scholarly journals Available water capacity and organic carbon storage profiles in soils developed from dark brown soil to boggy soil in Changbai Mountains, China

2020 ◽  
Vol 16 (No. 1) ◽  
pp. 11-21
Author(s):  
Dandan Yu ◽  
Feilong Hu ◽  
Kun Zhang ◽  
Li Liu ◽  
Danfeng Li

The available water capacity (AWC) is the most commonly used parameter for quantifying the amount of soil water that is readily available to plants. Specific AWC and soil organic carbon storage (SOCS) profiles are consequences of the soil development process. Understanding the distributions of AWC and SOCS in soil profiles is crucial for modelling the coupling between carbon and water cycle processes, and for predicting the consequences of global change. In this study, we determined the variations in the AWC and SOCS from the surface to a depth of 100 cm in soils developed from dark brown soil, skeletal dark brown soil, meadow dark brown soil, white starched dark brown soil, meadow soil, and boggy soil in the Changbai Mountains area of China. The AWC and SOCS profiles were calculated for each main soil group/subgroup using only the readily available variables for the soil texture and organic matter with the soil water characteristic equations. The results showed the following. (1) The AWC and SOCS decreased initially and then increased, before decreasing again in soils developed from dark brown soil to boggy soil, where the maximum SOCS occurred in the white starched dark brown soil, and the maximum AWC in the dark brown soil. (2) The SOCS was decreased by deforestation and concomitant soil erosion, but the negative impact of this decrease in the SOCS in the Changbai Mountains area was not caused completely by reductions in AWC. (3) In the soil development process from dark brown soil to boggy soil in response to deforestation, the AWC distribution differed in the profile and even among individual layers, whereas the SOCS was mainly present in the upper layer.

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 517
Author(s):  
Sunwei Wei ◽  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.


Geoderma ◽  
2008 ◽  
Vol 146 (1-2) ◽  
pp. 311-316 ◽  
Author(s):  
Wen-Ju Zhang ◽  
He-Ai Xiao ◽  
Cheng-Li Tong ◽  
Yi-Rong Su ◽  
Wan-sheng Xiang ◽  
...  

Geoderma ◽  
2006 ◽  
Vol 134 (1-2) ◽  
pp. 200-206 ◽  
Author(s):  
Huajun Tang ◽  
Jianjun Qiu ◽  
Eric Van Ranst ◽  
Changsheng Li

Geoderma ◽  
2010 ◽  
Vol 154 (3-4) ◽  
pp. 261-266 ◽  
Author(s):  
Fengpeng Han ◽  
Wei Hu ◽  
Jiyong Zheng ◽  
Feng Du ◽  
Xingchang Zhang

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