in the desired time scale is calculated.
SEI index[3]: Since the index SPI Single Entry, rain, The SPI index values under the influence of changes in temperature and evapotranspiration parameter that is powerful factor in the drought, it will not be. So to enter the effect of temperature and evapotranspiration in SPI, SEI (evapotranspiration index Standard) To calculate this index, before any measures should reference evapotranspiration for the period to be estimated.
define the rules for combining indicators SPI and SEI: Different classes index SPI and SEI rules or the same combination of conditional statements in the form if, as a class of SEPI index in the lead, is defined. This rule only a combination of different modes SPI and SEI indices that lead to SEPI index shows. In this regard, the rules can be combined to fit different for successive written and stored in the knowledge base. Since the output of the resultant composition, indices SPI and SEI are involved in determining the status of SEPI, Weight each of the indicators with regard to the effect of precipitation and temperature parameters on the severity of the drought was considered As a result, SPI indices and weights 0.667 and 0.333, respectively SEI were included in the calculations.
According to the results, according to the research, education Anfis model with 75 percent of the data series is well done SEPI and much has been done to ensure education is nearly 100 percent. So that the graphic maximum of 0.26 percent error in saghez station on a scale of 6 months and the lowest average error of 0.10 percent in Urmia station is on a scale of 6 months. In modeling, validation data, the average error modeling is naturally higher than the average training error. Most average forecast error saghez on a scale of 6 months at the station 0.34 percent and 0.10 percent, the lowest on a scale of Urmia station is 6 months. But the coding maximum of 0.65 percent error in saghez station on a scale of 6 months and the lowest average error of 0.32 percent in Tabriz station is on a scale of 6 months. SEPI index in the time scale of 6 and 12 months is used for investigate the characteristics of adaptive neuro-fuzzy inference system in order to drought and drought forecasting model. According to the findings in this study, the frequency of drought in the stations of Urmia and Saghez and Maragheh on a scale of 6 months is more than the scale of 12 months in the basin of Lake Urmia but in Tabriz and Mahabad Stations situation is the vice versa. The drought in Urmia Lake basin is increasing trend but temperature has increasing trend with more intensity. The highest and lowest percentage of drought was seen in Urmia and Mahabad station respectively. The results of the forecasting of index by ANFIS model showed that the most training error is in Tabriz station (0.51) and the lowest training error is in Maragheh station (0.36) in a scale of 12 months in coding. In validation data modeling the average of modeling error is higher than the average training error naturally. According to the definition of drought SEPI was presented based on amounts of 0.73 or higher or mild drought to higher floors as dry conditions arise The scale of 6 months in Urmia station with 13.14 percent to 10.89 percent saghez station, Tabriz stations with 5.58 percent, with a 5.1% Mahabad station and Maragheh with the amount of 4.82 percent, the drought has occurred. The time scale of 12 months in Tabriz station by 9%, saghez station with 7.26 percent, with 6.11 percent of Urmia station, Maragheh with 5.5% and the amount of Mahabad stations with a 3.44 percent, from months of study in the series, drought has occurred.
Results of SPEI are:1.Drought trend is increasing in urmia lake basin. Temperature has increasing trend extremely.
2.The highest percentage of drought is in Urmia station and its lowest is in Mahabad station.
3.Percent of frequency of drought in Urmia station, Saghez and Maragheh on a scale of 6 months is more than to 12 months, but the scale of Tabriz and Mahabad stations with the photos. Stations Tabriz and Mahabad is in the opposite situation.
Results of ANFIS Model are:In study area and in ANFIS model whatever forecasting coming years is shorter; confidence of forecasting will be more.
Due to the errors amount obtained in model validation, in study area forecasting of drought by ANFIS model was done with confidence 94%.
خلاصه ماشینی:
شاخص SEPI در مقیاس زمانی ٦ و ١٢ ماهه برای بررسی ویژگی خشکسالی و مدل سیستم استنتاج عصبی –فازی تطبیقی برای پیش بینی خشکسالی استفاده میشود با توجه به یافته های حاصل در این پژوهش ، درصد فراوانی وقوع خشکسالی در حوضه دریاچه ارومیه در ایستگاه های ارومیه و سقز و مراغه در مقیاس ٦ ماهه بیش تر از مقیاس ١٢ ماهه است اما در ایستگاه های تبریز و مهاباد شرایط بر عکس میباشد.
نیرآنجا و همکاران (٢٠١٣: ٤٢ ,Niran ana and et al) در مطالعه ی به تنوع مشاهده خشکسالیهای موسمی در سراسر هند با استفاده از شاخص پایش خشکسالی، یعنی شاخص Evapo تعرق بارش ، استاندارد شده (SPEI) پرداخته اند و به این نتیجه رسیداند که تجزیه و تحلیل همبستگی کانونی (CCA) نشان میدهد که بخش عمده ای از تنوع خشکسالی که توسط ال نینو/نوسان جنوبی (ENSO) و تنوع خشکسالیهای موسمی در سراسر هند به طور قابل توجه ی ، توسط ناهنجاری دمای سطح دریا استوایی تحت تاثیر قرار دارد.
در یک جمع بندی مربوط به پایش و بررسی خشکسالی در حوضه دریاچه ارومیه مهم ترین نتایج به دست آمده را میتوان به صورتی که در ادامه میآید فهرست کرد: در ایستگاه های مورد مطالعه ، در مقیاس زمانی ٦ ماهه شدت و تعداد تکرار خشکسالیها بیش تر از مقیاس ١٢ ماهه است اما در مقیاس ١٢ ماهه تداوم خشکسالیها بیش تر میباشد.