Climate change and hydrological extreme events Risks and - - PowerPoint PPT Presentation

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Climate change and hydrological extreme events Risks and - - PowerPoint PPT Presentation

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Climate change and hydrological extreme events Risks and perspectives for water management in Bavaria and Qubec Ralf Ludwig for the ClimEx group ICTP Trieste 16 June 2017 2 ICTP Trieste 16 June 2017 Partnership Background Major

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ICTP Trieste – 16 June 2017

Climate change and hydrological extreme events

Risks and perspectives for water management in Bavaria and Québec

Ralf Ludwig for the ClimEx group

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Partnership

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Background

« Extreme precipitation events over most of the mid-latitude land masses and over wet tropical regions will very likely become more intense and more frequent. » - IPCC AR5 report

Major flood events in Québec 1996/2011/2017 Major flood events in Bavaria 1999/2002/2005/2013/2016

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Research gaps

  • Confirm knowledge on whether and how climate change

contributes to higher magnitudes and frequencies of extreme events

  • Distinguish between the effects of natural variability and a

‘ clear’ climate change signal

  • Develop a pool of methods for improved analysis of hydro-

meteorological extreme events

  • Adapt regional water resources management effectively to

the risks associated with such dynamical changes

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Proj ect Rationale

  • A practical example of variability (from a statistical approach)…

the Danube

Schulz & Bernhard, 2016

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  • Use high-resolution RCM data for an improved identification of (potential)

hydro-meteorological extreme events

95th percentile of daily precipitation in the reference period 1970-2000 (EURO-CORDEX Proj ekt, RCM HIRHAM (EUR44 und EUR11)

New Challenges

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New Challenges

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  • Downscaling of a large

scale single GCM ensemble to determine the natural variability in extreme events (and separate from climate change signal) [here: CES M1 (30) over Northern Quebec]

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  • Use high-resolution hydrological modeling to better understand the processes

and patterns leading to hydrological extreme events

New Challenges

  • The question of scale imposes

great challenges to modeling, computing … process understanding!

  • New type of study requires the

powers of High Performance Computing (HPC)

  • The example from the past vs.

the present & future…

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Proj ect structure - workflow

Module A: CLIMATE Module B: HYDRO Module Z: Information Technology / Super Computing

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Proj ect structure - workflow

RCP 8.5 CanESM2 (+ Euro‐CORDEX) CRCM5 (+ Euro‐CORDEX), 0.11° QQM (+ LS, LIS, …) SCALMET (from 12km to <1km) WaSiM (500m, 3h), (HBV)

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Climate model domains

North American Domain European Domain

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North American Domain European Domain

Climate model domains

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Hydrological model domains

Québec domain Bavarian domain

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Module A –CLIMATE

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How does it look like in a Large S cale S ingle Model Ensemble?

Natural variability?

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With 50 model members, we are practically increasing the database 50‐fold; e.g. to estimate the robustness of natural variability in the time frame of 1981‐2010, we possess not 30 but 1500 (model years) in high temporal and spatial resolution (1‐3h, 12km)  estimation of rare extreme events in a given time period becomes much more robust…

Time (days since 1950-01-01)

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First results over NE-America

50 possible future changes for PRC (in % ) between 2030-2049 and 1980-1999 over Eastern North America from CanES M2-CRCM5 at a 12-km resolution

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First results over Europe (1991-2010)

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First results over Europe (1991-2010)

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First results over Europe (1991-2010)

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  • Climate change signal and

natural variability in the S M- LS

  • ensemble for Bavaria

(1960-2100)

  • Increasing seasonal

variability in precipitation (increase of P in winter, decrease in summer)

  • Increasing daily variability
  • S

trong temperature increase

*2075-2100 currently postprocessed

First results for Bavaria (1960-2100)

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First results on a catchment scale (Rott)

Monthly mean of daily precipitation (extremes)

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Extreme precipitation (valid? )

Hourly precipitation values for summer (JJA) in the Eastern Alpine Foreland for 1995- 2014: Observations (26), MOF (39), CRCM5 (146 tiles, 12km), REMO (234 tiles, 10km)

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Module B –HYDRO

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S cales in hydrological modeling?

  • Extreme events and temporal resolution…

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  • Extreme events and temporal resolution…

S cales in hydrological modeling?

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  • Extreme events and spatio-temporal resolution…

S cales in hydrological modeling?

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  • Extreme events and spatio-temporal resolution… high flows HQ

S cales in hydrological modeling?

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Rott (Ruhstorf = 1049 km²) MQ 9.2 m³/s MHQ 124 m³/s HQ 295 m³/s Iller (Kempten = 955 km²) MQ 46 m³/s MHQ 378 m³/s HQ 884 m³/s

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First hydrological model applications

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  • First results using HBV – Ex. Rott (gauge Ruhstorf); 10 runs per box

First hydrological model applications

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  • First results using HBV – Ex. Rott (gauge Ruhstorf); snow driven events

First hydrological model applications

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  • First results using HBV – Ex. Iller (gauge Kempten)

First hydrological model applications

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  • First results using WaSiM – Ex. Iller (gauge Kempten)

First hydrological model applications

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Module Z – IT

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  • Regional Ensemble Climate modeling – CanES

M2-CRCM5

  • Data storage and archiving
  • Calibration of hydrological models (global, local)
  • Visualisation of model results

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High Performance Computing

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High Performance Computing

Visualization (Europe in May 1999; CRCM5 driven with Reanalysis data

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Thank you!

visit us at: www.climex-project.org