Hydrological Modelling of Narmada basin in Central India using Soil and Water Assessment Tool (SWAT) T. Thomas, N. C. Ghosh, K. P. Sudheer National Institute of Hydrology, Roorkee (A Govt. of India Society under Ministry of Water Resources, RD & GR, Govt. of India)
Presentation Outline Introduction Objectives Study area Methodology Climate change detection in historical time horizons Hydrologic Modelling Impact assessment for future time horizons
Introduction Historical and Present time horizon: Warming is Unequivocal and Unprecedented (IPCC). Water is one of the sectors going to be impacted. Future time horizons: Global temperature change is likely to exceed 1.5 C for the end of the 21 st century relative to 1850. Expected to have significant impacts on the hydrological cycle.
Application of climate change science remains a challenge. Heavy analytical procedures Limited attention to implementation High level of uncertainty Political dimensions Indian sub-continent: combination of climate change rapid population growth land use change adaptation mechanisms Hydrologic modelling can be a useful tool for scenario analysis for water resources planning
Objectives Identification of climate change signals in Narmada basin through statistical analysis of historical climate variables. Modelling the watershed hydrology in Narmada basin by a physically based large scale hydrologic model. Assessment of the impact of climate change using projected climate scenarios (AR5) with emphasis on future water availability including extreme events.
Study Area Catchment area: 98796 sq. km. Non-snowfed river basin Sustained by base flows West flowing river Lies in the States of MP, Gujarat, Maharashtra and Chhattisgarh Average annual rainfall: 1178 mm Objectives
30 major & 150 medium projects planned in Narmada basin. Sustainability of the existing and upcoming projects uncertain. Hydrological impact assessments are much more imperative now. Non snow-fed perennial basin Sustained only by base flow Change in water balance components Predictions of hydrologic variables in future under climate change. Research gaps: impact assessment in data scarce areas Uncertainty issues, Non-stationarity issues.
Methodology SWAT Run with default parameter values LH-OAT for Sensitivity Analysis Establish Initial Parameter Ranges LH-sampling to assign 1000 groups of parameter values Observed Climatic Variables AND Hydrologic Variables (Precipitation, Temperature, Stream flows) Multi-site Calibration & Validation SWAT Model after incorporating existing dams TESTED SWAT MODEL Detection of Climate Change Signals in Present Climate AR5 RCM Climate Variables & Downscaled Climate Data for 2 RCP s and 4 time horizons Bias Correction of the Downscaled Data (QM) Addition of proposed dams in the basin Behavioural simulations B > 80%? Yes Select parameter values with corresponding NS greater than 0.50 Forecasted hydrological variables with existing dams Forecasted hydrological variables with existing and proposed dams No Use parameter range of last iteration as input range of next iteration Climate change impact assessments (existing dams only & with proposed and existing dams) Water availability, Flood, Drought Assessments (Indices based)
Extreme rainfall: Increased in recent times 1-day maximum P: Significant positive trend (z = 3.66) Particulars 1951-70 1989-08 1-day rainfall > 300 mm 1 9 1-day rainfall > 200 mm 8 17 Average 1-day maximum rainfall 203.9 mm 275.9 mm Temporal variation of amount of 1-day maximum rainfall in the basin (MK test z statistic = 3.66).
Frequency analysis: Increase in n-yr return period rainfall Grid cells with significant increasing trends in 5-year return period rainfall
Droughts: Increased in recent times Drought frequency more pronounced in the middle zone. Increase of drought in upper zone a cause of concern. SPI based drought duration for various zones
Extreme Temperatures: Increasing continuously 1-day maximum temperature : increasing @ 1.10 C/100 yr. (more than global average rate). 1-day minimum temperature: increasing @ 3.20 C/100 yr. S. Zones of Mann-Kendall test statistic & inference No. basin 1969-88 1990-2009 1. Upper zone +1.72 (not significant) +1.01 (not significant) 2. Middle zone +1.24 (not significant) +1.29 (not significant) 3. Lower zone +2.31 (significant) +2.11 (significant)
Hydrologic Modelling using SWAT Features of SWAT Physically based distributed model Continuous time model Long term yield model Uses readily available data Can be used for long term impact studies Processes Modelled Weather, Hydrology, Sedimentation Plant growth, Nutrient cycling Pesticide dynamics, Bacteria Management
SWAT setup for the Narmada up to Barmanghat
SWAT model setup for the Narmada upto Barmanghat (26000 sq. km). One Major Multi-purpose Project (Bargi Dam) for irrigation, power generation, industrial and domestic use. 10 projects being planned in the catchment : Rosra, Basania, Halon, Upper Narmada, Upper Burhner, Raghavpur, Atariya, Machhrewa, Sher and Chinki. Virgin simulation of the model has been carried out before setting up of reservoirs. Default run of model Bargi Reservoir (properties and inflows)..
Jan-88 Jun-88 Nov-88 Apr-89 Sep-89 Feb-90 Jul-90 Dec-90 May-91 Oct-91 Mar-92 Aug-92 Jan-93 Jun-93 Nov-93 Apr-94 Sep-94 Feb-95 Jul-95 Dec-95 May-96 Oct-96 Mar-97 Aug-97 Jan-98 Jun-98 Nov-98 Apr-99 Sep-99 Feb-00 Jul-00 Dec-00 Monthly discharge (cumecs) DEFAULT RUN: VIRGIN AND BARGI DAM 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 Calibrated (1988-00) Virgin (1988-00) 0
Sources of irrigation allocated: based on the actual water use of reservoir water in command areas & groundwater in non-command areas. SWAT Parameters for Stream flow simulation 1. Base flow recession coefficient: ALPHA_BF 2. GW Delay time : GW_DELAY 3. Revap coefficient : GW_REVAP 4. Soil evaporation coefficient : ESCO 5. Available Water Content : SOL_AWC 6. Saturated hydr. Conductivity : SOL_K 7. Manning s n : OV-N 7. Curve number : CN_f 8. Slope : SLOPE 9. Manning s n for channel : CH_N 10. Water depth (shallow aq.) :GWQMN 11. Slope length of main channel : CH_S 12. Snowfall temperature : SFTMP 13. Slope of sub-basin : SLSUBBSN 14. Surface lag : SURLAG One at a time (LH-OAT) Sensitivity Analysis has been performed for the parameters responsible for stream flow simulation.
Multi-site SWAT Calibration Multi-site calibration of SWAT model using 6 gauging sites (Dindori, Mohgaon, Manot, Belkheri, Patan and Barmanghat) Calibration: 1988-00, Validation: 2001-05 Sensitivity Analysis has been performed for the parameters responsible for stream flow simulation. CN2, SOL_AWC, ESCO, GW_REVAP, ALPHA_BF and GW_DELAY.
Jan-88 Jun-88 Nov-88 Apr-89 Sep-89 Feb-90 Jul-90 Dec-90 May-91 Oct-91 Mar-92 Aug-92 Jan-93 Jun-93 Nov-93 Apr-94 Sep-94 Feb-95 Jul-95 Dec-95 May-96 Oct-96 Mar-97 Aug-97 Jan-98 Jun-98 Nov-98 Apr-99 Sep-99 Feb-00 Jul-00 Dec-00 Monthly discharge (cumecs) Jan-88 Jun-88 Nov-88 Apr-89 Sep-89 Feb-90 Jul-90 Dec-90 May-91 Oct-91 Mar-92 Aug-92 Jan-93 Jun-93 Nov-93 Apr-94 Sep-94 Feb-95 Jul-95 Dec-95 May-96 Oct-96 Mar-97 Aug-97 Jan-98 Jun-98 Nov-98 Apr-99 Sep-99 Feb-00 Jul-00 Dec-00 Monthly discharge (cumecs) Model calibration 900 800 Observed_1980-00 Simulated flows at Patan 700 Calibrated (1984-00) 600 500 400 300 200 100 0 Month/Year 500 450 Observed_1980-00 Simulated flows at Dindori 400 Calibrated (1984-00) 350 300 250 200 150 100 50 0 Month/Year
Jan-88 Jun-88 Nov-88 Apr-89 Sep-89 Feb-90 Jul-90 Dec-90 May-91 Oct-91 Mar-92 Aug-92 Jan-93 Jun-93 Nov-93 Apr-94 Sep-94 Feb-95 Jul-95 Dec-95 May-96 Oct-96 Mar-97 Aug-97 Jan-98 Jun-98 Nov-98 Apr-99 Sep-99 Feb-00 Jul-00 Dec-00 Monthly discharge (cumecs) Jan-88 Jun-88 Nov-88 Apr-89 Sep-89 Feb-90 Jul-90 Dec-90 May-91 Oct-91 Mar-92 Aug-92 Jan-93 Jun-93 Nov-93 Apr-94 Sep-94 Feb-95 Jul-95 Dec-95 May-96 Oct-96 Mar-97 Aug-97 Jan-98 Jun-98 Nov-98 Apr-99 Sep-99 Feb-00 Jul-00 Dec-00 Monthly discharge (cumecs) 300 250 Observed_1980-00 Calibrated (1984-00) Simulated flows at at Belkheri 200 150 100 50 0 Month/Year 4500 4000 3500 Observed_1980-00 Calibrated (1988-00) Simulated flows at Barmanghat 3000 2500 2000 1500 1000 500 0 Month/Year
Observed & Simulated Hydrographs at Barmanghat 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Jan-88 Jul-88 Jan-89 Jul-89 Jan-90 Jul-90 Jan-91 Jul-91 Jan-92 Jul-92 Jan-93 Jul-93 Jan-94 Jul-94 Jan-95 Jul-95 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Discharge (cumecs) Observed Simulated 0 500 1000 1500 2000 2500 3000 3500 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Discharge (cumecs) Observed Simulated
Model performance S. No. Name of the gauging site Calibration Validation NSE RSR PBias NSE RSR PBias 1. Dindori 0.85 0.39-7.48 0.85 0.32-4.56 2. Mohgaon 0.35 0.81-37.60 0.90 0.28-5.05 3. Manot 0.95 0.23-8.91 0.97 0.14-1.57 4. Patan 0.63 0.61-50.45 0.68 0.51-42.59 5. Belkheri 0.90 0.32-5.80 0.70 0.48-18.61 6. Barmanghat 0.79 0.46 13.93 0.79 0.28 8.33
Climate Change Impact Assessments Global Climate Models (GCM) Representation Concentration Pathways (RCPs) Downscaling Regional Climate Models (RCMs) Bias Correction (Quantile mapping) Calibrated and Validated SWAT Model Multiple outputs of streamflow
Climate Models, RCPs and Extreme Events Indices 6 RCMs (0.50 o x 0.50 o ) CCSM4 CNRM-CM5 GFDL-CM3 ACCESS1.0 MPI-ESM-L NOR-ESM-M 4 time-horizons 1970-05 (historical) 2006-40 (near term) 2041-70 (mid term) 2071-99 (end term) 2 RCP Scenarios RCP4.5 RCP8.5 S. Index Rainfall range 1. Wet day > 2.50 mm 2. Heavy rainfall day 100 mm - 200 mm 3. Very heavy rainfall day > 200 mm Sl. # Index Range 1. Extremely hot days MaxT > 45 o C 2. Summer days MaxT > 40 o C 3. Warmer Days MaxT > 35 o C 4. Summer nights MinT > 25 o C Source: European Climate Assessment & Dataset (ECA&D)
Year Year Year Scenario Analysis: Higher rainfall variability RCM mean average annual rainfall under RCP4.5 2039 2036 2033 2030 2027 2024 2021 2018 2015 2012 2009 2006 0 200 400 600 800 1000 1200 1400 1600 1800 RCM-Mean average annual rainfall (mm) 2068 2065 2062 2059 2056 2053 2050 2047 2044 2041 0 200 400 600 800 1000 1200 1400 1600 1800 RCM-Mean average annual rainfall (mm) 2098 2095 2092 2089 2086 2083 2080 2077 2074 2071 0 200 400 600 800 1000 1200 1400 1600 1800 RCM-Mean average annual rainfall (mm)
1-day maximum rainfall (mm) Future scenario : Higher extreme rainfall events 450 400 350 300 250 200 150 100 50 0 2006-40 2041-70 2071-99 ACCESS1.0 CCSM4 CNRM-CM5 GFDL-CM3 MPI-ESM-LR Nor-ESM-M RCM-Mean Future 1-day maximum rainfall
Future scenario: Higher extreme temperatures Extremely hot days RCM Time horizon RCM mean RCP4.5 RCP8.5 2006-40 1 in 3.50 1 in 2.69 2041-70 1 in 2.73 1 in 2.61 2071-99 1 in 2.60 1 in 2.26 Average daily maximum temperature : CCSM4 Time horizon Maximum of MaxT ( o C) Minimum of MaxT ( o C) Scenario RCP4.5 RCP8.5 RCP4.5 RCP8.5 2006-40 45.67 46.06 21.84 22.40 2041-70 45.64 46.20 21.13 21.43 2071-99 46.22 46.65 22.28 22.78 The summer days, warmer days as well as the summer nights are projected to increase in all future time horizons
Annual dependable flow (cumecs) Annual dependable flow (cumecs) Annual dependable flow (cumecs) Future scenario: Lower water availability 700.0 600.0 500.0 400.0 300.0 200.0 100.0 0.0 HISTORICAL SIMULATED ACCESS1.0 CCSM4 CNRM-CM5 GFDL-CM3 MPI-ESM-LR NOR-ESM-M 5 10 20 50 75 90 95 Probability of Exceedance (%) 700.0 600.0 500.0 400.0 300.0 200.0 100.0 0.0 HISTORICAL SIMULATED ACCESS1.0 CCSM4 CNRM-CM5 GFDL-CM3 MPI-ESM-LR NOR-ESM-M 5 10 20 50 75 90 95 Probability of exceedance (%) 800.0 700.0 600.0 500.0 400.0 300.0 200.0 100.0 0.0 HISTORICAL SIMULATED ACCESS1.0 CCSM4 CNRM-CM5 GFDL-CM3 MPI-ESM-LR NOR-ESM-M 5 10 20 50 75 90 95 Probability of exceedance (%)
2006 2010 2014 2018 2022 2026 2030 2034 2038 2042 2046 2050 2054 2058 2062 2066 2070 2074 2078 2082 2086 2090 2094 2098 3month-SDI (June to August) Future scenario: More surface water droughts 1.00 0.50 0.00-0.50-1.00-1.50 Hydrological drought scenario at Barmanghat (RCP4.5)
Jun-71 Jul-72 Aug-73 Sep-74 Oct-75 Nov-76 Dec-77 Jan-79 Feb-80 Mar-81 Apr-82 May-83 Jun-84 Jul-85 Aug-86 Sep-87 Oct-88 Nov-89 Dec-90 Jan-92 Feb-93 Mar-94 Apr-95 May-96 Jun-97 Jul-98 Aug-99 12m-SPI Future scenario: More groundwater droughts 4 3 2 1 0-1 -2-3 Groundwater Drought Scenario RCP8.5
# of events/year # of events/year Increased frequency of dry and wet events 1.40 (a) dry events RCP8.5 1.40 (b) wet events RCP8.5 1.20 1.20 1.00 Extremely dry 1.00 Extremely wet 0.80 Severely dry 0.80 Severely wet Moderately dry Moderately wet 0.60 0.60 0.40 0.40 0.20 0.20 0.00 1988-05 2006-40 2041-70 2071-99 0.00 1988-05 2006-40 2041-70 2071-99
Conclusions SWAT can be effectively used for hydrologic modelling of large river basins and used effectively for scenario analysis. Increasing maximum and minimum temperature during the historical and all future time-horizons. Higher extreme rainfall events in the recent and future time-horizons. Lower water availability during all future time-horizons. Magnitude of low flows and high flows to decrease in future time-horizons. More frequent droughts in all future time-horizons. Simultaneous occurrences of droughts and wet events with changing rainfall pattern with frequent dry spells calls for optimal water utilisation. Supply-side management of the available water coupled with demand-side management can be considered as a viable climate change adaptation tool