Spatiotemporal variations of extreme precipitation and its potential driving factors in China’s North-South Transition Zone during 1960–2017

Wang, Liyuan; Chen, Shifa; Zhu, Wenbo; Ren, Han; Zhang, Lijuan; Zhu, Lianqi

doi:10.1016/j.atmosres.2020.105429

Cited by 53 publications

(22 citation statements)

References 79 publications

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“…The time phase of remote sensing data does not reach a very high degree of closeness, and there is a certain error in the results of the analysis of RSEI. Moreover, climate change has an important impact on the quality of ecosystems and the ecological environment and should be considered in the future [ 69 , 70 ]. Limited by the number of data acquisitions, this study only studies the 2017 Qinling-Daba Mountains’ RSEI and its influencing factors and has not carried out multi-dimensional studies on multiple years to reveal its temporal and spatial distribution characteristics.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Spatial Heterogeneity and Influencing Factors of Ecological Environment Quality in China’s North-South Transitional Zone

Yin

Chen

DONG

et al. 2022

IJERPH

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The ecological environment is important for the natural disaster prevention of human society. The monitoring of ecological environment quality has far-reaching practical significance for the functional construction of ecosystem services and policy coordination. Based on Landsat 8 operational land image (OLI)/thermal infrared sensor (TIRS) remote sensing image data, this study selected the normalized vegetation (NDVI), tasseled cap transformation humidity (WI), bare soil (SI), construction index (NDSI), and land surface temperature (LST) indexes from the aspects of greenness, humidity, dryness, and heat. Using spatial principal component analysis (SPCA) and the remote sensing ecological index (RSEI) analyzed the spatial differentiation characteristics and influencing factors of the original remote sensing ecological index (RSEI0). The results showed that: (1) the overall RSEI average value of the Qinling-Daba Mountains in 2017 was 0.61, and the ecological environment quality was at a “Good” level. Greenness contributed the most to the comprehensive index of the area, and vegetation distribution had a significant impact on the ecological environment quality of the study area. Heat is a secondary impact, and it has an inhibitory effect on habitat quality; (2) the overall distribution of regional ecological environment quality was quite different, with the ecological environment quality level showing a decreasing trend from low to high altitude; RSEI0 spatial heterogeneity at the optimal scale of 2 km was the largest, and the nugget effect was 88% which indicated a high degree of spatial variability, mainly affected by structural factors; (3) Slope, relief amplitude, elevation, the proportion of high-vegetation area, proportion of construction land area, and average population density significantly impact the spatial differentiation of RSEI0. The explanatory powers of slope and relief amplitude were 56.1% and 65.3%, respectively, which were the main factors affecting the spatial differentiation of the ecological environment quality in high undulation. The results can provide important scientific support for ecological environment construction and ecological restoration in the study area.

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Section: Discussionmentioning

confidence: 99%

Analysis of Spatial Heterogeneity and Influencing Factors of Ecological Environment Quality in China’s North-South Transitional Zone

Yin

Chen

DONG

et al. 2022

IJERPH

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“…Furthermore, previous studies in China have focused mostly on river basins located in northern China (characterized by arid or semiarid climates), such as the Yellow River (see Table 1), while only a few studies have been conducted on the Yangtze River Basin in southern China (characterized by a humid monsoon climate). In fact, the proportion of green water differs greatly between the Yangtze River (approximately 48.1% calculated within our studied basin, namely, the Xiangjiang River Basin) and the Yellow River (more than 80% as reported by previous papers) because the climate (e.g., precipitation) varies considerably between northern and southern China (Ye et al, 2013;Wang et al, 2021). Therefore, it is of practical significance to distinguish the blue and green water components (Mafuta, 2018) in a river basin characterized by a humid monsoon climate and to understand the implications of future climate change impacts on blue and green water resources (Abbaspour et al, 2009).…”

Section: Introductionmentioning

confidence: 44%

Impacts of Climate Change on Blue and Green Water Resources in the Xiangjiang River Basin of the Yangtze River, China

2021

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Green water resources, which are fundamental for plant growth and terrestrial ecosystem services, reflect precipitation that infiltrates into the unsaturated soil layer and returns to the atmosphere by plant transpiration and soil evaporation through the hydrological cycle. However, green water is usually ignored in water resource assessments, especially when considering future climate impacts, and green water modeling generally ignores the calibration of evapotranspiration (ET), which might have a considerable impact on green water resources. This study analyzes the spatiotemporal variations in blue and green water resources under historical and future climate change scenarios by applying a distributed hydrological model in the Xiangjiang River Basin (XRB) of the Yangtze River. An improved model calibration method based on remotely sensed MODIS ET data and observed discharge data is used, and the results show that the parallel parameter calibration method can increase the simulation accuracy of blue and green water while decreasing the output uncertainties. The coefficients (p-factor, r-factor, KGE, NSE, R2, and PBIAS) indicate that the blue and green water projections in the calibration and validation periods exhibit good performance. Blue and green water account for 51.9 and 48.1%, respectively, of all water resources in the historical climate scenario, while future blue and green water projections fluctuate to varying degrees under different future climate scenarios because of uncertainties. Blue water resources and green water storage in the XRB will decrease (5.3–21.8% and 8.8–19.7%, respectively), while green water flow will increase (5.9–14.7%). Even taking the 95% parameter prediction uncertainty (95 PPU) range into consideration, the future increasing trend of the predicted green water flow is deemed satisfactory. Therefore, incorporating green water into future water resource management is indispensable for the XRB. In general, this study provides a basis for future blue and green water assessments, and the general modeling framework can be applied to other regions with similar challenges.

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“…This study selected ten extreme temperature and precipitation indices to analyze the spatio-temporal variations in extreme events in the YRB. All these indices are recommended by the ETCCDI, which have been widely used in previous studies [42][43][44]. The definitions of these indices are listed in Table 1.…”

Section: Definition Of Extreme Temperature and Precipitation Indicesmentioning

confidence: 99%

Spatio-Temporal Variations in the Temperature and Precipitation Extremes in Yangtze River Basin, China during 1961–2020

et al. 2021

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Based on daily maximum temperature (Tmax), minimum temperature (Tmin), and precipitation of the Yangtze River Basin (YRB) from 1961 to 2020, we employed the trend analysis method and correlation analysis method to analyze spatiotemporal variations in 10 extreme indices and their associations with atmospheric and oceanic circulations. Results indicated that maximum Tmax (TXx), maximum Tmin (TNx), and minimum Tmin (TNn) all increased significantly, at rates of 0.19 °C, 0.19 °C, and 0.37 °C per decade, respectively, whereas minimum Tmax (TXn) did not show any significant trend. The diurnal temperature range (DTR) decreased by 0.09 °C per decade as minimum temperatures increased faster than maximum temperatures. TNx and TNn increased significantly in the majority of the YRB, but TXn showed no significant increases. TXn increased significantly in the upper reaches of the Yangtze River. The DTR increased significantly in the Jinsha River Basin and the lower reaches of the Yangtze River. Rx1day (maximum 1-day precipitation), SDII (Simple daily intensity index) and R99p (extremely wet-day precipitation) increased significantly, at rates of 1.12 mm, 0.09 mm, and 5.87 mm per decade, respectively, but the trends of Rx5day (maximum 5-day precipitation) and PRCPTOT (total wet-day precipitation) were not significant. However, the trends of precipitation extreme indices were not statistically significant in most of the YRB. In the future, maximum temperature and minimum temperature might increase while DTR might decrease. But, the trends of precipitation extremes in the future were ambiguous. Nearly all the extreme indices were related to the variability of Atlantic multidecadal oscillation (AMO) in the YRB. In addition, the correlations between extreme temperature indices and AMO are higher than that of extreme precipitation indices.

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Spatiotemporal variations of extreme precipitation and its potential driving factors in China’s North-South Transition Zone during 1960–2017

Cited by 53 publications

References 79 publications

Analysis of Spatial Heterogeneity and Influencing Factors of Ecological Environment Quality in China’s North-South Transitional Zone

Analysis of Spatial Heterogeneity and Influencing Factors of Ecological Environment Quality in China’s North-South Transitional Zone

Impacts of Climate Change on Blue and Green Water Resources in the Xiangjiang River Basin of the Yangtze River, China

Spatio-Temporal Variations in the Temperature and Precipitation Extremes in Yangtze River Basin, China during 1961–2020

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