کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8872824 | 1622872 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimating water consumption based on meta-analysis and MODIS data for an oasis region in northwestern China
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم زراعت و اصلاح نباتات
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چکیده انگلیسی
A severe water shortage in arid and semiarid regions in China has triggered regulations limiting the use of irrigation water in desert-oasis agricultural systems. To determine the actual water demand of agricultural systems and to develop standards for a rational use of water resources, maize evapotranspiration rates was simulated using meta-analysis of published data, determined land-use and soil properties in Zhangye oasis area and then estimated water consumption with MODIS for arid northwestern China for the 2014 growing season. Farmland in Zhangye oasis totaled 2189.88 km2 and accounted for 25.41% of the total oasis area; crop-protecting forestland totaled 8.56 km2 and accounted for 0.10% of the total oasis area, grassland and unutilized land accounted for 1.85%, 71.48% of the total oasis area, respectively. Soil moisture capacity at saturation ranged from 21.0 to 33.0% and at field capacity from 21.0 to 31.5%. Soil bulk density ranged from 1.36 to 1.58 g cmâ3. Using meta-analysis, the simulated maize evapotranspiration rate was 655.91± 20.53 mm. During 2014 growing season, minimum, optimum and maximum water consumption used for agricultural yields and oasis stability was 1.14Ã109 m3, 1.46Ã109 m3 and 1.75Ã109 m3, respectively. In future research, increased big picture and high frequency data will allow operational monitoring of water use and requirements and irrigation at a regional scale.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Agricultural Water Management - Volume 208, 30 September 2018, Pages 478-489
Journal: Agricultural Water Management - Volume 208, 30 September 2018, Pages 478-489
نویسندگان
Wenzhi Zhao, Xuexiang Chang, Xueli Chang, Dengrong Zhang, Bing Liu, Jun Du, Pengfei Lin,