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Stochastic Modelling of Annual and Maximum Daily Rainfall Using Markov Chain Model: Case of Ivory Coast
Relwinde Abdoul-Karim Nassa,
Amani Michel Kouassi,
Lassina Konate,
Kousso Marie Esther Kouaho
Issue:
Volume 11, Issue 6, December 2022
Pages:
143-156
Received:
7 September 2022
Accepted:
9 December 2022
Published:
10 December 2022
DOI:
10.11648/j.ajep.20221106.11
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Abstract: This work aims to simulate both annual rainfall and annual extreme daily rainfall, inside homogeneous climatic zones in Ivory Coast, for two intervals of period in the future: years 2031-2060 and 2071-2100. The methodological approach is based on the Markov Chain Model. Regarding the annual maximum daily rainfall, it is found that Nash-Stucliffe values range between 94.56% (Attiean Inside zone zone) and 97.36% (Sudanese zone zone), meanwhile the Markovian model at the annual scale was very conclusive with results performances ranging from 97.15% (Baoulean zone zone) to 98.83% (Mountain’s zone zone). These values are all greater than 60% and are very close to 100%, thus reflecting a good match between the observed values and the simulated values. In other words, the observed values and the model are consistent. These very satisfactory results make it possible to certify the performance of the designed model. The predicted rainfall amounts vary between 52.95 mm (Baoulean zone) and 244.10 mm (Attiean Littoral zone) with averages ranging from 69.41 mm (Baoulean zone) to 160.78 mm (Attiean Littoral zone) for the period 2031-2060. For the period 2071-2100, the heights of simulated extreme rainfall vary between 44.46 mm (Baoulean zone) and 222.9 mm (Attiean Littoral zone) with averages ranging from 70.85 mm (Baoulean zone) and 222.9 mm (Attiean Littoral zone). The different biases between the past annual maximum daily rainfall (1931-2020) and that of the middle of the 21st century (2031-2060) and that of the end of the 21st century (2071-2100) have been calculated. These bias values for the period 2031-2060 vary from -16.56% (Attiean Inside zone) to +36.74% (Attiean Littoral zone). Those of the period 2071-2100 fluctuate between -34.13% (Mountain’s zone) and +37.89% (Attiéen du littoral). These biases are significant and reflect an increase in extreme rainfall to come. The years 2031-2060 will then experience an increase in extreme daily rainfall in the areas of Attiean Littoral zone, Sudanese zone from the middle of the 21st century (2031-2060) to the end of the 21st century (2071-2100). Annual rainfall forecast heights range between 1,003 mm (Sudanese zone) and 1,155.84 mm (Attiean Littoral zone) with averages ranging from 1240.51 mm (Sudanese zone) to 1630.21 mm (Mountain’s zone) for the period 2031-2060. For the period 2071-2100, the simulated annual rainfall amounts vary between 1,007 mm (Attiean Inside zone) and 2179.66 mm (Attiean Littoral zone) with averages ranging from 1,214.07 mm (Baoulean zone) to 1,570.35 mm (Attiéen du littoral).
Abstract: This work aims to simulate both annual rainfall and annual extreme daily rainfall, inside homogeneous climatic zones in Ivory Coast, for two intervals of period in the future: years 2031-2060 and 2071-2100. The methodological approach is based on the Markov Chain Model. Regarding the annual maximum daily rainfall, it is found that Nash-Stucliffe va...
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Adaptation Response of Farmer to Climate Change and Variability in Gibe District of Hadiya Zone, SNNPR of Ethiopia
Solomon Umer,
Alemayehu Regassa
Issue:
Volume 11, Issue 6, December 2022
Pages:
157-164
Received:
1 November 2022
Accepted:
8 December 2022
Published:
28 December 2022
DOI:
10.11648/j.ajep.20221106.12
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Abstract: Climate change and variability across human borders and borders pose the greatest environmental, social, and economic concerns in many countries. Different mechanisms exist to react to the impacts of climate change and variability, including the adaptation. This study aim was to evaluate adaptation responses of farmers' to climate change and variability. Using an interview of household, key informant interviews, and focus group discussions, the study has collected qualitative and quantitative data using a stratified random sampling technique and a multiple stage sampling procedure. As a result, 299 household respondents were selected from three kebeles to provide primary data. National meteorological agency provided secondary data of rainfall and temperature. With the help of SPSS software version 20 and Microsoft Excel, the data were analyzed using descriptive statistical and multinomial logistic methods. The result reveals that seasonal and yearly rainfalls in the area are unpredictable with a declining tendency, while temperature is dramatically rising. Crop yield is also reducing and becoming highly unstable, according to the survey result, due to the effects of climate variability and change. Furthermore, the findings revealed that the majority of farmers are aware of climate change and its effects on crop production. Changes in crop type/or and variety, as well as proper soil and water conservation, are the most important climate change adaptation responses in the area. Similarly, findings revealed that raising agricultural community awareness and knowledge, providing to implement appropriate adaptation responses, are critical requirements for reducing the negative effects of climate change and variability. To improve crop production in the study area, it is critical to create way of adaptation responses and facilities to the climate variability and change adaptation responses to the smallholder farmers.
Abstract: Climate change and variability across human borders and borders pose the greatest environmental, social, and economic concerns in many countries. Different mechanisms exist to react to the impacts of climate change and variability, including the adaptation. This study aim was to evaluate adaptation responses of farmers' to climate change and variab...
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Developing the Smart Farming Index Across Countries
Issue:
Volume 11, Issue 6, December 2022
Pages:
165-170
Received:
8 December 2022
Accepted:
21 December 2022
Published:
29 December 2022
DOI:
10.11648/j.ajep.20221106.13
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Abstract: As the current agricultural challenges, including climate change, population growth, and water availability, become more pronounced, regions that are highly dependent on agriculture are seeking new ways to track productivity in an effort to boost agricultural output. Hence, an emerging concept of data-driven agriculture, or "Smart Farming," is becoming increasingly relevant in these regions. However, due to variations in available resources and technology across countries, it is difficult to objectify the effectiveness of these methodologies. Therefore, this paper aims to evaluate the potential effectiveness of Smart Farming in countries across different regions of the world to determine which nations have the strongest potential for driving gain through the use of such technology. The potential effectiveness of Smart Farming is assessed by 1) creating and using an index from a selection of datasets that represents every nation's agricultural environment, economic status, and resources available for the application; and 2) running Principal Component Analysis (PCA) on the dataset to weigh each nation's relations to the application and determine their rankings. The top 5 nations for the applicability of Smart Farming are Iceland, New Zealand, Australia, Norway, and Finland. These countries present a viable model for other nations to follow in order to achieve sustainable growth through the adoption of data-driven farming techniques.
Abstract: As the current agricultural challenges, including climate change, population growth, and water availability, become more pronounced, regions that are highly dependent on agriculture are seeking new ways to track productivity in an effort to boost agricultural output. Hence, an emerging concept of data-driven agriculture, or "Smart Farming," is beco...
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