Location: Soil Drainage ResearchTitle: Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models
|Zhu, Qing - Chinese Academy Of Sciences|
|Zhou, Zhiwen - Chinese Academy Of Sciences|
|Lv, Ligang - Chinese Academy Of Sciences|
|Liao, Kaihua - Chinese Academy Of Sciences|
|Feng, Huihui - Chinese Academy Of Sciences|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/9/2016
Publication Date: 2/1/2017
Publication URL: http://handle.nal.usda.gov/10113/5633745
Citation: Zhu, Q., Zhou, Z., Duncan, E.W., Lv, L., Liao, K., Feng, H. 2017. Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models. Journal of Hydrology. doi: 10.1016/j.jhydrol.2016.12.014.
Interpretive Summary: Soil moisture can be predicted using linear and non-linear models for periods of time in between manual measurements. Some research sites can afford continual, real time monitoring sensors, but not all locations can afford these sensors and must rely on manual measurements. By verifying models using manual and 'real time' data, scientists can predict soil moisture on hill slopes successfully.
Technical Abstract: Spatio-temporal variability of soil moisture (') is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time ' monitoring methods. This restricted the comprehensive and intensive examination of ' dynamics. In this study, we integrated the manual and real-time monitored data to depict the hillslope ' dynamics with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear (support vector machines-SVM) models were used to predict ' at 39 manual sites (collected 1-2 times per month) with ' collected at three real-time monitoring sites (collected every 5 mins). By comparing the accuracies of SMLR and SVM for each depth and manual site, an optimal prediction model was then determined at this depth at the site. Results showed that ' at the 39 manual sites can be reliably predicted (root mean square errors<0.028 m3 m-3) using both SMLR and SVM. The linear or non-linear relationship between ' at each manual site and at the three real-time monitoring sites was the main reason for choosing SMLR or SVM as the optimal prediction model. The subsurface flow dynamics was an important factor that determined whether the relationship was linear or non-linear. Depth to bedrock, elevation, topographic wetness index, profile curvature, and ' temporal stability influenced the selection of prediction model since they were related to the subsurface soil water distribution and movement. Using this approach, hillslope ' spatial distributions at un-sampled times and dates can be predicted. Missing information of hillslope ' dynamics can be acquired successfully.