scholarly journals Soil hydraulic conductivity as affected by vegetation restoration age on the Loess Plateau, China

2016 ◽  
Vol 8 (4) ◽  
pp. 546-555 ◽  
Author(s):  
Zongping Ren ◽  
Liangjun Zhu ◽  
Bing Wang ◽  
Shengdong Cheng
CATENA ◽  
2016 ◽  
Vol 143 ◽  
pp. 1-6 ◽  
Author(s):  
Chunlei Zhao ◽  
Ming’an Shao ◽  
Xiaoxu Jia ◽  
Mubasher Nasir ◽  
Chencheng Zhang

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114240 ◽  
Author(s):  
Pingzong Zhu ◽  
Guanghui Zhang ◽  
Hongxiao Wang ◽  
Baojun Zhang ◽  
Xue Wang

2021 ◽  
Vol 13 (12) ◽  
pp. 2358
Author(s):  
Linjing Qiu ◽  
Yiping Wu ◽  
Zhaoyang Shi ◽  
Yuting Chen ◽  
Fubo Zhao

Quantitatively identifying the influences of vegetation restoration (VR) on water resources is crucial to ecological planning. Although vegetation coverage has improved on the Loess Plateau (LP) of China since the implementation of VR policy, the way vegetation dynamics influences regional evapotranspiration (ET) remains controversial. In this study, we first investigate long-term spatiotemporal trends of total ET (TET) components, including ground evaporation (GE) and canopy ET (CET, sum of canopy interception and canopy transpiration) based on the GLEAM-ET dataset. The ET changes are attributed to VR on the LP from 2000 to 2015 and these results are quantitatively evaluated here using the Community Land Model (CLM). Finally, the relative contributions of VR and climate change to ET are identified by combining climate scenarios and VR scenarios. The results show that the positive effect of VR on CET is offset by the negative effect of VR on GE, which results in a weak variation in TET at an annual scale and an increased TET is only shown in summer. Regardless of the representative concentration pathway (RCP4.5 or RCP8.5), differences resulted from the responses of TET to different vegetation conditions ranging from −3.7 to −1.2 mm, while climate change from RCP4.5 to RCP8.5 caused an increase in TET ranging from 0.1 to 65.3 mm. These findings imply that climate change might play a dominant role in ET variability on the LP, and this work emphasizes the importance of comprehensively considering the interactions among climate factors to assess the relative contributions of VR and climate change to ET.


CATENA ◽  
2021 ◽  
Vol 202 ◽  
pp. 105293
Author(s):  
Yang Wu ◽  
WenJing Chen ◽  
Wulan Entemake ◽  
Jie Wang ◽  
HongFei Liu ◽  
...  

Author(s):  
Hui Wei ◽  
Wenwu Zhao ◽  
Han Wang

Large-scale vegetation restoration greatly changed the soil erosion environment in the Loess Plateau since the implementation of the “Grain for Green Project” (GGP) in 1999. Evaluating the effects of vegetation restoration on soil erosion is significant to local soil and water conservation and vegetation construction. Taking the Ansai Watershed as the case area, this study calculated the soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration, using the Chinese Soil Loess Equation (CSLE), based on rainfall and soil data, remote sensing images and socio-economic data. The effect of vegetation restoration on soil erosion was evaluated by comparing the average annual soil erosion modulus under two scenarios among 16 years. The results showed: (1) vegetation restoration significantly changed the local land use, characterized by the conversion of farmland to grassland, arboreal land, and shrub land. From 2000 to 2015, the area of arboreal land, shrub land, and grassland increased from 19.46 km2, 19.43 km2, and 719.49 km2 to 99.26 km2, 75.97 km2, and 1084.24 km2; while the farmland area decreased from 547.90 km2 to 34.35 km2; (2) the average annual soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration was 114.44 t/(hm²·a) and 78.42 t/(hm²·a), respectively, with an average annual reduction of 4.81 × 106 t of soil erosion amount thanks to the vegetation restoration; (3) the dominant soil erosion intensity changed from “severe and light erosion” to “moderate and light erosion”, vegetation restoration greatly improved the soil erosion environment in the study area; (4) areas with increased erosion and decreased erosion were alternately distributed, accounting for 48% and 52% of the total land area, and mainly distributed in the northwest and southeast of the watershed, respectively. Irrational land use changes in local areas (such as the conversion of farmland and grassland into construction land, etc.) and the ineffective implementation of vegetation restoration are the main reasons leading to the existence of areas with increased erosion.


Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 23 ◽  
Author(s):  
Mohammad Reza Neyshabouri ◽  
Mehdi Rahmati ◽  
Claude Doussan ◽  
Boshra Behroozinezhad

Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.


1980 ◽  
Vol 44 (1) ◽  
pp. 3-7 ◽  
Author(s):  
P. L. Libardi ◽  
K. Reichardt ◽  
D. R. Nielsen ◽  
J. W. Biggar

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