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Conditioning of Flow Projections under Climate Change on Hydrologic Signatures within the GLUE Framework

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2016
1416.pdf (35.72Kb)
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Todorović, Andrijana
Plavšić, Jasna
Despotović, Jovan
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Abstract
Climate change impact on water resources is generally quantified in terms of relative changes in characteristicflows (e.g. annual runoff, median annual flows, etc.) over a future period compared to the baseline one. Thesechanges are estimated under the assumed emission scenarios and with one or more modelling chains (combinationsof the Global and Regional Climate Models, and a hydrological model). Since different modelling chains yielddifferent projections, estimates of these relative changes are uncertain. High prediction uncertainty is reflected in awide 90 per cent prediction uncertainty band (90PPU) or in a distribution that resembles the uniform distribution.Therefore, research in robustness of the modelling chains has been conducted. The goal of the research is toappoint higher probabilities to the projections obtained by the more robust chains, and in that way reduce theuncertainty in flow projections under climate change.In this research, the hydrologic pr...ojections are conditioned on the hydrologic signatures within the GLUEframework. Namely, a relative change obtained with a modelling chain is assigned a likelihood depending on theperformance of the chain in terms of the hydrologic signatures over the baseline period. High flow projections (2ndpercentile of the daily flows) are conditioned on the high-segment of the flow duration curve (FDC), projectionsof the median flows are conditioned on the FDC mid-segment slope, and the projections of the low flows areconditioned on the FDC low-segment. The projections of total annual runoff are conditioned on the entire FDC.The likelihoods are quantified in terms of Nash-Sutcliffe efficiency coefficient (NSE) evaluated from the FDCs ofthe flows simulated by the modelling chains and the observed FDC.The methodology presented is applied to develop flow projections in the Kolubara River catchment in Ser-bia over the mid 21st century (2041-2070). Hydrologic projections are obtained by the HBV-light hydrologicmodel with input from five climate models (combinations of the Global and Regional Climate Models), all rununder A1B emission scenario. The outputs of the climate models are bias-corrected to reproduce distributions ofthe precipitation depths and temperatures observed in the baseline period (1961-1990). The best twenty parametersets out 25,000 sampled ones are kept in the analysis, resulting in 100 modelling chains.The GLUE conditioning did not significantly affect the median values of the projections, but only thewidth of the prediction bands. All modelling chains perform equally well in terms of the entire FDC and itsmid-segment. Therefore, the GLUE-conditioned projections of annual runoff volume and median flows are similarto the unconditioned ones (i.e. GLUE conditioning yields slightly narrower 90PPUs). Model efficiency in the highflow domain differs between the modelling chains: however, the GLUE conditioning leads to somewhat narrower90PPU. Only few modelling chains performed well in the low flow domain, therefore the width of the 90PPU wasconsiderably reduced by conditioning (from 61.5% to 36.8%)

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EGU General Assembly 2016, 2016, 18, 14398-14398
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  • Geophysical Research Abstracts
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https://hdl.handle.net/21.15107/rcub_grafar_1418
URI
https://grafar.grf.bg.ac.rs/handle/123456789/1418
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  • Radovi istraživača / Researcher's publications
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GraFar
TY  - CONF
AU  - Todorović, Andrijana
AU  - Plavšić, Jasna
AU  - Despotović, Jovan
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1418
AB  - Climate change impact on water resources is generally quantified in terms of relative changes in characteristicflows (e.g. annual runoff, median annual flows, etc.) over a future period compared to the baseline one. Thesechanges are estimated under the assumed emission scenarios and with one or more modelling chains (combinationsof the Global and Regional Climate Models, and a hydrological model). Since different modelling chains yielddifferent projections, estimates of these relative changes are uncertain. High prediction uncertainty is reflected in awide 90 per cent prediction uncertainty band (90PPU) or in a distribution that resembles the uniform distribution.Therefore,  research  in  robustness  of  the  modelling  chains  has  been  conducted.  The  goal  of  the  research  is  toappoint higher probabilities to the projections obtained by the more robust chains, and in that way reduce theuncertainty in flow projections under climate change.In  this  research,  the  hydrologic  projections  are  conditioned  on  the  hydrologic  signatures  within  the  GLUEframework. Namely, a relative change obtained with a modelling chain is assigned a likelihood depending on theperformance of the chain in terms of the hydrologic signatures over the baseline period. High flow projections (2ndpercentile of the daily flows) are conditioned on the high-segment of the flow duration curve (FDC), projectionsof the median flows are conditioned on the FDC mid-segment slope, and the projections of the low flows areconditioned on the FDC low-segment. The projections of total annual runoff are conditioned on the entire FDC.The likelihoods are quantified in terms of Nash-Sutcliffe efficiency coefficient (NSE) evaluated from the FDCs ofthe flows simulated by the modelling chains and the observed FDC.The  methodology  presented  is  applied  to  develop  flow  projections  in  the  Kolubara  River  catchment  in  Ser-bia  over  the  mid  21st  century  (2041-2070).  Hydrologic  projections  are  obtained  by  the  HBV-light  hydrologicmodel with input from five climate models (combinations of the Global and Regional Climate Models), all rununder A1B emission scenario. The outputs of the climate models are bias-corrected to reproduce distributions ofthe precipitation depths and temperatures observed in the baseline period (1961-1990). The best twenty parametersets out 25,000 sampled ones are kept in the analysis, resulting in 100 modelling chains.The  GLUE  conditioning  did  not  significantly  affect  the  median  values  of  the  projections,  but  only  thewidth  of  the  prediction  bands.  All  modelling  chains  perform  equally  well  in  terms  of  the  entire  FDC  and  itsmid-segment. Therefore, the GLUE-conditioned projections of annual runoff volume and median flows are similarto the unconditioned ones (i.e. GLUE conditioning yields slightly narrower 90PPUs). Model efficiency in the highflow domain differs between the modelling chains: however, the GLUE conditioning leads to somewhat narrower90PPU. Only few modelling chains performed well in the low flow domain, therefore the width of the 90PPU wasconsiderably reduced by conditioning (from 61.5% to 36.8%)
C3  - EGU General Assembly 2016
T1  - Conditioning of Flow Projections under Climate Change on Hydrologic Signatures within the GLUE Framework
EP  - 14398
SP  - 14398
VL  - 18
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1418
ER  - 
@conference{
author = "Todorović, Andrijana and Plavšić, Jasna and Despotović, Jovan",
year = "2016",
abstract = "Climate change impact on water resources is generally quantified in terms of relative changes in characteristicflows (e.g. annual runoff, median annual flows, etc.) over a future period compared to the baseline one. Thesechanges are estimated under the assumed emission scenarios and with one or more modelling chains (combinationsof the Global and Regional Climate Models, and a hydrological model). Since different modelling chains yielddifferent projections, estimates of these relative changes are uncertain. High prediction uncertainty is reflected in awide 90 per cent prediction uncertainty band (90PPU) or in a distribution that resembles the uniform distribution.Therefore,  research  in  robustness  of  the  modelling  chains  has  been  conducted.  The  goal  of  the  research  is  toappoint higher probabilities to the projections obtained by the more robust chains, and in that way reduce theuncertainty in flow projections under climate change.In  this  research,  the  hydrologic  projections  are  conditioned  on  the  hydrologic  signatures  within  the  GLUEframework. Namely, a relative change obtained with a modelling chain is assigned a likelihood depending on theperformance of the chain in terms of the hydrologic signatures over the baseline period. High flow projections (2ndpercentile of the daily flows) are conditioned on the high-segment of the flow duration curve (FDC), projectionsof the median flows are conditioned on the FDC mid-segment slope, and the projections of the low flows areconditioned on the FDC low-segment. The projections of total annual runoff are conditioned on the entire FDC.The likelihoods are quantified in terms of Nash-Sutcliffe efficiency coefficient (NSE) evaluated from the FDCs ofthe flows simulated by the modelling chains and the observed FDC.The  methodology  presented  is  applied  to  develop  flow  projections  in  the  Kolubara  River  catchment  in  Ser-bia  over  the  mid  21st  century  (2041-2070).  Hydrologic  projections  are  obtained  by  the  HBV-light  hydrologicmodel with input from five climate models (combinations of the Global and Regional Climate Models), all rununder A1B emission scenario. The outputs of the climate models are bias-corrected to reproduce distributions ofthe precipitation depths and temperatures observed in the baseline period (1961-1990). The best twenty parametersets out 25,000 sampled ones are kept in the analysis, resulting in 100 modelling chains.The  GLUE  conditioning  did  not  significantly  affect  the  median  values  of  the  projections,  but  only  thewidth  of  the  prediction  bands.  All  modelling  chains  perform  equally  well  in  terms  of  the  entire  FDC  and  itsmid-segment. Therefore, the GLUE-conditioned projections of annual runoff volume and median flows are similarto the unconditioned ones (i.e. GLUE conditioning yields slightly narrower 90PPUs). Model efficiency in the highflow domain differs between the modelling chains: however, the GLUE conditioning leads to somewhat narrower90PPU. Only few modelling chains performed well in the low flow domain, therefore the width of the 90PPU wasconsiderably reduced by conditioning (from 61.5% to 36.8%)",
journal = "EGU General Assembly 2016",
title = "Conditioning of Flow Projections under Climate Change on Hydrologic Signatures within the GLUE Framework",
pages = "14398-14398",
volume = "18",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1418"
}
Todorović, A., Plavšić, J.,& Despotović, J.. (2016). Conditioning of Flow Projections under Climate Change on Hydrologic Signatures within the GLUE Framework. in EGU General Assembly 2016, 18, 14398-14398.
https://hdl.handle.net/21.15107/rcub_grafar_1418
Todorović A, Plavšić J, Despotović J. Conditioning of Flow Projections under Climate Change on Hydrologic Signatures within the GLUE Framework. in EGU General Assembly 2016. 2016;18:14398-14398.
https://hdl.handle.net/21.15107/rcub_grafar_1418 .
Todorović, Andrijana, Plavšić, Jasna, Despotović, Jovan, "Conditioning of Flow Projections under Climate Change on Hydrologic Signatures within the GLUE Framework" in EGU General Assembly 2016, 18 (2016):14398-14398,
https://hdl.handle.net/21.15107/rcub_grafar_1418 .

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