Prof Nagesh Kumar Climate Change Impacts Nagesh

Prof D Nagesh Kumar, Dept of Prof D Nagesh Kumar, Dept of Civil Engg, IIScCivil Engg, IISc

Prof. D. Nagesh KumarDepartment of Civil Engineering

Indian Institute of ScienceBangalore – 560012

URL: http://www.civil.iisc.ernet.in/~nageshAcknowledgement:

Drs A Anandhi , V V Srinivas &Prof Ravi S Nanjundiah

Prof D Nagesh Kumar, Dept of Civil Engg, IISc

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OutlineIntroduction Climate Change IPCC ScenariosDownscaling for Hydrologic InvestigationsDownscaling of Hydro-meteorological Variables for a River BasinRemote Sensing (RS), GIS & DEM for HydrologyRS for Land Use/ Land Cover GIS for Watershed Delineation DEM for Drainage Pattern Estimation using SRTM Data Streamflow Projections using SWATAV SWAT ModelInputs for AV SWAT ModelStreamflow ProjectionsConclusions


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Climate Change


Climate …… has direct/indirect impact on hydrology… creates nonstationarity in hydrologic time series… has the power to change the socio-economic status of the country… has the power to change ICE age to present day and may be from

present day to FIRE age!

… Downscaling to river basin scale… Hydrologic modeling and streamflow projections

Should have the answer to the following …… How does climate impact on hydrology?

(precipitation, rainfall-runoff process, water resources management)… How to deal with the nonstationarity in hydrologic time series?… How to be prepared for the changed scenario?

Why do we need to understand climate?

What does a hydrologist need to do?

How to assess the climate impact on hydrology?


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Climate Change ScenarioClimate Change scenario refers to a plausible future climate that has been

constructed for explicit use in investigating the potential consequences of anthropogenic climate change and natural climate variability

IPCC - Intergovernmental Panel on Climate ChangeThe 4 storylines describe the way world population, land use changes,

new technologies, energy resources, economies and political structure may evolve over the next few decades.


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IPCC ScenariosFour different narrative storylines: A1, A2, B1, B2

Describe consistently the relationships between emission driving forces and their evolution and add context for the scenario quantification

Represents different demographic, social, economic, technological and environmental developments

A1 storyline: Future world of very rapid economic growthGlobal population that peaks in mid-century

and declines thereafterRapid introduction of new and more efficient

technologies.

A2 storyline:Very heterogeneous worldContinuously increasing global populationEconomic development is primarily regionally

oriented Per capita economic growth and technological

change are more fragmented and slower than in other storylines


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IPCC Scenarios contd..B1 storyline:

Convergent world with the same global population that peaks in mid-century and declines thereafter, as in the A1 storylineRapid changes in economic structures toward a service and information economy with reductions in material intensity and the introduction of clean & resource-efficient technologies

B2 storyline:Emphasis is on local solutions to economic, social, and environmental sustainabilityContinuously increasing global population at a rate lower than A2Intermediate levels of economic development, and less rapid and more diverse technological change than in the B1 and A1 storylines


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IPCC Scenarios contd..

Four qualitative storylines yield four sets of scenarios called “families” :A1, A2, B1 and B2Six scenario groups drawn from the four families:

one group each in A2, B1, B2, and three groups within the A1 family, characterizing alternative developments of energy

technologies:A1FI (fossil fuel intensive), A1B (balanced), and A1T (predominantly non-fossil fuel)

SRES - IPCC Special Report on Emission Scenarios


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Range of Green House Gas Emissions in SRES scenarios

Broad Range of Global Warming Projections:1.0 - 3.5° C by 2100 or 1.5 - 4.5° C for a CO2 doubling


Climate Scenario Development

Contain a sufficient number of variables

Basic set of climate variables: maximum and minimum temperature, precipitation, radiation/cloud cover, specific/relative humidity or vapor pressure and wind speed

Spatially compatible - changes in one region are physically consistent with those in another region and with global changes

Consistent with the broad range of global warming projections:

1.0 - 3.5°c by 2100, or 1.5 - 4.5°c for a CO2 doubling


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Climate Scenario Development

Four basic scenario types

Synthetic or IncrementalAnalogueClimate Models

General Circulation Models (GCMs)Others


General Circulation Models

Most complete modelsModels the three dimensionality of the atmosphereMain components:

Dynamics Large scale atmospheric transports are done through numerical schemesComputed either in rectangular grid space or in spectral space

PhysicsComputed in grid space even for spectral GCMs

Pertains to surface and to vertical motion To limit isolated phenomena such as thunderstorms, to specific locations

Other


Dynamics in GCM’s

Any GCM must be formulated with some fundamental considerations for

Conservation of momentum

Conservation of mass

Conservation of energy

Ideal gas law

Computed either in rectangular grid space or in spectral space


Scenarios from GCM’s

Major source of information for constructing scenarios

Most advanced tools currently available for simulating the response of the global climate system to changing atmospheric composition

Two types of experimentsEquilibrium

Transient

Cold start

Warm start


Downscaling to River Basin Scale

To enhance the information from GCMs in order to bridge the gap between what is supplied by climate models and what is required by impacts researchers

Schematic illustration of the (a) real world (b) world as represented by GCMs

Small-scale affects (such as topography) important to local climate are poorly represented in GCM

300k

m

50km

10km

1m

Poin

t

Global Climate Models supply...

Impact models require ...

Courtesy: The Canadian Climate Impacts Scenarios (CCIS) Project

Courtesy: The Canadian Climate Impacts Scenarios (CCIS) Project

GCMs impacts

large gap

downscaling


Climate Model Grid Points

RCM

GCM

two way nesting

one way nesting

RCM and GCM run simultaneouslyFeedback from RCM to GCM

Driving RCM with GCMNo feedback from RCM to GCM

20 vertical Levels-soil

19 vertical levels-atmosphere

1.25 km1.25 km

2.5lat 3.75long

300 km

F is modeled dynamically (through RCM)

F is modeled empirically from observational (or modeled) data


Dynamical downscaling

The RCM is coupled to a global model which regularly providesboundary conditions to the RCM during the integration Regional Climate Model

Global Climate Model


Support Vector Machine

• The SVM uses kernel functions to transform the data to a higher, possibly infinite, dimensional space.

• A linear solution, in the higher dimensional feature space, corresponds to a non-linear solution in the original lower dimensional input space.

• The mapped objects are linearly separable

• An optimal line is constructed rather than a complex curve to separate objects


Select probable predictors (PPs)

Prepare scatter plots, and compute cross-correlations (CCs) between PPs in NCEP and GCM data sets, and between PPs in NCEP and the predictand data

Select/update thresholds Tng & Tnp

CCs = thresholds

75% of FVs form the training set

Potential predictors (POPs) for downscaling

Standardize the monthly data of POPs extracted from NCEP and GCM datasets

Extract PCs and PDs from standardized NCEP data of POPs to

prepare feature vectors (FVs) depicting months

Obtain PCs of standardized GCM data of POPs along PDs extracted from NCEP data to prepare feature

vectors (depicting months)

25% of FVs form the validation set

Calibration and verification of SVM Model

Grid search method to find optimum parameter range

Genetic algorithm to determine optimum

parameter value

Comparison of downscaled values with observed values

from past record and estimation of error

SVM model validation

Is the performance accepted

Yes

Validated SVM model

Future projections of predictand

Yes

No

No

Parameters

PDs

Methodology for Downscaling using SVM


Impact of Climate Change in 12 River basins in India

Source: NATCOM report by

Gosain et al. (2004)


India

ArabianSea

Malaprabha BasinPart of Krishna Basin in KarnatakaThe climate of the study region is dry, except in monsoon months Avg. annual rainfall:1051mmCatchment Area:2564 sq km

GCM output is downscaled using Support Vector Machine (SVM)Six Cardinal variables are downscaled

PrecipitationMaximum TemperatureMinimum TemperatureWind SpeedRelative HumiditySolar Radiation

Map showing NCEP & GCM grid points and rain gauge locations in Malaprabha reservoir catchment


Predictors for downscaling different hydrometeorological variables

Max. & Min. Temperature

Group A – Ta 925, Ua 925, Va 925Group B – latent heat, sensible heat, shortwave radiation and longwave radiation fluxes Group C – comprises of all predictors in Groups A and B

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PrecipitationTemperature at 925mb, 700mb, 500mb, 200mb (Ta 925, Ta 700, Ta 500, Ta 200),geo-potential height at 925mb, 500mb, 200mb (Zg 925, Zg 500, Zg 200),specific humidity at 925mb, 850mb (Hus 925, Hus 850), zonal (Ua) and meridional wind velocities (Va) at 925mb, 200mb (Ua 925, Va 925, Ua 200, Va 200),precipitable water (prw) and surface pressure (ps).

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Predictand(river basin scale

2500 km2)

Predictors (Spatial resolution of 2.5o X 2.5o 75,000 km2 )

Sl.No. ≈

≈


StreamflowGroup A – 15 predictors, same as predictors selected for precipitation*Group B –precipitation, max. & min. temperature, wind speed, relative humidity, cloud cover

6Cloud coverPrecipitable water5Relative humidityTa 925, Hus 925, Ta sur, and LH4Wind speedUa 925, Va 9253

Predictand(river basin scale)

Predictors (Spatial resolution of 2.5o X 2.5o)

Sl.No.

* at river basin scale

Predictors for downscaling different hydrometeorological variables – contd..


Downscaling RH from CGCM3 for different Scenarios

Predictandvalue

Past – generally 1978-2000

The future 100 year simulations are divided into five 20-year periods for each of the IPCC scenarios

Downscaled using NCEP predictors

Downscaled using GCM predictors

Depicts the mean trend of RH projected by GCM Indicates the mean annual RH (historical )

Theoretical and Applied Climatology, Springer, 2012.


Downscaled Rainfall from CGCM3 for different Scenarios*

* International Journal of Climatology, Royal Meteorological Society (RMetS), UK, 2008


Downscaled Maximum Temperature*

• Results show that Tmax is projected to increase in future for A1B, A2, and B1 scenarios.• Projected increase in Tmax is high for A2 scenario, whereas it is least for B1 scenario.

This is because among the scenarios considered, the scenario A2 has the highest concentration of carbon dioxide (CO2) equal to 850 ppm, while the same for A1B, B2 and COMMIT scenarios are 720 ppm, 550 ppm and ≈370 ppm respectively.



Downscaled Minimum Temperature*



Downscaled Wind Speed


Downscaled Cloud Cover

Solar radiation is obtained from downscaled cloud cover and temperature data


Integration of RS, GIS, DEM and Hydrological Models

RS GIS DEM Hydrological Model


• Hydrological model is a good tool for understanding and managing phenomena related to hydrological processes

• RS provides essential inputs for hydrologic models

• GIS provides a platform for simulation of hydrological model

• DEM provides inputs essential for topography

• RS, GIS & DEM combined with mathematical models provide a convenient platform for handling, compiling and presenting large amounts of spatial data essential to river basin management

Integration of RS, GIS, DEM and Hydrological Models


Land use/ land cover theme of the Malaprabha reservoir catchment derived from IRS data


Major soil groups in the Malaprabha reservoir catchment derived from RS and NBSS & LUP, Nagpur


DEM of the catchment of Malaprabha reservoir


Stream network in the catchment of Malaprabha reservoir obtained from AVSWAT model using DEM


Drainage sub-basin outlets that are fixedin the catchment of Malaprabha


Sub-basins formed by AVSWAT for the outlets fixed in the catchment of Malaprabha reservoir


Longest stream in each sub-basin obtained from AVSWAT model


Observed and simulated monthly streamflows (in mm)

0

50

100

150

200

250

300

350

4 4 5 5 6 6 7 7 8 8 9 9 0 0

Observed SWAT simulated

P = 0.97

Model performance during the validation period (1994 –2000)


Streamflow projections made for various IPCC scenarios for Malaprabha Basin obtained from AV-SWAT model

Streamflow in mm


Conclusions

• Climate change and its variability will have significant impact on Hydrology of river basin

• It is important to understand and quantify these impacts

• Remote Sensing, GIS and DEM are very useful tools to assess water resources of a river basin

• Impact of climate change on hydrology and water resources should be investigated for effective planning and management of water resources

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Future Scope• Detection and Attribution of climate change & its variability

• Uncertainty due to GCMs, Scenarios, Downscaling techniques, Stationarity assumption etc

• Proper representation of monsoon rainfall system in GCMs

• Land use/ Land cover changes and their contributions

• Climate change impact on ground water resources

• Climate change impact on Cropping pattern & Agricultural Production

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Thank you

Prof Nagesh Kumar Climate Change Impacts Nagesh

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