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The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

Rainfall trend analysis is essential for managing water resources, agriculture, disaster management, and climate change research. The current study aims to examine annual rainfall variability and trend over 38 meteorological stations in Iraq during the period (1980-2021) using three tests: linear regression analysis (LRA), Mann-Kendall (MK), and the innovative trend analysis (ITA). The results of the three different tests showed that most stations recorded a decreasing trend except for four stations in ITA, six stations in MK, and seven stations in the LRA test, which exhibit a positive trend. However, three stations, Emaidyah, Rabiah, and Biji, showed an increasing trend for all three tests. The ITA test recorded more significant results (14 stations) than the other two tests. The larger significant result appeared in the significance level of 95 % (nine in LRA, eight in MK, and five in ITA). The outcomes of the three trend-detection approaches by assessing the statistical significance levels, 90 %, 95 %, and 99 %, revealed no significant trend in 16 stations dispersed throughout the various climatic zones of Iraq. Only Emadiayah station indicated a positive trend at the significance of 99 %. The overall results showed that the ITA test outperformed the MK and LRA tests since it produced more significant results. [ABSTRACT FROM AUTHOR]

Rise in mean temperature put a great deal of uncertainty about how weather and climate extremes may play out, particularly in India's varied climatic zones. Consequently, it is important to understand the possible changes in both magnitude and direction of weather and climate extremes like rainfall for different warming levels of 1.5 and 2°C scenarios concerning preindustrial and present levels. Hence in the present study, the precipitation behavior of seven North Indian states that is, Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand carefully studied using CMIP5 models. Future projections of precipitation has been done for the Paris Agreement global warming level of 1.5 and 2°C scenarios. Along with model validation and future projections of precipitation, the return period of extreme rainfall is also discussed to understand the behavior of the occurrence of extreme precipitation. Statistical analysis shows that the ensemble means have the least error as compared to the other six CMIP5 models. Therefore, future analysis has been done with the ensemble mean. Our findings show that the precipitation is likely to decrease in the 1.5°C scenarios, while it is likely to increase in the 2°C scenarios. The occurrence and intensity of extreme rainfall events are likely to be more frequent in all the models. The return period of the extreme rainfall events is likely to increase in all the states in both the warming scenarios. A three‐fold rise is likely to increase extreme rainfall events in the 2°C scenario. Key Points: Study performs a detailed analysis of rainfall behavior of the northern states India during preindustrial, present, 1.5 and 2° scenariosSix climate models were selected out of 26 CMIP5 climate models based on the capability of representing the monsoon rainfall over IndiaIn the 2°C scenario, the recurrence period for intense rainfall events will likely to increase by a factor of 3 [ABSTRACT FROM AUTHOR]

In current study impact of climate change on net revenue of rice crop in Khyber Pakhtunkhwa (KP) Pakistan was assessed by cross-sectional Ricardian method. Data was collected from 180 rice growers in different climatic zone of Khyber Pakhtunkhwa including southern, central and northern regions. Key cost components were identified by simple budgeting technique. It reveals that land rent account for 29.11% of total cost, followed by labor (26.47%), threshing (8.53%) and tractor hours (8.31%). Per acre net returns noted vary across climatic zones, with northern zone yielding the highest at Rs. 30190.76/-followed by southern (Rs. 29957.12/-) and central zone (Rs. 27340.72/-). Temporal analysis having range from 1986-2021 reveals an upward trend for temperature in all three stages across the three zones. While rainfall patterns exhibit hill shaped curve during sowing and vegetative stages and a U-shaped curve during harvesting stage. Controlled variables including tractor hours, labor days, urea, DAP, and irrigation show positive and significant correlations with net revenue. A 1% increase in these variables results net revenue increase by 0.172 %, 0.175%, 0.019%, 0.061% and 0.113%, respectively. Study revealed non-linear relationship between temperature, rainfall and net revenue. Temperature's impact on net revenue follow inverted-U shaped with a critical temperature of 31 C°, beyond which rice crop yield decrease. Rainfall's effect for net return is U-shaped with a minimum rainfall level of 41 mm for study period. It is concluded that temperature increase in southern and central zone adversely affect crop yield because average temperature (34.88 °C, 34.77 °C) already exceeds the optimal level. Based on findings study recommended mechanized farming practices, such as use of rice combine harvester, mechanical drier and automatic planters to reduce production costs. Further, optimizing the use of basic inputs and implementing nature-based measures such as developing vegetative cover to control temperature could enhance net returns. Effective information sharing among stakeholders and timely action are essential to mitigate climate change risk in study area. [ABSTRACT FROM AUTHOR]

Daily rainfall data obtained from 1025 rain gauges spread across the country over 51 years (1951-2001) are subjected to correlation analysis to identify homogeneous rainfall zones over India. In contrast to earlier studies, which were based on seasonal/annual rainfall, the present study identifies homogeneous rainfall regions with the help of seasonal [southwest monsoon (SWM) and northeast monsoon (NEM)] and annual rainfall. India is divided into 26 (20) homogeneous rainfall zones using annual and SWM (NEM) rainfall. The delineated homogeneous regions are compared and contrasted with those defined by earlier studies, employing a variety of schemes. The interseries correlations of rainfall within each zone are found to be better when the zones are identified by the present study than by other studies. The tests that are performed to evaluate coherency of zones reveal that the zones are homogeneous not only at different temporal scales (interannual and intraseasonal) but also in terms of rain amount, rain frequency, and rain type. Although the delineation of coherent zones is done using interannual/seasonal rainfall data, these zones exhibit coherency in rainfall variations at intraseasonal scale. Nevertheless, the degree of homogeneity is different for rainfall variations occurring at different temporal scales. Further, the zones show better coherency in excess rainfall years than in deficit rainfall years. Longer-term utility of the delineated zones is studied by examining delineated zones and their coherency in the first and second half of the total data period. Although the regions remain the same in both the periods, the coherency is reduced in the second half, suggesting that the homogeneity of regions may vary in the future. [ABSTRACT FROM AUTHOR]

Daily rainfall data from about 200 automatic weather stations are operationally interpolated onto a regular grid of points covering all of New Zealand at a resolution of 0.05 degrees lat/long (approximately 5 km). These data, as well as daily values for 10 other climate variables, are known as NIWA's Virtual Climate Station (VCS) data. There is a need to understand the accuracy of the daily rainfall estimates, particularly for hydrological assessments. The provision of historic daily rainfall data from more than 700 Regional Council locations (many of which are no longer operational) not used in the generation of the VCS estimates, has enabled a robust error assessment. Using a regression model approach (where the VCS rainfall is the independent variable and the Regional Council rainfall is the dependent variable), bias corrections were determined and applied to every VCS daily rainfall ≥ 1 mm. The bias-corrected VCS rainfall mean absolute error for locations below about 500 m elevation is approximately 2-4 mm (95% of the range) when rain days only (VCS rainfall ≥ 1 mm) are analysed. The error (and its spatial variation) is higher in areas above 500 m elevation, at approximately 5-15 mm. For daily VCS rainfalls > 40 mm (heavy rain days), the errors are approximately 8-12 mm (low elevation areas) and 10-40 mm (high elevation areas), and for monthly rainfall totals Journal of Hydrology (NZ) 51 (1): 25-44 2012 © New Zealand Hydrological Society (2012) the corresponding errors are approximately 10-15 mm and 10-120 mm. For some uses of daily rainfall data, the installation of good quality telemetered rain gauges is the only option. However, if gauges can't be installed or the data collection cannot be maintained long-term, then using the bias-corrected VCS rainfall data is a viable option that is often a significant improvement over using data from distant stations that may not be in the same catchment or even located in a similar climatic zone. [ABSTRACT FROM AUTHOR]