Impact of land cover transformation on urban heat islands in Harbin, China.

At the local and regional climate scale, one of the most studied environmental issues is urban heat island (UHI). UHI is a thermal anomaly caused by temperature differences between urban and rural settings, which adds heat to the atmosphere and makes people feel uncomfortable. This study explores the influence of new land-cover data on UHI simulations using the high-resolution Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM) in the city of Harbin. A comparison was performed between the new Tsinghua University (TU) land cover dataset with the default United States Geological Survey (USGS) and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover datasets. The results of this study revealed that the new TU land cover data had better representation and more realistic land cover changes than the default datasets. The diurnal, seasonal, and long-term nighttime UHIs of air and surface temperatures were higher than the daytime UHIs for both downtown Harbin and the satellite towns. We discovered that coal-burning during winter had a significant influence on UHI in Harbin. Moreover, the results from our buffer revealed a rapid increase in the UHIs of satellite towns, thus revealing the need to focus on the effects of UHI in satellite towns in the future. Therefore, the timely updating of land cover datasets in the WRF model and implementing mitigation strategies will help improve the urban climatic comfort.

Driving forces of UHI changes in China's major cities from the perspective of land surface energy balance.

As remarkable human-induced temperature anomalies on the land surface, variations of urban heat island (UHI) and its driving factors have been investigated in numerous studies. However, few studies discussed the spatiotemporal heterogeneity of the driving forces exerted by land surface energy fluxes, i.e., net radiation, sensible heat, latent heat and heat storage, on UHI behaviors at large scale and long term. In this study, a comprehensive application of multisource datasets and statistical methods have been implemented based on land surface energy balance theory, the spatiotemporal variations of surface UHI intensity (urban-rural temperature difference) and changes of their driving forces have been quantified. The results demonstrate the dynamics of UHI intensity in 32 major cities of China from 2003 to 2017 are generally coherent with the common perception, the overall surface UHI intensity is 4.57 K higher in summer than in winter. The spatial variations of the fluxes that alter UHI intensity can be largely attributed to the varied energy interactions between vegetated/paved surface and atmosphere and the differences of background temperature and precipitation, the contribution of latent heat to UHI changes declines nearly 40% from semiarid/arid climate at the north to subtropical humid climate at the south, while the contributions of other fluxes are stable. The temporal changes of the effect of these fluxes, however, imply more complex mechanisms. The contributions of sensible heat and latent heat to UHI intensity variations are three times and eight times larger in the warm season than in the cold season respectively, indicating the influence of seasonality of background temperature, precipitation and vegetation. The low contributions of these fluxes in the cold season also suggest the significant effect of other driving forces such as anthropogenic heat, especially in semiarid/semihumid climate zones. This study highlights the temporal shifts of major driving forces of UHI intensity, the mitigation tactics for UHI in different cities and seasons should be customized for better validity.

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas.

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO2 emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/Tair ) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/Tair and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. We found that the top drivers of LST/Tair , explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/Tair variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/Tair is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High-resolution Imagery in Commercial Vineyards.

Estimation of surface energy fluxes using thermal remote sensing-based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in Ts and Tc estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service's Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.