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Showing 2 results for Lake Urmia

Batol Zynali, Sayyad Asghari Saraskanroud, Vahid Saffarian Zangir,
Volume 4, Issue 1 (4-2017)
Abstract

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%.


[1] - The combination of indices SPI (Standardized Precipitation Index) and SEI (evapotranspiration index standard) based on the rules of the Fuzzy Inference System.

[2] - Standardized Precipitation Index

[3] - Standardized Evapotranspiration


Sorayya Ebrahimi, Abdolreza Rahmanye Fazli, Farhad Azizpour,
Volume 9, Issue 3 (12-2022)
Abstract

Factors affecting the adaptation of rural settlements to the water crisis of Lake Urmia Case study: Miandoab County

Problem statement
In recent years, Lake Urmia, the largest lake in Iran, has faced severe water shortages, which has raised concerns in terms of economic, social and environmental consequences in the surrounding communities, especially in rural areas. Livelihood dependence of rural community stakeholders, to the natural resources and agricultural products have caused the harmful effects of drying Urmia Lake to be more visible. The drying up of Lake Urmia is not limited to this lake, but human communities have also suffered a lot from their sphere of influence. Due to the human effects of the drying of Lake Urmia,  it is necessary to analyze the effects of this phenomenon from a human perspective in research. Identifying the adaptive capacity of rural community stakeholders makes it possible to adopt appropriate management strategies to reduce the damage caused by lake drying. Therefore, despite the importance of the subject of this research, it seeks to study the factors and forces affecting the adaptation capacity of rural settlements in the face of the drying crisis of Lake Urmia in the city of Miandoab and so on.

Research Methodology
In terms of methodology, strategy and design, the present study is a combination of (mixed), sequential and explanatory exploratory, respectively. In this study, for a detailed study of community mentalities, a discourse on effective factors to increase the adaptive capacity of rural settlements in the face of drying or water retreat of Lake Urmia, the combined method of (Q) was selected. The research discourse community included local managers (governorate experts, heads and employees of government departments, districts, rural districts and Islamic councils) as well as local experts in the sample villages of Miandoab city. Targeted sampling method (snowball) was used to select the statistical sample. Q statements were also compiled using first-hand sources (expert opinions, local managers, field observations, etc.) and codified sources (books, articles, publications, etc.) using the library and field methods. The Q questionnaire was also used to assess the attitude of experts. In order to analyze the data of the Q (Q) method matrices, heuristic factor analysis based on the individual method (Stanfson method) was used.

Description and interpretation of results
 In reviewing the findings of the exploratory factor analysis model with KMO criterion, Bartlett test confirmed the sufficient number of samples and its appropriateness for the research. To investigate the most important influencing factors, the specific value and percentage of variance were calculated and the number of factors was determined by pebble diagram and Kaiser Guttman criterion. The results showed that the most important factors and forces affecting the increase of adaptation capacity to the drying of Lake Urmia in the sample villages of Miandoab are: 1) Increasing economic capital and the use of natural resources, 2) Increasing social capital and investment, 3) Developing infrastructure facilities and improving the skills of villagers, 4) Economic diversification and improving rural management .. Among these factors, the first factor with a specific value of 5.40 and a percentage of variance of 24.55 was recognized as the most important factor and effective force in increasing the adaptation capacity of the studied villages against the drying of Lake Urmia. Thus, economic and natural factors, as the most important assets of the villagers, are endangered at any time by the drying up and retreat of the water of Lake Urmia and have a direct impact on the livelihood of the villagers.

Keywords: Adaptation capacity, Lake Harumiyeh, Miandoab County.

 

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