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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 4 – Item 3 S_Muddu

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IUKWC Workshop November 2016: Developing Hydro-climatic Services for Water Security
Session 4.3 Sekhar Muddu

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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 4 – Item 3 S_Muddu

  1. 1. Modeling hydrological changes using multiscale remote sensing in India Indo-UK Workshop on Developing Hydro-Climatic Services for Water Security 29th – 30th November 2016, IITM Pune http://civil.iisc.ernet.in/~mudduhttp://civil.iisc.ernet.in/~muddu Sekhar Muddu Department of Civil Engineering & Interdisciplinary Centre for Water Resources Indian Institute of Science, Bangalore Email: sekhar.muddu@gmail.com
  2. 2.  Water Budget components from RS.  ET estimation from Energy Balance method & India product at 5 km grid resolution. Soil moisture estimation from remote sensing & mapping crop-water stress regions.  Groundwater budget & Baseflow modeling: Hydrology outlook.  Framework for addressing climate change impacts on groundwater. Outline of the presentation 2
  3. 3. θ∆−∆−−=+ GWEPQQ GWs Surface & Groundwater Water Budget • Qs = Surface water runoff • QGW = Base flow • P = Precipitation • E = Evapotranspiration • ∆GW = Groundwater storage change • ∆θ= Change in soil moisture 3
  4. 4. Hydrological Modeling (conventional) Optical RS (e.g. IRS-LISS) Optical RS (e.g. IRS-LISS) Soil/crop parameters Weather variables DHMDHM Runoff, Recharge, Root zone soil moisture AET SSM Root zone soil moisture, AET & recharge at plot scale for entire watershed Root zone soil moisture, AET & recharge at plot scale for entire watershed Model 4 A C Q Rf ET Ro OVF2 TF1 TF2 TFn OVF1 OVFn S1 S2 SnP1 P2 Pn Rf Int Ovf TF Q Hydrological model BF GWD
  5. 5. Modeling approach with Remote Sensing MODISMODIS Active microwave Active microwave Passive microwave Passive microwave AET SSM SSM Soil/crop parameters Weather variables DHMDHM Runoff, Recharge, Root zone soil moisture AET SSM Data assimilation/parameter estimationData assimilation/parameter estimation Improved root zone soil moisture, AET & recharge at plot size for entire watershed Improved root zone soil moisture, AET & recharge at plot size for entire watershed Model Satellite 5 Optical RS (e.g. IRS-LISS) Optical RS (e.g. IRS-LISS) TRMM/ GPM TRMM/ GPM SARSAR
  6. 6. θ∆−∆−−=+ GWEPQQ GWs Surface & Groundwater Water Budget • Qs = Surface water runoff • QGW = Base flow • P = Precipitation • E = Evapotranspiration • ∆GW = Groundwater storage change • ∆θ= Change in soil moisture 6
  7. 7. Evapotranspiration Modeling – Energy Balance Method 7  The ET was estimated using S-SEBI and Triangle methods. Five sites with about 200 plus clear sky images from Terra and Aqua were used during 2009-12. It was observed that the Triangle method performed well with ground observations from the BREB towers of Energy, Mass Exchange project of SAC, ISRO and further extended through INCOMPASS (NERC-MoES) project Eswar, Sekhar, Bhattacharya (2013) Journal of Geophysical Res- Atmosphere 0 10 20 30 40 50 01-Jan 26-Feb 22-Apr 17-Jun 12-Aug 07-Oct 02-Dec PET AET Evapotranspiration(mm) Berambadi, Karnataka 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 LatentHeatfromSatelite(Wm-2) LatentHeat from AMS towers (Wm-2) Madhya Pradesh Dehradun Karnataka Rajasthan WestBengal R2 = 0.57 RMSE = 46 Wm-2 Bias = 24 Wm-2 MAE = 38 Wm-2 BREB Tower @ Beechanahalli Eddy flux Tower @ Beechanahalli
  8. 8. Modeling Evapotranspiration from Remote Sensing 8  The EF was estimated using S-SEBI and Triangle methods. Five sites and 150 plus clear sky images from Terra and Aqua were used during 2009-12. It was observed that the Triangle method compared well with the EF obtained from the BREB observations. EF at 250m was disaggregated using DEFrac model. 0 10 20 30 40 50 01-Jan 26-Feb 22-Apr 17-Jun 12-Aug 07-Oct 02-Dec PET AET Evapotranspiration(mm) Eswar, Sekhar, Bhattacharya (2013) Journal of Geophysical Res- Atmosphere
  9. 9. Mean diurnal cycles: Energy fluxes
  10. 10. Disaggregation of LST : Comparative analysis of different vegetation indices Temperature vegetation dryness index (Downscaled Soil Moisture?) Eswar, R., Sekhar, M., Bhattacharya, B. (2015) Disaggregation of LST over India: Comparative analysis of different vegetation indices, International Journal of Remote Sensing
  11. 11. 2010 ET Product ET product is from 2001- 2014 at 0.05 degree spatial resolution with 8- day frequency. The approach used for ET estimate is based on the Triangle method using MODIS (Eswar et al., 2013 & 2016). The method was validated using data from BREB tower sites of Energy, Mass Exchange project of SAC, ISRO. Eswar, Sekhar, Bhattacharya (2013) Journal of Geophysical Res- Atmosphere Eswar, Sekhar, Bhattacharya (2016) International Journal of Remote Sensing 11
  12. 12. Validation Sites 0 1 2 3 4 5 0 1 2 3 4 5 ET (mm/day) RS Model FluxTower RMSE = 0.7 mm 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 21-03-2012 14-04-2012 08-05-2012 01-06-2012 25-06-2012 19-07-2012 12-08-2012 05-09-2012 29-09-2012 23-10-2012 16-11-2012 10-12-2012 01-01-2013 25-01-2013 18-02-2013 14-03-2013 07-04-2013 01-05-2013 25-05-2013 18-06-2013 12-07-2013 05-08-2013 29-08-2013 Rainfall RS Model Flux Tower Evapotranspiration(mm/day) Rainfall(mm/week) Kabini CZO 12
  13. 13. Landuse&LandCover Sub-basins&Grids 750 800 850 900 950 2003 2005 2007 2009 2011 2013 2015 Kabini Lakshmanthirtha Hemavathy Shimsha Suvarnavathy Arakavathy AnnualET(mm) 2005 2007 2009 2011 2013 2015 Kabini Lakshmanthirtha Hemavathy Shimsha Suvarnavathy Arakavathy 700 750 800 850 900 950 2003 2005 2007 2009 2011 2013 2015 AnnualET(mm) 700 750 800 850 900 950 2003 2005 2007 2009 2011 20 Bhavani Chinar2 Chinar1 Noyal Amaravati Delta AnnualET(mm) 14
  14. 14. Cauvery:Districts 0.2 0.4 0.6 0.8 1.0 1.2 Kodagu Chikmagalur Hassan Mandya BangaloreUrban Tumkur BangaloreRural Mysore Chamarajanagar Annual(ET/P) 2014-2013 0.4 0.6 0.8 1.0 1.2 2002 2004 2006 2008 2010 2012 2014 Karnataka Tamilnadu Annual(ET/P) Karnataka 15
  15. 15. • Groundwater storage changes can be estimated at a good spatial resolution if the RHS is known - depletion is strongly linked to (P-AET) where ET/P =1 or >1 • Crop/ AET is a good sensor of excessive groundwater draft and so these hot spots can be delineated and modeled in priority. • In regional modeling, AET can be proxy for spatial variations of draft. GWEP ∆∝− ( )GWs QQEPGW +−−=∆ Modeling Groundwater storage changes at finer resolution 16
  16. 16. Mean (P-ET) = 200 mm For 10 years (2003-2013) (P-AET) = 2000mm = 2m GWL change = 2m / Sy ≈ = 20 m or = 40 m Precipitation – ET Trends 450 484 961 1071 681 997 927 848 867 841 539 652 806 -517 -473 24 25 -310 43 -136 -179 -206 -164 -444 -317 -194 -800 -300 200 700 1200 2002 2004 2006 2008 2010 2012 Mean Rainfall (mm/y) ET in mm/yearRainfall,(P-ET) P-ET mm/y 17 Sy = 0.1 or 0.05
  17. 17. Major crops – Sunflower, Marigold, Sorghum, Maize, Turmeric = 64% 18 October 2013 Major crops – Sunflower, Marigold, Sorghum, Maize, Turmeric = 66% September 2014 Comparison of ET from Energy & Water balance 0 5 10 15 20 25 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Water balance Energy balance Evapotranspiration(mm) P-ET mm/y 0 2 4 6 8 25-11-200102-02-200215-04-200226-06-200206-09-200217-11-200225-01-200307-04-200318-06-200329-08-200309-11-200317-01-200429-03-200409-06-200420-08-200431-10-200409-01-200522-03-200502-06-200513-08-200524-10-200501-01-200614-03-200625-05-200605-08-200616-10-200627-12-200606-03-200717-05-200728-07-200708-10-200719-12-200726-02-200808-05-200819-07-200829-09-200810-12-200818-02-200901-05-200912-07-200922-09-200903-12-200910-02-201023-04-201004-07-201014-09-201025-11-201002-02-201115-04-201126-06-201106-09-201117-11-201125-01-201206-04-201217-06-201228-08-201208-11-201217-01-201330-03-201310-06-201321-08-201301-11-2013 Mean daily Evapotranspiration (mm) Evapotranspiration(mm)
  18. 18. Salient features  Works in all weather condition  Spatial resolution = 500 m  Temporal resolution = 1 day SMOS satellite (CNES) MAPSM Algorithm RISAT-1 satellite (ISRO) Soil moisture persistence below a threshold is important indicator of water stress to crops Soil Moisture Modeling from Remote Sensing θ∆−∆−−=+ GWEPQQ GWs INNOVATIONS
  19. 19. Where, SM is soil moisture [v/v], is field capacity [-], is wilting point [-], F is the CDF, BC is the backscatter coefficient [dB], SM retrieval from active microwave Existing Enviroscansites ProposedHydra probe sites #49
  20. 20. Temporal resolution (days) Spatialresolution (m) 10 20 30 102 103 104 105 Active Passive MAPSM Passive microwave has good temporal resolution (1-3 days), but poor spatial resolution (~40 km) Active microwave has good spatial resolution (less than 100 m), but poor temporal resolution (~ 30 days) MAPSM provides soil moisture at both good temporal (3 days) and spatial resolution (less than 500 m) Merging Active and Passive Soil Moisture (MAPSM) Passive microwave:  SMOS  SMAP  AMSR2 Active microwave:  RISAT  RADARSAT-2  PALSAR MAPSM is applied over Karnataka state using RISAT and SMOS data. Next slides show the validation results and output for the same. 21
  21. 21. RADARSAT-2 retrieved soil moisture BC= f (Vegetation, Soil roughness, Soil moisture) Tomer et al. (2015) Retrieval and multi-scale validation of soil moisture from multi-temporal SAR data in a tropical region. Remote Sensing, 7(6), 8128-8153 22
  22. 22. MAPSM: Merged Active and Passive Soil Moisture Tomer et al. (2017) MAPSM: A Conceptual Spatio-temporal Algorithm to Merge Active and Passive Soil Moisture. Remote Sensing.
  23. 23. Remote Sensed high resolution relative soil moisture for KarnatakaRemote Sensed high resolution relative soil moisture for Karnataka Spatial resolution: 500 m; Temporal resolution: 1 day Relative soil moisture: 0 means driest possible 1 means wettest possible Latitude Longitude 24 RMSE 0.057 Correlation coefficient 0.81 RMSE 0.036 Correlation coefficient 0.79 Courtesy: Sat Tomer
  24. 24. COSMOS at Berambadi Cosmos, Steven’s Hydra probes & OTT @ K Madahalli, Chamarajanagar TQ. Monsoon dynamics and thermodynamics from the land surface, through convection to the continental scale (INCOMPASS). Sponsored by MoES and NERC. Courtesy: Ross Morrison & Jon Evans
  25. 25. Cumulative persistence of soil moisture below a threshold Example: Upper Cauvery 26 INNOVATIONS
  26. 26. Relative soil moisture time series plot Mandya Region (red shaded region) Soil moisture time series in location 1 – in Mandya district (red shaded region) has indicated relative soil moisture below 0.2 for nearly three months in Kharif 2016 27 INNOVATIONS
  27. 27. θ∆−∆−−=+ GWEPQQ GWs Surface & Groundwater Water Budget • Qs = Surface water runoff • QGW = Base flow • P = Precipitation • E = Evapotranspiration • ∆GW = Groundwater storage change • ∆θ= Change in soil moisture 28
  28. 28. UNESCO (2012) Irrigated area Map using RS Thenkabail et al. (2010) Net Irrigated area = 146 MhaNet Irrigated area = 146 Mha Major Irrigation = 55 MhaMajor Irrigation = 55 Mha Minor Irrigation = 91 MhaMinor Irrigation = 91 Mha Groundwater abstraction trends 29
  29. 29. Ground water – Stream flow links  Modeling the base flow to streams: “Hydrology outlook” for dry season flow (December-April) mainly depends on groundwater system 30
  30. 30. Groundwater budget Runoff Groundwater discharge Groundwater balance equation ( ) ( ) ( ),y G S net dh S R I D B O dt = + − − + Recharge Discharge ,y net dh S rR D h dt λ= − − λh corresponds to groundwater discharge term. In the current GEC approach this is not used & hence this information is missing. 31 Improved & simpler model
  31. 31. Local Scale Modeling for Processes & Parameters 10 15 20 25 30 35 Apr-10 Apr-11 Apr-12 Apr-13 Apr-14 Apr-15 Depthtogroundwater(m) Observed GWL(m) Simulated GWL(m) #103 5.7 5.7 5.3 8.2 9.3 0 5 10 15 2010 2011 2012 2013 2014 Honnegowdanahalli Rechargefactor(%) Rainfall (mean) = 900 mm, Recharge = 65 ± 25 mm GW Discharge = 40 ± 10 mm, Draft = 25 ± 5 mm 32UPSCAPE Project (2016-19) Sponsored by MoES and NERC.
  32. 32. Lat:11.83 N Long: 76.12 E Area:1260 km2 Record:1973-2012 Base flow modeling @ Muthakere (Kabini Basin) 0 20 40 60 80 15-Nov 30-Nov 15-Dec 30-Dec 14-Jan 29-Jan 13-Feb 28-Feb Dischargecum/sec 1999-2008 1990-1998 1975-1985 33
  33. 33. 0 5 10 15 20 25 Apr-90 Apr-92 Apr-94 Apr-96 Apr-98 Apr-00 Apr-02 Apr-04 SimulatedBaseflow(Cumec) Modeled Base flow  Aquifers have longer memory and base flows in a particular year would depend on previous years rainfall & uses.  Further baseflow modeling is helpful for closing the groundwater budget in the basin/sub-basin especially recharge (which is often uncertain due to specific yield). 34
  34. 34. Runoff Modeling and Trend Analysis over Main Land of India Source: ISRO
  35. 35. Grid # 6 Prerequisite: Developing gridded groundwater level data set for India to study impacts of climate change. An Example: Climate Change - Ground water 36 Error bars for GCMs
  36. 36. Kabini Critical Zone Observatory • What controls the resilience, response and recovery of a hydrological system to perturbations of climate and land-use changes? • How can sensing technology and modeling be integrated for simulation and forecasting of essential hydrological variables? • How can theory, data and mathematical models from natural- and social- sciences be integrated to simulate, manage river basin/catchments goods and services ? Hydrological observatories in the world Kabini basin observatory- Response of tropical ecosystems to global changes in Southern India CEFIRSE LMI 37
  37. 37.  Numerical modeling improved through groundwater level data mapped at higher granularity, AET from RS & simpler groundwater models for improved total water & groundwater budgets.  Base flow modeling of river basins/sub-basins and gearing up for “hydrology outlook”.  Developing gridded groundwater data, calibrating simpler models for the grids for modeling the climate change impacts and delineating grids requiring adaptation measures.  Soil moisture retrieval at 500 m spatial resolution at 2-day frequency through active & passive microwave satellites for mapping regions with persistence of crop-water stress. SUMMARY 38
  38. 38. 40 Source: CGWB Ganga basin (0.65 Million Km2 ) Hydrometeorological feedbacks and changes in water storage and fluxes in Northern India
  39. 39. 41 450 mm 2.5 mm Pumping (mm) Fall & Rise Falling Falling 2003-2012 Groundwater System behavior  Specific yield estimated using split- season approach (GEC, 1997). Groundwater pumping from Minor Irrigation Surveys.
  40. 40. One degree grids 0 500 1000 1500 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 (Precipitation - Evapotranspiration) mm 10, 11, 12, 13, 14, 20, 21, 22 27, 28 33, 34 38, 39, 40 43, 44 47, 48 52 (P-AET) Groundwater Pumping induced Evapotranspiration Evapotranspiration simulated is able to capture the trends of AET induced by groundwater pumping 42 Grid 11 2.5 5.0 7.5 10.0 May-03 May-04 May-05 May-06 May-07 May-08 May-09 May-10 May-11 May-12 Depthtogroundwater(m) Grid 20 Grid 43 y = 0.5x R² = 0.803 0 250 500 750 1000 0 500 1000 1500 2000 Hilly areas Precipitation (mm) Evapotranspiration(mm) ET influenced by groundwater pumping
  41. 41. 43  Gridded GWLs - #87 grids  Spatial resolution : 0.5o  Temporal : Monthly  Period : 1980-2012 Modeling the climate change impact on GW system
  42. 42. 44 Recharge function Pumping function Subash, Y., et al. In: Sustainable Water Resources Management, ASCE Book Chapter. (In Press).

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