Trends in rainfall, rainy days and 24 h maximum rainfall are investigated using the Mann-Kendall non-parametric test at twenty-four sites of subtropical Assam located in the northeastern region of India. The trends are statistically confirmed by both the parametric and non-parametric methods and the magnitudes of significant trends are obtained through the linear regression test. In Assam, the average monsoon rainfall (rainy days) during the monsoon months of June to September is about 1606 mm (70), which accounts for about 70% (64%) of the annual rainfall (rainy days). On monthly time scales, sixteen and seventeen sites (twenty-one sites each) witnessed decreasing trends in the total rainfall (rainy days), out of which one and three trends (seven trends each) were found to be statistically significant in June and July, respectively. On the other hand, seventeen sites witnessed increasing trends in rainfall in the month of September, but none were statistically significant. In December (February), eighteen (twenty-two) sites witnessed decreasing (increasing) trends in total rainfall, out of which five (three) trends were statistically significant. For the rainy days during the months of November to January, twenty-two or more sites witnessed decreasing trends in Assam, but for nine (November), twelve (January) and eighteen (December) sites, these trends were statistically significant. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the well-reported climatic warming in monsoon and post-monsoon seasons may have implications for human health and water resources management over bio-diversity rich Northeast India. 相似文献
Strong and rapid greenhouse gas (GHG) emission reductions, far beyond those currently committed to, are required to meet the goals of the Paris Agreement. This allows no sector to maintain business as usual practices, while application of the precautionary principle requires avoiding a reliance on negative emission technologies. Animal to plant-sourced protein shifts offer substantial potential for GHG emission reductions. Unabated, the livestock sector could take between 37% and 49% of the GHG budget allowable under the 2°C and 1.5°C targets, respectively, by 2030. Inaction in the livestock sector would require substantial GHG reductions, far beyond what are planned or realistic, from other sectors. This outlook article outlines why animal to plant-sourced protein shifts should be taken up by the Conference of the Parties (COP), and how they could feature as part of countries’ mitigation commitments under their updated Nationally Determined Contributions (NDCs) to be adopted from 2020 onwards. The proposed framework includes an acknowledgment of ‘peak livestock’, followed by targets for large and rapid reductions in livestock numbers based on a combined ‘worst first’ and ‘best available food’ approach. Adequate support, including climate finance, is needed to facilitate countries in implementing animal to plant-sourced protein shifts.
Key policy insights
Given the livestock sector’s significant contribution to global GHG emissions and methane dominance, animal to plant protein shifts make a necessary contribution to meeting the Paris temperature goals and reducing warming in the short term, while providing a suite of co-benefits.
Without action, the livestock sector could take between 37% and 49% of the GHG budget allowable under the 2°C and 1.5°C targets, respectively, by 2030.
Failure to implement animal to plant protein shifts increases the risk of exceeding temperate goals; requires additional GHG reductions from other sectors; and increases reliance on negative emissions technologies.
COP 24 is an opportunity to bring animal to plant protein shifts to the climate mitigation table.
Revised NDCs from 2020 should include animal to plant protein shifts, starting with a declaration of ‘peak livestock’, followed by a ‘worst first’ replacement approach, guided by ‘best available food’.
In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. 相似文献