Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China

Abstract

Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere-biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past three decades in the Upper Heihe River Basin (UHRB), a complex multiple use watershed in arid northwestern China. We apply empirical orthogonal function (EOF) and singular value decomposition (SVD) analysis to isolate and identify the spatial patterns of satellite-derived leaf area index (LAI) and their close relationship with the variability of an aridity index (AI = Precipitation/Potential Evapotranspiration). Results show that UHRB has become increasingly warm and wet during the past three decades. In general, the rise of air temperature and precipitation had a positive impact on mean LAI at the annual scale. At the monthly scale, LAI variations had a lagged response to climate. Two major coupled spatial change patterns explained 29% and 41% of the LAI dynamics during 1983-2000 and 2001-2010, respectively. The strongest connections between climate and LAI were found in the southwest part of the basin prior to 2000, but they shifted towards the north central area afterwards, suggesting that the sensitivity of LAI to climate varied over time, and that human disturbances might play an important role in altering LAI patterns. At the basin level, the positive effects of regional climate warming and precipitation increase as well as local ecological restoration efforts overwhelmed the negative effects of overgrazing. The study results offer insights about the coupled effects of climatic variability and grazing on ecosystem structure and functions at a watershed scale. Findings from this study are useful for land managers and policy makers to make better decisions in response to climate change in the study region.

Quantifying Climate Change and Ecological Responses within the Yangtze River Basin, China

“…A study of arid and semi-arid regions in China demonstrated that precipitation significantly positively affected GPP, NPP, and LAI, while the temperature was negatively correlated with NPP, and had no correlation with GPP [21]. Hao et al [22] found that increases in temperature and precipitation in north-west China during the past three decades had a positive impact on annual mean LAI. In most parts of China, the temperature is the main climatic factor affecting the change of NPP.…”

Prospects for the sustainability of social-ecological systems (SES) on the Mongolian plateau: five critical issues

“…However, within-country migration to Ulaanbaatar is accompanied by the depopulation of rural areas, while in IM a strong influx of population from other Chinese provinces has compensated the rural (and, in particular, nomadic) population decline. These variations in socioecological parameters are compounded by spatiotemporal variation in climate (Liu et al 2013, as well as interactive feedbacks among the elements of SES (Hardin 1968, Chen et al 2015b, Hao et al 2016a, Allington et al 2017, Hessl et al 2018.…”

Future Projected Changes in Local Evapotranspiration Coupled with Temperature and Precipitation Variation

“…These two RCP scenarios are more representative and widely used for future climate forcing. The RCP4.5 scenario explores the long-term climate system response to the stabilizing the anthropogenic components of radiative forcing [33], while the RCP8.5 scenario represents the highest greenhouse gas emissions [34]. We chose these two typical climate change scenarios to focus the effect of climate change on evapotranspiration in the future based on normal and high emissions, respectively, including the effects of low and middle emissions of RCP 2.6 and RCP 6.0 on evapotranspiration.…”

“…Since variables in terrestrial ecosystems are characterized by non-linearity and high dimensionality, the Empirical Orthogonal Function (EOF) analysis and Singular Value Decomposition (SVD) analysis techniques are widely used to represent the variation in dominant spatiotemporal patterns; the relative importance of each pattern is in explaining observed variation across space [33][34][35]. First, according to the spatial mode, EOF was conducted to analyze temporally varying spatial patterns of annual evapotranspiration, and corresponding time coefficients that describe the magnitude of the variation of the spatial mode.…”