hydrological perspective of climate change impact assessment

Hydrological Perspective of Climate Change Impact Assessment

Professor Ke-Sheng ChengDept. of Bioenvironmental Systems Engineering

National Taiwan University

Distinguished Lecture - Hydrological Sciences Section

Department of Bioenvironmental Systems Engineering, National Taiwan University

• The scale issue of climate change studies• An example of climate change impact

assessment focusing on changes in design storms.

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Outline


• 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


• 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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Climatological

Hydrological

Scales for flood risk assessment


• 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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• 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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• 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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– 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.


– 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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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)


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Climate 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


• 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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Design rainfall depthsFor 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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• 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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• Characteristics of storm events• Number of storm events• Duration of a storm event• Total rainfall depth• Time variation of rainfall intensities

• These 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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• A GCM – stochastic model integrated approach – Climatological projection by GCMs

• Changes in the means of storm characteristics• For examples,

– Average number of typhoons per year – Average duration of typhoons– Average event-total rainfall of typhoons

– Hydrological projection by a stochastic storm rainfall simulation model

• Generating 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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Characteristics of storm events1 Number of storm events2 Onset of storm occurrences3 Duration of a storm event4 Total rainfall depth5 Time variation of rainfall intensity

Design rainfall depthsFor example, 24-hr, 100-year rainfall depth

Characteristics 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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• Emission scenario: A1B• Baseline period: 1980 – 1999• Projection period

– Near future: 2020 – 2039– End of century: 2080 – 2099

• GCM model: 24 GCMs statistical downscaling• Hydrological scenario: changes in storm

characteristics

Climate change scenarios andGCM outputs

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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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Annual counts of storm events estimated by ANN

Maiyu Typhoo

n

ConvectiveFrontal

South

Center

North

07/28 - 08/01, 2014 21Department of Bioenvironmental Systems Engineering, National Taiwan University


Gauge observations MRI (1979 - 2003)

MRI (2015 – 2039) MRI (2075 - 2099)

Storm characteristics (average duration of typhoon)

Source: NCDR, Taiwan

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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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Time(hr)

Rain

rat

e

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


Season-specific storm characteristics

Rain

falls

(m

m)

Frontal

Convective, Typhoon Front

al 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



• Simulating occurrences of storms and their rainfall rates• Preserving seasonal variation and temporal

autocorrelation of rainfall process.• Duration and event-total depth• Inter-event times• Percentage of total rainfalls in individual intervals

(Storm hyetographs)

Stochastic Storm Rainfall Simulation Model (SSRSM)

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• Simulating occurrences of storm events of various storm types– Number of events per year

• Poisson distribution for typhoon and Mei-Yu

– Inter-event time• Gamma or log-normal distributions

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• Simulating joint distribution of duration and event-total depth– Bivariate gamma distribution (e.g. typhoons)– Log-normal-Gamma bivariate– Non-Gaussian bivariate distribution was

transformed to a corresponding bivariate standard normal distribution with desired correlation matrix.

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Bivariate gamma (X,Y)

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• Simulating percentages of total rainfalls in individual intervals (Simulation of storm hyetographs)– Based on the simple scaling property

• Durations 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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Modeling the storm hyetograph

Probability density of x(15)

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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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Truncated gamma density (parameters estimation, including the truncation level)

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37Department of Bioenvironmental Systems Engineering, National Taiwan University

Effect of modeling truncated data with an untruncated density

cX

XX vx

xF

xfxf

T ,

)(

)()(

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Parameters estimation Truncated gamma distribution

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][

][ tt

XEKE 2

][][

t

t

XVarKVar

tt

XK

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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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– Rainfall percentages should sum to 100%• Truncated gamma distributions• Conditional simulation is necessary• 1st order Markov process

– Conditional simulation of first order truncated gamma Markov process

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Duration

Total depth

Time(hr)

Rain

rate

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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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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• Empirical cumulative distribution functions

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

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• Extreme rainfall assessment– Annual maximum rainfall depth– Hydrologic frequency analysis

• Seasonal rainfall assessment• Water resources management

Application of simulation results

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Impact on design storm depths

(Projection period: 2020-2039)

Tainan Kaoshiung

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• 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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• ReferencesWu, 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 + uncertainties• Climate extremes vs weather/hydrological

extremes• Support changes and their interpretations• Coping with uncertainties by using multiple

model ensembles• Different meanings of the same terminology in

different fields.• Importance of communications

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Conclusions


• Communications– We 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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