ENSO and IOD analysis on the occurrence of floods in Pakistan

Iqbal, Asif; Hassan, Syed Ahmad

doi:10.1007/s11069-017-3158-y

Cited by 22 publications

(16 citation statements)

References 51 publications

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“…Much has been written about ENSO's links with disasters (e.g. Bouma et al 1997;Dilley and Heyman 1995;Iqbal and Hassan 2018;Rodríguez-Morata et al 2018) and resulting impacts on policy (Broad, Pfaff, and Glantz 2002;Glantz 2000Glantz , 2001Glantz , 2015Tozier de la Poterie et al 2018). At the international level, for example, UNESCAP (2016) discussed the need to invest in ENSO early warning systems, emphasising the role that seasonal forecasts could play in decisionmaking.…”

Section: Discussionmentioning

confidence: 99%

Precipitation responses to ENSO and IOD in the Maldives: Implications of large-scale modes of climate variability in weather-related preparedness

Foley

Kelman

2020

International Journal of Disaster Risk Reduction

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This research seeks to address the extent to which indices of large-scale modes of climate variability (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) can be linked to physical differences in the local mean and extreme rainfall conditions experienced in the Maldives in order to suggest implications for disaster risk reduction (DRR). While some significant differences in precipitation metrics do occur at the local level between different phases of the large-scale modes of climate variability studied, they do not occur for all sites studied. While the constrained availability of historical meteorological data in the region is a limiting factor in this analysis, these findings suggest that with respect to decision-making related to extreme precipitation, ENSO/IOD forecasting may be most helpful on local scales, when supplementing an approach of on-going readiness in which communities are prepared to effectively manage hazards in any phase of the large-scale modes of climate variability studied. These conclusions are based on only precipitation; results may vary for other impacts including changing sea levels and sea surface temperatures.

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Section: Discussionmentioning

confidence: 99%

Precipitation responses to ENSO and IOD in the Maldives: Implications of large-scale modes of climate variability in weather-related preparedness

Foley

Kelman

2020

International Journal of Disaster Risk Reduction

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“…These intervals are transition probabilities or transition states. Which is a better way to understand the effect of CI on El-Nino, La-Nina and non-active (ENSO) because Markov technique provides future occurrence of events probability on behalf of current event probability without using preceding events (Akhter et al 2019;Iqbal and Hassan 2018). The three-state classification of La-Nina, El-Nino and nonactive ENSO corresponding CI is defined below as…”

Section: Formation (Transition Probability Matrices) Of Extreme Events and CI Intensitiesmentioning

confidence: 99%

“…The transient state is the expected number of steps until the chain enters a recurring class (such that X 0 = j). This approach can be used for irreducible Markov chain (Akhter et al 2019;Hassan et al 2016;Lawler 2004;Iqbal & Hassan, 2018).…”

Section: Expected Number Of Visitsmentioning

confidence: 99%

“…The variety of the sun powered cycle may shape the atmosphere occasions like El Nino, La Nino and non-active(ENSO). These occasions are caused outrageous condition like the inconstancy of precipitation, dry seasons and flood fiasco and so forth (Iqbal and Hassan 2018). As indicated by the sun oriented cycle, moderates wind field was untimely in the past in stratosphere and troposphere to the built up connect among ENSO and mathematical effect (Akhter et al 2019).…”

Section: Introductionmentioning

confidence: 98%

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El-Nino, La-Nina and Non-Active ENSO Events Relation with Solar Activity in the Perspective of Markov Process

Akhter

Abbas

Hassan

2022

Preprint

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Past investigations have been done against the sun powered atmosphere relationship and discovered huge comprehension between them. Our goal is to comprehend the sun based atmosphere progress with the assistance of Markov chain lattices. In that association we utilized solar (CI) and climate(ENSO) dataset from 1950 to 2016. All through the period atmosphere information are conveyed into El Nino, La Nino and non-active(ENSO) occasions and discovered solid connection with related CI information. The critical solid 2-dimensional correlation supported connection between El Nino and non-active(ENSO) with related CI networks (0.9865 and 0.8358) separately. So additionally, both El Nino and non-active(ENSO) with contrasting CI shows the higher number of visits in state 3 while La Nino shows a higher number of visits in state 1. Our examination shows the Markov cycle and embraced the main request Markov chain property for CI, El-Nino, La Nina and Non-active(ENSO) boundaries. The obtained results transitional probabilities of climate events with solar activity may be helpful to understand the future variation of events chances using Markov technique.

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“…A plethora of publications have been devoted to particular continents, in their entirety, or to specific regions. There have been numerous source items referring to Asia [12][13][14][15][16][17][18][19][20][21][22], North America , and Europe [44][45][46][47][48][49][50][51]. References on Australia [52][53][54][55][56], South America [57][58][59][60], and Africa [61][62][63][64] are also abundant.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Modeling of Climate Variability Impact on River Runoff

Norel

Krawiec

Kundzewicz

2021

Water

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The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere–ocean system: ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation). The global river flow reconstructions (ERA-20-CM-R) for 18 study areas on six continents and climate variability indices for the period 1901–2010 were used. The split-sample approach was applied, with the period 1901–2000 used for training and 2001–2010 used for testing. The quality measures used in this paper were mean absolute error, dynamic time warping, and top extreme events error. We demonstrated that a machine learning approach (convolution neural network, CNN) trained on climate variability indices can model the river runoff better than the long-term monthly mean baseline, both in univariate (per-cell) and multivariate (multi-cell, regionalized) settings. We compared the models to the baseline in the form of heatmaps and presented results of ablation experiments (test time ablation, i.e., jackknifing, and training time ablation), which suggested that ENSO is the primary determinant among the considered indices.

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ENSO and IOD analysis on the occurrence of floods in Pakistan

Cited by 22 publications

References 51 publications

Precipitation responses to ENSO and IOD in the Maldives: Implications of large-scale modes of climate variability in weather-related preparedness

Precipitation responses to ENSO and IOD in the Maldives: Implications of large-scale modes of climate variability in weather-related preparedness

El-Nino, La-Nina and Non-Active ENSO Events Relation with Solar Activity in the Perspective of Markov Process

Machine Learning Modeling of Climate Variability Impact on River Runoff

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