AN OVERVIEW OF HYDROCLIMATOLOGY RESEARCH AT NCSU

SANKAR ARUMUGAMUNIVERSITY FACULTY SCHOLAR

ASSOCIATE PROFESSORDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING

Presentation Outline• Climate Variability – Seasonal to Interannual

– Climate Forecasts and Model Uncertainty– Water Allocation and Water Supply (Falls Lake)– Water and Energy Management– Nutrient Forecasts over the Southeast US

• Climate Change – Long-term Planning– Near-term Climate Change – System Design, Capacity Expansion– Water Sustainability and Climate (Cat 3) Project

• Opportunities and Challenges

Climate Variability and Change• Climate Variability

– Structured Interannual and Longer Variations – Due to internal feedback processes– Adaptive seasonal/interannual water management

• Climate Change and Land Use– Increased CO2 concentration and population growth– Non-stationary hydroclimatology– Relevant to System Design, Planning and Capacity

Expansion Projects, Instream Ecology

Large Scale Hydroclimatology & Water Management

Hydroclimate Dynamics • Diagnosis • Physical Significance • Assimilation

Climatic Indices Land Surface Indices

Hydrologic Fluxes Estimation • Modeling • Forecasting

Water Management • System Design • Impact/Assessment • Allocation/Operation

Understanding & Monitoring of Large Scale Hydroclimatic Systems

General Circulation Model

“Downscaling”Regional Climate Model

Hydrologic Model

Climatic Predictors/ProjectionsLand-Surface Conditions

Statistical Model

Streamflow Projections/Forecasts

Downscaling Climate Information – Two Approaches

Dynamical Downscaling Statistical Downscaling

Precipitation &Temperature

Hydroclimatic Risk on Water Management• Forecast Producers

– Climate Scientists and Hydrologists– Express seasonal streamflow uncertainty as terciles/ensembles

• Forecast Consumers– Water Managers and Reservoir Operators– Often risk averse; No reward for using forecasts– Difficulty interpreting/relating forecasts to releases– Often manages the system based on rule curves Need not quantify

conditional risk

Forecasting Summer Flows into Falls Lake

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200

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1991 1993 1995 1997 1999 2001 2003 2005

Stre

amflo

w (f

t3 /sec

)

Year

ResamplingRegressionMultimodelObserved

Correlation = 0.52

Golembesky and Sankar,JWRM, 2009

Probability of Meeting the Target Storage

0200400600800100012001400160018002000

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1991 1993 1995 1997 1999 2001 2003 2005

Obs

erve

d St

ream

flow

(cfs

)

Prob

(ST

< S T

* )

Year

Regression

Resampling

Multimodel

Observed

0.33 percentile

0.66 percentile

Golembesky and Sankarasubramanian,JWRM, 2009

Skill in predicting Winter Precipitation

9

Skill is Varying•Spatially•Temporally•Across the models

Multimodel Combination•Improves skill•Reduces uncertainties

Methods Include•Pooling ensembles: equal weights•Long term skill•Skill based on dominant predictor state (Devineni et al., 2008)

Devineni and Sankar, MWR, 2010

Increased Hydropower – Angat Reservoir, Manila

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1987 1989 1991 1993 1995 1997 1999 2001

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Hyd

ropo

wer

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erat

ed (

in G

WH

)

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Observed In

flowActualUpdated ForecastOctober ForecastObserved

How Climate Forecasts can improve long-term operation?

0.1

1.0

10.0

100.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0Storage/Demand

% Im

prov

emen

t

Correlation = 1.0Correlation = 0.75Correlation = 0.5

Sankarasubramanian et al., 2009, WRR

Forecast Portal - Automation

Precipitation Forecasts (Pt)

from GCMs

IRI Data Library

PredictandModel

Observed Streamflow (Qt-1)

Statistical Downscaling

Model Forecasted

Streamflow (Qt)

Training Period : 1957-1989Archived Forecasts : 1990-till date

Skill Measures:Correlation, Mean Square Skill Score, Rank Probability Skill Score

Predictors

State Climate Office of NC

Portal automatically downloads Updated Monthly/Seasonal Precipitation Forecasts from GCMs between 15-18 of each month

Inflow Forecast Portal –http://www.nc-climate.ncsu.edu/inflowforecast

Site Winter Jan Feb Mar

Falls Lake 0.50 0.62 0.62 0.56Jordan Lake 0.75 0.60 0.65 0.50Kerr Scott 0.69 0.55 0.70 0.88Philpot 0.68 0.57 0.68 0.87Catawba (South Fork) 0.96 0.73 0.59 0.52Rocky Creek 0.67 0.67 0.67 0.49

Skill - Correlation – Seasonal and Monthly

Example: Falls Reservoir

Storage Forecasts

Climate-Water-Energy : Opportunities and Challenges• For seasonal forecasts to be useful

– Initial storage should constrain the allocation; If not, 100% reliability; • Use end of season target storage constraint

– If initial storage does not constrain allocation– If skill is good only during a particular season– To enforce restrictions for below normal conditions– To reduce spillage and increase allocation (primarily hydropower) for above

normal conditions• Perspectives from Forecasting

– Update the forecasts within the season (very beneficial for hydropower systems)

– Multimodel climate forecasts are better, since it reduces overconfidence of individual models

– Provides better correspondence between forecast probability and its observed frequency

Climate Forecasts and Water Quality• Instream Water Quality

– Streamflow – dominant predictor– Nonpoint loadings – high flow seasons– Point Sources – Low flow seasons

• Climate – Streamflow – Water Quality– Seasonal Water Quality Management– Pre-season water quality forecasts conditional on Climate information– Optimal load allocation – water quality trading

Oh and Sankarasubramanian et al., 2012, HESS

R2 = R2 (Simulated)*R2 (LOADEST)

Water Quality Management• Water Quality Trading

– Successful programs in Neuse and Tar basins– Association - Municipal and Industrial discharges– Target nutrient loadings – Point and nonpoint– Trading – Point to point and point to nonpoint– Point to nonpoint – Cost share program for BMP

• Opportunity to use Forecasts– Pre-season estimates of loadings from runoff– Optimal loadings between point and nonpoint– Forecasts work better with contracts/trading

Water Sustainability under Near-term Climate Change: A Cross-Regional Analysis Incorporating Socio-Ecological Feedbacks and Adaptations

NCSU : Sankar Arumugam, Emily Zechman, Kumar Mahinthakumar, Tushar Sinha, Seung Seo, Raj Bhowmik, Shams Al-Amin; NOAA: Ken Kunkel and Wei LiuASU : John Sabo, Albert Ruthi, Kelli Larson, Deborah AyodaleFIU : John Kominoski, Megan Hagler

http://www.waterforthesunbelt.org/

Nea

r-te

rm C

limat

e C

hang

e

Nea

r-te

rm C

limat

e C

hang

e

Hawkins and Sutton, 2009, BAMS

Near-term Climate Change & Water Sustainability over Sunbelt

Hydrology Ecology

Climate

Policy/Human Decisions

Arid/Humid

CV of FlowsHigh/Low Native/Non-native

High/Low

Water RightsPrior/Riparian

ReservoirsOver-year/Within-year

Southwest/Southeast

Reliability-Resiliency Impacts – CMIP3

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0.8 1.0 1.2 1.4 1.6 1.8

Failu

re P

roba

bilit

y o

f Ta

rget

Rel

ease

s

Resilience Index, m

Operating Level - 216

Obs BCCR CCCMA CNRM CSIRO

Sankarasubramanian et al., 2013, WRR

Resiliency = (1- mean annual Release/ mean annual inflow)/CV

A Cross-Regional Synthesis on Water Sustainability under Near-term Climate Change incorporating Socio-Ecological Feedbacks (NSF # : 1204368)

Hydrology Ecology

Climate

Policy/Human Decisions

CMIP5 hindcasts straddle observed temperature trend

over the Sunbelt

Bayesian non-parametric statistical downscaling better

preserves observed cross-correlation fields

Limited or no difference in change Ratio between ensemble mean CMIP5 forcings and using

all CMIP5 ensembles

Fish detection (Extinction) probability increased (decreased)

with increased flow

Reservoir releases can better serve ecological demands

Cross-regional comparison show significant differences in

drought response

Non-Native (native) fish species respond negatively

(positively) to flow anomalies

% change between 1985-2005 in municipal water use per capita consumption - figure above - is reduced (increased) in rich (poorer) counties

and change in agricultural water use depends on micro and sprinkler irrigation adaptations

• Forecasts are more useful than climatology– Within year storage systems (typically humid basins) than over

year (arid basins) systems – Reducing system losses (spill and evaporation)– Systems with low storage/annual demand ratio– Multiple uses constraining the allocation process

• Near-term Climate Change Projections– 10-year updated and 30-year updated hindcasts– Adaptive Management and feedback representation– Climate variability vs Climate Change

Climate-Water-Energy : Opportunities and Challenges