Dynamic response patterns of profile soil moisture wetting events under different land covers in the Mountainous area of the Heihe River Watershed, Northwest China

Tian, Jie; Zhang, Baoqing; He, Chansheng; Han, Zhibo; Bogena, Heye; Huisman, Johan Alexander

doi:10.1016/j.agrformet.2019.03.006

Cited by 67 publications

(30 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This sensor minimizes the influence of salinity and texture and is regarded as an accurate sensor in a wide range of soil media [37]. The error of the data was estimated to be within ± 3% [38], and this sensor has been used in many studies [3,37,39]. The 5TE probes were inserted horizontally in the soil profiles at depths of 2.5 cm, 7.5 cm, 12.5 cm, 22.5 cm, and 40 cm to measure the SWC and soil temperature at each sampling site.…”

Section: Methodsmentioning

confidence: 99%

“…Atmospheric precipitation is the main source of water supply to soils in the arid regions of Northwest China. The magnitude and process of precipitation have a strong impact on the SWC [3]. SWC response to precipitation in the arid areas was analyzed by comparing the time series of the SWC and the precipitation under different vegetation conditions in the study area of the typical APE during 2017 and 2018 ( Figure 5).…”

Section: Soil Water Content Response To Precipitationmentioning

confidence: 99%

“…The soil water content (SWC) is usually defined as water stored in unsaturated soil layers [1,2]. It plays an important role in the global water cycle by controlling the distribution of evaporation and infiltration [3]. It is also a critical link between the atmosphere and the biosphere; it influences the interaction of the hydrological processes in the earth system over both space and time [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The spatiotemporal variation in SWC refers to the obvious difference and diversity of soil moisture characteristics in different regions, at different times, locations, and soil layers [6,7]. Traditionally, SWC can be measured by in-situ observations, remote sensing, and laboratory measurements [3,8]. In recent years, the method of satellites [9,10], proximal neutron [11], and gamma radiation stations [12] have been greatly improved, which promoted the development of soil moisture estimation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Soil Water Content and Temperature Dynamics under Grassland Degradation: A Multi-Depth Continuous Measurement from the Agricultural Pastoral Ecotone in Northwest China

Yang

Wang

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

The agricultural pastoral ecotone (APE) in Northwest China is an ecological transition zone in the arid area with a very fragile ecosystem. In recent years, the ecosystem has deteriorated sharply, and increasing desertification has made the regional ecosystem more vulnerable and sensitive. In this study, we analyzed (using classical statistical methods) spatial and temporal variations in soil water content (SWC) from 14 September 2016 to 22 April 2019 for high and low vegetation in two grassland sites in Yanchi County, Ningxia. The results showed that the largest average seasonal SWC occurred in autumn. The SWC of the first three layers (0 ÷ 15 cm) of the soil profile responded strongly to precipitation, whereas the SWC in deeper soil (30 ÷ 50 cm) could only be recharged markedly after continuous precipitation. Additionally, the growing process of plants proved to be a cause of variability in soil moisture profiles. Vegetation degradation sped up the course of desertification and decreased soil organic carbon content. These changes left the soil increasingly desiccated and enhanced soil variability. Meanwhile, vegetation degradation also prompted changes in soil temperature and shortened the soil’s frozen time in winter. With the acceleration of global warming, if the process of vegetation degeneration continues and soil temperatures keep rising, the ecosystem is likely to undergo irreversible degradation.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Soil Water Content Response To Precipitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Soil Water Content and Temperature Dynamics under Grassland Degradation: A Multi-Depth Continuous Measurement from the Agricultural Pastoral Ecotone in Northwest China

Yang

Wang

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…The entire data set used in this study thus consists of six in situ SM time series at depths of 5, 15, 25, 40, 60 cm, and 0-70 cm for each of the 35 stations. Due to the influence of soil freezing in winter, the soil moisture time series were limited to the growing season (May to October, Tian et al, 2019) of 2014 to 2016. The half-hourly measurements were averaged to obtain daily SM values that will be used for the estimation of subsurface SM (Wagner et al, 1999).…”

Section: Datasetsmentioning

confidence: 99%

Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

Tian¹,

Han²,

Bogena³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Profile soil moisture (SM) in mountainous areas are significant in water resources management and ecohydrological studies of downstream arid watersheds. Satellite products are useful in providing spatially distributed SM information, but only have limited penetration depth (e.g. top 5 cm). In contrast, in situ observations can provide multi-depth measurements, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using statistical methods, but this requires sufficient coupling strength among surface and subsurface SM. This study evaluates methods to calculate subsurface SM from surface SM and an application to the satellite SM product based on a SM observation network in the Qilian Mountains (China) established since 2013. First, we used cross-correlation to analyze the coupling strength among surface (0–10 cm) and subsurface (10–20, 20–30, 30–50, 50–70 cm, and profile of 0–70 cm) SM. Our results indicated an overall strong coupling among surface and subsurface SM in this study area. Afterwards, three different methods were tested to estimate subsurface SM from in-situ surface SM: the exponential filter (ExpF), artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods. The results showed that both ANN and ExpF methods were able to provide accurate estimates of subsurface soil moisture at 10–20 cm, 20–30 cm, and for the profile of 0–70 cm using surface (0–10 cm) soil moisture only. Specifically, the ANN method had the lowest estimation error (RSR) of 0.42, 0.62 and 0.49 for depths of 15 and 25 cm and profile SM, respectively, while the ExpF method best captured the temporal variation of subsurface soil moisture. Furthermore, it could be shown that the performance of the profile SM estimation was not significantly lower with using an area-generalized Topt (optimum T) compared to the station-specific Topt. In a final step, the ExpF method was applied to the satellite SM product (Soil Moisture Active Passive Level 3: SMAP_L3) to estimate profile SM, and the resulting profile SM was compared to in situ observations. The results showed that the ExpF method was able to estimate profile SM from SMAP_L3 surface products with reasonable accuracy (median R of 0.718). It was also found that the combination of ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in the mountainous areas in comparison to the SMAP_L4 root zone product. Overall, it was concluded that the ExpF method is able to estimate profile SM using SMAP surface products in the Qilian Mountains.

show abstract

Differences in the Effects of Three Water Elements on Vegetation in Different Climatic Regions: Insights From the Yellow River Basin, China

Jin,

Fan,

et al. 2024

Ecohydrology

View full text Add to dashboard Cite

The effects of water on vegetation have always been a concern. It is an important support as well as a major limiting factor with respect to vegetation growth. By analysing the spatiotemporal changes and correlations between precipitation (PRE), soil moisture (SM), vapour pressure deficit (VPD) and normalized difference vegetation index (NDVI) in the Yellow River Basin, we explored the different effects of three water elements on vegetation in different climatic regions. Our findings reveal the following: (1) NDVI and the three water elements report an increasing trend in the Yellow River Basin, with NDVI increasing most significantly. 92.1% of the Yellow River Basin showed an increase in NDVI. (2) Vegetation in the Yellow River Basin was most positively affected by PRE, followed by SM and VPD. PRE mainly affected the natural vegetation on both sides of the boundary between the arid and semi‐arid regions and the semi‐humid regions. SM mainly affected the natural vegetation in the arid and semi‐arid regions, whereas VPD mainly affected the crops in the irrigation areas, and the irrigation areas in arid regions were affected the most. These findings contribute to a deeper understanding of the relationship between water elements and vegetation.

show abstract

Dynamic response patterns of profile soil moisture wetting events under different land covers in the Mountainous area of the Heihe River Watershed, Northwest China

Cited by 67 publications

References 89 publications

Soil Water Content and Temperature Dynamics under Grassland Degradation: A Multi-Depth Continuous Measurement from the Agricultural Pastoral Ecotone in Northwest China

Soil Water Content and Temperature Dynamics under Grassland Degradation: A Multi-Depth Continuous Measurement from the Agricultural Pastoral Ecotone in Northwest China

Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

Differences in the Effects of Three Water Elements on Vegetation in Different Climatic Regions: Insights From the Yellow River Basin, China

Contact Info

Product

Resources

About