Application of Multi-Index Decision Analysis to Management Scenarios Considering Climate Change Prediction in the Zayandeh Rud River Basin

Javadinejad, Safieh; Ostad‐Ali‐Askari, Kaveh; Eslamian, Saeid

doi:10.1007/s41101-019-00068-3

Cited by 50 publications

(32 citation statements)

References 39 publications

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“…The dangers of drought and climate change have the potential to turn into disastrous and devastating powers for human communities in the absence of risk reduction systems [33]. Living among natural hazards does not necessarily mean damage and vulnerability, but the lack of resilience and the amount of knowledge and perception of a population independent of the degree of the type and the risk of causing damage [22,23]. This is why world-class changes in risk perceptions are evolving so that the dominant approach has been to deconstruct and reduce vulnerability to increased resilience to disasters [24,45].…”

Section: Introductionmentioning

confidence: 99%

“…Resilience is one of the most important factors in the realization of sustainability. The introduction of the concept of resilience to disaster management issues was raised since 2005, and gradually became more important in both the theoretical and practical aspects of reducing the risks of accidents [22,23]. In recent years, concepts such as resilient societies and resilient livelihoods are commonly used in scientific studies.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Prioritization the Effective Factors on Increasing Farmers Resilience Under Climate Change and Drought

2020

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California is severely exposed to drought and damage due to the climate change and drought belt, which has a major impact on agriculture. So, after the drought crisis, there are various reactions from farmers. The extent of the damage caused by the socioeconomic, environment and the extent of the resistance of farmers to this crisis is manifested in a variety of ways. Recognizing the population’s resilience and the involved human groups is a tool for preventing a catastrophe-based increase in life-threatening areas in high-risk areas. Sometimes the inability to manage this phenomenon (especially under the climate change) leads to farmers’ desertification and agricultural land release, which itself indicates a low level of resilience and resilience to the crisis. The recent drought under the climate change condition in California and the severity of the damage sustained by farmers continue to be vulnerable. The present study seeks to prioritize and prioritize resilience of farmers to the crisis under the climate change. This study simulated drought condition with using PDSI value for current and future time period. In order to calculate PDSI values, the climatic parameters extracted from CMIP5 models and downscaled under the scenario of RCP 8.5. Also in order to understand the resilience of the agriculture activities under the climate change, this study was performed using statistical tests and data from the questionnaire completed in the statistical population of 320 farmers in the Tulare region in California. The findings of the research by t test showed that the average level of effective factors in increasing the resilience of farmers in the region is low. This is particularly significant in relation to the factors affecting government policies and support. So that only the mean of five variables is higher than the numerical desirability of the test and the other 15 variables do not have a suitable status for increasing the resilience of the farmers. Also, the results of the Vikor model showed that most of the impact on their resilience to drought and climate change was the development of agricultural insurance, the second important impact belongs to drought monitoring system, climate change and damage assessment, and variable of attention to knowledge is in third place of the important factor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Prioritization the Effective Factors on Increasing Farmers Resilience Under Climate Change and Drought

2020

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“…The incidence and severity of heat waves in parts of the world have increased in recent years [26][27][28] . The French heat wave in the summer of 2003, as well as the heat of the summer of 2010, covered a large part of the northern hemisphere such as Russia, Kazakhstan, Iran, China, Africa and parts of Europe [29,30] . The occurrence of drought and fire in the forests of western Russia and the unprecedented flood event in widespread parts of Pakistan can be cited as the most significant consequences of a severe heat wave in 2010 [31,32] .…”

Section: Introductionmentioning

confidence: 99%

Investigation of the effect of climate change on heat waves

Javadinejad

Dara

Jafary

2020

Resour Environ Inf Eng

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The purpose of this research is to identify the heat waves of the South Sea of Iran and compare the conditions in the present and future. To reach this goal, the average daily temperature of 35 years has been used. Also, in order to predict future heat waves, the maximum temperature data of four models of the CMIP5 model series, according to the RCP 8.5 scenario, has been used for the period 2040-2074. In order to reverse the output of the climatic models, artificial neural networks were used to identify the thermal waves, and the Fumiaki index was used to determine the thermal waves. Using the programming in MATLAB software, the days when their temperature exceeded 2 standard deviations as a thermal wave were identified. The results of the research show that the short-term heat waves are more likely to occur. Heat waves in the base period have a significant but poorly developed trend, so that the frequency has increased in recent years. In the period from 2040 to 2074, the frequency of thermal waves has a significant decreasing trend, but usually with low coefficients. However, for some stations from 2040 to 2074, the frequency of predicted heat waves increased.

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“…Accurate assessments of groundwater levels allow water directors, engineers, and stakeholders to improve policies designed to prevent or decrease detrimental impacts, e.g., a pumping deficit in water wells, land surface collapse, aquifer compression, and poor water quality (Prinos et al, 2002). Furthermore, these evaluations, along with predictive modelling, are beneficial in developing a better understanding of the dynamics and underlying factors that affect groundwater (Javadinejad et al, 2019b). This understanding can help to balance the needs of urban, agricultural, and industrial water uses, and to trade off profits and prices of water protection (Adamowski and Fung Chan, 2011;Moosavi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Modelling groundwater level fluctuation in an Indian coastal aquifer

Javadinejad

Dara

Jafary

2020

WSA

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Estimating groundwater level (GWL) fluctuations is a vital requirement in hydrology and hydraulic engineering, and is commonly addressed through artificial intelligence (AI) models. The purpose of this research was to estimate groundwater levels using new modelling methods. The implementation of two separate soft computing techniques, a multilayer perceptron neural network (MLPNN) and an M5 model tree (M5-MT), was examined. The models are used in the estimation of monthly GWLs observed in a shallow unconfined coastal aquifer. Data for the water level were collected from observation wells located near Ganjimatta, India, and used to estimate GWL fluctuation. To do this, two scenarios were provided to achieve optimal input variables for modelling the GWL at the present time. The input parameters applied for developing the proposed models were a monthly time-series of summed rainfall, the mean temperature (within its lag times that have an effect on groundwater), and historical GWL observations throughout the period 1996–2006. The efficiency of each proposed model for Ganjimatt was investigated in stages of trial and error. A performance evaluation showed that the M5-MT outperformed the MLPNN model in estimating the GWL in the aquifer case study. Based on the M5-MT approach, the development of this model gives acceptable results for the Indian coastal aquifers. It is recommended that water managers and decision makers apply these new methods to monitor groundwater conditions and inform future planning.

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Application of Multi-Index Decision Analysis to Management Scenarios Considering Climate Change Prediction in the Zayandeh Rud River Basin

Cited by 50 publications

References 39 publications

Analysis and Prioritization the Effective Factors on Increasing Farmers Resilience Under Climate Change and Drought

Analysis and Prioritization the Effective Factors on Increasing Farmers Resilience Under Climate Change and Drought

Investigation of the effect of climate change on heat waves

Modelling groundwater level fluctuation in an Indian coastal aquifer

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