Projection of Forest Vegetation Change by Applying Future Climate Change Scenario MIROC3.2 A1B

Abstract Background Several water resources projects are under planning and implementation in the Baro-Akobo basin. Currently, the planning and management of these projects is relied on historical data. So far, hardly any study has addressed water resources management and adaptation measures in the face of changing water balances due to climate change in the basin. The main bottleneck to this has been lack of future climate change scenario base data over the basin. The current study is aimed at developing future climate change scenario for the basin. To this end, Regional Climate Model (RCM) downscaled data for A1B emission scenario was employed and bias corrected at basin level using observed data. Future climate change scenario was developed using the bias corrected RCM output data with the basic objective of producing baseline data for sustainable water resources development and management in the basin. Result The projected future climate shows an increasing trend for both maximum and minimum temperatures; however, for the case of precipitation it does not manifest a systematic increasing or decreasing trend in the next century. The projected mean annual temperature increases from the baseline period by an amount of 1 °C and 3.5 °C respectively, in 2040s and 2090s. Similarly, evapotranspiration has been found to increase to an extent of 25% over the basin. The precipitation is predicted to experience a mean annual decrease of 1.8% in 2040s and an increase of 1.8% in 2090s over the basin for the A1B emission scenario. Conclusion The study resulted in a considerable future change in climatic variables (temperature, precipitation, and evapotranspiration) on the monthly and seasonal basis. These have an implication on hydrologic extremes-drought and flooding, and demands dynamic water resources management. Hence the study gives a valuable base information for water resources planning and managers, particularly for modeling reservoir inflow-climate change relations, to adapt reservoir operation rules to the real-time changing climate.

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Evaluation of long term forest fires in India with respect to state administrative boundary, forest category of LULC and future climate change scenario: A Geospatial Perspective

Forest Research Papers ◽

10.2478/frp-2018-0034 ◽

2018 ◽

Vol 79 (4) ◽

pp. 335-343

Author(s):

Firoz Ahmad ◽

Md Meraj Uddin ◽

Laxmi Goparaju

Keyword(s):

Climate Change ◽

Forest Fire ◽

Forest Fires ◽

Climate Change Scenario ◽

Fire Frequency ◽

Future Climate ◽

Future Climate Change ◽

Change Scenario ◽

Future Climate Change Scenario

Abstract Analysing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of the advanced tools that has enormous capacity to evaluate the number of data sets simultaneously and to analyse the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India’s state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship. The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percentage (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to the total country’s forest fire counts. The various LULC categories which represent the forest show some notable forest fire trends. The category ‘Deciduous Broadleaf Forest’ retain the highest fire frequency equivalent to 38.1% followed by ‘Mixed Forest’ (25.6%), ‘Evergreen Broadleaf Forest’ (16.5%), ‘Deciduous Needle leaf Forest’ (11.5%), ‘Shrub land’ (5.5%), ‘Evergreen Needle leaf Forest’ (1.5%) and ‘Plantations’ (1.2%). Monthly seasonal variation of forest fire events reveal the highest forest fire frequency percentage in the month of ‘March’ (55.4%) followed by ‘April’ (28.2%), ‘February’ (8.1%), ‘May’ (6.7%), ‘June’ (0.9%) and ‘January’ (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern; therefore, future forest fires will be more intensified in large parts of India, whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in the lower Sivalik range of Himalaya. The deciduous forests will further degrade in future. The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analysed at the country level in view of the future climate scenario. Such analysis gives insight to the policymakers to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario.

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Model and Scenario Variations in Predicted Number of Generations of Spodoptera litura Fab. on Peanut during Future Climate Change Scenario

PLoS ONE ◽

10.1371/journal.pone.0116762 ◽

2015 ◽

Vol 10 (2) ◽

pp. e0116762 ◽

Cited By ~ 12

Author(s):

Mathukumalli Srinivasa Rao ◽

Pettem Swathi ◽

Chitiprolu Anantha Rama Rao ◽

K. V. Rao ◽

B. M. K. Raju ◽

...

Keyword(s):

Climate Change ◽

Spodoptera Litura ◽

Climate Change Scenario ◽

Future Climate ◽

Future Climate Change ◽

Change Scenario ◽

Future Climate Change Scenario

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Investigation of the Peer-to-Peer energy trading performances in a local community under the future climate change scenario in Sweden

Energy Reports ◽

10.1016/j.egyr.2021.12.032 ◽

2022 ◽

Vol 8 ◽

pp. 989-1001

Author(s):

Pei Huang ◽

Marco Lovati ◽

Jingchun Shen ◽

Jiale Chai ◽

Xingxing Zhang

Keyword(s):

Climate Change ◽

Climate Change Scenario ◽

Local Community ◽

Peer To Peer ◽

Future Climate ◽

Future Climate Change ◽

Change Scenario ◽

Energy Trading ◽

The Future ◽

Future Climate Change Scenario

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Future Climate Change Scenario over Maharashtra, Western India: Implications of the Regional Climate Model (REMO-2009) for the Understanding of Agricultural Vulnerability

Pure and Applied Geophysics ◽

10.1007/s00024-020-02642-6 ◽

2021 ◽

Author(s):

Rahul S. Todmal

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Change Scenario ◽

Climate Model ◽

Regional Climate ◽

Future Climate Change ◽

Change Scenario ◽

Western India ◽

Agricultural Vulnerability ◽

Future Climate Change Scenario

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An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, Using CMIP5 Climate Models

Journal of Climate ◽

10.1175/jcli-d-16-0536.1 ◽

2017 ◽

Vol 30 (17) ◽

pp. 6701-6722 ◽

Cited By ~ 15

Author(s):

Daniel Bannister ◽

Michael Herzog ◽

Hans-F. Graf ◽

J. Scott Hosking ◽

C. Alan Short

Keyword(s):

Climate Change ◽

Sichuan Basin ◽

Climate Models ◽

Distribution Functions ◽

Future Climate ◽

First Century ◽

Future Climate Change ◽

Change Scenario ◽

The Sichuan Basin ◽

Twenty First Century

The Sichuan basin is one of the most densely populated regions of China, making the area particularly vulnerable to the adverse impacts associated with future climate change. As such, climate models are important for understanding regional and local impacts of climate change and variability, like heat stress and drought. In this study, climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are validated over the Sichuan basin by evaluating how well each model can capture the phase, amplitude, and variability of the regionally observed mean, maximum, and minimum temperature between 1979 and 2005. The results reveal that the majority of the models do not capture the basic spatial pattern and observed means, trends, and probability distribution functions. In particular, mean and minimum temperatures are underestimated, especially during the winter, resulting in biases exceeding −3°C. Models that reasonably represent the complex basin topography are found to generally have lower biases overall. The five most skillful climate models with respect to the regional climate of the Sichuan basin are selected to explore twenty-first-century temperature projections for the region. Under the CMIP5 high-emission future climate change scenario, representative concentration pathway 8.5 (RCP8.5), the temperatures are projected to increase by approximately 4°C (with an average warming rate of +0.72°C decade−1), with the greatest warming located over the central plains of the Sichuan basin, by 2100. Moreover, the frequency of extreme months (where mean temperature exceeds 28°C) is shown to increase in the twenty-first century at a faster rate compared to the twentieth century.

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