Soil moisture dynamics and dominant controls at different spatial scales over semiarid and semi-humid areas

Suo, Lizhu; Huang, Mingbin; Zhang, Yongkun; Duan, Liangxia; Shan, Yan

doi:10.1016/j.jhydrol.2018.05.036

Cited by 46 publications

(13 citation statements)

References 57 publications

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“…This was confirmed by root development and water uptake in this study (Fig. 11) and by field observations in this region (Suo et al, 2020). In the top 2.0 m soil layer, infiltration events replenish and plant roots deplete the soil water alternately.…”

Section: Evolution Of Drying Soil Layerssupporting

confidence: 87%

Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China

Luo

Sun

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Plant root–soil water interactions are fundamental to vegetation–water relationships. Soil water availability and distribution impact the temporal–spatial dynamics of roots and vice versa. In the Loess Plateau (LP) of China, where semi-arid and arid climates prevail and deep loess soil dominates, drying soil layers (DSLs) have been extensively reported in artificial forestland. While the underlying mechanisms that cause DSLs remain unclear, they hypothetically involve root–soil water interactions. Although available root growth models are weak with respect to simulating the rooting depth, this study addresses the hypothesis of the involvement of root–soil water interactions in DSLs using a root growth model that simulates both the dynamic rooting depth and fine-root distribution, coupled with soil water, based on cost–benefit optimization. Evaluation of field data from an artificial black locust (Robinia pseudoacacia L.) forest site in the southern LP positively proves the model's performance. Further, a long-term simulation, forced by a 50-year climatic data series with varying precipitation, was performed to examine the DSLs. The results demonstrate that incorporating the dynamic rooting depth into the current root growth models is necessary to reproduce soil drying processes. The simulations revealed that the upper boundary of the DSLs fluctuates strongly with infiltration events, whereas the lower boundary extends successively with increasing rooting depth. Most infiltration was intercepted by the top 2.0 m layer, which was the most active zone of infiltration and root water uptake. Below this, the percentages of fine roots (5.0 %) and water uptake (6.2 %) were small but caused a persistently negative water balance and consequent DSLs. Therefore, the proposed root–water interaction approach succeeded in revealing the intrinsic properties of DSLs; their persistent extension and the lack of an opportunity for recovery from the drying state may adversely affect the implementation of artificial afforestation in this region as well as in other regions with similar climates and soils.

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Section: Evolution Of Drying Soil Layerssupporting

confidence: 87%

Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China

Luo

Sun

et al. 2022

Hydrol. Earth Syst. Sci.

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“…where J is the number of dependent variables X , and F is the number of components. W j f is the weight of variable j and component f . S S Y f is the sum of squares that is explained by the variance for component f , and S S Y T o t a l is the total sum of squares that is explained by the dependent variables (Suo et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…W jf is the weight of variable j and component f . SSY f is the sum of squares that is explained by the variance for component f , and SSY Total is the total sum of squares that is explained by the dependent variables (Suo et al, 2018). The threshold of VIP was usually set to 1, and an independent variable with a VIP value >1 was considered important, otherwise it was unimportant (Mehmood et al, 2012;Mukherjee et al, 2015;Suo et al, 2018).…”

Section: The Importance Magnitude Of Built Environment Variablesmentioning

confidence: 99%

“…SSY f is the sum of squares that is explained by the variance for component f , and SSY Total is the total sum of squares that is explained by the dependent variables (Suo et al, 2018). The threshold of VIP was usually set to 1, and an independent variable with a VIP value >1 was considered important, otherwise it was unimportant (Mehmood et al, 2012;Mukherjee et al, 2015;Suo et al, 2018). Therefore, when the VIP value of a built environment variable was >1, the variable was considered transferable with an important impact on the ridership.…”

Section: The Importance Magnitude Of Built Environment Variablesmentioning

confidence: 99%

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Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

Fang¹,

Zhang

Zheng³

2022

Transactions in Urban Data, Science, and Technology

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New stations (such as metro stations) will bring remarkable changes to the local transportation and economic development. Understanding patterns of factors which importantly impact on public transit ridership in the surrounding areas of new stations is essential to their construction planning, like estimating the possible ridership. Built environment variables with high importance magnitude, which were thought applicable to estimate public transit ridership in other areas of the same category, were described as transferable variables (TVs) in this study. A transferability analysis method of the built environment for the ridership estimation was constructed by adopting partial least square regression (PLSR) based on available data. Taking Wuhan, China as an example, this study analyzed the changes and differences of the built environment variables in different categories of pedestrian catchment areas (PCAs) of metro stations on the importance and transferability magnitude for the metro and taxi ridership, based on the metro and taxi data of one week in January, April, and June. Performances of the ridership estimation based on TVs and all the built environment variables were compared. This study inferred that (1) most of the land use variables (about 85%) showed important influence on the metro and taxi ridership, while only about 18% of the other variables showed key impact. The importance magnitude of the built environment variables was mainly related to PCA categories and public transportation modes, but less related to time. (2) Highly important built environment variables also tended to be highly transferable. Transferability magnitude of the built environment variables for the ridership was related to PCA categories and types of public transport. (3) Compared to all the built environment variables, using TVs, the relative accuracy of the metro and taxi ridership estimation was around 20% and 18% higher respectively.

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“…The previously discussed interactions between rainfall and the forest canopy add to the heterogeneity of the climate, soil physical, chemical and biological attributes, and topography. Forest traits affect the spatiotemporal distribution of the soil moisture in this environment (Gao et al, 2011; Rodrigues et al, 2020; Suo, Huang, Zhang, Duan, & Shan, 2018). Conversely, the soil moisture spatiotemporal variability drives species diversity, tree growth and mortality, evapotranspiration, preferential flow paths (favouring infiltration and groundwater recharge) and soil moisture redistribution.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms

Oliveira

Rodrigues

Morais

et al. 2021

European J Soil Science

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Understanding the spatiotemporal behaviour of soil moisture in tropical forests is fundamental because it mediates processes such as infiltration, groundwater recharge, runoff and evapotranspiration. This study aims to model the spatiotemporal dynamics of soil moisture in an Atlantic forest remnant (AFR) through four machine learning algorithms, as these dynamics represent an important knowledge gap under tropical conditions. Random forest (RF), support vector machine, average neural network and weighted k‐nearest neighbour were studied. The abilities of the models were evaluated by means of root mean square error, mean absolute error, coefficient of determination (R2) and Nash‐Sutcliffe efficiency (NS) for two calibration approaches: (a) chronological and (b) randomized. The models were further compared with a multilinear regression (MLR). The study period spans from September 2012 to November 2019 and relies on variables representing the weather, geographical location, forest structure, soil physics and morphology. RF was the best algorithm for modelling the spatiotemporal dynamics of the soil moisture with an NS of 0.77 and R2 of 0.51 in the randomized approach. This finding highlights the ability of RF to generalize a dataset with contrasting weather conditions. Kriging maps highlighted the suitability of RF to track the spatial distribution of soil moisture in the AFR. Throughfall (TF), potential evapotranspiration (ETo), longitude (Long), diameter at breast height (DBH) and species diversity (H) were the most important variables controlling soil moisture. MLR performed poorly in modelling the spatiotemporal dynamics of soil moisture due to the highly nonlinear condition of this process. Highlights Modelling soil moisture in an Atlantic forest through machine learning. Machine learning algorithms are powerful tools to address the spatiotemporal dynamics of soil moisture. Climate, position and forest variables drive the spatiotemporal pattern of soil moisture. Random forest is the best algorithm to simulate soil moisture dynamics.

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Soil moisture dynamics and dominant controls at different spatial scales over semiarid and semi-humid areas

Cited by 46 publications

References 57 publications

Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China

Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China

Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms

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Soil moisture dynamics and dominant controls at different spatial scales over semiarid and semi-humid areas

Cited by 46 publications

References 57 publications

Modelling the artificial forest (&lt;i&gt;Robinia pseudoacacia&lt;/i&gt; L.) root–soil water interactions in the Loess Plateau, China

Modelling the artificial forest (&lt;i&gt;Robinia pseudoacacia&lt;/i&gt; L.) root–soil water interactions in the Loess Plateau, China

Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms

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Product

Resources

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Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China

Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China