Statistically downscaling climate change projection of precipitation and temperature over the semi-arid Yarmouk Basin, Jordan

2021 ◽  
Vol 24 (3/4) ◽  
pp. 261
Author(s):  
Moayyad S. Shawaqfah ◽  
Fayez A. Abdulla ◽  
Alham W. Al Shurafat
Mousaion ◽  
2016 ◽  
Vol 33 (3) ◽  
pp. 1-24
Author(s):  
Emmanuel Elia ◽  
Stephen Mutula ◽  
Christine Stilwell

This study was part of broader PhD research which investigated how access to, and use of, information enhances adaptation to climate change and variability in the agricultural sector in semi-arid Central Tanzania. The research was carried out in two villages using Rogers’ Diffusion of Innovations theory and model to assess the dissemination of this information and its use by farmers in their adaptation of their farming practices to climate change and variability. This predominantly qualitative study employed a post-positivist paradigm. Some elements of a quantitative approach were also deployed in the data collection and analysis. The principal data collection methods were interviews and focus group discussions. The study population comprised farmers, agricultural extension officers and the Climate Change Adaptation in Africa project manager. Qualitative data were subjected to content analysis whereas quantitative data were analysed to generate mostly descriptive statistics using SPSS.  Key findings of the study show that farmers perceive a problem in the dissemination and use of climate information for agricultural development. They found access to agricultural inputs to be expensive, unreliable and untimely. To mitigate the adverse effects of climate change and variability on farming effectively, the study recommends the repackaging of current and accurate information on climate change and variability, farmer education and training, and collaboration between researchers, meteorology experts, and extension officers and farmers. Moreover, a clear policy framework for disseminating information related to climate change and variability is required.


2002 ◽  
Vol 33 (5) ◽  
pp. 415-424 ◽  
Author(s):  
Cintia B. Uvo ◽  
Ronny Berndtsson

Climate variability and climate change are of great concern to economists and energy producers as well as environmentalists as both affect the precipitation and temperature in many regions of the world. Among those affected by climate variability is the Scandinavian Peninsula. Particularly, its winter precipitation and temperature are affected by the variations of the so-called North Atlantic Oscillation (NAO). The objective of this paper is to analyze the spatial distribution of the influence of NAO over Scandinavia. This analysis is a first step to establishing a predictive model, driven by a climatic indicator such as NAO, for the available water resources of different regions in Scandinavia. Such a tool would be valuable for predicting potential of hydropower production one or more seasons in advance.


Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 927
Author(s):  
Jamshad Hussain ◽  
Tasneem Khaliq ◽  
Muhammad Habib ur Rahman ◽  
Asmat Ullah ◽  
Ishfaq Ahmed ◽  
...  

Rising temperature from climate change is the most threatening factor worldwide for crop production. Sustainable wheat production is a challenge due to climate change and variability, which is ultimately a serious threat to food security in Pakistan. A series of field experiments were conducted during seasons 2013–2014 and 2014–2015 in the semi-arid (Faisalabad) and arid (Layyah) regions of Punjab-Pakistan. Three spring wheat genotypes were evaluated under eleven sowing dates from 16 October to 16 March, with an interval of 14–16 days in the two regions. Data for the model calibration and evaluation were collected from field experiments following the standard procedures and protocols. The grain yield under future climate scenarios was simulated by using a well-calibrated CERES-wheat model included in DSSAT v4.7. Future (2051–2100) and baseline (1980–2015) climatic data were simulated using 29 global circulation models (GCMs) under representative concentration pathway (RCP) 8.5. These GCMs were distributed among five quadrants of climatic conditions (Hot/Wet, Hot/Dry, Cool/Dry, Cool/Wet, and Middle) by a stretched distribution approach based on temperature and rainfall change. A maximum of ten GCMs predicted the chances of Middle climatic conditions during the second half of the century (2051–2100). The average temperature during the wheat season in a semi-arid region and arid region would increase by 3.52 °C and 3.84 °C, respectively, under Middle climatic conditions using the RCP 8.5 scenario during the second half-century. The simulated grain yield was reduced by 23.5% in the semi-arid region and 35.45% in the arid region under Middle climatic conditions (scenario). Mean seasonal temperature (MST) of sowing dates ranged from 16 to 27.3 °C, while the mean temperature from the heading to maturity (MTHM) stage was varying between 12.9 to 30.4 °C. Coefficients of determination (R2) between wheat morphology parameters and temperature were highly significant, with a range of 0.84–0.96. Impacts of temperature on wheat sown on 15 March were found to be as severe as to exterminate the crop before heading. The spikes and spikelets were not formed under a mean seasonal temperature higher than 25.5 °C. In a nutshell, elevated temperature (3–4 °C) till the end-century can reduce grain yield by about 30% in semi-arid and arid regions of Pakistan. These findings are crucial for growers and especially for policymakers to decide on sustainable wheat production for food security in the region.


2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
T. Mesbahzadeh ◽  
M. M. Miglietta ◽  
M. Mirakbari ◽  
F. Soleimani Sardoo ◽  
M. Abdolhoseini

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.


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