Numerical simulation of the impact of land cover change on regional climate in China

2017

Sustainability

Abstract:The spatial and temporal characteristics and driving factors analysis of regional land use are the core scientific problems in the research of ecological environment and human sustainable development. It is also an important basis for the government to formulate land management policy. Based on the land use maps of 2000, 2005, 2010 and 2015, this article analyzed the spatiotemporal pattern of land use change in Tianjin, and determined the relative importance of each driving factor of land use change. The main features of land use change were the continuous expansion of built-up land (1386.89 km 2 /74.73% gains) and the decrease of arable land area (1181.60 km 2 /16.84% losses). The area and intensity of land use change were not completely consistent, such as Wuqing and Jixian. The hotspots of land use change mainly were located in the main urban region in Tianjin, around the suburban settlements and Binhai New Area. The landscape pattern in the research region has also changed significantly. The Largest patch index (LPI) and largest shape index (LSI) of arable land showed an increasing trend, and the degree of landscape fragmentation of arable land was deepened. The trend of landscape index of built-up land was similar to that of arable land, but the change intensity was more severe. In addition, the article also used the stepwise regression analysis in the multiple regression to analyze the relative importance of various driving factors, indicating that the driving factors of the built-up land and arable land change were obviously different in different periods. Government policies also have a significant impact on land use change, such as establishing the Tianjin Binhai New Area (TBNA).

Section: Introductionmentioning

confidence: 99%

Characterizing Spatiotemporal Pattern of Land Use Change and Its Driving Force Based on GIS and Landscape Analysis Techniques in Tianjin during 2000–2015

2017

Sustainability

“…The deforestation activity resulted in cooling consequence due to albedo effect in our study area (Lawrence and Chase 2010;Wang et al 2013). But the albedo-cooling effect could be dampened by 30 to 50 % by non-radiative process (Boisier et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…The biogeophysical forcing of LUCC may, in some regions, be of similar magnitude or larger than that of greenhouse gas-induced climate change (e.g., Bonan 2008;Avila et al 2012;Mahmood et al 2013). Avila et al (2012) also demonstrated that impacts of LUCC on indices of temperature extreme were equal to the impacts of doubling of CO 2 , which pointed out the influence of LUCC on regional climate (e.g., Gao et al 2003;Lawrence and Chase 2010;Boisier et al 2012;Wang et al 2013). So information on global mean radiative forcing and global mean surface temperature change may be less useful than information on regional climate changes including precipitation (Betts et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Lawrence and Chase (2010), using Community Climate System Model (CCSM) and MODIS LUCC data, revealed that the deforestation in the past reduced the LAI but increased albedo in all four seasons; air temperature was increased in summer and autumn but decreased in winter and spring. Wang et al (2013), using WRF model and MODIS LUCC data, also discovered that LAI reduced, albedo increased, and surface temperature decreased in both summer and winter from 2006 to 2011. The cause primarily comes from the influence of radiative process.…”

Section: Introductionmentioning

confidence: 99%

“…So temperature reduction caused by deforestation firstly roots in increased albedo. Many studies showed this consistent trends and the associated near-surface cooling as the leading impact of LUCC (e.g., Feddema et al 2005;Wang et al 2013). The LUCC-induced cooling is directed by increase in albedo, but its amplitude is 30 to 50 % smaller than the one that would be expected from the sole radiative changes (Boisier et al 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of land use/cover change on land surface energy partitioning and climate in Northeast China

Tao

et al. 2014

Theor Appl Climatol

The Simple Biosphere Model (SiB2) and the 2× 2 km resolution National Land use/Land Cover database were used to investigate the effects of Land Use/Cover Change (LUCC) on land surface energy balance and climate in Jilin Province, northeast China, from 1990 to 2005. The spatial patterns of the components of surface energy balance (i.e., net radiation (R n ), latent heat (LH), sensible heat (SH), and albedo (α)) and climate (i.e., canopy temperature (T c ), diurnal temperature range (DTR)), as well as the roles of land cover type in variations of energy balance and climate, were investigated. The results showed that there were general similar trends in R n , LH, SH, and α in the LUCC process. The spatial patterns of T c and DTR also showed consistent relationships with LUCC processes. Leaf area index (LAI) and canopy conductance (g c ) were found to be the key factors in controlling the spatial patterns of the components of surface energy balance and T c . Using linear correlation method, the gaps of the components of surface energy balance were well-explained by the differences of LAI and g c , and R n had a better correlation with T c and DTR, in the process of LUCC. The surface energy partitioning of R n into LH and SH could not only dampen or strengthen the temperature difference, but also change the relative size of albedo-based R n when the albedo gap was small, between land cover types.

Changes in reference evapotranspiration over China during 1960–2012: Attributions and relationships with atmospheric circulation

Chai

Sun

Chen

et al. 2018

Hydrological Processes

This study investigates reference evapotranspiration (ET0) trends in China from 1960 to 2012 based on the Penman–Monteith equation and gridded meteorological measurements. Under the combined impacts of factors influencing ET0 (i.e., net radiation [RN], mean temperature [TAVE], vapour pressure deficit [VPD], and wind speed [WND]), both seasonal and annual ET0 for the whole China and more than half of the grids decreased over the past 53 years. The attribution analyses suggest that for the whole China, the WND is responsible for annual and seasonal ET0 decreases (excluding summer, where RN is responsible). Across China, the annual cause of WND with the largest spatial extent (43.1% of grids) mainly derives from north of the Changjiang River Basin (CJRB), whereas VPD (RN) as a cause is dispersedly distributed (within and to the south of the CJRB). In summer, RN is dominant in more than half of the grids, but the dominance of VPD and WND accounts for approximately 90% of grids during the remaining seasons. Finally, the correlation coefficients between ET0 and the Atlantic Oscillation (AO), North AO, Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO) indices with different lead times are calculated. For the whole China, annual and seasonal ET0 always significantly correlate with these indices (excluding the IOD) but with varied lead times. Additionally, near half of the grids show significant and maximum (i.e., the largest one between ET0 and a certain index with a lead time of 0–3 seasons) correlation coefficients of ET0 with PDO in spring and summer, ENSO in autumn, and AO in winter. This study is not only significant for understanding ET0 changes, but it also provides preliminary and fundamental reference information for ET0 prediction.