Hydrological Perspective of Climate Change Impact
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Hydrological Perspective of Climate Change Impact

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Hydrological Perspective of Climate Change Impact




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Presentation on theme: "Hydrological Perspective of Climate Change Impact"— Presentation transcript:

Slide1

Hydrological Perspective of Climate Change Impact Assessment

Professor Ke-Sheng ChengDept. of Bioenvironmental Systems EngineeringNational Taiwan University

Distinguished Lecture - Hydrological Sciences Section

Slide2

The scale issue of climate change studiesAn example of climate change impact assessment focusing on changes in design storms.

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Outline

Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide3

Climate changes have had profound impacts on climate and weather of our lives.The impacts of climate change vary with the scales of interest.

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The scale issue

Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide4

As scientists, we can assess the impacts of climate changes on all scales of variables of interest. However, practical actions for coping with climate changes are almost exclusively implemented in country and regional/local scales.Although hydrologists and climatologists may conduct studies in similar scales, there are also scales which are of unique interests to hydrologists.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide5

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Climatological

Hydrological

Scales for flood risk assessment

Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide6

Climatologists focus on climate-scale changes.Changes in annual or long-term average rainfalls of global to regional scales.Hydrologist are more concerned about the impacts of climate change on hydrological extremes such as floods and droughts.Such hydrological extremes are results of extreme weather events.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Studies related to climate changes usually involve multiple disciplines.Terminologies commonly used by one discipline may not be familiar to other disciplines and, in some cases, terminologies actually cause misunderstandings or misinterpretations of the research results.Effective and good communications are important in disseminating research outputs.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Climatologists focus on climate-scale changes.Changes in annual or long-term average rainfalls of global to regional scales.Impact of Climate Change on River Discharge Projected by Multimodel Ensemble (Nohara et al., 2006, Journal of Hydrometeorology)At the end of the twenty-first century, the annual mean precipitation, evaporation, and runoff increase in high latitudes of the Northern Hemisphere, southern to eastern Asia, and central Africa.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Future changes in precipitation and impacts on extreme streamflow over Amazonian sub-basins (Guimberteau et al., 2013, Environ. Res. Lett.)Hydrological annual extreme variations (i.e. low/high flows) associated with precipitation (and evapo-transpiration) changes are investigated over the Amazon River sub-basins.Evaluating changes in mean annual flow (MAF), high flow (highest decile of MAF), low flow (lowest decile of MAF) over the 1980 – 2000 period and two periods of the 21st century.

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Mean annual flow is the

average daily flow for the individual year or multi-year period of interest. [http://streamflow.engr.oregonstate.edu/analysis/annual/]

This study investigated changes in hydrological extremes which were associated with an annual resolution.

Department of Bioenvironmental Systems Engineering, National Taiwan University

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Temperature dependence of global precipitation extremes (Liu et al., 2009, Geophysical Research Letters)For Taiwan, the top 10% heaviest rain increases by about 140% for each degree increase in global temperature.The top 10% bin rainfall intensity was defined as 13 mm/hr which was calculated based on long-term average daily rainfall intensities.The above climatological rainfall extreme is much lower than the 79 mm design rainfalls (for 90-minute duration and 5-year return period) of the Taipei City.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Example

Contours of the 100-year return period daily rainfall depth based on observed data and high-resolution downscaled rainfalls.

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Based on site observations

Based on high-resolution downscaled rainfalls.

Contours exhibit higher degree of spatial continuity.

(A)

(B)

Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide12

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C

limate change impact assessment focusing on changes in design storms in Taiwan

Cheng, K.S., Lin, G.F., Chen, M.J., Wu, Y.C, Wu, M.F.Hydrotech Research Institute, NTU

Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide13

In assessing the impact of climate change, hydrologists often are interested in changes in rainfall extremes, such as rainfall depths of high return periods (i.e., design storms such as rainfall depth of 24-hour, 100-year).Such rainfall extremes are results of extreme weather events which are characteristic of relatively small spatial and temporal scales and cannot be resolved by GCMs.

Scale mismatch in climate projection and hydrological projection

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Design rainfall depths

For example, 24-hr, 100-year rainfall depth

Characteristics of extreme storm events

From GCM outputs to design storm depths – a problem of scale mismatch (both temporal and spatial)

24 GCMs

Projections in coarse spatial and time scales.

(200 – 300 km; monthly)

RCM

Projections in finer spatial scale

.(5km; monthly)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Rainfall extremes represent quantities of high percentiles.Predicting extreme values is far more difficult than predicting the means.We may have reasonable confidence on climate projections (for example, long-term average seasonal rainfalls), whereas our confidence on extreme weather projections is generally low.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Characteristics of storm eventsNumber of storm eventsDuration of a storm eventTotal rainfall depthTime variation of rainfall intensitiesThese characteristics are random in nature and can be described by certain probability distributions.Although the realized values of these storm characteristics of individual storm events represent weather observations, their probability distributions are climate (long term and ensemble) properties.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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A GCM – stochastic model integrated approach Climatological projection by GCMsChanges in the means of storm characteristicsFor examples, Average number of typhoons per year Average duration of typhoonsAverage event-total rainfall of typhoonsHydrological projection by a stochastic storm rainfall simulation modelGenerating realizations of storm rainfall process using storm characteristics which are representative of the projection period.Preserving statistical properties of the all storm characteristics.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Characteristics of storm events

1 Number of storm events

Onset of storm occurrences Duration of a storm event Total rainfall depth Time variation of rainfall intensity

Design rainfall depthsFor example, 24-hr, 100-year rainfall depthCharacteristics of extreme storm events

Weather Generator

(Richardson type)

Projections in finer time scale.(5km; daily)

ANN

Stochastic storm rainfall simulation

Projections in point (spatial) and hourly (time) scales.

Conceptual flowchart

24 GCMs

Projections in coarse spatial and time scales.

(200 – 300 km; monthly)

RCM

Projections in finer spatial scale

.

(5km; monthly)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Emission scenario: A1BBaseline period: 1980 – 1999Projection periodNear future: 2020 – 2039End of century: 2080 – 2099GCM model: 24 GCMs statistical downscalingHydrological scenario: changes in storm characteristics

Climate change scenarios andGCM outputs

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide20

Near future (2020 – 2039)

Near future (2080 – 2099)

Changes in monthly rainfalls (Statistical downscaling, Ensemble average with standard deviation adjustment)

Taipei area

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Annual counts of storm events estimated by ANN

Maiyu

Typhoon

Convective

Frontal

South

Center

North

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide22

Gauge observations

MRI (1979 - 2003)

MRI (2015 – 2039)

MRI (2075 - 2099)

Storm characteristics (average duration of typhoon)

Source:

NCDR, Taiwan

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide23

Gauge observations

MRI (1979 - 2003)

MRI (2015 – 2039)

MRI (2075 - 2099)

Storm characteristics (average event-total rainfalls of typhoon)

Source:

NCDR, Taiwan

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Time(hr)

Rainrate

Duration

Total depth

Inter-arrival time

Duration

Duration

Duration

Inter-arrival time

Storm characteristics

Duration

Event-total depth

Inter-arrival(or inter-event) time

Time variation of rain-rates

Stochastic storm rainfall process

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Season-specific storm characteristics

Rainfalls (mm)

Frontal

Convective, Typhoon

Frontal

Mei-Yu

Jan- April

May - June

July - October

Nov - Dec

Storm type

Period

Frontal

Nov - April

Mei-Yu

May - June

Convective

July - October

Typhoon

July - October

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Simulating occurrences of storms and their rainfall ratesPreserving seasonal variation and temporal autocorrelation of rainfall process.Duration and event-total depthInter-event timesPercentage of total rainfalls in individual intervals (Storm hyetographs)

Stochastic Storm Rainfall Simulation Model (SSRSM)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide27

Simulating occurrences of storm events of various storm typesNumber of events per yearPoisson distribution for typhoon and Mei-YuInter-event timeGamma or log-normal distributions

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide28

Simulating joint distribution of duration and event-total depthBivariate gamma distribution (e.g. typhoons)Log-normal-Gamma bivariateNon-Gaussian bivariate distribution was transformed to a corresponding bivariate standard normal distribution with desired correlation matrix.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide29

Bivariate gamma (X,Y)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Simulating percentages of total rainfalls in individual intervals (Simulation of storm hyetographs)Based on the simple scaling propertyDurations of all events of the same storm types are divided into a fixed number of intervals (e.g. 24 intervals).For a specific interval, rainfall percentages of different events are identically and independently distributed (IID).Rainfall percentages of adjacent intervals are correlated.The simple scaling leads to the Horner equation fitting of the IDF curves.

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Simple scaling (Random fractal)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide34

Modeling the storm hyetograph

Probability density of

x

(15)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Taking all the above properties into account, we propose to model the dimensionless hyetograph by a

truncated gamma Markov process

.

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Truncated gamma density (parameters estimation

, including the truncation level

)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Effect of modeling truncated data with an

untruncated density

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Parameters estimation Truncated gamma distribution

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Stochastic simulation

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Example 1

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Example 2

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CHECK

Validation by stochastic simulation

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Slide51

Rainfall percentages should sum to 100%Truncated gamma distributionsConditional simulation is necessary1st order Markov processConditional simulation of first order truncated gamma Markov process

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide52

Duration

Total depth

Time(hr)

Rainrate

Duration

Duration

Duration

Each simulation run yields an annual sequence of hourly rainfalls. 500 runs were generated for each rainfall station.

(Duration, total depth) bivariate simulation

Time of storm occurrences

first-order Truncated Gamma-Markov simulation

Hourly rainfall sequence

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Examples of hourly rainfall sequence (Kaoshiung)

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Validation of the simulation results using baseline period observations

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Hyetograph Simulation results (Typhoons)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide56

Empirical cumulative distribution functions

Time-to-peak and peak rainfall percentage (Typhoons)

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Department of Bioenvironmental Systems Engineering, National Taiwan University

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Extreme rainfall assessmentAnnual maximum rainfall depthHydrologic frequency analysisSeasonal rainfall assessmentWater resources management

Application of simulation results

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide58

Impact on design storm depths

(Projection period:

2020-2039

)

Tainan

Kaoshiung

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Department of Bioenvironmental Systems Engineering, National Taiwan University

Slide59

Changes in storm characteristics were derived using monthly rainfall outputs of multiple GCMs and an ANN model. The SSRSM is highly versatile.Can provide rainfall data of different temporal scales (hourly, daily, TDP, monthly, yearly)Can facilitate the data requirements for various applications (disaster mitigation, water resources management and planning, etc.)

Summary

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References

Wu, Y.C., Hou, J.C., Liou, J.J., Su, Y.F.,

Cheng, K.S., 2012. Assessing the impact of climate change on basin-average annual typhoon rainfalls with consideration of multisite correlation. Paddy and Water Environment, DOI 10.1007/s10333-011-0271-5. Liou, J.J. Su, Y.F., Chiang, J.L., Cheng, K.S., 2011. Gamma random field simulation by a covariance matrix transformation method. Stochastic Environmental Research and Risk Assessment, 25(2): 235 – 251, DOI: 10.1007/s00477-010-0434-8. Cheng, K.S., Hou, J.C., Liou, J.J., 2011. Stochastic Simulation of Bivariate Gamma Distribution – A Frequency-Factor Based Approach. Stochastic Environmental Research and Risk Assessment, 25(2): 107 – 122, DOI 10.1007/s00477-010-0427-7. Cheng, K.S., Hou, J.C., Wu, Y.C., Liou, J.J., 2009. Assessing the impact of climate change on annual typhoon rainfall – A stochastic simulation approach. Paddy and Water Environment, 7(4): 333 – 340, DOI 10.1007/s10333-009-0183-9. Cheng, K.S., Chiang, J.L., and Hsu, C.W., 2007. Simulation of probability distributions commonly used in hydrologic frequency analysis. Hydrological Processes, 21: 51 – 60.

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Physical processes + uncertaintiesClimate extremes vs weather/hydrological extremesSupport changes and their interpretationsCoping with uncertainties by using multiple model ensemblesDifferent meanings of the same terminology in different fields.Importance of communications

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Conclusions

Department of Bioenvironmental Systems Engineering, National Taiwan University

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CommunicationsWe should not evaluate the performance of GCMs by making a point-to-point comparison of their outputs of the baseline (present-day) period to observed data of the same period.Ii is also not appropriate to compare projected data of GCMs to observations when they become available. Projected data of GCMs were generated under certain scenarios which may not be fully realized in the future.

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Thanks for your patience!

Department of Bioenvironmental Systems Engineering, National Taiwan University

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