Groundwater‐dependent distribution of vegetation in Hailiutu River catchment, a semi‐arid region in China

Lv, Jingjing; Wang, Xu‐Sheng; Zhou, Yangxiao; Qian, Kun; Wan, Li; Eamus, Derek; Zheng-ping, Tao

doi:10.1002/eco.1254

Cited by 73 publications

(42 citation statements)

References 12 publications

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“…Thus, plant density tends to be larger when groundwater is available than in nearby vegetation that does not have access to groundwater. Lv et al (2012) used a remotely sensed vegetation index (normalised difference vegetation index; NDVI; 300 m resolution) to examine changes in depth-to-groundwater within a small region in northern China. NDVI is a reliable measure of the chlorophyll content ("greenness") in leaves and vegetation cover (Gamon et al, 1995;Carlson and Ripley, 1997;Huete et al, 2002).…”

Section: Satellite-based Approachesmentioning

confidence: 99%

“…Similarly, increased Huber value, (Table 3). Consequently, response functions for individual traits are readily apparent; examples include changes with depth-to-groundwater in rates of photosynthesis (Horton et al, 2001), plant cover (Elmore et al, 2006), NDVI (Lv et al, 2012) and crown dieback (Horton et al, 2001). However, few studies have examined multiple traits across multiple scales and then provided an integrated "ecosystem-scale" response function to differences in groundwater availability.…”

Section: Multiple Traits Across Leaf Branch Whole-tree and Standmentioning

confidence: 99%

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Groundwater-dependent ecosystems: recent insights from satellite and field-based studies

Eamus

Zolfaghar

Villalobos‐Vega

et al. 2015

Hydrol. Earth Syst. Sci.

132

112

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Abstract. Groundwater-dependent ecosystems (GDEs) are at risk globally due to unsustainable levels of groundwater extraction, especially in arid and semi-arid regions. In this review, we examine recent developments in the ecohydrology of GDEs with a focus on three knowledge gaps: (1) how do we locate GDEs, (2) how much water is transpired from shallow aquifers by GDEs and (3) what are the responses of GDEs to excessive groundwater extraction? The answers to these questions will determine water allocations that are required to sustain functioning of GDEs and to guide regulations on groundwater extraction to avoid negative impacts on GDEs.We discuss three methods for identifying GDEs: (1) techniques relying on remotely sensed information; (2) fluctuations in depth-to-groundwater that are associated with diurnal variations in transpiration; and (3) stable isotope analysis of water sources in the transpiration stream.We then discuss several methods for estimating rates of GW use, including direct measurement using sapflux or eddy covariance technologies, estimation of a climate wetness index within a Budyko framework, spatial distribution of evapotranspiration (ET) using remote sensing, groundwater modelling and stable isotopes. Remote sensing methods often rely on direct measurements to calibrate the relationship between vegetation indices and ET. ET from GDEs is also determined using hydrologic models of varying complexity, from the White method to fully coupled, variable saturation models. Combinations of methods are typically employed to obtain clearer insight into the components of groundwater discharge in GDEs, such as the proportional importance of transpiration versus evaporation (e.g. using stable isotopes) or from groundwater versus rainwater sources.Groundwater extraction can have severe consequences for the structure and function of GDEs. In the most extreme cases, phreatophytes experience crown dieback and death following groundwater drawdown. We provide a brief review of two case studies of the impacts of GW extraction and then provide an ecosystem-scale, multiple trait, integrated metric of the impact of differences in groundwater depth on the structure and function of eucalypt forests growing along a natural gradient in depth-to-groundwater. We conclude with a discussion of a depth-to-groundwater threshold in this mesic GDE. Beyond this threshold, significant changes occur in ecosystem structure and function.

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Section: Satellite-based Approachesmentioning

confidence: 99%

Section: Multiple Traits Across Leaf Branch Whole-tree and Standmentioning

confidence: 99%

Groundwater-dependent ecosystems: recent insights from satellite and field-based studies

Eamus

Zolfaghar

Villalobos‐Vega

et al. 2015

Hydrol. Earth Syst. Sci.

132

112

View full text Add to dashboard Cite

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“…When groundwater is available to vegetation, plant density tends to be larger than adjacent areas where groundwater is unavailable. Lv et al (2012) used remotely sensed images of a vegetation index (the Normalised Difference Vegetation Index; NDVI) to assess changes in NDVI as a function of depth-togroundwater in northern China. A 25 m resolution digital elevation model and groundwater bore data were used to generate a contour map of groundwater depths across the 2600 km 2 catchment.…”

Section: Application Of Vegetation Indices Derived From Rsmentioning

confidence: 99%

Groundwater Dependent Ecosystems: Classification, Identification Techniques and Threats

Eamus

Springer

et al. 2016

Integrated Groundwater Management

Self Cite

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This chapter begins by briefly discussing the three major classes of groundwater dependent ecosystems (GDEs), namely: (I) GDEs that reside within groundwater (e.g. karsts; stygofauna); (II) GDEs requiring the surface expression of groundwater (e.g. springs; wetlands); and (III) GDEs dependent upon sub-surface availability of groundwater within the rooting depth of vegetation (e.g. woodlands; riparian forests). We then discuss a range of techniques available for identifying the location of GDEs in a landscape, with a primary focus of class III GDEs and a secondary focus of class II GDEs. These techniques include inferential methodologies, using hydrological, geochemical and geomorphological indicators, biotic assemblages, historical documentation, and remote sensing methodologies. Techniques available to quantify groundwater use by GDEs are briefly described, including application of simple modelling tools, remote sensing methods and complex modelling applications. This chapter also outlines the contemporary threats to the persistence of GDEs across the world. This involves a description of the "natural" hydrological attributes relevant to GDEs and the

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“…However, apart from the influence of climatic change, NDVI is also strongly related to the groundwater table level (GTL) in arid and semi-arid regions [2,[17][18][19]. Many investigators have also reported the relationship between vegetation and groundwater availability (Lv et al, 2013;Jin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Jin et al (2016) found a linear correlation between NDVI and groundwater depth in the Qaidam basin [2]. The distribution of GTL is becoming more heterogeneous in the middle reaches of Hei River as a result of increasing cultivated land area and runoff from Yingluo station [18]. The distribution of vegetation is also highly heterogeneous and depends on multiple factors.…”

Section: Introductionmentioning

confidence: 99%

Modeling NDVI Using Joint Entropy Method considering Hydro-Meteorological Driving Factors in the Middle Reaches of Hei River Basin

Zhang¹,

Su²,

Singh³

et al. 2017

Preprint

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Terrestrial vegetation dynamics are closely influenced by a multitude of factors.This study investigated the relationships between vegetation patterns and their main influencing factors. The joint entropy method was employed to evaluate the dependence between normalized difference vegetation index (NDVI) and coupled variables in the middle reaches of Hei River basin. Based on the spatial distribution of mutual information, the whole study area was divided into five sub-regions. In each sub-region, nested statistical models were applied to model the NDVI on the grid and regional scales, respectively. Results showed that the annual average NDVI increased with a rate of 0.005/a in recent 11 years. In the desert regions, the NDVI increased significantly with an increase in precipitation and temperature, and high accuracy of retrieving NDVI model was obtained by coupling precipitation and temperature, especially in sub-region I. In the oasis regions, groundwater was also an important factor driving vegetation growth, and the rise of groundwater level contributed to the growth of vegetation. However, the relationship was weaker in artificial oasis regions (sub-region III and sub-region V) due to the influence of human activities, such as irrigation. The overall correlation coefficient between the observed NDVI and modeled NDVI was observed to be 0.97. Outcomes of this study are suitable for ecosystem monitoring, especially under the realm of climate change.Further studies are necessary and should consider more factors, such as runoff and irrigation.

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Groundwater‐dependent distribution of vegetation in Hailiutu River catchment, a semi‐arid region in China

Cited by 73 publications

References 12 publications

Groundwater-dependent ecosystems: recent insights from satellite and field-based studies

Groundwater-dependent ecosystems: recent insights from satellite and field-based studies

Groundwater Dependent Ecosystems: Classification, Identification Techniques and Threats

Modeling NDVI Using Joint Entropy Method considering Hydro-Meteorological Driving Factors in the Middle Reaches of Hei River Basin

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