Exploring grassland ecosystem water use efficiency using indicators of precipitation and soil moisture across the Mongolian Plateau

Liu, Xinyi; Lai, Quan; Yin, Shi; Bao, Yuhai; Song, Qian; Bayarsaikhan, Sainbuyan; Bu, Lingxin; Mei, Li; Li, Zhi‐Ru; Niu, Jialong; Yang, Yongrong

doi:10.1016/j.ecolind.2022.109207

Cited by 19 publications

(9 citation statements)

References 78 publications

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“…The higher precipitation under the high concentration emission scenario can reduce the restriction of precipitation factors on the distribution of species and expand the suitable habitat of species. On the contrary, the increased precipitation under the low concentration emission scenario can not reduce the limit, but will reduce the available water for species to absorb with global warming [12,14,52]. This is consistent with the change law of the total suitable habitat of C. medica L. var.…”

Section: Plos Onesupporting

confidence: 79%

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model

Xia

Kazim²,

Nasir³

et al. 2023

PLoS ONE

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Climatic variables are important conditions for plant growth, development and reproduction. Citrus medica L. var. sarcodactylis Swingle (Rutaceae: Citrus) is one of the traditional bulk Chinese medicinal materials in China with the effects of bacteriostasis, anti-inflammatory, anti-oxidation, anti-cancer cells, regulating the immun. Analyzing the impact of climate change on geographical distribution of C. medica L. var. sarcodactylis can provide strong support for its production layout and agricultural zoning. In our paper, MaxEnt and ArcGIS were applied to simulate the suitable areas of C. medica L. var. sarcodactylis in China from the perspectives of bioclimate, soil, topographic factors and human activities, and the future climate scenarios generated by global climate models (GCMs) were selected to predict its suitable areas in 2050s and 2090s. Results showed that, 1) Under current climate condition, areas of the total, most, moderately and poorly suitable habitats of C. medica L. var. sarcodactylis in China were 177.36×104 km2, 22.27×104 km2, 51.96×104 km2 and 103.13×104 km2 respectively. The range of the most suitable habitat was the narrowest, which was located in the middle east of Sichuan, western Chongqing in the upstream of the Yangtze River Basin, southern Guizhou and western Guangxi in the upstream of the Pearl River Basin, central and southern Yunnan and Southeast Tibet in the Middle-Lower reaches of the Southwest River Basin and western Taiwan. 2) Under the future climate change scenarios, the total suitable area showed a significant increase trend in 2090s, and the change of most, moderately and poorly suitable habitats showed no obvious law. 3) Under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the centroid of the most suitable habitat of C. medica L. var. sarcodactylis would move to the northwest, southeast and southwest respectively.

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Section: Plos Onesupporting

confidence: 79%

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model

Xia

Kazim²,

Nasir³

et al. 2023

PLoS ONE

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“…For example, forest ecosystems are usually higher than grassland [10]. The PUE of vegetation in extraordinarily arid and humid areas may be lower, and the PUE of vegetation in areas with annual precipitation of 475 mm may be higher [11]. The temporal and spatial distribution of PUE was also significantly correlated with some biological characteristics of vegetation, such as vegetation coverage and leaf area index.…”

Section: Introductionmentioning

confidence: 93%

“…The correlation between PUE and precipitation, air temperature, potential evapotranspiration, wind speed, and saturated vapor pressure was calculated according to Equations ( 6)- (11). Each grid point's most important control factors were obtained by comparing the spatial correlation, and their spatial distribution characteristics are shown in Figure 8.…”

Section: Controlling Factorsmentioning

confidence: 99%

Spatiotemporal Variation Characteristics and Driving Force Analysis of Precipitation Use Efficiency at the North Foot of Yinshan Mountain

Yang,

Liu,

Tao

et al. 2023

Water

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The northern foothills of Yinshan Mountain are situated in northern China’s agricultural and pastoral ecotone, serving as a crucial ecological barrier. To comprehensively assess the impact of grassland resource restoration in this region since the initiation of the Grain-for-Green conversion project in 2000, this study analyzes the spatiotemporal characteristics of precipitation use efficiency (PUE) and investigates climate-driven factors during 2001–2021. The results showed that the grassland types at the north foot of Yinshan could be divided into four categories: warm-arid, warm subtropical semidesert (WSS), warm temperate-arid, warm temperate zonal semidesert (WZS), warm temperate-semiarid, warm temperate typical steppe (WTS), and warm temperate-subhumid forest steppe (WFT). The NPP of the four grassland species were 151.34 (WSS), 196.72 (WZS), 283.33 (WTS), and 118.06 gC·m−2 (WFT), and correspondingly, the PUE of the four grassland species were 0.66 (WSS), 0.66 (WZS), 0.80 (WTS), and 0.57 gC·m−2·mm−1 (WFT). From 2001 to 2021, PUE in grassland showed an overall upward trend, rising from 0.57 to 0.99 gC·m−2·mm−1. The trend analysis found that the vegetation ecological area of the northern foot of Yinshan became better, of which 54.36% was improved and 15.72% was degraded. It is worth pointing out that WSS had the highest degree of improvement, while WFT was in a degraded state. The climate driving force analysis shows that the regional contribution of precipitation is 19.57%, temperature is 28.33%, potential evapotranspiration is 13.65%, wind speed is 10.79%, and saturated vapor pressure is 27.66%.

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“…As one of the manifestations of water use efficiency, PUE is defined as the ratio of biomass to precipitation (Sun et al, 2019). Studies have shown that PUE is an important indicator for analysing and evaluating the response characteristics of vegetation production capacity to spatial and temporal changes in precipitation at the regional scale (Hu et al, 2009; Liu et al, 2022; Wang, Zhang, et al, 2022), and it is an ideal ecological parameter to understand the relationship between climate and vegetation productivity (Jiang et al, 2022; Zhang et al, 2021). Analysing the spatial variation of PUE across geographic and climatic gradients can help to assess ecosystem function and predict the impact of climate change on vegetation productivity.…”

Section: Introductionmentioning

confidence: 99%

Distribution, trends and drivers of precipitation use efficiency in the loess plateau

Shi,

Zhang,

Ding

et al. 2024

Hydrological Processes

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Against the background of climate change, the global carbon and water cycle has undergone significant changes, and it is of great significance to explore the interrelationships of the carbon and water cycles in different regions to cope with future climate change. In this study, based on gross primary productivity (GPP) and precipitation (PRE) data, the precipitation use efficiency (PUE) of the Loess Plateau (LP) was calculated, and the Sen trend analysis and Mann‐Kendall test were used to analyse the temporal and spatial variation characteristics of PUE, as well as the first‐order difference method was used to derive the relative contributions to quantify the impact of vegetation growth and meteorological factors on PUE. The results show that (1) from 2001 to 2018, the spatial distribution pattern of vegetation PUE exhibited an increasing trend from northwest to southeast. On the time scale, the multi‐year average value of PUE was 1.17 gC m−2 mm−1, showing an overall upward trend. Among the different land cover types, paddy fields had the highest PUE level, and sparse grassland had the lowest; (2) LAI had the highest relative contribution to vegetation PUE, followed by temperature (Temp), net radiation of the ground surface (RN), specific humidity (Shum), and wind speed (WS). LAI contributes positively to 88.9% of the area. RN positively influences high‐altitude areas, while Shum had a larger area with a negative contribution. The contribution of Temp increases from northwest to southeast, and WS has balanced positive and negative contributions; (3) LAI is the dominant factor for the spatial and temporal variation of PUE in the LP, and the size of the areas where different meteorological factors dominate the changes of PUE are as follows: WS > Shum>RN > Temp. At high altitudes, the dominant meteorological driver is WS, while at low altitudes, the dominant climate driver is Shum. This study is of guiding significance for the ecological restoration and management of the LP, and it can also provide a scientific basis for the improvement of ecosystems and the sustainable management of water resources in the context of global climate change.

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Exploring grassland ecosystem water use efficiency using indicators of precipitation and soil moisture across the Mongolian Plateau

Cited by 19 publications

References 78 publications

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model

Spatiotemporal Variation Characteristics and Driving Force Analysis of Precipitation Use Efficiency at the North Foot of Yinshan Mountain

Distribution, trends and drivers of precipitation use efficiency in the loess plateau

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