Variation of Surface Soil Moisture and its Implications Under Changing Climate Conditions 1.

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Presentation transcript:

Variation of Surface Soil Moisture and its Implications Under Changing Climate Conditions 1

Outline: 2 2

Motivation Problem Statement: Surface soil Moisture is highly variable in space and time and often difficult to predict using LSMs Problem Statement: Surface soil Moisture is highly variable in space and time and often difficult to predict using LSMs Linkage: Global climate models need accurate initial and boundary conditions for better prediction and forecasting Linkage: Global climate models need accurate initial and boundary conditions for better prediction and forecasting 3 3

Importance of surface soil moisture 4 4

Study Region: (Illinois & Indiana) 19 sites in Illinois Soil Moisture data at 10 cm depth ( Global soil Moisture Data Bank (Robock et al, 2000) Climate Variables : Precip, Air temp., Soil Temp., Solar Radiation, Potential ET ( Illinois Climate Network (ICN) Soil Type: Silt Loam, silt clay loam 11 sites in Indiana Soil temperature data Monthly Stream Flow Data from USGS for 5 gauging stations Latent Heat Flux Data 19 sites in Illinois Soil Moisture data at 10 cm depth ( Global soil Moisture Data Bank (Robock et al, 2000) Climate Variables : Precip, Air temp., Soil Temp., Solar Radiation, Potential ET ( Illinois Climate Network (ICN) Soil Type: Silt Loam, silt clay loam 11 sites in Indiana Soil temperature data Monthly Stream Flow Data from USGS for 5 gauging stations Latent Heat Flux Data 5 5 AmeriFlux Sites

Objectives:Objectives: 6 6

Annual anomalies and soil moisture Annual anomalies show good relationship with soil moisture anomaly _____ Soil Moisture ______ Precipitation. ______ Air Temperature ______ Potential Evapotranspiration Observed Data 7 7

Monthly Stream flow Simulation Calibration Period ( ) Validation Period ( ) Total Period ( ) Observed Simulated Nash-Sutcliffe Eff. ( ) Calibration Period ( ) Validation Period ( ) Total Period ( ) Observed Simulated Nash-Sutcliffe Eff. ( ) 8 8

Solar Radiation Simulations VIC model predicts solar radiation in a good agreement with observation The over prediction for spring season It may influence spring season soil moisture dynamics VIC model predicts solar radiation in a good agreement with observation The over prediction for spring season It may influence spring season soil moisture dynamics Obs Sim.

Soil Temperature Simulation (10 cm) Soil Temperature effects long wave radiation, sensible and ground heat flux There is a good agreement between simulated and observed soil temperature, however model under predicts summer soil temperature Soil Temperature effects long wave radiation, sensible and ground heat flux There is a good agreement between simulated and observed soil temperature, however model under predicts summer soil temperature Obs Sim 10

Soil Temperature Simulations in Indiana (10 cm) Obs Sim The under prediction is more in summer than winter It will influence summer ET, soil temperature and therefore soil moisture The under prediction is more in summer than winter It will influence summer ET, soil temperature and therefore soil moisture

Latent Heat Flux Simulations Observed data from Ameriflux network 3 Sites in Illinois and Indiana Might be land use effect 12

Model captures less temporal variability than observed soil moisture Over prediction in summer might be due to under prediction of solar radiation and soil temperature Site no -11: outwash plains Site no – 12 : poorly drained soil on knoll Site no -34: Gravely loamy sand Model captures less temporal variability than observed soil moisture Over prediction in summer might be due to under prediction of solar radiation and soil temperature Site no -11: outwash plains Site no – 12 : poorly drained soil on knoll Site no -34: Gravely loamy sand Soil Moisture Simulation (0-10 cm) Obs Sim. 13

Simulated monthly anomaly of soil moisture shows less persistence Soil Moisture Persistence (Obs. Vs Sim.) Obs Sim. 14

Overall Model Performance for 16 sites (Monthly Time Scale) Coefficient of variation for simulated soil moisture is under predicted 15

Soil Moisture Interaction (Avg. of 16 sites) Annual soil moisture anomalies have strong correlation with sensible and latent heat fluxes anomalies Simulation Period ( ) Annual soil moisture anomalies have strong correlation with sensible and latent heat fluxes anomalies Simulation Period ( ) 16

Projected Change (PCM.A1 ( ) – Observed Forcing ( ) ) Seasonal Average FluxesAnnual Average Fluxes 17

Projected Climate Impact on Land Surface Variables (Avg. of 16 sites) IPCC AR3 Climate model output (PCM, HadCM3, GFDL ) Scenarios: A1, B1 and SresA1F1, SersB1 ____Observed ( ) ___ GFDL ( ) ___ PCM ( ) ____HadCM3 ( ) Projections: Decrease in soil moisture – increase in soil temp- increase in ET- increase in LHF- decrease in SHF IPCC AR3 Climate model output (PCM, HadCM3, GFDL ) Scenarios: A1, B1 and SresA1F1, SersB1 ____Observed ( ) ___ GFDL ( ) ___ PCM ( ) ____HadCM3 ( ) Projections: Decrease in soil moisture – increase in soil temp- increase in ET- increase in LHF- decrease in SHF 18

Conclusions: VIC model simulates surface soil moisture, soil temperature and other variables which are in good agreement with observations Soil moisture anomalies of observed data show good correlation with other climatic variables Climate model projections show decrease in soil moisture, increase in ET, increase in ST which might lead to significant changes in energy fluxes 19