Zhiqiang Zhao

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.

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.

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.

Uncertainty of future projections of species distributions in mountainous regions

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Climate extremes in the Kobresia meadow area of the Qinghai-Tibetan Plateau, 1961–2008

The Alpine Kobresia meadow is a major vegetation type on the Qinghai-Tibetan Plateau in western China. It is important both in preserving local and regional water and carbon and as a valuable pasture resource; however, meadow degradation has been spreading under both climatic and human disturbance. Climate extremes and related disasters are more discernible by local people than gradual trends and may have rapid and direct impact on the ecosystem; however, research on the resilience of the Kobresia meadow is rare. This study uses time series of 21 objectively defined indices of daily temperature (12) and precipitation (9) extremes from representative weather stations in the Kobresia meadow region to analyse the spatial pattern and regional trend in climate extremes over the period of 1961–2008. A general tendency towards more warm extremes is found over the Kobresia meadow and is more noticeable at night, although the increase in the warmest daytime temperature is larger than that of the warmest night-time temperature. The meadow region is, however, not experiencing a uniform tendency in terms of precipitation extremes, except for a decrease in consecutive dry days, which seems to occur especially at higher altitudes. Regionally, there seems no obvious trend in rain intensity, but a quasi-decadal fluctuation occurs. The middle and eastern Kobresia meadow area has experienced relatively milder extreme climatic change especially in night-time cold temperatures and in precipitation. Targeted measures towards sustaining the Kobresia meadow need to take these regional differences in climatic extremes into account.