Hydrological Perspective of Climate Change Impact

Hydrological Perspective of Climate Change Impact Assessment Distinguished Lecture - Hydrological Sciences Section

Professor Ke-Sheng Cheng Dept. of Bioenvironmental Systems Engineering National Taiwan University

Outline • The scale issue of climate change studies • An example of climate change impact assessment focusing on changes in design storms.

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The scale issue • 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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• 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. 07/28 - 08/01, 2014

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Scales for flood risk assessment

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• 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. 07/28 - 08/01, 2014

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

– 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 evapotranspiration) 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. This study investigated changes in hydrological extremes which were associated with an annual resolution. 07/28 - 08/01, 2014

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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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Example •

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

(A)

Based on site observations

(B)

Based on high-resolution downscaled rainfalls. Contours exhibit higher degree of spatial continuity.

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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

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Scale mismatch in climate projection and hydrological projection • 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.

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24 GCMs

Projections in coarse spatial and time scales. (200 – 300 km; monthly)

RCM

Projections in finer spatial scale. (5km; monthly)

From GCM outputs to design storm depths – a problem of scale mismatch (both temporal and spatial) Design rainfall depths For example, 24-hr, 100-year rainfall depth Characteristics of extreme storm events

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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. 07/28 - 08/01, 2014

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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. 07/28 - 08/01, 2014

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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. 07/28 - 08/01, 2014

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24 GCMs

Projections in coarse spatial and time scales. (200 – 300 km; monthly)

Projections in finer spatial scale. (5km; monthly)

RCM

Weather Generator (Richardson type)

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

Conceptual flowchart

ANN

Stochastic storm rainfall simulation

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

1 2 3 4 5

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

Design rainfall depths For example, 24-hr, 100-year rainfall depth Characteristics of extreme storm events

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Climate change scenarios and GCM outputs • 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

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

Near future (2020 – 2039) 07/28 - 08/01, 2014

Near future (2080 – 2099) Department of Bioenvironmental Systems Engineering, National Taiwan University

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Annual counts of storm events estimated by ANN Frontal

Maiyu

Typhoon

Convective

North

Center

South

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Storm characteristics (average duration of typhoon)

Gauge observations

MRI (1979 - 2003)

Source: NCDR, Taiwan

Department of Bioenvironmental Systems Engineering, MRI (2075 - 2099) National Taiwan University

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Storm characteristics (average event-total rainfalls of typhoon)

Gauge observations

MRI (1979 - 2003)

Source: NCDR, Taiwan

Department of Bioenvironmental Systems Engineering, MRI (2075 - 2099) National Taiwan University

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Stochastic storm rainfall process Storm characteristics

•Duration •Event-total depth •Inter-arrival(or inter-event) time •Time variation of rain-rates Inter-arrival time

Inter-arrival time

Rainrate

Total depth

Department of BioenvironmentalDuration Systems Engineering, Duration Duration

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National Taiwan University

Duration

Time(hr) 24

Season-specific storm characteristics

Rainfalls (mm)

Frontal

Jan- April 07/28 - 08/01, 2014

Storm type

Period

Frontal

Nov - April

Mei-Yu

May - June

Convective

July - October

Typhoon

July - October

Mei-Yu

May - June

Convective, Typhoon

Frontal

July - October

Nov - Dec

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Stochastic Storm Rainfall Simulation Model

(SSRSM)

• 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)

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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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Effect of modeling truncated data with an untruncated density

f X ( x) f XT ( x)  ,   x  vc FX ( x )

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Parameters estimation Truncated gamma distribution

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Kt 

Xt  



E[ K t ] 

E[ X t ]  

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

Var[ K t ] 

Var[ X t ]

2

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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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Each simulation run yields an annual sequence of hourly rainfalls. 500 runs were generated for each rainfall station. Time of storm occurrences (Duration, total depth) bivariate simulation first-order Truncated Gamma-Markov simulation Rainrate

Hourly rainfall sequence Total depth

Time(hr)

Duration

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Duration

Duration

Duration

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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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Time-to-peak and peak rainfall percentage (Typhoons) • Empirical cumulative distribution functions

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Application of simulation results • Extreme rainfall assessment – Annual maximum rainfall depth – Hydrologic frequency analysis

• Seasonal rainfall assessment • Water resources management

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Impact on design storm depths

Tainan

Kaoshiung

(Projection period: 2020-2039)

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Summary • 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.)

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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/s10333-0090183-9. 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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Conclusions • 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 07/28 - 08/01, 2014

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• 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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Thanks for your patience!

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