Shuangcheng Li

Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology.

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation’s dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R2) were found from the results of the GWR models. Moreover, the spatial patterns of the local R2 of NDVI-precipitation are much clearer than the R2 of NDVI-temperature in the semi-arid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.

Climate adaptation, institutional change, and sustainable livelihoods of herder communities in northern Tibet

The Tibetan grassland social-ecological systems are widely held to be highly vulnerable to climate change. We aim to investigate livelihood adaptation strategies of herder households and the types of local institutions that shaped those adaptation strategies. We examined the barriers and opportunities for strengthening adaptive capacity of local herder communities. We designed and implemented a household survey in the herder communities of northern Tibet. The survey results showed that migratory grazing has become less feasible. Storage, diversification, and market exchange have become the dominant adaptation strategies. The adaptation strategies of local herders have been reshaped by local institutional change. Local governmental and market institutions played the dominant roles in reshaping climate adaptation strategies. Although the present livelihood adaption strategies related to sedentary grazing have improved productivity and profitability of the herding livelihood, they have led to continuous deterioration of pastures. The local grazing system has become more and more dependent on artificial feeding and inputs from outside the grazing system. Purchasing forage has become one of the dominant adaptation strategies of local herder households. Multilevel regression modeling of this adaptation behavior showed that explanatory variables related to climate variability, household capital, and local institutional arrangements had statistically significant relationships with the adoption of this adaptation strategy. The results implies that building household capital and promoting the coordination among local governmental, market, and communal institutions are critical for strengthening adaptive capacity of the Tibetan herder communities.

Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: a case study of Guizhou Karst Plateau, China

Knowing the spatial relationships between the normalized difference vegetation index (NDVI) and environmental variables is of great importance for monitoring rocky desertification. This article investigated the spatially non-stationary relationships between NDVI and environmental factors using geographically weighted regression (GWR) at multi-scales. The spatial scale-dependency of the relationships between NDVI and environmental factors was identified by scaling the bandwidth of the GWR model, and the appropriate bandwidth of the GWR model for each variable was determined. All GWR models represented significant improvements of model performance over their corresponding ordinary least squares (OLS) models. GWR models also successfully reduced the spatial autocorrelations of residuals. The spatial relationships between NDVI and environmental factors significantly varied over space, and clear spatial patterns of slope parameters and local coefficient of determination (R 2) were found from the results of the GWR models. The study revealed detailed site information on the different roles of related factors in different parts of the study area, and thus improved the model ability to explain the local situation of NDVI.

Vulnerability of the Tibetan Pastoral Systems to Climate and Global Change

The impacts of climate and global change on Tibetan pastoral systems have become increasingly evident. Thus, a significant research endeavor is to explore the combined effects of these changes on the livelihoods of herder households and communities, on the adaptation strategies they adopted to respond to the current and expected risks associated with these changes, and on the emerging opportunities that can strengthen their resilience and adaptive capacity. We performed an integrated analysis of the dynamics of Tibetan pastoral systems influenced by climate and global changes by using the analytical framework developed by Ostrom. Climate and global changes have significantly altered the attributes of and the interactions within Tibetan pastoral systems, thus posing great challenges to their sustainable development. We used Nagqu County, a remote area of the northern Tibetan Plateau of China, as a case study to analyze the adaptation strategies adopted by local herders to respond to multiple stressors, as well as the emerging opportunities that they can take advantage of to increase their adaptive capacity. Findings show that although local herders have developed various adaptation strategies, such as planting forage grass, buying fodder from the market, renting pastures, joining formal or informal cooperatives, and diversifying livelihoods, social, cultural, and institutional challenges still exist. To enhance the adaptive capacity of herders and to reduce their vulnerability, we recommend that future rangeland policies and programs promote: (1) comprehensive support for formal or informal pastoral cooperatives, (2) development of the rangeland economy to take advantage of the multifunctionalities of rangeland ecosystems, and (3) revitalization of the mobility paradigm to allow the flexible use of rangelands.

IDENTIFYING SPATIAL PATTERNS AND DYNAMICS OF CLIMATE CHANGE USING RECURRENCE QUANTIFICATION ANALYSIS: A CASE STUDY OF QINGHAI–TIBET PLATEAU

The climate system is a prototypical nonlinear complex system exhibiting nonstationary temporal variation and complicated spatial patterns. One of the ideal locations for studying climate systems is the Qinghai–Tibet Plateau (QTP), which is considered an amplifier of global climate change. In this study, recurrence quantification analysis (RQA) was used to analyze the annual temperature series of 17 stations in different climate zones of the QTP, based on station observation data of daily temperature (minimum, maximum and mean) from 1961 to 2008. Spatial patterns and variation of RQA indices of Determinism (DET) and Kolmogorov (K2) entropy suggested that there are marked differences in temperature pattern in the QTP. Correlation analysis between RQA indices of temperature series and environmental factors, such as topographical variation and Normalized Difference Vegetation Index suggest that both the source and effect of climate complexity are nonlinear. Results of this study indicate that RQA measurement was indeed an efficient approach to analyze the dynamics of a climate system.

Spatiotemporal Decompositions of Summer Drought in China and Its Teleconnection with Global Sea Surface Temperatures during 1901–2012

AbstractThe teleconnection between the summer (June–August) Palmer drought severity index (PDSI) in China and seasonal global sea surface temperatures (SSTs) is investigated at both spatial and temporal scales during 1901–2012. Three pairs of coupled spatial patterns for China’s PDSI and global SST anomalies are identified using the singular value decomposition (SVD) method. With a combination of ensemble empirical mode decomposition (EEMD) and multiple linear regression (MLR) analysis, it is found that the first mode, the sea ice loss–global warming pattern, causes wetness over north and northeastern China and drying over Inner Mongolia. The North Pacific Current (NPC) mode shows that a warmer NPC corresponds to a wetter summer over eastern China and a drier one over the Tibetan Plateau. Both NPC and Pacific decadal oscillation (PDO) affect moisture variability in northern China and over the Tibetan Plateau, with the NPC mode more important in the centennial scale, while the PDO mode is more important in...

Water memory effects and their impacts on global vegetation productivity and resilience

Memory effects refer to the impacts of antecedent climate conditions on current vegetation productivity. This temporal linkage has been found to be strong in arid and semi-arid regions. However, the dominant climatic factors that determine such patterns are still unclear. Here, we defined’water-memory effects’ as the persistent effects of antecedent precipitation on the vegetation productivity for a given memory length (from 1 to up to 12 months). Based on satellite observations and climate data, we quantified the length of water-memory effects and evaluated the contributions of antecedent precipitation on current vegetation. Our results showed that vegetation productivity was highly dependent on antecedent precipitation in arid and semi-arid regions. The average length of water memory was approximately 5.6 months. Globally, water-memory effects could explain the geographical pattern and strength of memory effects, indicating that precipitation might be the dominant climatic factor determining memory effects because of its impact on water availability. Moreover, our results showed vegetation in regions with low mean annual precipitation or a longer water memory has lower engineering resilience (i.e. slower recovery rate) to disturbances. These findings will enable better assessment of memory effects and improve our understanding of the vulnerability of vegetation to climate change.

Vulnerability of the global terrestrial ecosystems to climate change

Climate change has far-reaching impacts on ecosystems. Recent attempts to quantify such impacts focus on measuring exposure to climate change but largely ignore ecosystem resistance and resilience, which may also affect the vulnerability outcomes. In this study, the relative vulnerability of global terrestrial ecosystems to short-term climate variability was assessed by simultaneously integrating exposure, sensitivity, and resilience at a high spatial resolution (0.05°). The results show that vulnerable areas are currently distributed primarily in plains. Responses to climate change vary among ecosystems and deserts and xeric shrublands are the most vulnerable biomes. Global vulnerability patterns are determined largely by exposure, while ecosystem sensitivity and resilience may exacerbate or alleviate external climate pressures at local scales; there is a highly significant negative correlation between exposure and sensitivity. Globally, 61.31% of the terrestrial vegetated area is capable of mitigating climate change impacts and those areas are concentrated in polar regions, boreal forests, tropical rainforests, and intact forests. Under current sensitivity and resilience conditions, vulnerable areas are projected to develop in high Northern Hemisphere latitudes in the future. The results suggest that integrating all three aspects of vulnerability (exposure, sensitivity, and resilience) may offer more comprehensive and spatially explicit adaptation strategies to reduce the impacts of climate change on terrestrial ecosystems.