Generating long-term high-resolution land-use change datasets for regional climate modeling in CORDEX domains

2021 ◽  
Author(s):  
Peter Hoffmann ◽  
Diana Rechid ◽  
Vanessa Reinhart ◽  
Christina Asmus ◽  
Edouard L. Davin ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Land Cover ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Local Scale ◽  
Modeling Studies

<p>Land-use and land cover (LULC) are continuously changing due to environmental changes and anthropogenic activities. Many observational and modeling studies show that LULC changes are important drivers altering land surface feedbacks and land-atmosphere exchange processes that have substantial impact on climate on the regional and local scale. Yet, most long-term regional climate modeling studies do not account for these changes. Therefore, within the WCRP CORDEX Flagship Pilot Study LUCAS (Land Use Change Across Scales) a new workflow was developed to generate high-resolution annual land cover change time series based on past reconstructions and future projections. First, the high-resolution global land cover dataset ESA-CCI LC (~300 m resolution) is aggregated and converted to a 0.1° resolution, fractional plant functional type (PFT) dataset. Second, the land use change information from the land-use harmonized dataset (LUH2), provided at 0.25° resolution as input for CMIP6 experiments, is translated into PFT changes employing a newly developed land use translator (LUT). The new LUT was first applied to the EURO-CORDEX domain. The resulting LULC maps for past and future - the LUCAS LUC dataset - can be applied as land use forcing to the next generation RCM simulations for downscaling CMIP6 by the EURO-CORDEX community and in the framework of FPS LUCAS. The dataset includes land cover and land management practices changes important for the regional and local scale such as urbanization and irrigation. The LUCAS LUC workflow is applied to further CORDEX domains, such as Australasia and North America. The resulting past and future land cover changes will be presented, and challenges regarding the application of the new workflow to different regions will be addressed. In addition, issues related to the implementation of the dataset into different RCMs will be discussed.</p>

scholarly journals Land Use Change in Central Florida and Sensitivity Analysis Based on Agriculture to Urban Extreme Conversion

2012 ◽  
Vol 4 (3) ◽  
pp. 200-211 ◽  
Author(s):  
José L. Hernández ◽  
Syewoon Hwang ◽  
Francisco Escobedo ◽  
April H. Davis ◽  
James W. Jones
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Areas ◽  
Climate Modeling ◽  
Regional Climate ◽  
Weather Conditions ◽  
Regional Climate Modeling ◽  
Land Uses ◽  
Central Florida ◽  
Rural And Urban

Abstract This paper explored recent land use and land cover change in western central Florida, examining both socioeconomic and biophysical influences on land transformation and the impacts of that change. Between 1995 and 2006, a growth in population resulted in the conversion of agricultural areas, grasslands, and upland forests to urban areas. Additionally, the amount of extractive land uses (e.g., mining) increased by 21.8%, water reservoirs by 19.9%, and recreation areas by 13.3%. Regional climate modeling experiments suggest that the overall effects of land use change (LUC) on mesocale climates in summer days resulted in modified temperatures that were modulated by the new LU characteristics, local and synoptic atmospheric circulations, and the distance of rural and urban land uses from the shoreline. The difference between the extreme and actual LU simulations for temperature, wind speed, wind direction, and precipitation presented higher variability in the inland urbanized and rural zones. Results can be used to better understand the basic influences of LUC and urbanization on key climate parameters, and urban heat island effects in peninsular Florida under typical weather conditions.

scholarly journals High-resolution land-use land-cover change data for regional climate modelling applications over Europe – Part 2: Historical and future changes

2021 ◽  
Author(s):  
Peter Hoffmann ◽  
Vanessa Reinhart ◽  
Diana Rechid ◽  
Nathalie de Noblet-Ducoudré ◽  
Edouard L. Davin ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Environmental Changes ◽  
Regional Climate ◽  
Climate Modelling ◽  
Historical Changes ◽  
The Impact

Abstract. Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS – "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC Version 1.0 at 0.1° resolution for Europe Hoffmann et al. (2021b,c). The plant functional type distribution for the year 2015 (i.e. LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method based on a cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (submitted). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive realistic LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to remotely-sensed PFT time series. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the next generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

Spatiotemporal Pattern Analysis of Rapid Urban Expansion Using GIS and Remote Sensing

2010 ◽  
Vol 1 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Hyun Joong Kim
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Urban Expansion ◽  
Local Scale ◽  
Spatiotemporal Pattern ◽  
Land Use Land Cover ◽  
Modeling Studies

Rapidly growing urban areas tend to reveal distinctive spatial and temporal variations of land use/land cover in a locally urbanized environment. In this article, the author analyzes urban growth phenomena at a local scale by employing Geographic Information Systems, remotely sensed image data from 1984, 1994, and 2004, and landscape shape index. Since spatial patterns of land use/land cover changes in small urban areas are not fully examined by the current GIS-based modeling studies or simulation applications, the major objective of this research is to identify and examine the spatial and temporal dynamics of land use changes of urban growth at a local scale. Analytical results demonstrate that sizes, locations, and shapes of new developments are spatio-temporally associated with their landscape variations and major transportation arteries. The key findings from this study contribute to GIS-based urban growth modeling studies and urban planning practices for local communities.

scholarly journals Helping to Make Sense of Regional Climate Modeling: Professional Development for Scientists and Decision-Makers Anytime, Anywhere

2016 ◽  
Vol 97 (7) ◽  
pp. 1173-1185 ◽  
Author(s):  
Peter J. Walton ◽  
Morgan B. Yarker ◽  
Michel D. S. Mesquita ◽  
Friederike E. L. Otto
Keyword(s):  
Professional Development ◽  
High Resolution ◽  
Climate Models ◽  
Online Courses ◽  
Climate Modeling ◽  
Regional Climate ◽  
Climate Science ◽  
Regional Climate Modeling ◽  
Decision Makers ◽  
E Learning

Abstract Globally, decision-makers are increasingly using high-resolution climate models to support policy and planning; however, many of these users do not have the knowledge needed to use them appropriately. This problem is compounded by not having access to quality learning opportunities to better understand how to apply the models and interpret results. This paper discusses and proposes an educational framework based on two independent online courses on regional climate modeling, which addresses the accessibility issue and provides guidance to climate science professors, researchers, and institutions who want to create their own online courses. The role of e-learning as an educational tool is well documented, highlighting the benefits of improved personal efficiency through “anywhere, anytime” learning with the flexibility to support professional development across different sectors. In addition, improved global Internet means increased accessibility. However, e-learning’s function as a tool to support understanding of atmospheric physics and high-resolution climate modeling has not been widely discussed. To date, few courses, if any, support understanding that takes full advantage of e-learning best practices. There is a growing need for climate literacy to help inform decision-making on a range of scales, from individual households to corporate CEOs. And while there is a plethora of climate information online, educational theory suggests that people need to be guided in how to convert this information into applicable knowledge. Here, we present how the experience of the courses we designed and ran independent of each other, both engaging learners with better understanding benefits and limitations of regional climate modeling, lead to a framework of designing e-learning for climate modeling.

Impact of land cover characterization on regional climate modeling over West Africa

2015 ◽  
Vol 46 (1-2) ◽  
pp. 637-650 ◽  
Author(s):  
Mouhamadou Bamba Sylla ◽  
Jeremy S. Pal ◽  
Guiling L. Wang ◽  
Peter J. Lawrence
Keyword(s):  
Land Cover ◽  
West Africa ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling

scholarly journals Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

2020 ◽  
Vol 12 (24) ◽  
pp. 4048
Author(s):  
Yrneh Ulloa-Torrealba ◽  
Reinhold Stahlmann ◽  
Martin Wegmann ◽  
Thomas Koellner
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Historical Maps ◽  
River Catchment ◽  
Main River ◽  
Object Based

The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

Changes in climate extremes over Bangladesh at 1.5 °C, 2 °C, and 4 °C of global warming with high-resolution regional climate modeling

2020 ◽  
Vol 140 (3-4) ◽  
pp. 1451-1466 ◽  
Author(s):  
Md Jamal Uddin Khan ◽  
A. K. M. Saiful Islam ◽  
Sujit Kumar Bala ◽  
G. M. Tarekul Islam
Keyword(s):  
Global Warming ◽  
High Resolution ◽  
Climate Modeling ◽  
Regional Climate ◽  
Climate Extremes ◽  
Regional Climate Modeling

scholarly journals Impacts of long-term disturbance on soil health with relation to vegetation distribution in a tropical ecosystem

2020 ◽  
Vol 2 (4) ◽  
pp. 149-156
Author(s):  
C. N. Basweti ◽  
◽  
S. Otor ◽  
S. Manohar ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Soil Health ◽  
Integrated Approach ◽  
Soil Productivity ◽  
Land Cover Changes ◽  
Vegetation Distribution ◽  
Grassland Soil

Land-use and land-cover changes are the main cause of soil degradation and associated human and environmental problems. The study was conducted in Mai Mahiu ecosystem, Kenya whose aim was to assess long-term (1985 to 2015) impacts of land-use and land-cover changes on soil health with disturbance-induced vegetation distribution. Landsat archive was utilized to detect land-use change for 30 years at an interval of 15 years and analysed based on supervised image classification. Four land-use practices (undisturbed forest, disturbed forest, cropland and grassland) were selected and soil sampled to 15 cm depth for soil analyses. In this period, cropland increased by 135% at the expense of natural forest while built-up areas increased by three times. Soil bulk density increased significantly (p<0.001) from 0.93±0.02 g cm-3 in forest soil to 1.27±0.02 g cm-3 in disturbed grassland. Soil pH had significant change (p=0.002) that ranged between 6.19±0.14 and 7.18±0.12. Soil organic carbon declined significantly (p=0.008) with land-use change with losses of up to 63% recorded in disturbed grassland. Total nitrogen levels declined from 0.34% in the forest to 0.15% in disturbed grassland soil. The pronounced changes in land-use and land-cover in Mai Mahiu have negatively affected the soil health with a potential drop in soil productivity and ecosystem provisioning. An integrated approach, enforcement of relevant laws and policy implementation are recommended to restoring and maintaining soil quality of this ecosystem.

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