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CLIMATE CHANGE IMPACTS ON WATER ARENA OF A RIVER BASIN IN INDIA. D. Roy ,S. Begam , S. Jana and S. Sinha School of Water Resources Engineering Jadavpur University Kolkata,India. Presented by Dr . Debasri Roy Associate Professor School of Water Resources Engineering
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CLIMATE CHANGE IMPACTS ON WATER ARENA OF A RIVER BASIN IN INDIA D. Roy ,S. Begam, S. Jana and S. Sinha School of Water Resources Engineering Jadavpur University Kolkata,India Presented by Dr. DebasriRoy Associate Professor School of Water Resources Engineering JadavpurUniversity
Background • Climate change is currently an issue of great concern . • Flood is expected to occur more frequently in certain regions. • Drought related and competing water issues is expected to intensify in other regions. • Rainfall distribution pattern is also expected to change. • These changes could imply some changes in water resources in different parts of the world. • South Asia in general and India in particular, are considered particularly vulnerable to climate change and its adverse socio-economic effects. • Reasons: low adaptive capacities to withstand the adverse impacts of climate change due to the high dependence of the majority of the population on climate-sensitive sectors like agriculture and forestry and lack of financial resources. • Vast regional variabilities exist in India that affect the adaptive capacity of the country to climate change. • Therefore, there is a need to evaluate the impact of climate on water resources in India at regional and local level.
In this scenario, attempt has been made to assess the impacts of climate induced changes on the water scenario in the upper portion uptoGhatshila gauging site (area 14472 sq. km. , river length 175 km. )and lying between the latitudes 22018’ N and 22037’N and longitudes 86038’E and 870E)of the interstate basin of the Subarnarekha river (co-basin riparian states are Jharkhand , Orissa and West Bengal ) of eastern part of India.
Subarnarekha river basin • The smallest (0.6% of geographical area of the country) of the fourteen major river basins of India(19,296 sq.km). • The river length is 450km. • It originates in Jharkhand highlands (23˚18’ N, 85˚11’E , elevation 740m). • It drains a sizable portions of the three States of Jharkhand, Orissa and West Bengal and finally debouches into the Bay of Bengal. • Average annual rainfall 1350 mm. • Annual yield of water constitutes about 0.4% of the country’s total surface water resources. • Annual utilisable water resources have been estimated to be 9.66 MCM
Work The work comprises: • Development of hydrologic model of the basin with the help of the catchment simulation model viz. Hydrologic Modeling System (HEC-HMS 3.5) developed by the Hydrologic Engineering Center, USA using historical data . • Running of the model for future period under Q0, Q1 and Q14 simulations of A1B scenario—generated using regional climate model (RCM) PRECIS (Providing Regional Climates for Impacts Studies) developed by the Hadley Centre, UK and run at the Indian Institute of Tropical Meteorology (IITM), Pune, India at 50 km × 50 km horizontal resolution over the South Asian domain for A1B scenario (Special Report on Emissions Scenarios (SRES) prepared under the Intergovernmental Panel on Climate Change (IPCC) coordination. • Analyzing precipitation, potential evapotranspiration, streamflow under changed climate scenario and those under historical scenario to ascertain impact of climate change on water resources in the basin.
Climate Change Scenario • The IPCC SRES scenario set comprises four scenario families: A1, A2, B1 and B2. The A1 family includes three groups reflecting a consistent variation of the scenario (A1T, A1FI and A1B). Hence, the SRES emissions scenarios consist of six distinct scenario groups, all of which are plausible and together capture the range of uncertainties associated with driving forces. • Scenario A1: • The A1 scenario family describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies.
A1FI scenario : fossil intensive • A1T scenario : non-fossil energy sources • A1B scenario: balance across all sources where balance is defined as not relying too heavily on one particular energy source • Boundary conditions from three simulations from a 17-member Perturbed Physics Ensemble generated using Hadley Center Coupled Model (HadCM3) for the Quantifying Uncertainty in Model Predictions (QUMP) project have been used to drive PRECIS at IITM, Pune, India for the period 1961–2098 in order to generate an ensemble of future climate change scenarios (Q0, Q1 and Q14 ) for the Indian region at 50 km × 50 km horizontal resolution for A1B scenario.
Model Evaluation • The criteria for model evaluation adopted involves the following: • Sensitivity Analysis --- The sensitivity analysis of the model was performed to determine the important parameters which needed to be precisely estimated to make accurate prediction of basin yield. • Percentage error in simulated volume (PEV) • Percentage error in simulated peak (PEP), and • Net difference of observed and simulated time to peak (NDTP) • Nash–Sutcliffe model efficiency (EFF) Volo = observed runoff volume (m3) Volc = computed runoff volume (m3)
Qpo = observed peak discharge (m3/s) Qpc = computed peak discharge (m3/s) Tpo = time to peak of observed discharged(h) Tpc = time to peak of computed discharge (h) Qoi = ith ordinate of the observed discharge (m3/s) = mean of the ordinates of observed discharge (m3/s) Qci = ith ordinate of the computed discharge (m3/s)
Calibration Analysis Stream flow hydrograph Non-monsoon 1999
Calibration Analysis Stream flow hydrograph Monsoon 1999
Performance measures table of the model for calibration years
Validation Analysis Stream flow hydrograph Non-monsoon 2004
Stream flow hydrograph Monsoon 2004
Performance measures table of the model for validation years
Annual rainfall in all the projected years are found to be normal or above normal (1.5 – 35)% except for 5 years. • the highest value is 1860.2 mm • the lowest one 925.6 mm lower (by 32%)
Annual rainfall for historical and future years under Q0, Q1 and Q14 simulations
The annual rainfall for Q0 simulation in the projected years (except 2040 and 2050) is found to be higher than the other two simulations----close to Q14 simulation. Rainfall for Q14 in 2040 and 2050 is higher than historical average(22% and 28%). • annual rainfall lowest for Q1 simulation and also lower than historical average(21 to 51%) .
Monsoonal rainfall ( July, August and September) and March rainfall in all the projected years do not show significant deviation (compared to non-monsoonal rainfall)from historical values. • Non-monsoonal rainfall in future periods show marked deviation (increase) from historical ones. • The highest increase (1331.6 %) is found for the month of May in decade of 2020 and the second highest increase (774.4 %) in the month of Nov. in decade of 2030 and the third highest increase in the month of Dec. and Jan. in 2050.
Q1 simulation • The highest increase (586%) in rainfall is found for the month of Nov in 2050 following the second highest increase (470%) in the month of December 2050,October 2050 and February 2050. • A noticeable increase has also been found for February 2030 March 2020,September 2030 only. • A decrease in monthly rainfall values (-36% to-95%) from corresponding historical values has been observed for almost all the months with a maximum decrease (95%) in month of June, 2040.
Q14 simulation • A noticeable increase in monthly rainfall has been found in April2020, November and December of 2030 and 2050 with maximum increase (1766%) in December 2050 • Monthly rainfall deviation for Q0 ,Q14 almost similar for 2030 and 2050.
Annual 24-h maximum rainfall • Projected to be lower than the historical highest for the future years excepting for five years. • The quantum of decrease in the value ranges from 20 % to 80% • Projected to be lower than the historical highest for Q1 and Q14 simulations. • is highest for Q14 (excepting 2030) • Rainfall is higher for Q1 than for Q0 excepting 2030 and 2040
Monthly variation of Potential Evapotranspiration Q0 simulation
The annual distribution of projected monthly PET values is found to follow the pattern of historical average PET values. • For Q0 simulation monthly deviation is small(-13to +13%). • Monthly PET values lie close to the historical one for the year of 2014 – 2020. • The monthly PET values for FEB to APR for the decade of 2020 is found to be higher than the historical one. • The monthly PET values for APR to JUN and SEP, OCT for the decade of 2030 is found to be higher than the historical one. • As per Q1 and Q14 simulations, monthly PET values in projected years are higher than corresponding historical values (excepting for the year 2020)---larger increase has been found in quantum of monthly PET during the months of March, April, May(for Q1~19%) and June .
Streamflow Hydrographs Q1 simulation Q0 simulation Q14 simulation
Flow pattern • No change in pattern of stream flow over that of historical flow is observed in the projected years for Q1 simulation • As per Q0 simulation,(4) of the years showed annual peak in May and (8) in October and (1) of the years in November • As per Q14 simulation , annual peak flow is observed in May and in October (rather than in monsoon)in 2040 and 2050 respectively.
Deviation in annual stream-flow volume (MCM) from historical stream-flow volume for projected years under Q0, Q1 and Q14 simulations
The stream-flow volumes for projected years (excluding year 2014 and 2020) are higher than the corresponding historical one. The highest increase(166%) during 2031-40,followed by 2021-2030 (147%) and 2014-2020 For Q1 simulation annual stream-flow volumes for all the projected years have been found to be lower (range 32% to 70%) than the average historical value and for 2020 and 2030 for Q14. The annual stream-flow volumes have been found to lie very close to the historically observed flow volume for 2040 under Q0 & Q14 simulations and also for 2050 under Q14 simulation only.
Deviation of monthly flow volume (future decadal average) from historical flow for Q0 simulation
Stream-flow volumes during monsoon in the projected years show smaller deviation (10 to 50%) from historical values compared to those in non-monsoon.(35% to 270%----even higher in month of May) • Stream-flow volumes for projected months from January to April (excluding year 2014- 2020) are lower than the corresponding historical one. • From October to December, stream-flow volumes are higher than the corresponding historical one showing maximum variation in the month May for two future periods (2021-2030 & 2031-2040).
Peak flows for Q0 simulation have been found to be lower than the historically observed annual highest peak --- the peak flow approaches historical value for three years only – and on one occasion peak flow is higher than historically observed 2ndand 3rdhighest peak flow. • Annual peak flows (1st, 2nd and 3rd highest) in the years 2020,2030, 2040, 2050 have been found to be much lower than hist. av. in Q0, Q1 and Q14 simulation. • Peak flow is the lowest for Q1 simulation among the three.
Flow Duration Curve Analysis Flow-duration Curve for historical observed data
Flow characteristic of the stream during historical and future years was found to be similar. Non-perennial flow condition was found to exist in both historical and projected years 80% of time the discharge of the stream was found to equal or exceed 80 cumec ,117 cumec and 107 cumec in 2014-20,2021-30 and in 2031-40, (against historical flow of 20 cumec) and 90% dependable flow for those period was found to exceed 22.3,57.8 and 36.2 cumec (against historical flow of 8.3 cumec).
