Impact of land use and land cover changes on temperature trends over India

Nayak, Sridhara; Mandal, Mrinal Kanti

doi:10.1016/j.landusepol.2019.104238

Cited by 67 publications

(45 citation statements)

References 39 publications

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“…The impacts of land use/cover types and land cover changes are more regional than the global impacts of increased GHGs. This result has also been confirmed in many regional climate change studies 41,42 . The results of the correlation analysis also show that vegetation cover, air humidity, and distance from water bodies can significantly regulate surface temperature, which is similar to the findings of many studies on urban thermal environments [43][44][45][46] .…”

Section: Discussionsupporting

confidence: 78%

Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China

Tan

et al. 2020

Sci Rep

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the Dongting Lake area (china) is a climate change-sensitive and ecologically fragile area and plays a crucial role in the regulation of the regional climate. in recent decades, rapid social and economic development has led to increased land use/land cover (LULc) changes in the Dongting Lake area, which affect the surface energy balance and hydrological processes. Its contemporary variability under climate change remains highly uncertain. therefore, we retrieved the Land surface temperature (LSt) from the Landsat 7 data and explored its relationship with the LULC types. The results showed that LST is significantly affected by surface type. LST varied significantly across LULC types, with higher LSTs in built-up land, reed beach land, forest land, and paddy fields than in water bodies, mud beaches, marshlands, and riparian forests. Water bodies play an important regulatory role in reducing LSt and mitigating thermal effects on the ground. The winter LST in the study area increased by approximately 3.5 °C, which may be related to the decrease in the area of Dongting Lake water bodies, water fields and reed flats after the Three Gorges Reservoir was impounded. Compared with the relationship between the nDVi, DeM, and distance from the water body, the negative correlation between the nDMi and LST was stronger and more stable and had the greatest effect on LST. These insights improve the understanding of the land change consequences on the temporal dynamics of LSt. Land surface temperature (LST) is a reflection of the energy flow in the interactions between the land surface and atmosphere and between the land surface and biosphere. The change in LST is an intuitive regional climate response to global climate change and has important research significance in agriculture, hydrology, ecology, environment, climate, and biogeochemistry1 1-6. LULC is the interface between the atmosphere and biosphere for material and energy exchange and has a clear influence on LST. Currently, a wide consensus has been reached on the influence of LULC change on regional climate through various processes, such as water and heat flux between land and air 7 , surface wind speed, and boundary layer turbulence8 8,9. Exploring the mechanism of interaction between LULC change and local climate has become a popular research topic 10-12. LULC change directly causes changes in the physical characteristics of the land surface, which affects a variety of factors that determine regional climate, such as radiation, heat, and water vapor exchange 13-15. LULC change also alters the composition of surface attachments such as vegetation type and vegetation density 16,17. In addition, LULC change has associated effects such as demographic changes and changes in regional chemical composition 18,19. This relationship has led to changes in terrestrial carbon stocks and their fluxes 20-22 , resulting in a variety of outcomes, including changes in atmospheric greenhouse gas levels 23-25. Therefore, the relationship between LST and LUC change should be investigated...

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Section: Discussionsupporting

confidence: 78%

Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China

Tan

et al. 2020

Sci Rep

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“…As a result, OMR has the ability to remove the signals of large-scale temperature variations induced by greenhouse gases while retaining the local signals [24]. Based on the OMR method, Kalnay et al, Mario et al, and Nayak et al investigated the impacts of LULC changes on climate changes in the United States, Argentina, and western India [27][28][29]. Lim et al investigated the sensitivity of surface climate change to land types over the Northern Hemisphere and found that surface warming was higher in areas that are barren, anthropogenically developed, or covered with needle-leaf forests, whereas surface warming appeared to be suppressed in highly vegetated areas [30].…”

Section: Introductionmentioning

confidence: 99%

Overestimating Impacts of Urbanization on Regional Temperatures in Developing Megacity: Beijing as an Example

Cao

Huang

Liu

et al. 2019

Advances in Meteorology

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Land-use and land cover changes may have important local, regional, and global climatic impacts by modifying the underlying land surface conditions, which in turn influence the exchange of energy and moisture between the land surface and atmosphere. Many studies have shown that urbanization has contributed to climate warming, and the amount of warming has varied. As the capital of China and one of the world’s megacities, Beijing has experienced rapid urbanization over the past 30 years. In this study, we quantitatively investigated the impacts of urbanization on regional temperatures based on observations from meteorological stations and National Centers for Environmental Prediction (NCEP) reanalysis data and overestimating of the impacts were found. Comparing the temperature trends of land-use types, forest showed stronger inhibitory effects on temperature increase (−0.085°C/10a). Cropland also had a negative effect on climate warming yearly and seasonally, especially in winter (−1.133°C/10a) and spring (−0.299°C/10a). Conversely, the urban area showed strong warming effects (0.438°C/10a). The conversion of cropland to urban land appeared to show the highest warming trend (0.548°C/10a). However, the cooling effect of forest and grassland with high vegetation coverage inhibited climatic warming attributed to rapid urbanization. In addition, planting trees or grass along roadsides and increasing green parks and green roofs can also suppress surface warming. Therefore, the actual warming effects of urbanization on temperatures were overestimated in megacities or urban agglomeration regions. The results showed that the green space and landscape configuration should be considered in urban planning to increase green space and reduce the influence of urban heat island effect.

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“…Additionally, understanding the regional manifestations of recent climate trends can help us to prepare for future climatic effects on agricultural productivity. Among the various parameters of climatic variability [29], rainfall [30] and temperature [31,32] play the most determining roles [33,34] in defining agricultural conditions. This is also true for the districts being studied here.…”

Section: Resultsmentioning

confidence: 99%

Impact of Climate Variability on Crop Yield in Kalahandi, Bolangir, and Koraput Districts of Odisha, India

Panda

Sahu

Behera

et al. 2019

Climate

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Most tropical regions in the world are vulnerable to climate variability, given their dependence on rain-fed agricultural production and limited adaptive capacity owing to socio-economic conditions. The Kalahandi, Bolangir, and Koraput districts of the south-western part of Odisha province of India experience an extreme sub-humid tropical climate. Based on the observed changes in the magnitude and distribution of rainfall and temperature, this study evaluates the potential impact of climate variation on agricultural yield and production in these districts. The study is conducted by taking into account meteorological data like rainfall and temperature from 1980 to 2017 and crop productivity data from 1980–81 to 2016–17. Additionally, climate variability indices like Monsoon Index, Oceanic Nino Index, and NINO-3 and NINO 3.4 are used. To analyse the data, various statistical techniques like correlation and multiple linear regression are used. The amount of monsoon rainfall is found to have a significant impact on crop productivity, compared to temperature, in the study area, and as a result the Monsoon Index has a determining impact on crop yield among various indices.

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Impact of land use and land cover changes on temperature trends over India

Cited by 67 publications

References 39 publications

Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China

Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China

Overestimating Impacts of Urbanization on Regional Temperatures in Developing Megacity: Beijing as an Example

Impact of Climate Variability on Crop Yield in Kalahandi, Bolangir, and Koraput Districts of Odisha, India

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