26 فروردین 1403
علي ديندارلو

علی دیندارلو

مرتبه علمی: استادیار
نشانی: دانشکده مهندسی کشاورزی - گروه مهندسی تولید و ژنتیک گیاهی
تحصیلات: دکترای تخصصی / مهندسی کشاورزی
تلفن: 07731221300
دانشکده: دانشکده مهندسی کشاورزی

مشخصات پژوهش

عنوان Geostatistics-based spatial distribution of soil moisture and temperature regime classes in Mazandaran province, northern Iran
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
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مجله Archives of Agronomy and Soil Science
شناسه DOI
پژوهشگران سید مصطفی عمادی (نفر اول) ، علیرضا شهریاری (نفر دوم) ، فردین صادق زاده (نفر سوم) ، باهی جلیلی سهبردان (نفر چهارم) ، علی دیندارلو (نفر پنجم)

چکیده

Soil moisture regime (SMR) and soil temperature regime (STR) classes as soil classification criterions are required by US Soil Taxonomy because they affect genesis, use, and management of soils. The lack of sufficient soil moisture and temperature data requires the characterization of the pedoclimate on the basis of climatic data processed by simulation models. This research was conducted to consider the new approach for SMR and STR mapping. The objectives of this study were to compare the four interpolation schemes including ordinary kriging (OK), cokriging (Co-K), inverse distance weighting, and conditional simulation for interpolating the monthly mean total precipitation (MMTP) and monthly mean air temperature (MMAT) and to apply the Java Newhall simulation model for the MMTP and MMAT predictive values at each node of 1 km2 grids across the Mazandaran province, northern Iran, for delineating the SMR and STR classes. The semivariogram analyses showed moderate to strong spatial dependence of data sets. The accuracy of interpolators varied within months for both MMTP and MMAT data sets. In most cases, OK and Co-K methods had the highest accuracy with lower mean error, root mean square error, and higher concordance correlation coefficient. The predictive maps show high diversity of SMR classes including Aridic, Ustic, Udic, and Xeric. The STR classes comprise Mesic, Thermic, and Cryic regimes. Results herein indicated that geostatistical approaches can potentially provide the opportunity for mapping of SMR and STR classes in data scarce regions.