Interannual variability of winter precipitation over northwest India and adjoining region: impact of global forcings

Kar, S. C.; Rana, Sapna

doi:10.1007/s00704-013-0968-z

Cited by 63 publications

(47 citation statements)

References 23 publications

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“…It has a direct influence on the interannual variability of the westerly winds (westerly disturbances) and their water content traversing Europe, the Mediterranean and the Middle East region into the mountains of the UIB (Yadav et al, 2009a;Syed et al, 2010;Filippi et al, 2014). Indices of the NAO have been linked with UIB weather station winter precipitation (Archer and Fowler, 2004;Afzal et al, 2013;Filippi et al, 2014), western Indus Basin's winter snow cover and weather station precipitation (Hasson et al, 2014), Pakistan weather station temperature (del Río et al, 2013) and winter precipitation in north-west India (Kar and Rana, 2014).…”

Section: Climate Influencesmentioning

confidence: 99%

“…The commonly used SOI (Southern Oscillation Index) has been linked with winter Hindu Kush Himalayan region precipitation (Afzal et al, 2013), Indian Summer Monsoon precipitation (Ashok et al, 2004;Ashok and Saji, 2007), central southwest Asian winter precipitation (Syed et al, 2006), Pakistan weather station temperature (del Río et al, 2013) and north-west India winter precipitation (Kar and Rana, 2014). Stronger links have been reported between ENSO, western disturbances and interannual winter precipitation variability in recent decades (Yadav et al, 2009a, b).…”

Section: Climate Influencesmentioning

confidence: 99%

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Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Charles

Wang

Ahmad

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (AprilSeptember) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April-September), early Kharif (April-June) and late Kharif (July-September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

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Section: Climate Influencesmentioning

confidence: 99%

Section: Climate Influencesmentioning

confidence: 99%

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Charles

Wang

Ahmad

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…It has a direct influence on the interannual variability of the westerly winds (westerly disturbances) and their water content traversing Europe, the Mediterranean and the Middle East region into the mountains of the UIB (Yadav et al, 2009a;Syed et al, 2010;Filippi et al, 2014). Indices of the NAO have been related to: UIB station winter precipitation (Archer and Fowler, 2004;Afzal et al, 2013;Filippi et al, 2014); western Indus basin's winter snow cover and station precipitation 20 (Hasson et al, 2014); Pakistan station temperature (del Río et al, 2013); and winter precipitation in northwest India (Kar and Rana, 2014).…”

Section: Climate Influencesmentioning

confidence: 99%

“…Himalayan region precipitation (Afzal et al, 2013); Indian Summer Monsoon Precipitation (Ashok et al, 2004;Ashok and Saji, 2007); central southwest Asian winter precipitation (Syed et al, 2006); Pakistan station temperature (del Río et al, 2013); and northwest India winter precipitation (Kar and Rana, 2014). Stronger links have been reported between ENSO, western disturbances and interannual winter precipitation variability for recent decades (Yadav et al, 2009a;Yadav et al, 2009b).…”

Section: Climate Influencesmentioning

confidence: 99%

Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

Charles

Wang

Ahmad

et al. 2017

Preprint

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Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the 15 BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April-September) streamflow forecasts for inflow to Pakistan's two largest Upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESP) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM 20 for this location. Cross validation of the streamflow forecasts show that the BJP approach using two predictors (March flow and an ENSO climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April-September), early Kharif (April-June) and late Kharif (July-September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early 25Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.Hydrol. Earth Syst. Sci. Discuss., https://doi

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“…In the northern and northwestern parts of India, the winters during El Niño events tend to be wet and cold, and La Niña winters tend to be warm and dry (Yadav et al 2009(Yadav et al , 2010Kar and Rana 2013). The wintertime temperature variability over this region has been relatively less explored because of heterogeneity in terms of topography, surface characteristics and variability of weather and climate conditions.…”

Section: Introductionmentioning

confidence: 99%

Seasonal prediction skill of winter temperature over North India

Tiwari

Kar

Mohanty

et al. 2015

Theor Appl Climatol

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The climatology, amplitude error, phase error and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art General Circulation Models (GCMs) have been examined for the winter (December -January-February) seasons over the North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from

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Interannual variability of winter precipitation over northwest India and adjoining region: impact of global forcings

Cited by 63 publications

References 23 publications

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

Seasonal prediction skill of winter temperature over North India

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