scholarly journals Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1260 ◽  
Author(s):  
Khalid Alotaibi ◽  
Abdul Ghumman ◽  
Husnain Haider ◽  
Yousry Ghazaw ◽  
Md. Shafiquzzaman

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

2021 ◽  
Vol 123 ◽  
pp. 67-81
Author(s):  
Takeshi Kuramochi ◽  
Leonardo Nascimento ◽  
Mia Moisio ◽  
Michel den Elzen ◽  
Nicklas Forsell ◽  
...  

2014 ◽  
Vol 1010-1012 ◽  
pp. 2094-2101
Author(s):  
Long Xi Han ◽  
Jia Jia Zhai ◽  
Lin Zhang

The opportunities and challenges in the field of Chinese renewable energy were analyzed through the impact of global greenhouse gas (GHG) emission reduction trade, especially CDM on Chinese renewable energy, combined with the enhancement of awareness of voluntary emission reduction, relationship between emission reduction trade and renewable energy, changes in the international trade environment and the rise of the domestic trading system. It is suggested that the renewable energy industry integrates with GHG emission reduction trading system in China and explores the huge double benefit of emission reduction and income increase with market means, providing a reference for the smooth implementation of nationwide CN ETS including varies industries in the carbon trading market in the future, and striving for the speaking right for China to set the marketing price of international GHG emission reduction trading in the future.


2018 ◽  
Vol 10 (2) ◽  
pp. 134 ◽  
Author(s):  
Cesar Revoredo-Giha ◽  
Neil Chalmers ◽  
Faical Akaichi

2019 ◽  
Vol 11 (2) ◽  
pp. 497 ◽  
Author(s):  
Olimpia Neagu ◽  
Mircea Teodoru

The aim of the paper is to examine the long-term relationship between economic complexity, energy consumption structure, and greenhouse gas emission, within a panel of European Union countries and two subpanels: (i) European economies with higher economic complexity and (ii) European economies with a lower level of economic complexity. Taking into consideration the heterogeneity among European countries, the heterogeneous panel technique is used, including panel estimation through fully modified least squares (FMOLS) and dynamic ordinary least squares (DOLS). The empirical findings indicate a long-term equilibrium relationship between economic complexity, energy consumption structure and greenhouse gas emission within all three panels. Economic complexity and energy consumption structure have a statistically significant impact on greenhouse gas emission within all panels, but the influence is higher within the subpanel of countries with a lower level of economic complexity, suggesting a higher risk of pollution as the economic complexity grows and as the energy balance inclines in favor of non-renewable energy consumption. Our paper suggests that the economic complexity is a variable that must be taken into consideration when national economic and energy policies are shaped. Finally, policy implications for each panel of countries are discussed.


2013 ◽  
Vol 04 (03) ◽  
pp. 1350008 ◽  
Author(s):  
NIKOLINKA SHAKHRAMANYAN ◽  
UWE A. SCHNEIDER ◽  
BRUCE A. McCARL

Climate change may affect the use of pesticides and their associated environmental and human health impacts. This study employs and modifies a partial equilibrium model of the US agricultural sector to examine the effects of alternative regulations of the pesticide and greenhouse gas emission externality. Simulation results indicate that without pesticide externality regulations and low greenhouse gas emission mitigation strategy, climate change benefits from increased agricultural production in the US are more than offset by increased environmental costs. Although the combined regulation of pesticide and greenhouse gas emission externalities increases farmers' production costs, their net income effects are positive because of price adjustments and associated welfare shifts from consumers to producers. The results also show heterogeneous impacts on preferred pest management intensities across major crops. While pesticide externality regulations lead to substantial increases in total water use, climate policies induce the opposite effect.


2021 ◽  
Vol 9 ◽  
Author(s):  
Jun-Ming Zhang ◽  
Min-Li Song ◽  
Zhen-Jian Li ◽  
Xiang-Yong Peng ◽  
Shang Su ◽  
...  

Akebia quinata, also known as chocolate vine, is a creeping woody vine which is used as Chinese herbal medicine, and found widely distributed in East Asia. At present, its wild resources are being constantly destroyed. This study aims to provide a theoretical basis for the resource protection of this plant species by analyzing the possible changes in its geographic distribution pattern and its response to climate factors. It is the first time maximum entropy modeling (MaxEnt) and ArcGIS software have been used to predict the distribution of A. quinata in the past, the present, and the future (four greenhouse gas emission scenarios, namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Through the prediction results, the impact of climate change on the distribution of A. quinata and the response of A. quinata to climate factors were analyzed. The results showed that the most significant climatic factor affecting the distribution pattern of A. quinata was the annual precipitation. At present, the suitable distribution regions of A. quinata are mainly in the temperate zone, and a few suitable distribution regions are in the tropical zone. The medium and high suitable regions are mainly located in East Asia, accounting for 51.1 and 81.7% of the worldwide medium and high suitable regions, respectively. The migration of the geometric center of the distribution regions of A. quinata in East Asia is mainly affected by the change of distribution regions in China, and the average migration rate of the geometric center in each climate scenario is positively correlated with the level of greenhouse gas emission scenario.


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