文章基本信息

标题：Estimating Deep Drainage Using Deep Soil Moisture Data under Young Irrigated Cropland in a Desert‐Oasis Ecotone, Northwest China
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作者：Yongyong Zhang ; Wenzhi Zhao ; Tyson E. Ochsner 等
期刊名称：Vadose Zone Journal
电子版ISSN：1539-1663
出版年度：2019
卷号：18
期号：1
页码：1-9
DOI：10.2136/vzj2018.10.0189
出版社：Soil Science Society of America, Inc.
摘要：Core Ideas Deep drainage rates under young cropland in the desert‐oasis region were investigated. Deep drainage rates were estimated by a simulation approach based on deep soil moisture data. Annual deep drainage averaged 468 mm; the annual deep drainage coefficient averaged 43%. Deep drainage reduces agricultural water productivity under cropland recently converted from native desert soils (i.e., young cropland) and increases the risks of nutrient and pesticide leaching into groundwater in the desert‐oasis ecotone. However, the deep drainage rates under young cropland in these oasis environments remain unclear, especially for winter irrigation, a common practice in Northwest China. The objective of this study was to estimate the deep drainage rate using the HYDRUS‐1D model based on soil moisture data in the deep vadose zone. Soil moisture at depths ranging from 0 to 200 cm was measured using HydraProbe II soil sensors in maize ( Zea mays L.) and wheat ( Triticum aestivum L.) fields in 2015 and 2017, respectively. Using a novel simulation approach based on soil moisture data in the deep vadose zone, the HYDRUS‐1D model provided reliable estimates of deep drainage as confirmed by comparison with estimates from the soil water balance method and prior studies in the region. The annual deep drainage averaged 468 mm, and the annual deep drainage coefficient averaged 43% in the young croplands. The winter irrigation amount averaged 265 mm, and the deep drainage coefficient during winter averaged 21% in the young croplands. The sandy soil of the young cropland and inefficient irrigation scheduling are detrimental to water conservation, causing relatively large deep drainage losses and enhancing the risks of groundwater pollution.
关键词：MAE; mean absolute error