Precipitation in the Karakoram-Himalaya: a CMIP5 view

Palazzi, Elisa; Hardenberg, Jost von; Terzago, Silvia; Provenzale, Antonello

doi:10.1007/s00382-014-2341-z

Cited by 100 publications

(84 citation statements)

References 92 publications

(97 reference statements)

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“…This finding is in agreement with a number of studies targeting alpine catchments such as the Hindu Kush-Himalayan region (Palazzi et al, 2014;Lutz et al, 2016), the Pacific Northwest of the US (Jung et al, 2012), western Oregon (Chang and Jung, 2010) and the Southern Alps of New Zealand (Zammit and Figure 13. Relative contribution of the four uncertainty sources to the overall uncertainty of the seasonal runoff signal for (a) winter, (b) spring, (c) summer and (d) autumn.…”

Section: Discussionsupporting

confidence: 81%

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)

Jobst

Kingston

Cullen

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. As climate change is projected to alter both temperature and precipitation, snow-controlled mid-latitude catchments are expected to experience substantial shifts in their seasonal regime, which will have direct implications for water management. In order to provide authoritative projections of climate change impacts, the uncertainty inherent to all components of the modelling chain needs to be accounted for. This study assesses the uncertainty in potential impacts of climate change on the hydro-climate of a headwater sub-catchment of New Zealand's largest catchment (the Clutha River) using a fully distributed hydrological model (WaSiM) and unique ensemble encompassing different uncertainty sources: general circulation model (GCM), emission scenario, bias correction and snow model. The inclusion of snow models is particularly important, given that (1) they are a rarely considered aspect of uncertainty in hydrological modelling studies, and (2) snow has a considerable influence on seasonal patterns of river flow in alpine catchments such as the Clutha. Projected changes in river flow for the 2050s and 2090s encompass substantial increases in streamflow from May to October, and a decline between December and March. The dominant drivers are changes in the seasonal distribution of precipitation (for the 2090s +29 to +84 % in winter) and substantial decreases in the seasonal snow storage due to temperature increase. A quantitative comparison of uncertainty identified GCM structure as the dominant contributor in the seasonal streamflow signal (44-57 %) followed by emission scenario (16-49 %), bias correction (4-22 %) and snow model (3-10 %). While these findings suggest that the role of the snow model is comparatively small, its contribution to the overall uncertainty was still found to be noticeable for winter and summer.

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Section: Discussionsupporting

confidence: 81%

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)

Jobst

Kingston

Cullen

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…10 A large part of the GCM related uncertainty was found to be caused by the precipitation signal, which became especially uncertain during the winter season. This finding is in agreement with a number of studies targeting alpine catchments such as the Hindu-Kush-Himalayan region (Palazzi et al, 2014;Lutz et al, 2016), the Pacific Northwest of the US (Jung et al, 2012), Western Oregon (Chang and Jung, 2010) and the Southern Alps of New Zealand (Zammit and Woods, 2011). Hence constraining and accounting for the uncertainty associated with the precipitation output of GCMs and RCMs remains a major 15 research challenge in hydrological impact studies.…”

Section: Discussionsupporting

confidence: 81%

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (Clutha River, New Zealand)

Jobst¹,

Kingston²,

Cullen³

et al. 2017

Preprint

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Abstract.As climate change is projected to alter both temperature and precipitation, snow controlled mid-latitude catchments are expected to experience substantial shifts in their seasonal regime, which will have direct implications for water management. In order to provide authoritative projections of climate change impacts, the uncertainty inherent to all 10 components of the modelling chain needs to be accounted for. This study assesses the uncertainty in potential impacts of climate change on the hydro-climate of New Zealand's largest catchment (the Clutha River) using a fully distributed hydrological model (WaSiM) and unique ensemble encompassing different uncertainty sources: General Circulation Model (GCM), emission scenario, bias correction and snow model. The inclusion of snow models is particularly important, given that (1) they are a rarely considered aspect of uncertainty in hydrological modelling studies, and (2) snow has a considerable 15 influence on seasonal patterns of river flow in alpine catchments such as the Clutha. Projected changes in river flow for the 2050s and 2090s encompass substantial increases in streamflow from May to October, and a decline between December and March. The dominant drivers are changes in the seasonal distribution of precipitation (for the 2090s +25 to +76% in winter) and substantial decreases in the seasonal snow storage due to temperature increase. A quantitative comparison of uncertainty identified GCM structure as the dominant contributor in the seasonal streamflow signal (44-57%) followed by emission 20 scenario (16-49%), bias correction (4-22%) and snow model (3-10%). While these findings suggest that the role of the snow model is comparatively small, its contribution to the overall uncertainty was still found to be noticeable for winter and summer.

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“…In agreement with the general behaviour of several regional (as well as global) climate models which are known to exhibit wet biases over mountainous areas especially in winter (e.g. Kotlarski et al, 2014;Palazzi et al, 2015), also this WRF simulation overestimates precipitation and localized precipitation extremes over the Alps (Pieri et al, 2015).…”

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confidence: 54%

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology

Terzago¹,

Palazzi²,

Hardenberg³

2018

Preprint

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Abstract. Stochastic rainfall downscaling methods usually do not take into account orographic effects or local precipitation features at spatial scales finer than those resolved by the large-scale input field. For this reason they may be less reliable in areas with complex topography or with sub-grid surface heterogeneities. Here we test a simple method to introduce realistic fine-scale precipitation patterns into the downscaled fields, with the objective of producing downscaled data more suitable for climatological and hydrological applications as well as for extreme events studies. The proposed method relies on the we separately analyze areas with precipitation climatology higher or lower than the median calculated over all the points in the domain, we find that the Probability Density Function (PDF) of the real precipitation is better reproduced using the modified RainFARM rather than the standard RainFARM method. In fact, the modified method successfully assigns more precipitation to areas where precipitation is on average more abundant according to a reference long-term climatology. 20The results of the E-OBS downscaling show that the modified RainFARM introduces improvements in the representation of precipitation amplitudes. While for low-precipitation areas the downscaled and the observed PDFs are in excellent agreement, for high-precipitation areas residual differences persist, mainly related to known E-OBS deficiencies in properly representing the correct range of precipitation values in the Alpine region. The downscaling method discussed is not intended to correct the bias eventually present in the coarse-scale data, so possible biases should be adjusted before applying the downscaling 25 procedure.1 Nat. Hazards Earth Syst. Sci. Discuss., https://doi

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Precipitation in the Karakoram-Himalaya: a CMIP5 view

Cited by 100 publications

References 92 publications

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (Clutha River, New Zealand)

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology

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