Ecosystem Evapotranspiration as a Response to Climate and Vegetation Coverage Changes in Northwest Yunnan, China

Yang, Hao; Luo, Peng; Wang, Jun; Mou, Chengxiang; Li, Mo; Wang, Zhiyuan; Fu, Yao; Lin, Hao; Yang, Yongping; Bhatta, Laxmi Dutt

doi:10.1371/journal.pone.0134795

Cited by 29 publications

(26 citation statements)

References 61 publications

(59 reference statements)

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“…Much previous research has demonstrated a positive correlation between temperature and ET in study areas such as the Guanzhong region of Shanxi [51], northeast China [52], and the Songhua River basin [53]. However, the most important factor in the change of ET is precipitation [43] in the Tibetan Plateau and northwest Yunnan [19], which is in agreement with our study both in temporal distribution and in spatial distribution ( Figures 2 and 4(b)). The ET value is highest in the summer, when the precipitation is also largest [54].…”

Section: Dynamics Of Et the Spatial Distribution Of Et Oversupporting

confidence: 91%

“…The most common way is to use remote sensing data to calculate the evapotranspiration by employing the surface energy balance with parameters such as meteorological data [18], which has been used in China, including in the northwest of Yunnan province [19]. Long-term estimation methods of evapotranspiration mainly involve water balance [20][21][22], combination, and complementary methods [23,24], and measurement methods mainly involve sap flow, lysimeter, and eddy covariance methods, which underestimate the measurement [25].…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Patterns and Controls of Evapotranspiration across the Tibetan Plateau (2000–2012)

Zhang

Sun

Xiong

2017

Advances in Meteorology

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Evapotranspiration (ET) is a key factor to further our understanding of climate change processes, especially on the Tibetan Plateau, which is sensitive to global change. Herein, the spatial patterns of ET are examined, and the effects of environmental factors on ET at different scales are explored from the years 2000 to 2012. The results indicated that a steady trend in ET was detected over the past decade. Meanwhile, the spatial distribution shows an increase of ET from the northwest to the southeast, and the rate of change in ET is lower in the middle part of the Tibetan Plateau. Besides, the positive effect of radiation on ET existed mainly in the southwest. Based on the environment gradient transects, the ET had positive correlations with temperature ( > 0.85, < 0.0001), precipitation (R > 0.89, p < 0.0001), and NDVI (R > 0.75, p < 0.0001), but a negative correlation between ET and radiation (R = 0.76, p < 0.0001) was observed. We also found that the relationships between environmental factors and ET differed in the different grassland ecosystems, which indicated that vegetation type is one factor that can affect ET. Generally, the results indicate that ET can serve as a valuable ecological indicator.

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Section: Dynamics Of Et the Spatial Distribution Of Et Oversupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Patterns and Controls of Evapotranspiration across the Tibetan Plateau (2000–2012)

Zhang

Sun

Xiong

2017

Advances in Meteorology

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“…In order to understand accurately the correlation and interaction mechanisms among hydrological variables in the changing environment, and to develop scientific strategies to cope with the changing climate, it is necessary to separate and quantify the contributions of climate change and human activities to hydrological variations (e.g., runoff and drought) [33][34][35] and even ecology elements (e.g., ecosystem evapotranspiration ET, gross primary productivity GPP) [29]. At present, the common models used for evaluating the impacts include Soil Water Assessment Tool (SWAT) model [31], Yellow river water balance model (YRWBM) model [33], Budyko-type equations [34], Penmann-Monteith model [35], and statistical analysis methods [29,36,37] mainly include integration of regression analysis and the double cumulative curve. Recently, statistical analysis methods are paid more attention due to they need less data and parameter calibration compared with hydrological models, and suitable to the data-scare watershed.…”

Section: Discussionmentioning

confidence: 99%

“…Penmann-Monteith model [35], and statistical analysis methods [29,36,37] mainly include integration of regression analysis and the double cumulative curve. Recently, statistical analysis methods are paid more attention due to they need less data and parameter calibration compared with hydrological models, and suitable to the data-scare watershed.…”

Section: Discussionmentioning

confidence: 99%

Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China

Wei

Zhang

Gong

et al. 2020

IJERPH

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The purpose of this study is to illustrate intrinsic correlations and their temporal evolution between hydro-meteorological elements by building three-element-composed system, including precipitation (P), runoff (R), air temperature (T), evaporation (pan evaporation, E), and sunshine duration (SD) in the Wuding River Basin (WRB) in Loess Plateau, China, and to provide regional experience to correlational research of global hydro-meteorological data. In analysis, detrended partial cross-correlation analysis (DPCCA) and temporal evolution of detrended partial-cross-correlation analysis (TDPCCA) were employed to demonstrate the intrinsic correlation, and detrended cross-correlation analysis (DCCA) coefficient was used as comparative method to serve for performance tests of DPCCA. In addition, a novel way was proposed to estimate the contribution of a variable to the change of correlation between other two variables, namely impact assessment of correlation change (IACC). The analysis results in the WRB indicated that (1) DPCCA can analyze the intrinsic correlations between two hydro-meteorological elements by removing potential influences of the relevant third one in a complex system, providing insights on interaction mechanisms among elements under changing environment; (2) the interaction among P, R, and E was most strong in all three-element-composed systems. In elements, there was an intrinsic and stable correlation between P and R, as well as E and T, not depending on time scales, while there were significant correlations on local time scales between other elements, i.e., P-E, R-E, P-T, P-SD, and E-SD, showing the correlation changed with time-scales; (3) TDPCCA drew and highlighted the intrinsic correlations at different time-scales and its dynamics characteristic between any two elements in the P-R-E system. The results of TDPCCA in the P-R-E system also demonstrate the nonstationary correlation and may give some experience for improving the data quality. When establishing a hydrological model, it is suitable to only use P, R, and E time series with significant intrinsic correlation for calibrating model. The IACC results showed that taking pan evaporation as the representation of climate change (barring P), the impacts of climate change on the non-stationary correlation of P and R was estimated quantitatively, illustrating the contribution of climate to the correlation variation was 30.9%, and that of underlying surface and direct human impact accounted for 69.1%. IntroductionComplex hydro-meteorological systems contain various interactions among hydro-meteorological elements [1,2]. Investigating cross-correlations and teleconnections between hydro-meteorological signals benefits to deeply understanding the whole hydrological system and to serve for water resources management [3]. Under the influences of changing climate and anthropogenic disturbance [4], the single hydro-meteorological time series and their interaction relationships tend to exhibit complex [5,6], non-stationary [7], and multi-scale [8] chang...

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“…These studies concluded that afforestation efforts contributed to the vegetation coverage increase in northwestern Yunnan Province. A multivariate linear regression analysis indicated that the factor of vegetation coverage variation explained 14.82% of the ETa variation in this region [20]. …”

Section: Introductionmentioning

confidence: 99%

Variation of Vegetation Ecological Water Consumption and Its Response to Vegetation Coverage Changes in the Rocky Desertification Areas in South China

et al. 2016

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Over the past several decades, rocky desertification has led to severe ecological problems in karst areas in South China. After a rocky desertification treatment project was completed, the vegetation coverage changed greatly and, consequently, increased the ecology water consumption (approximately equal to the actual evapotranspiration) of the regional vegetation. Thus, it intensified the regional water stresses. This study explored the changes in the actual evapotranspiration (ETa) response to the vegetation coverage changes in the rocky desertification areas in South China based on the precipitation (P), potential evapotranspiration (ETp) and NDVI (the normalized difference vegetation index) datasets. The revised Bagrov model was used to simulate the actual evapotranspiration changes with the supposed increasing NDVI. The results indicated that the average NDVI value was lower when the rocky desertification was more severe. The ETa, evapotranspiration efficiency (ETa/ETp) and potential humidity (P/ETp) generally increased with the increasing NDVI. The sensitivity of the ETa response to vegetation coverage changes varied due to different precipitation conditions and different rocky desertification severities. The ETa was more sensitive under drought conditions. When a drought occurred, the ETa exhibited an average increase of 40~60 mm with the NDVI increasing of 0.1 in the rocky desertification areas. Among the 5 different severity categories of rocky desertification, the ETa values’ responses to NDVI changes were less sensitive in the severe rocky desertification areas but more sensitive in the extremely and potential rocky desertification areas. For example, with the NDVI increasing of 0.025, 0.05, 0.075, and 0.1, the corresponding ETa changes increased by an average of 2.64 mm, 10.62 mm, 19.19 mm, and 27.58 mm, respectively, in severe rocky desertification areas but by 4.94 mm, 14.99 mm, 26.80, and 37.13 mm, respectively, in extremely severe rocky desertification areas. Understanding the vegetation ecological water consumption response to the vegetation coverage changes is essential for the vegetation restoration and water stresses mitigation in rocky desertification areas.

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Ecosystem Evapotranspiration as a Response to Climate and Vegetation Coverage Changes in Northwest Yunnan, China

Cited by 29 publications

References 61 publications

Spatial-Temporal Patterns and Controls of Evapotranspiration across the Tibetan Plateau (2000–2012)

Spatial-Temporal Patterns and Controls of Evapotranspiration across the Tibetan Plateau (2000–2012)

Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China

Variation of Vegetation Ecological Water Consumption and Its Response to Vegetation Coverage Changes in the Rocky Desertification Areas in South China

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