Several recent studies have shown that the El Niño–Southern Oscillation (ENSO) index has a significant influence on various climatic and hydrologic signals across the globe. This study attempts to identify the nature and strength of possible teleconnections between the Ganges River flow and ENSO, and to develop a model which can capture, at least in part, the natural variability of flow, and provide a large forecasting lead‐time. The motivation came from the fact that, in the past, hydrologic forecasts of the basin through rainfall‐runoff modelling could provide a lead‐time on the order of the basin response time, which is several days or so. Such a short forecasting lead‐time is not adequate to hedge against extreme events (flood or drought) in large river basins. This is, perhaps, the first attempt to relate flows in the Ganges with ENSO.Our analysis suggests that a significant relationship exists between the natural variability of the Ganges annual flow and ENSO index. Through further investigation, we show that the rate of change of ENSO index is also statistically related to the Ganges flow. A statistical model that combines all these indicators to forecast annual flow in the Ganges is proposed. This model uses current flow data, predicted ENSO data and its gradient to forecast flow in the Ganges with a forecasting lead‐time of 1 year. The model also provides a quantitative measure of forecasting uncertainty. A key advantage of this model is that it does not require rainfall and stream flow information from upstream areas and countries. We have used 45 years of data for model development and calibration, and 15 years of data for validation. It is encouraging to note that all four of the validation forecasts during the El Niño and La Niña events are within the 95% confidence intervals. These results demonstrate the strength of the proposed approach and suggest further exploration of this long‐range forecasting methodology for other major rivers in the world. Copyright © 2001 Royal Meteorological Society

The research contribution in this paper is twofold. First, an investigative survey on cloud computing is conducted with the main focus on gaps that is slowing down cloud adoption as well as reviewing the threat remediation challenges. Next, some thoughts are constructed on novel approaches to address some of the widely discussed attack types using machine learning techniques. Such thoughts captured through a series of experiments are expected to give researchers, cloud providers and their customers' additional insight and tools to proactively protect themselves from known or perhaps even unknown security issues that follow the same patterns.

Iraq has been experiencing water resources scarcity, and is vulnerable to climate change. Analysis of historical data revealed that the region is experiencing climate change to a degree higher than generally reported elsewhere. The relationship between climate change and its effect on water resources of a region has been sparsely addressed in published literature. To fill that gap this research work first investigates if there has been a significant change in climate in the region, which has been found to be true. In the next stage, the research projects future climatic scenarios of the region based on six oft-used General Circulation Model (GCM) ensembles, namely CCSM4, CSIRO-Mk3.6.0, GFDL-ESM2M, MEROC5, HadGEM2-ES, and IPSL-CM5A-LR. The relationship between climate change and its impact on water resources is explored through the application of the popular, widely used SWAT model. The model depicts the availability of water resources, classified separately as blue and green waters, for near and distant futures for the region. Some of the findings are foreboding and warrants urgent attention of planners and decision makers. According to model outputs, the region may experience precipitation reduction of about 12.6% and 21% in near (2049–2069) and distant (2080–2099) futures, respectively under RCP8.5. Those figures under RCP4.5 are 15% and 23.4%, respectively and under RCP2.6 are 12.2% and 18.4%, respectively. As a consequence, the blue water may experience decreases of about 22.6% and 40% under RCP8.5, 25.8% and 46% under RCP4.5, and 34.4% and 31% under RCP2.6 during the periods 2049–2069 and 2080–2099, respectively. Green water, by contrast, may reduce by about 10.6% and 19.6% under RCP8.5, by about 14.8% and 19.4% under RCP4.5, and by about 15.8% and 14.2% under RCP2.6 during the periods 2049–2069 and 2080–2099, respectively. The research further investigates how the population are adapting to already changed climates and how they are expected to cope in the future when the shift in climate is expected to be much greater.