پهنه بندی شرق دریاچه ارومیه بر مبنای شاخص توزیع زمانی بارش با روش وارد و K- Means

نویسندگان

چکیده

با توجه به اهمیت بارش و کمبود منابع آب، تحلیل مکانی بارش‌های روزانه توأم با فواصل زمانی مربوطه، یکی از ضروریات می‌باشد. هدف این مطالعه، معرفی یک شاخص برای شناسایی مناطق مستعد از نظر توان دیم‌کاری، با استفاده از داده‌های توأم بارش و فواصل زمانی آن‌ها در شرق حوضة دریاچه ارومیه می‌باشد. با استفاده از مقدار بارش و فواصل زمانی آن، در دورة آماری سال 1370 تا 1392 شاخص توزیع زمانی بارش برای 23 ایستگاه محاسبه شد. برای خوشه‌بندی شاخص توزیع زمانی بارش، روش‌های K-Means و وارد به‌کار گرفته شد. آزمون همگنی خوشه‌های به‌دست‌آمده از طریق روش آماره H انجام گرفت. مقایسة نواحی همگن حاصل از خوشه‌بندی با دو روش ذکرشده با توزیع مکانی خطوط هم‌شاخص نشان داد که روش K-Means نواحی همگن را بهتر از روش وارد تفکیک کرد. بخش‌های جنوبی، مرکزی، شمال شرقی و جنوب غربی ناحیه مورد مطالعه توانایی کشت دیم بیشتری را نسبت به سایر مناطق در شرق دریاچه ارومیه دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Regionalization of the East Part of Urmia Lake Basin Based on temporal distribution of Precipitation using the K- Means and Ward Methods

نویسندگان [English]

  • Parva Mohammadi
  • Ahmad Fakherifard
چکیده [English]

Considering the importance of precipitation and scarcity of water resources, spatial analysis of daily rainfall together with corresponding time intervals is one of the requirements. Among the atmospheric factors affecting the rainfed farming, precipitation is the most important factor in numerous studies about the rainfed cultivation. In order to reduce the destruction threat of water resources and resolve the future’s food needs of the people, the importance of rainfed agriculture would be inevitable. In this regard, one of the effective measurements that can be done to find rainy areas with the possibility of rainfed cultivation. The greatest water consumption is corresponding to the agricultural sector. According to the water crisis in the country, decline in the groundwater levels and the importance of agriculture in job creation, the water resource management is essential. Therefore, it will have a value of higher management to determine for the fertile lands in the current situation. Despite the reduction in rainfall, it is also necessary to analytically study the rainfed and determine the fertile lands in the country, especially in the Urmia Lake basin. This study aimed to introduce an index for identification of the suitable areas from the viewpoint of rainfed ability, by using daily rainfall in corresponding with the time intervals for each year in the east part of the Urmia Lake basin.
First a program is written in Fortran, using the rainfall data, the proportion of rainfall intervals is determined, then the Fortran program is developed for this propose, based on the concept of the temporal intensity precipitation and their related mechanistic, an index is defined which is the main outcome of the Fortran programming. The index was a criterion for analysis and disintegration of the rainfed quantity. The idea of introducing an index is based on the ratio of rainfall to the interval corresponding. If the ratio quantity was high, it represents the water supply, in other words, the rainfed capability will be great which can play the role of an index rating. In this index, the rainfall depth has positive role and intervals have negative role in the assessment. When the index amount is high, the rainfed capability will increase. Because the amount of precipitation must be bigger (which is the sign of the large amount of rainfall or the smaller interval time) an increase in the amount of rainfall would be followed, or in compound, causes an increase in the proportion, which shows the increase of rainfed ability. The statistical period was 1992-2014 for 23 rainfall stations. The index values were calculated for each year of all stations. Clustering is one of the most useful classification methods. In the cluster analysis, one attempts to actual observations of each cluster, which have the most similarity in terms of variables together. In this study, the clustering methods of the K-Means and Ward were carried out to deline the homogeneous regions based on the developed index values. Clustering in the rainfall stations was considered as a variable, the correlation matrix 23 * 23, where 23 is related to the number of the years stations used. The homogeneity of the clusters were checked through the H-Statistics method and the homogeneous clusters were shown in the GIS environment.
In order to use the K- Means method, the whole study area was considered as two clusters and the results of the H-Statistics homogeneity test showed that the two clusters are homogeneous. The first cluster has a mild homogeneity while the second one is completely homogeneous. In the clustering index using the Ward method, the study area was divided into two clusters. The observation of thehomogeneous clusters with the H-Statistics homogeneity test showed that the both clusters are mild homogeneous. On the other hand, from the viewpoint of spatial variation, Iso-Index lines were drawn over the study area. Identifing the rainy areas was conducted by examining the lines of these areas w are suitable for the rainfed agriculture and have a better temporal rainfall distribution. The comparison of two maps regionalization with two methods and the Iso-Index lines show that the regionalization by K- means method, the first part of this regionalization with Iso-Index lines have the same incremental direction, that suggests more rain stations in this area in comparison with the second area. The second cluster of homogeneous region was obtained using the Ward method, having similarity with Iso-Index only in the limited stations. The comparison of the regions resulted from clustering methods with the spatial distribution of the Iso-Index lines over the study area implied that the K-Means method isolated the regions better than the Ward method. The results show that the south, northeast, northwest and the center parts of the study area were more eligible for the rainfed agriculture than the other parts. These areas, regarding to agriculture, temporal distribution of precipitation and richness of groundwater, are better.

کلیدواژه‌ها [English]

  • Clustering
  • Homogeneity Test
  • Rainfed
  • spatial distribution