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Water 2017, 9(5), 354; doi:10.3390/w9050354

Soil Moisture Stochastic Model in Pinus tabuliformis Forestland on the Loess Plateau, China

1
College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
2
Ji County Forest Ecosystem Research Station, Linfen 042200, China
3
Beijing Collaborative Innovation Center for Eco-Environmental Improvement with Forestry and Fruit Trees, Beijing 102206, China
4
Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
5
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Xianyang 712100, China
6
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
7
Beijing Water Consulting Co., Ltd., Beijing 100048, China
*
Author to whom correspondence should be addressed.
Academic Editors: Timothy R. Green and Michele Mossa
Received: 25 March 2017 / Revised: 6 May 2017 / Accepted: 15 May 2017 / Published: 18 May 2017
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Abstract

As an important restrictive factor of ecological construction on the Loess Plateau, the study of soil moisture dynamics is essential, especially under the impact of climate change on hydrological processes. In this study, the applicability of the Laio soil moisture stochastic model on a typical plantation Pinus tabuliformis forestland on the Loess Plateau was studied. On the basis of data concerning soil properties, climate, and plants of the typical forestland during the period 2005–2015 in the Chinese National Ecosystem Research Network (Ji County Station) in Ji County, Shanxi, model results were acquired and compared with observed soil moisture from 2005 to 2015 in the study area. The genetic algorithm method was used to optimize model parameters in the calibration process. In the calibration and validation periods, the relative error between numerical characteristics of simulated and observed soil moisture values was mostly within 10%, and model evaluation index J was close to 1, indicating that the Laio model had good applicability in the study area. When calibrating the model, it was recommended to use soil moisture data with a sampling interval of no more than 10 days so as to reduce the loss of soil moisture fluctuation information. In the study area, the Laio model was strongly sensitive to variations of input parameters, including maximum evapotranspiration rate Emax, average rainfall depth α, and average rainfall frequency λ, which should be paid more attention for stable and reliable simulation results. This study offers a method to obtain soil moisture data at ungauged sites. Results from this study provide guidance for Laio model application on the Loess Plateau. View Full-Text
Keywords: soil moisture dynamics; Laio stochastic model; sampling interval; parameter sensitivity analysis; Loess Plateau soil moisture dynamics; Laio stochastic model; sampling interval; parameter sensitivity analysis; Loess Plateau
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Chang, Y.-F.; Bi, H.-X.; Ren, Q.-F.; Xu, H.-S.; Cai, Z.-C.; Wang, D.; Liao, W.-C. Soil Moisture Stochastic Model in Pinus tabuliformis Forestland on the Loess Plateau, China. Water 2017, 9, 354.

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