scholarly journals Determining Robust Impacts of Land-Use-Induced Land Cover Changes on Surface Climate over North America and Eurasia: Results from the First Set of LUCID Experiments

2012 ◽  
Vol 25 (9) ◽  
pp. 3261-3281 ◽  
Author(s):  
Nathalie de Noblet-Ducoudré ◽  
Juan-Pablo Boisier ◽  
Andy Pitman ◽  
G. B. Bonan ◽  
V. Brovkin ◽  
...  
Keyword(s):  
Land Use ◽  
North America ◽  
Land Cover ◽  
Greenhouse Gases ◽  
Land Surface ◽  
Climate Models ◽  
Heat Fluxes ◽  
Sea Ice Extent ◽  
Available Energy ◽  
The Impact

The project Land-Use and Climate, Identification of Robust Impacts (LUCID) was conceived to address the robustness of biogeophysical impacts of historical land use–land cover change (LULCC). LUCID used seven atmosphere–land models with a common experimental design to explore those impacts of LULCC that are robust and consistent across the climate models. The biogeophysical impacts of LULCC were also compared to the impact of elevated greenhouse gases and resulting changes in sea surface temperatures and sea ice extent (CO2SST). Focusing the analysis on Eurasia and North America, this study shows that for a number of variables LULCC has an impact of similar magnitude but of an opposite sign, to increased greenhouse gases and warmer oceans. However, the variability among the individual models’ response to LULCC is larger than that found from the increase in CO2SST. The results of the study show that although the dispersion among the models’ response to LULCC is large, there are a number of robust common features shared by all models: the amount of available energy used for turbulent fluxes is consistent between the models and the changes in response to LULCC depend almost linearly on the amount of trees removed. However, less encouraging is the conclusion that there is no consistency among the various models regarding how LULCC affects the partitioning of available energy between latent and sensible heat fluxes at a specific time. The results therefore highlight the urgent need to evaluate land surface models more thoroughly, particularly how they respond to a perturbation in addition to how they simulate an observed average state.

scholarly journals Inferring past land use-induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

2013 ◽  
Vol 10 (3) ◽  
pp. 1501-1516 ◽  
Author(s):  
J. P. Boisier ◽  
N. de Noblet-Ducoudré ◽  
P. Ciais
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Surface Albedo ◽  
Climate Models ◽  
Vegetation Type ◽  
Seasonal Pattern ◽  
Satellite Observations ◽  
Secondary Importance ◽  
Past Land Use

Abstract. Regional cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land use-induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the extent of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We derived monthly albedo climatologies for croplands and four other land cover types from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. We then reconstructed the changes in surface albedo between preindustrial times and present-day by combining these climatologies with the land cover maps of 1870 and 1992 used by seven land surface models (LSMs) in the context of the LUCID ("Land Use and Climate: identification of robust Impacts") intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter, and larger than 2% in summer between 1870 and 1992 over areas that experienced intense deforestation in the northern temperate regions. The historical surface albedo changes estimated with MODIS data were then compared to those simulated by the various climate models participating in LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the MODIS-based reconstructions, that is, larger albedo increases in winter than in summer, driven by the presence of snow. However, individual models show significant differences between the simulated albedo changes and the corresponding reconstructions, despite the fact that land cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how LSMs parameterize albedo. Another reason, of secondary importance, results from differences in their simulated snow extent. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point out deficiencies of the models. We therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate LSMs.

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

scholarly journals Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES)

2019 ◽  
Vol 12 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Chantelle Burton ◽  
Richard Betts ◽  
Manoel Cardoso ◽  
Ted R. Feldpausch ◽  
Anna Harper ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Fire Disturbance ◽  
Substantial Contribution ◽  
Surface Model ◽  
The Impact

Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

scholarly journals Influence of Land Use Land Cover on Cyclone Track Prediction – A Study During Aila Cyclone

2012 ◽  
Vol 6 (1) ◽  
pp. 33-41 ◽  
Author(s):  
K. V.S. Badarinath ◽  
D. V. Mahalakshmi ◽  
Satyaban Bishoyi Ratna
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Mesoscale Model ◽  
Surface Processes ◽  
Land Use Land Cover ◽  
Land Surface Processes ◽  
Mm5 Model ◽  
Land Cover Data ◽  
The Impact

Land-surface processes are one of the important drivers for weather and climate systems over the tropics. Realistic representation of land surface processes in mesoscale models over the region will help accurate simulation of numerical forecasts. The present study examines the influence of Land Use/ Land Cover Change (LULC) on the forecasting of cyclone intensity and track prediction using Mesoscale Model (MM5). Gridded land use/land cover data set over the Indian region compatible with the MM5 model were generated from Indian Remote Sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) for the year 2007-2008. A case study of simulation of ‘Aila’ cyclone has been considered to see the impact of these two sets of LULC data with the use of MM5 model. Results of the study indicated that incorporation of current land use/land cover data sets in mesoscale model provides better forecasting of cyclonic track.

scholarly journals Thirty Years of Land Use/Land Cover Changes and Their Impact on Urban Climate: A Study of Kano Metropolis, Nigeria

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1106
Author(s):  
Auwalu Faisal Koko ◽  
Yue Wu ◽  
Ghali Abdullahi Abubakar ◽  
Akram Ahmed Noman Alabsi ◽  
Roknisadeh Hamed ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Urban Climate ◽  
Land Use Land Cover ◽  
Spectral Indices ◽  
The Impact

Rapid urban expansion and the alteration of global land use/land cover (LULC) patterns have contributed substantially to the modification of urban climate, due to variations in Land Surface Temperature (LST). In this study, the LULC change dynamics of Kano metropolis, Nigeria, were analysed over the last three decades, i.e., 1990–2020, using multispectral satellite data to understand the impact of urbanization on LST in the study area. The Maximum Likelihood classification method and the Mono-window algorithm were utilised in classifying land uses and retrieving LST data. Spectral indices comprising the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were also computed. A linear regression analysis was employed in order to examine the correlation between land surface temperature and the various spectral indices. The results indicate significant LULC changes and urban expansion of 152.55 sq. km from 1991 to 2020. During the study period, the city’s barren land and water bodies declined by approximately 172.58 sq. km and 26.55 sq. km, respectively, while vegetation increased slightly by 46.58 sq. km. Further analysis showed a negative correlation between NDVI and LST with a Pearson determination coefficient (R2) of 0.6145, 0.5644, 0.5402, and 0.5184 in 1991, 2000, 2010, and 2020 respectively. NDBI correlated positively with LST, having an R2 of 0.4132 in 1991, 0.3965 in 2000, 0.3907 in 2010, and 0.3300 in 2020. The findings of this study provide critical climatic data useful to policy- and decision-makers in optimizing land use and mitigating the impact of urban heat through sustainable urban development.

scholarly journals DETERMINING THE IMPACTS OF LAND COVER/USE CATEGORIES ON LAND SURFACE TEMPERATURE USING LANDSAT8-OLI

2016 ◽  
Vol XLI-B8 ◽  
pp. 251-256 ◽  
Author(s):  
F. Bektas Balcik ◽  
E. M. Ergene
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Tasseled Cap ◽  
Tasseled Cap Transformation ◽  
The Impact ◽  
Use Categories

Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.

scholarly journals Quantifying the impact of land cover changes on hydrological extremes in India

2020 ◽  
Author(s):  
Shaini Naha ◽  
Miguel A. Rico-Ramirez ◽  
Rafael Rosolem
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Land Surface ◽  
Distributed Model ◽  
Model Parameters ◽  
Land Cover Changes ◽  
Area Index ◽  
Mahanadi River ◽  
The Impact

Abstract. Several research studies have addressed the effects of future climate changes on the hydrological regime of Mahanadi river basin located in eastern part of India. However, studies investigating the effects of future land cover changes on hydrology are limited owing to the lack of availability of projected land cover scenarios. Our study investigates how the hydrology of Mahanadi river basin would respond to the current and future land cover scenarios under a large-scale hydrological modelling framework. Both historical and future land cover scenarios from the recently released, Land use Harmonisation (LUH2) project for CMIP6, indicates cropland and forest are the major land cover types in the basin with a noticeable increase in the cropland (23.3 %) at the expense of forest (22.65 %) by the end of year 2100 compared to the baseline year, 2005. A physically semi-distributed model, the Variable Infiltration Capacity has been set up and implemented over the Mahanadi river basin system for the time period 1990–2010. The uncertain model parameters were subjected to Sensitivity Analysis and calibrated within a Monte Carlo framework. The best set of calibrated models obtained is used in conjunction with the harmonized set of present and future land use scenarios from LUH2 at 25 km by 25 km resolution to generate an ensemble of model simulations that captures a range of plausible impacts of land cover changes on discharge and other hydrological components of the basin. Overall, model simulation results indicate an increase in the extreme flows (i.e., 95th percentile or higher) in the range of 0.12 to 21 % at multiple subcatchments within the basin. This increase can be attributed to the direct conversion of forested areas to agriculture (on the order of 30,000 km2) that has reduced the Leaf Area Index and subsequently reduces the Evapotranspiration (ET). These changes ultimately affect other water balance components at the land surface, resulting in an increase in surface runoff and baseflow, respectively.

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