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Isotope-based water-use efficiency of major greening plants in a sponge city in northern China

  • Changchao Li,

    Roles Data curation, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

    Affiliation Institute of Environmental Research, Shandong University, Qingdao, China

  • Miansong Huang ,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    hms@capitalwater.cn

    Affiliations Ningxia Capital Sponge City Construction & Development Co., Ltd, Guyuan, China, Beijing Capital Co., Ltd, Beijing, China

  • Jian Liu,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Institute of Environmental Research, Shandong University, Qingdao, China

  • Shuping Ji,

    Roles Investigation, Writing – original draft, Writing – review & editing

    Affiliation Institute of Environmental Research, Shandong University, Qingdao, China

  • Ruiqi Zhao,

    Roles Investigation, Writing – review & editing

    Affiliation Institute of Environmental Research, Shandong University, Qingdao, China

  • Di Zhao,

    Roles Software, Writing – review & editing

    Affiliation Institute of Environmental Research, Shandong University, Qingdao, China

  • Ruilian Sun

    Roles Funding acquisition, Writing – review & editing

    Affiliation Institute of Environmental Research, Shandong University, Qingdao, China

Isotope-based water-use efficiency of major greening plants in a sponge city in northern China

  • Changchao Li, 
  • Miansong Huang, 
  • Jian Liu, 
  • Shuping Ji, 
  • Ruiqi Zhao, 
  • Di Zhao, 
  • Ruilian Sun
PLOS
x

Abstract

To tackle urban water issues, the Chinese government has promoted the construction of sponge cities in recent years. Thirty cities have been designated as experimental sites to serve as models for future sponge city construction, as more than 80% of the built-up urban areas in China must reach the standards of sponge cities by 2030. Greening plants play an important role in sponge cities, and water-use efficiency (WUE) is a vital index to determine whether plants could adapt to and grow healthily in environments with water deficits. In this study, WUE of greening plants was quantified by measuring the stable carbon isotope fractionation. Suitable plants for the green spaces in Guyuan sponge city, in northern China, were selected based on their WUE, and the main factors affecting WUE were studied in four habitats within the city. Plant species identity had the greatest effect on WUE, while habitat and plant life form had lower effect, illustrating that WUE is a relatively stable and reliable index for the classification of plant species. We can improve the WUE and ecological function of green spaces in sponge cities by using isotope technology to select suitable plant species with high WUE. To our knowledge, this study is the first to select plant species for sponge city by using this method, providing a quick and scientific method for the selection of greening plants for future sponge cities.

Introduction

Sponge city is a new type of urban stormwater management concept. It is based on the premise that, like sponges, cities should be “elastic” in order to adapt to environmental changes and cope with natural disasters [1]. During rainfall, the sponge city absorbs, accumulates, seeps, and purifies water. When needed, it releases and uses the stored water [1]. The concept of sponge cities in China is similar in function to Low Impact Development, Sustainable Drainage Systems, and Best Management Practice in other countries. However, this concept draws on internationally successful construction experience and practice cases, while taking into account the local conditions, climate, geography, and other factors specific to China, so it has more comprehensive connotations and far-reaching significance [2]. In April 2015, China designated 16 cities as the first batch of experimental sites for sponge cities; in April 2016, 14 additional cities were designated as the second batch of sponge city experimental sites. The construction of sponge cities will promote the transformation and development of China’s traditional stormwater management and serves as the benchmark and guidelines for future urban rainwater constructions in China [3].

Greening plants form an important part of urban ecosystems. In sponge cities, greening plants can absorb and utilize nitrogen, phosphorus, and potassium (present in precipitation and residential drainage) as nutrients; they can absorb and degrade pesticides and metal ions, and purify water by increasing the presence of dissolved oxygen [4]. Plants can reduce runoff and peak flow by intercepting precipitation [5], increasing rainwater seepage and evaporation, and increasing groundwater recharge. The greening plants can also absorb dust particles, reduce noise, and beautify the environment, thus, giving residents a more comfortable living environment [6, 7].

Water-use efficiency (WUE) is an important index of sponge city greening plants, especially for sponge cities in arid regions. Plants with high WUE have strong drought tolerance, which can reduce the need for artificial irrigation, lower maintenance costs, and increase urban ecosystem services [8, 9]. At present, the methods for determining the WUE of plants include field measurement, leaf gas exchange technology, and stable carbon isotope technology [10, 11]. The stable carbon isotope ratio (denoted as δ13C) is positively correlated with the WUE of a plant [12, 13], and larger δ13C values imply higher WUE [1416]. Therefore, δ13C can be used as an evaluation index for plant WUE [17, 18]. The advantages of stable carbon isotope technology are that (1) it allows for long-term measurements, overcoming the shortcomings of conventional methods that can only study short-term and instantaneous WUE, (2) it limits the destruction of plants during sampling, (3) it is not limited by factors such as sampling time, sampling location, and the underlying surface conditions of study area, and (4) the plant material is convenient for storage and measurement [19]. Since stable carbon isotope technology has become a recognized method for estimating the long-term WUE of plants [20], we used this method to measure the WUE of greening plants in the study area.

Studies on the WUE of plants by using stable carbon isotope technology have shown a strong correlation between the δ13C value and WUE with minimal interference due to other factors [21, 22]. Carbon isotope technology has been used to estimate the changes in the WUE of grasslands in the past decades [23, 24]. Different plant genotypes have different responses to water availability [25]; therefore, the δ13C value is an effective index for selecting genotypes with higher WUE [26]. There are several studies on the selection or evaluation of greening plants in sponge cities [2729]. For example, waterlogging resistance and drought-tolerant of greening plants in Shenzhen sponge city have been studied to provide guidelines for the construction of other sponge cities [27, 28]. The growth adaptability and flood tolerance of six common herbaceous garden plants have been compared to provide a screening method for the selection of greening plants in sponge cities [29]. However, to our knowledge, there is no study on the WUE of greening plants in sponge cities based on isotope technology.

The city of Guyuan is in the transition zone between the semi-arid to the arid and moderate temperate zone of China. Because of global climate change, the ecological environment in Guyuan has become more fragile in recent years [30]. As a result, natural disasters are more frequent, and drought is the most prominent environmental phenomenon [30]. Moreover, water deficit has become the dominant factor restricting agricultural production in Guyuan. In 2016, the city of Guyuan was selected to form a part of the second batch of sponge city experimental sites in China, aiming to solve the water shortage through the construction of a sponge city. Sponge cities are constructed mainly to solve two types of urban ecological problems, waterlogging and water shortage, and Guyuan is a representative of cities with water shortages. Therefore, in Guyuan, greening plants with high WUE should be identified and chosen based on their ability to adapt to the environment and boost the ecological functions of the sponge city.

The objectives of this study were (1) to explore the main factors affecting WUE of greening plants in Guyuan, (2) to explain whether the WUE of green spaces in sponge cities can be improved by utilizing plant species with high WUE, and (3) to provide a convenient and scientific method for selecting greening plants for sponge cities.

Materials and methods

Ethics statement

We collected soil and plant samples for our study with the official permission of Ningxia Capital Sponge City Construction & Development CO., LTD. There is no endangered or protected plant species were sampled.

Study area

The study area is in Guyuan, a city of roughly 1.23 million people and approximately 10540 km2 in area. The city is located in the south of the Ningxia Hui Autonomous Region (35° 14′ N-36° 32′ N, 105° 19′ E-106° 58′ E). The southern part of Guyuan is a semi-humid zone in the middle temperate zone.

The annual average temperature in Guyuan is 6.9°C and the annual average precipitation is low (166.9–647.3 mm) [31]. The precipitation of Guyuan is more in the south and less in the north and the distribution between seasons is uneven, with heavy rainfall in summer and autumn, and less rainfall in winter and spring [31]. Guyuan is situated at a high altitude, has good atmospheric transparency, high radiation intensity, long sunshine hours, and abundant illumination resources [32].

Sample collection and preparation

Four main habitats of greening plants were chosen, namely, residential green spaces (Denoted by A), street green spaces (Denoted by B), park green spaces (Denoted by C), and the riverside wetland (Denoted by D) in which twelve, nine, twelve, and four sampling plots were selected, respectively, as shown in Fig 1, and the geographic coordinates of all the 37 sampling plots were listed in S1 Table.

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Fig 1. Map of sampling plots collected from Guyuan.

(1) The map of China; (2) the map of Guyuan City; and the locations of the sampling plots are shown in (3) and (4). The circles: the sampling plots of residential green spaces; the asterisks: the sampling plots of street green spaces; the diamond: the sampling plots of park green spaces; the squares: the sampling plots of the riverside wetland.

https://doi.org/10.1371/journal.pone.0220083.g001

In each plot, mixed topsoil (0–20 cm) was collected from five subsamples. Half of the topsoil from each subsample was stored in an ice bucket for determination of ammonium nitrogen (NH4+-N) content and nitrate nitrogen (NO3-N) content in the soil, and the other half was stored at room temperature for determination of other soil indexes, including soil pH, soil organic matter (SOM), total nitrogen (TN), and total phosphorus (TP) content.

In each plot, depending on leaf size, 5 to 20 healthy and complete leaves (mostly 10) of each plant species were picked and placed into a coded envelope until further analysis. A total of 32 plant species were collected in 37 plots, with at least four replicates per species. The 32 plant species were numbered from 1 to 32, and the species names corresponding to each species number were listed in S2 Table.

Measurement

The soil NH4+-N content and NO3-N content were extracted with a 2 mol·L−1 KCl solution at 1:5 (soil:water) and measured with a continuous-flow auto-analyzer (AA3, Seal Analytical Inc., Southampton, UK). Soil pH was measured in a 1:2.5 (soil:water) mixture using a glass electrode pH meter (PHS-2F, Shanghai INESA Instrument Co., Ltd, China). The SOM was determined with the K2Cr2O7-H2SO4 wet oxidation method, the soil TN content was determined with the Kjeldahl method using an automatic Kjeldahl analyzer (Haineng-K9860, Hanon Instruments Co., Ltd., Jinan, China), and the soil TP content was determined using acid-soluble molybdenum antimony blue colorimetry with an ultraviolet-visible spectrophotometer (UV-5500, Shanghai Metash Instruments Co., Ltd., China) [33].

The leaves of the plants were rinsed with deionized water, blotted with tissue paper to remove the water completely, then treated at 115°C for 15 minutes for deactivation of enzymes, and oven dried at 70°C for 48 hours. The dried leaf samples were ground, sieved, and measured with a stable isotope ratio mass spectrometer (IsoPrime100; Elementar, Germany) to obtain the δ13C values of plants surveyed.

Quantifying WUE from δ13C

Farquhar [33] derived the relationship between WUE and δ13C value as follows: (1) where a is the fractionation occurring due to diffusion in air (4.4 ‰), b is the net fractionation caused by carboxylation (27 ‰) [23, 34]; Ca is the concentration of atmospheric CO2 (390 μL·L−1) [35]; δa is the δ13C value of air (-8.8 ‰) [36], δp is the δ13C value of the plant; θ is the ratio of carbon consumed by respiration in leaves and other organs throughout the growing season (0.3) [37]; and 1.6 is the ratio of diffusivities of water vapor and CO2 in air [24, 38]; and VPD is the vapor pressure deficit between the inside and outside of the leaf, that was calculated from the daily average meteorological data on the sampling date [39, 40]: (2) where T is the temperature of leaf,°C, set T to the average temperature of the leaves when sampling (20°C); RH is atmospheric relative humidity, set it to the average relative humidity when sampling; 0.611 is saturated vapor pressure on a pure horizontal surface at 0°C. Substituting (2) into (1), (3)

Ca = 390 μL·L−1; θ = 0.3; a = 4.4 ‰; b = 27 ‰; δa = -8.8 ‰; δp = δ13C; T = 20°C; RH = 0.6.

Substituting all the data will yield the relationship between WUE and δ13C values: (4)

Data analysis

Differences in soil indexes were analyzed using a one-way ANOVA with a post hoc Duncan test. The interaction effect of habitats and species identity on WUE were analyzed using two-way ANOVA. Differences in WUE between trees, shrubs, and herbs, and between evergreen and deciduous plants were analyzed using nonparametric tests. The tests were performed using IBM SPSS Statistics 22. A probability of 0.05 or lower was considered as significant in testing the null hypothesis of no differences in WUE and in other calculated values. Intraspecific differences in plants were calculated by the coefficient of variation using Microsoft Excel 2016. The map of sampling plots was drawn using ArcGIS 10.2.2, and other graphs were drawn using Origin 2018.

Results

Soil condition

The results of soil pH, SOM content, TP content, TN content, NH4+-N content, and NO3-N content were list in Table 1, and the soil samples were evaluated according to the results of the second national soil survey in China. Thus, soil fertility was divided into six levels, with the first level being the most fertile and the sixth level the most barren. Accordingly, the soil pH values in Guyuan are comparatively higher. The SOM was generally low, except for the soil samples in habitat D that reached the fourth-level soil standard, and the SOM in the other three habitats is lower than the fourth-level soil standard. The TN content of all the soils was less than 0.5 g·kg−1, so all the soils belong to the sixth-level soil standard. The soil TP content was at the fourth-level soil standard. The NH4+-N and NO3-N content was also generally low. Thus, the soil in the habitats of the main greening plants was alkaline and barren.

Effects of habitats on WUE

The WUE values of different plant species in each habitat are shown in Fig 2, and the WUE conditions of the green spaces in the four habitats are shown in Fig 3. The results of one-way ANOVA showed that there were significant differences in the WUE of different habitats (P < 0.001); the WUE values of habitats B and D were significantly higher than those of habitat A and C (P < 0.05). The WUE of the same plant species in habitat A and B and in habitat B and C were found out and shown in Fig 4. It was found that the WUE of same plant species between different habitats did not differ significantly.

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Fig 2. The WUE of the plant species surveyed in each habitat.

A, B, C and D represent residential green spaces, street green spaces, park green spaces and the riverside wetland respectively. Plant species are represented by numbers on abscissa axis: 1-Verbena officinalis L., 2-Cirsium setosum (Willd.) Besser ex M.Bieb., 3-Picea asperata Mast., 4-Dianthus chinensis L., 5-Ulmus pumila ‘Jinye’, 6-Rumex acetosa L., 7-Rosa chinensis Jacq. var. spontanea (Rehd. et Wils.) Yü et Ku, 8-Tamarix ramosissima Lbd., 9-Iris tectorum Maxim., 10-Prunus × cisterna ‘Pissardii’, 11-Zoysia tenuifolia, 12-Sedum spectabile, 13-Amygdalus triloba (Lindl.) Ricker, 14-Juniperus formosana Hayata, 15-E. crus-galli, 16-Lavandula angustifolia Mill., 17-Prunus Cerasifera Ehrh. f. atropurpurea (Jacq.) Rehd., 18-Trifolium repens L., 19-Iris lacteal Pall. var. chinensis (Fisch.) Koidz., 20-Rosa xanthina Lindl., 21-Euonymus phellomanus Loes., 22-Hosta ventricosa (Salisb.) Stearn, 23-Salix matsudana Koidz., 24-Lythrum salicaria L., 25-Typha orientalis C. Presl, 26-Platycladus orientalis (L.) Franco, 27-Rudbeckia hirta L., 28-Artemisia mongolica (Fisch. ex Besser) Fisch. ex Nakai, 29-Salix babylonica L., 30-Forsythia viridissima Lindl., 31-Coreopsis lanceolata L., 32-Syringa oblata Lindl.

https://doi.org/10.1371/journal.pone.0220083.g002

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Fig 3. The WUE conditions of the four habitats.

A, B, C and D represent residential green spaces, street green spaces, park green spaces and the riverside wetland respectively.

https://doi.org/10.1371/journal.pone.0220083.g003

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Fig 4.

The WUE of the same species in A—B and in B—C. A, B, and C represent residential green spaces, street green spaces and park green spaces respectively. The names of the species corresponding to the species numbers are shown in the notes of Fig 2.

https://doi.org/10.1371/journal.pone.0220083.g004

Effects of species identity on WUE

The WUE values of 32 plant species, as shown in Fig 5, varied significantly (F = 42.313, P < 0.001), and they were between 1.6 mmol C·mol−1 H2O and 30.8 mmol C·mol−1 H2O. After multiple comparisons, it was found that the WUE of 5-Ulmus pumila ‘Jinye’ was significantly lower than that of all other species; while the WUE of 15-E. crus-galli was significantly higher than those of all other species.

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Fig 5. The WUE of 32 plant species surveyed.

Different labels (a-l) are significantly different between the WUE of plant species (P < 0.05). The names of the species corresponding to the species numbers are shown in the notes of Fig 2.

https://doi.org/10.1371/journal.pone.0220083.g005

The studied plants were classified as herbs, shrubs, and trees and further classified as evergreen or deciduous plants (Fig 6). There were no significant differences in the WUE of herbs, shrubs, and trees or in WUE of evergreen plants and deciduous plants. This indicates that the WUE values did not differ significantly between different plant life forms.

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Fig 6. The WUE of different plant life forms.

(1) the WUE of herbs, shrubs and trees; (2) the WUE of evergreen plants and deciduous plants.

https://doi.org/10.1371/journal.pone.0220083.g006

Interaction effect of habitats and species identity on WUE

The two-way ANOVA results show that the species identity had a significant effect on WUE (P = 0.00), while the effect of different habitats on WUE did not differ significantly between species (P = 0.06). The interaction between habitat and species identity had a significant effect on WUE.

Intraspecific differences in the WUE of plants

By analyzing the coefficient of variation of each species, it was found that the coefficient of variation of most species was less than 20%, and only three species had a coefficient of variation of more than 20% (Table 2); thus, the degree of variation within a species was relatively small.

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Table 2. The mean, SD (standard deviation) and CV (coefficient of variation) of the WUE of the sampled species.

https://doi.org/10.1371/journal.pone.0220083.t002

Discussion

The choice of plants in sponge cities is especially important because sponge cities, like Guyuan, often face water shortages, and water availability is a major limiting factor in community structure and ecosystem function. High WUE guarantees the healthy growth of plants and the maintenance of good ecological functions. Therefore, WUE is an important index that should be considered when selecting plants for water-deficient sponge cities.

Habitat had a significant effect on the WUE of plant community; however, if the species in different habitats are the same, the effect of habitat on WUE is non-significant. This illustrates that habitat can affect WUE mainly depending on the species composition within the habitat. Therefore, we can improve the WUE of green spaces by selecting suitable plant species. Our results are consistent with those of a previous study in which the WUE in dryland habitats were optimized by selecting a specific mixture of plant species [38].

Some studies have shown that habitat or other environmental factors might have a significant effect on WUE or carbon isotope composition [39, 41, 42], mainly due to large differences in habitat geography, soil properties, or climate. For example, it was found that soil salinity and nutrient conditions had effects on plant carbon isotope composition through greenhouse control experiments or searching for habitats with significant differences [43, 44]. However, the soil properties and climate differences are usually not big among different habitats in one city; therefore, the conclusions of this study are reliable, and the WUE of green spaces can be improved by utilizing suitable plant species.

Consistent with the results of previous studies [4547], the significant effect of species identity on WUE indicated that WUE was mainly controlled by genetic factors. The lack of significant differences among the WUE of herbs, shrubs, and trees and between evergreen and deciduous plants suggest that WUE might not differ significantly between plant life forms in a small area. The coefficient of variation of most species was less than 20%, showing that the change of sampling plots had a negligible effect on WUE for most species. The results of this study illustrate that WUE differs mainly between species, instead of different habitats or different plant life forms. Therefore, the WUE is a relatively stable indicator. To improve the WUE of green areas, the key is to select the correct species of plants.

The significant interaction between species identity and habitat indicates that the effect of habitat on WUE might depend on the species composition within the habitat and the effect of species identity on WUE might differ between habitats. The former indication corroborates our conclusion that the WUE of green spaces could be altered by utilizing different plant species, and the latter indication explains why the coefficient of variation of several species is relatively high.

WUE is a relatively stable indicator that is mainly influenced by the plant species and less by other factors; therefore, plants can be selected by comparing their WUE (choosing plants with high WUE), and in so doing, the WUE of green spaces can be improved. The average carbon isotope of plants (-28.74 ‰) [48] surveyed worldwide was converted to the WUE value, and the plants surveyed were divided into two categories according to this value: (1) higher WUE plant species and (2) the lower WUE plant species. The 1-Verbena officinalis L., 5-Ulmus pumila ‘Jinye’, 9-Iris tectorum Maxim., 10-Prunus × cisterna ‘Pissardii’, 11-Zoysia tenuifolia, 16-Lavandula angustifolia Mill., 23-Salix matsudana Koidz., 24-Lythrum salicaria L., 25-Typha orientalis C. Presl, 27-Rudbeckia hirta L., and 28-Artemisia mongolica (Fisch. ex Besser) Fisch. ex Nakai were classified as lower WUE plant species, and the remaining twenty-one species were classified as higher WUE plant species. The WUE of 15-E. crus-galli, in particular, was much higher than that of other species, mainly because it is a C4 plant [49, 50]. The WUE of C4 plants is generally higher than that of C3 plants [51, 52], and so C4 plants might be taken into account when selecting plants for improving the WUE of the plant community. Since Guyuan has poor soil characteristics and low rainfall, the twenty-one higher WUE plant species could be considered to select to ensure healthy plant coverage and improve the WUE of the green space.

In practical applications, when selecting greening plants for sponge cities, priority can be given to the class of higher WUE plant species that have strong adaptability to water-deficient environments and better ecological functions, while the lower WUE plant species can only be planted in relatively humid environment. Furthermore, the plant species with higher WUE can be utilized to improve the WUE of green spaces. This method is also suitable for plant selecting in non-sponge cities when the WUE is considered.

To our knowledge, this study is the first to select plant species for sponge city by using stable carbon isotope technology, providing a scientific method for rapid plant species selection for the construction of large-scale sponge cities.

Supporting information

S1 Table. The geographic coordinates of sampling plots.

https://doi.org/10.1371/journal.pone.0220083.s001

(DOCX)

S2 Table. The names of the 32 plant species studied.

https://doi.org/10.1371/journal.pone.0220083.s002

(DOCX)

S3 Table. The data set of plant δ13C and WUE.

https://doi.org/10.1371/journal.pone.0220083.s003

(XLSX)

Acknowledgments

Thanks to professional editors at Editage for their English editing.

References

  1. 1. Cui G, Zhang Q, Zhan Z, Chen Y. Research progress and discussion of sponge city construction. Water Resources Protection. 2016; 32(2): 1–4. (in Chinese).
  2. 2. Wu D, Zhan S, Li Y, Tu M, Zhen J, Guo Y, et al. New trends and practical research on the sponge cities with Chinese characteristics. China Soft Science. 2016; (1): 79–97. (in Chinese).
  3. 3. Yang Y, Lin G. A review on sponge city. South Architecture. 2015; (3): 59–64. (in Chinese).
  4. 4. Tanner CC. Plants for constructed wetland treatment systems—A comparison of the growth and nutrient uptake of eight emergent species. Ecological Engineering. 1996; 7(1): 59–83.
  5. 5. Barbier S, Philippe B, Gosselin F. Influence of several tree traits on rainfall partitioning in temperate and boreal forests: a review. Annals of Forest Science. 2009; 66(6): 602.
  6. 6. Berg AVED, Maas J, Verheij RA, Groenewegen PP. Green space as a buffer between stressful life events and health. Social Science & Medicine. 2010; 70(8): 1203–1210. pmid:20163905
  7. 7. Pauleit S, Andersson E, Anton B, Buijs A, Haase D, Hansen R, et al. Urban green infrastructure–connecting people and nature for sustainable cities. Urban Forestry & Urban Greening. 2019; 40: 1–3. https://doi.org/10.1016/j.ufug.2019.04.007
  8. 8. Condon AG, Richards RA, Rebetzke GJ, Farquhar GD. Breeding for high water-use efficiency. Journal of Experimental Botany. 2004; 55(407): 2447–2460. pmid:15475373
  9. 9. Mccarthy HR, Pataki DE, Jenerette GD. Plant water-use efficiency as a metric of urban ecosystem services. Ecological Applications. 2011; 21(8): 3115–3127.
  10. 10. Cao S, Feng Q, Si J, Chang Z, Zhuomacuo , Xi H, et al. Summary on research methods of water use efficiency in plant. Acta Ecologica Sinica. 2009; 29(7): 3882–3892. http://ir.casnw.net/handle/362004/20731 (in Chinese).
  11. 11. Luo Y, Zhao X, Huang Y, Na S, Feng J. Research progress on plant water use efficiency and its measuring methods. Journal of Desert Research. 2009. (in Chinese).
  12. 12. Scartazza A, Mata C, Matteucci G, Yakir D, Moscatello S, Brugnoli E. Comparisons of δ13C of photosynthetic products and ecosystem respiratory CO2 and their responses to seasonal climate variability. Oecologia. 2004; 140(2): 340–351. pmid:15150655
  13. 13. Damesin C, Lelarge C. Carbon isotope composition of current-year shoots from Fagus sylvatica in relation to growth, respiration and use of reserves. Plant, Cell and Environment. 2003; 26(2): 207–219.
  14. 14. Sun ZJ, Livingston NJ, Guy RD, Ethier GJ. Stable carbon isotopes as indicators of increased water use efficiency and productivity in white spruce (Picea glauca (Moench) Voss) seedlings. Plant, Cell and Environment. 1996; 19(7): 887–894.
  15. 15. Farquhar GD, Richards RA. Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Aust. J. Plant Pkysiol. 1984; 11(6): 539–552.
  16. 16. Marshall JD, Zhang J. Carbon isotope discrimination and water-use efficiency in native plants of the north-central rockies. Ecology. 1994; 75(7): 1887–95.
  17. 17. Lajtha K, Michener RH. Stable isotopes in ecology and environmental science. Freshwater Biology. 1994; 54(2): 434–435.
  18. 18. Peterson BJ, Fry B. Stable isotopes in ecosystem studies. Annual Review of Ecology and Systematics. 1987; 18(1): 293–320.
  19. 19. Leffler AJ, Evans AS. Physiological variation among Populus fremontii populations: short- and long-term relationships between δ13C and water availability. Tree Physiology. 2001; 21(15): 1149–1155. pmid:11581022
  20. 20. Farquhar GD, Hubick KT, Condon AG, Richards RA. Carbon isotope fractionation and plant water-use efficiency. Stable Isotopes in Ecological Research. Springer New York; 1989. https://doi.org/10.1007/978-1-4612-3498-2_2
  21. 21. Hubick KT, Farquhar GD, Shorter R. Correlation between water-use efficiency and carbon isotope discrimination in diverse peanut (arachis) germplasm. Functional Plant Biology. 1986; 13(6): 803–816.
  22. 22. Lockhart E, Wild B, Richter A, Simonin K, Merchant A. Stress-induced changes in carbon allocation among metabolite pools influence isotope-based predictions of water use efficiency in Phaseolus vulgaris. Functional Plant Biology. 2016; 43(12): 1149.
  23. 23. Barbosa ICR, Köhler IH, Auerswald K, Lüps P, Schnyder H. Last-century changes of alpine grassland water-use efficiency: a reconstruction through carbon isotope analysis of a time-series of Capra ibex horns. Global Change Biology. 2010; 16(4): 1171–1180.
  24. 24. Saurer M, Siegwolf RTW, Schweingruber FH. Carbon isotope discrimination indicates improving water-use efficiency of trees in northern Eurasia over the last 100 years. Global Change Biology. 2004; 10(12): 2109–2120.
  25. 25. Moghaddam A, Raza A, Vollmann J, Ardakani MR, Wanek W, Gollner G, et al. Carbon isotope discrimination and water use efficiency relationships of alfalfa genotypes under irrigated and rain-fed organic farming. European Journal of Agronomy. 2013; 50(5): 82–89.
  26. 26. Wright GC, Nageswara Rao RC, Farquhar GD. Water-use efficiency and carbon isotope discrimination in peanut under water deficit conditions. Crop Science. 1994; 34(1): 92–97.
  27. 27. Ren J, Zhai W, Wang Y, Bai W, Ren H. Screening of plants for bioretention areas in Shenzhen during the construction of sponge city. Tianjin Agricultural Sciences. 2017; 23(3): 97–102. (in Chinese).
  28. 28. Pan X, He G, Wu X, Chen X, Liu Z. Comparison of waterlogging tolerance in 10 species of native plants in Shenzhen to select plants suitable for preparing submerged green belts. Pratacultural Science. 2018; 35(11): 2622–2630. CNKI:SUN:CYKX.0.2018-11-009 (in Chinese).
  29. 29. Gao Q, Shen G, Yang T, Xue H, Wang K, Shi F. Comparison study on physiological responses to water logging stress of six common herbaceous plants. Acta Scientiarum Naturalium Universitatis Nankaiensis. 2018; 51(2): 1–8. CNKI:SUN:NKDZ.0.2018-02-001 (in Chinese).
  30. 30. Guo X, Zhang X, Liu J, Kang Y. The water requirements of main crops in response to climatic changes in recent years in Guanyuan region of Ningxia. Barley and Cereal Sciences. 2017; 34(5):50–55. (in Chinese).
  31. 31. Han S. Signature analysis of climate change in Guyuan of Ningxia in recent 50 years. Ningxia Engineering Technology. 2010; 9(1): 68–70. (in Chinese).
  32. 32. Sang J, Liu Y, Lin L. Characteristic of solar radiation in Ningxia and integrated evaluation on utilization potential of solar energy. Journal of Desert Reserch. 2006; 26(1): 122–125. (in Chinese).
  33. 33. Lu R. Analytical methods for soils and agricultural chemistry. Beijing: China Agricultural Science and Technology Press; 2000 (in Chinese).
  34. 34. Farquhar GD, Ehleringer JR, Hubick KT. Carbon isotope discrimination and photosynthesis. Ann Rev Plant Physiol Plant Mol Biol. 1989; 40(40): 503–537.
  35. 35. Shi Y, Zhou G, Jiang Y, Wang H, Xu Z. Sensitive indicators of Stipa bungeana response to precipitation under ambient and elevated CO2 concentration. International Journal of Biometeorology. 2018; 62(2): 141–151. pmid:28864884
  36. 36. Sun B, Dilcher DL, Beerling D, Zhang C, Yuan D, Kowalski E. Variation in Ginkgo biloba L. leaf characters across a climatic gradient in China. Proceedings of the National Academy of Sciences of the United States of America. 2003; 100(12): 7141–7146. pmid:12777617
  37. 37. Evans JR. Nitrogen and photosynthesis in the flag leaf of wheat (Triticum aestivum L.). Plant Physiology. 1983; 72(2): 297–302. pmid:16662996
  38. 38. Pronger J, Campbell DI, Clearwater MJ, Mudge PL, Rutledge S, Wall AM, et al. Toward optimisation of water use efficiency in dryland pastures using carbon isotope discrimination as a tool to select plant species mixtures. Science of The Total Environment. 2019; 665: 698–708. pmid:30780015
  39. 39. Abbate PE, Dardanelli JL, Cantarero MG, Maturano M, Mclchiori RJM, Sucro EE. Climatic and water availability effects on water-use efficiency in wheat. Crop Science. 2004; 44(2): 474–483.
  40. 40. Hu J, Moore DJP, Riveros-Iregui DA, Burns SP, Monson RK. Modeling whole-tree carbon assimilation rate using observed transpiration rates and needle sugar carbon isotope ratios. New Phytologist, 2010, 185(4): 1000–1015. pmid:20100209
  41. 41. Forster BP, Handley LL, Pakniyat H, Scrimgeour CB, Nevo E, Raven JA. Genetic and ecological factors controlling carbon isotope discrimination in barley. Aspects of Applied Biology. 1994.
  42. 42. Ponton S, Flanagan LB, Alstad KP, Johnson BG, Morgenstern K, Kljun N, et al. Comparison of ecosystem water-use efficiency among Douglas-fir forest, aspen forest and grassland using eddy covariance and carbon isotope techniques. Global Change Biology. 2006; 12 (2): 294–310.
  43. 43. Brugnoli E, Lauteri M. Effects of salinity on stomatal conductance, photosynthetic capacity, and carbon isotope discrimination of salt-tolerant (Gossypium hirsutum L.) and salt-sensitive (Phaseolus vulgaris L.) C3 non-halophytes. Plant Physiology. 1991; 95(2):628–635. pmid:16668029
  44. 44. O"Leary M H. Carbon isotope fractionation in plants. Royal Society of New Zealand Wellington New Zealand. 1981; 20(4):553–567.
  45. 45. Schmidt G, Gebauer G, Widmann K. Influence of nitrogen supply and temperature on stable carbon isotope ratios in plants of different photosynthetic pathways (C3, C4, CAM). Isotopenpraxis Isotopes in Environmental and Health Studies. 1993; 29 (1–2): 5.
  46. 46. Eleki K, Cruse RM, Albrecht KA. Root segregation of C3 and C4 species using carbon isotope composition. Crop Science. 2005; 45(3): 879–882.
  47. 47. Huang J, Lin G, Huang X. Comparative studies on water use efficiency of rhizophoraceae plants grown in different environments. Acta Phytoecologica Sinica. 2005; 29(4): 530–536. (in Chinese).
  48. 48. Korner C, Farquhar GD, Roksandic Z. A global survey of carbon isotope discrimination in plants from high altitude. Oecologia. 1988; 74(4):623–632. pmid:28311772
  49. 49. Ehleringer JR, Lin ZF, Field CB, Sun GC, Kuo CY. Leaf carbon isotope ratios of plants from a subtropical monsoon forest. Oecologia. 1987; 72(1): 109–114. pmid:28312905
  50. 50. Rose JD. Photosynthesis and production in a changing environment: A field and laboratory manual. Phytochemistry; 1993.
  51. 51. Bender MM. Variations in the 13C/12C ratios of plants in relation to the pathway of photosynthetic carbon dioxide fixation. Phytochemistry. 1971; 10(6): 1239–1244.
  52. 52. Martinelli LA, Devol AH, Victoriat RL, Richey JE. Stable carbon isotope variation in C3 and C4 plants along the Amazon River. Nature (London). 1991; 353(6339): 57–59.