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      Assessing the Impact of Long-Term ENSO, SST, and IOD Dynamics on Extreme Hydrological Events (EHEs) in the Kelani River Basin (KRB), Sri Lanka

      , , ,
      Atmosphere
      MDPI AG

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          Abstract

          Hydrological extremes are common throughout the world and can be considered a globally significant phenomenon with severe environmental and social consequences. In recent decades, especially in the second half of the 20th century, Extreme Hydrological Events (EHEs) have attracted extensive attention. Physiological and anthropogenic factors have increased the frequency and severity of hydrological extremes worldwide in the last few decades. Recently, it has become a significant environmental issue in Sri Lanka. Both floods and droughts are widespread throughout the country, and the influence of floods is becoming more common every year. Currently, the frequency and severity of EHEs in the Kelani River Basin (KRB), Sri Lanka, are very common and have increased due to climate variations. Therefore, this study focused mainly on evaluating the EHEs and the impact of long-term El Niño Southern Oscillation (ENSO), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) dynamics on extreme events. Rainfall Anomaly Index (RAI) and Extreme Precipitation Indices (EPIs) were calculated to examine the EHEs and their spatial variability. In addition, the relationships between EHEs and ENSO were investigated using several climate indices based on SST anomalies. Both observed and satellite-derived daily precipitation from 1951 to 2019 were used to assess the EHEs in the KRB. The trend of EHEs and the change points were evaluated using the Pettitt test, and teleconnection with global indices was examined using the correlation coefficient in the R application. The result of the study revealed that the pattern of EHEs varied spatially from 1951 to 2019. The strong La Niña years showed a high degree of teleconnection with EHEs in April (r = 0.622 at 0.05 significance level) and August (r = −0.732 at 0.05 significance level). NINO3.4 and the Southern Oscillation Index (SOI) have shown a significant positive impact on EHEs in the Northeast Monsoon (NEM) period. This research on KRB will be a popular scientific measure that can provide scientific results and solutions for the comprehensive decision-making process in the future. Investigating the global physical changes that influence EHEs is critical to taking the necessary steps to reduce the severity of hydrological extremes in Sri Lanka.

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          Detecting causality in complex ecosystems.

          Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.
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            Human contribution to more-intense precipitation extremes.

            Extremes of weather and climate can have devastating effects on human society and the environment. Understanding past changes in the characteristics of such events, including recent increases in the intensity of heavy precipitation events over a large part of the Northern Hemisphere land area, is critical for reliable projections of future changes. Given that atmospheric water-holding capacity is expected to increase roughly exponentially with temperature--and that atmospheric water content is increasing in accord with this theoretical expectation--it has been suggested that human-influenced global warming may be partly responsible for increases in heavy precipitation. Because of the limited availability of daily observations, however, most previous studies have examined only the potential detectability of changes in extreme precipitation through model-model comparisons. Here we show that human-induced increases in greenhouse gases have contributed to the observed intensification of heavy precipitation events found over approximately two-thirds of data-covered parts of Northern Hemisphere land areas. These results are based on a comparison of observed and multi-model simulated changes in extreme precipitation over the latter half of the twentieth century analysed with an optimal fingerprinting technique. Changes in extreme precipitation projected by models, and thus the impacts of future changes in extreme precipitation, may be underestimated because models seem to underestimate the observed increase in heavy precipitation with warming.
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              Inferring causation from time series in Earth system sciences

              The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                ATMOCZ
                Atmosphere
                Atmosphere
                MDPI AG
                2073-4433
                January 2023
                December 30 2022
                : 14
                : 1
                : 79
                Article
                10.3390/atmos14010079
                4f71b785-626b-471b-8c7d-d3a06a5a79fa
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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