Spatial and temporal characteristics of reference evapotranspiration and its climatic driving factors over China from 1979–2015

Wang, Zongzhi; Ye, Ailing; Wang, Lihui; Liu, Kelin; Cheng, Liang

doi:10.1016/j.agwat.2018.12.006

Cited by 51 publications

(41 citation statements)

References 35 publications

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“…It is the first high spatial–temporal resolution gridded near‐surface meteorological dataset developed specifically for the study of land surface processes in China and was made through the fusion of remote sensing products, reanalysis datasets, and in situ station data. Due to its continuous temporal coverage and consistent quality, the CMFD is one of the most widely used climate datasets in China and is especially widely used in the TP with good applicability (Yang et al ., 2010; Lu et al ., 2018; Nury et al ., 2019; Wang et al ., 2019).…”

Section: Methodsmentioning

confidence: 99%

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Lun

Liu

Cheng

et al. 2021

Intl Journal of Climatology

124

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General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

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Section: Methodsmentioning

confidence: 99%

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Lun

Liu

Cheng

et al. 2021

Intl Journal of Climatology

124

View full text Add to dashboard Cite

show abstract

“…Since the non-parametric Mann-Kendall (MK) test has the ability to quantify the trend and significance of the long time-series data, it has been widely applied for trend analysis in hydrological and climatological data time series [22,46,47]. In this study, the MK test was used to detect the changing trends of dry and wet conditions in the Poyang Lake basin.…”

Section: Trend Analysismentioning

confidence: 99%

Analysis of Dry/Wet Variations in the Poyang Lake Basin Using Standardized Precipitation Evapotranspiration Index Based on Two Potential Evapotranspiration Algorithms

Liu

2019

Water

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Global warming has resulted in unevenly distributed changes in precipitation and evapotranspiration, which has some influence on dry/wet conditions, thus exerting a tremendous impact on national life and the social economy, especially agricultural production. In order to characterize the dry/wet variations in the Poyang Lake basin during 1958–2013, based on the potential evapotranspiration (PET) estimated by the Thornthwaite (TH) and Penman–Monteith (PM) formulas, two types of Standardized Precipitation Evapotranspiration Index (SPEI), namely SPEI_th and SPEI_pm, were calculated in this study. The characteristic of dry/wet variations in the Poyang Lake basin was analyzed and a comparative analysis of two SPEIs was conducted. The results indicate that both SPEI series showed a wet trend in the Poyang Lake basin on an annual scale as well as seasonal scales during 1958–2013, except for spring and autumn. A drying trend was observed in spring, while in autumn, the dry and wet conditions in two SPEIs had opposite trends. However, all trends from two SPEIs were not significant, except for summer SPEI_pm. Meanwhile, significant positive correlations were detected between precipitation and two SPEIs, with the correlation coefficients above 0.95, whereas negative correlations were detected between PET and two SPEIs, with the correlation coefficients ranging from −0.17 to −0.85. This indicates that precipitation was the main climatic factor to determine change in dry/wet conditions in the Poyang Lake basin. Although there were obvious differences between the accumulated values of the Penman–Monteith-based PET (ET_pm) and Thornthwaite-based PET (ET_th), trends in the SPEI_pm series were generally consistent with those in the SPEI_th series, revealing that the method for PET calculation was not critical to the change in dry/wet conditions. Moreover, the results of the conditional probability of SPEI_pm and SPEI_th show that both SPEI_pm and SPEI_th could detect wet or dry events that were identified by SPEI_pm or SPEI_th.

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“…This may be due to relatively lower precipitation in the western plains where the ET 0 makes more contribution to water balance (P-ET o ), and thus explains a higher amount of the SPEI total variability. Regarding the order of sensitivities, Wang et al [32] analyzed the impact of drought evolution on climate variables using the standardized regression coefficient and concluded that the most sensitive variable in south China, north China and the Qinghai-Tibet plateau was P, followed by U 2 , T mean , RH, and Rs. Moreover, in northwest China, the most sensitive variable was identified as U 2 , followed by P, T mean , RH, and Rs.…”

Section: Discussionmentioning

confidence: 99%

“…order of sensitivities, Wang et al [32] analyzed the impact of drought evolution on climate variables using the standardized regression coefficient and concluded that the most sensitive variable in south China, north China and the Qinghai-Tibet plateau was P, followed by U2, Tmean, RH, and Rs. Moreover, in northwest China, the most sensitive variable was identified as U2, followed by P, Tmean, RH, and Rs.…”

Section: Spei Sensitivity Analysis At Different Time Scalesmentioning

confidence: 99%

“…Thus, sensitivity analysis of drought indices to meteorological variables can be applied to analyze the relationship between meteorological variables and drought events [30]. In recent years, the PM equation has been widely used in the sensitivity analysis of ET o [31][32][33]. Yao et al [34] observed distinct sensitivities of P and reference evapotranspiration (ET o ) to the SPEI in Xinjiang, and drought condition was mainly determined by the ET o in the arid zone.…”

Section: Introductionmentioning

confidence: 99%

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Global Sensitivity Analysis of the Standardized Precipitation Evapotranspiration Index at Different Time Scales in Jilin Province, China

Chen

Chi

2020

Sustainability

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The Standardized Precipitation Evapotranspiration Index (SPEI) has been widely applied, due to its multi-scalar features and the ability to identify different drought types. However, its sensitivity to climatic variables still remains unclear, especially at different time scales. Therefore, this study investigates the sensitivity of SPEI to average temperature (Tmean), diurnal temperature ranges (Tdelta), relative humidity (RH), solar radiation (Rs), wind speed (U2), geothermal flux (G), and precipitation (P) from 1957 to 2017 using the extended Fourier Amplitude Sensitivity Test at different time scales in Jilin Province, China. Jilin Province experienced a significant rise in Tmean, and a sharp decrease in Tdelta, Rs, and U2. P is undoubtedly the most influential factor to the SPEI among the meteorological variables, which explained 59.9%–97.9% of the total variability, especially during the main crop growing season (from May to September). While Tmean, RH, or U2 observably affect the SPEI and cannot be neglected during the nongrowing season. In terms of spatial distribution, the SPEI was mainly affected by P in the eastern region, while it was also influenced by Tmean, RH, and U2 as well in the western region. The sensitivity of the SPEI differs in time scales: P > Tmean > RH > U2 > Rs > G > Tdelta (1 to 6 month), P > U2 > RH ≈ Tmean > G > Rs > Tdelta (7 to 18 month), and P > U2 > G > Tmean > RH > Rs > Tdelta (more than 24 month time scale), respectively. The results have the potential to provide a reference for agricultural production and management in Jilin Province, China.

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Spatial and temporal characteristics of reference evapotranspiration and its climatic driving factors over China from 1979–2015

Cited by 51 publications

References 35 publications

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Analysis of Dry/Wet Variations in the Poyang Lake Basin Using Standardized Precipitation Evapotranspiration Index Based on Two Potential Evapotranspiration Algorithms

Global Sensitivity Analysis of the Standardized Precipitation Evapotranspiration Index at Different Time Scales in Jilin Province, China

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