کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5770967 1629905 2017 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Research papers3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
پیش نمایش صفحه اول مقاله
Research papers3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter
چکیده انگلیسی


- Soil water dynamics were monitored and nowcasted in 3D using EM induction data.
- An empirical (ANN) model was built to predict soil water content from EM data.
- A physical model was fitted to predict soil water dynamics.
- An ensemble Kalman filter combined the empirical and the physical models.
- Water balance across the field was estimated for real-time irrigation management.

Mapping and immediate forecasting of soil water content (θ) and its movement can be challenging. Although inversion of apparent electrical conductivity (ECa) measured by electromagnetic induction to calculate depth-specific electrical conductivity (σ) has been used, it is difficult to apply it across a field. In this paper we use a calibration established along a transect, across a 3.94-ha field with varying soil texture, using an ensemble Kalman filter (EnKF) to monitor and nowcast the 3-dimensional θ dynamics on 16 separate days over a period of 38 days. The EnKF combined a physical model fitted with θ measured by soil moisture sensors and an Artificial Neural Network model comprising σ generated by quasi-3d inversions of DUALEM-421S ECa data. Results showed that the distribution of θ was controlled by soil texture, topography, and vegetation. Soil water dried fastest at the beginning after the initial irrigation event and decreased with time and soil depth, which was consistent with classical soil drying theory and experiments. It was also found that the soil dried fastest in the loamy and duplex soils present in the field, which was attributable to deep drainage and preferential flow. It was concluded that the EnKF approach can be used to improve the irrigation efficiency by applying variable irrigation rates across the field. In addition, soil water status can be nowcasted across large spatial extents using this method with weather forecast information, which will provide guidance to farmers for real-time irrigation management.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 549, June 2017, Pages 62-78
نویسندگان
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