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Impact of climate change on ecohydrologic processes of Sierra mountain watersheds: importance of fine scale variation of microclimate, topography, vegetation and soil Kyongho Son and Naomi Tague University of California, Santa Barbara 2012 Annual Southern Sierra CZO meeting Research topics 1) Effect of DEM resolution on model estimates of ecohydrologic response to climate. 2) Effect of vegetation parameters (LAI and canopy fraction) on mode estimates of ecohydrologic response to climate. 3) Effect of soil parameter uncertainty on model estimates of ecohydrologic response to climate 4) Strategic sampling design for soil moisture, transpiration and microclimate. 5) Climate warming effect on ecohydrologic responses of two small Sierra mountain watersheds. Study Sites Bull Watersheds Providence Watersheds P301 B201 B203 P303 B204 P304 D104 Wolverton Watershed T003 Ecohydrologic modeling (RHESSys) RHESSysframework Model Vertical hydrologic processes Carbon and nitrogen processes Horizontal hydrologic processes 1) DEM resolution effects on the sensitivity of ecohydrologic predictions to inter-annual climate variability Two hypotheses: 1) Estimates for rain-snow transition watersheds are more sensitive to DEM resolution than snow-dominated watersheds 2) Estimates for watersheds with flashy hydrologic responses are more sensitive to DEM resolution than watersheds with slow hydrologic responses. * hydrologic response time is the function of climate, soil properties (soil depth, porosity and Ksat), and slope. 1) Watershed Classification Watershed Precipitation type slope Soil depth Drainage rates Descriptive name Hypothesized Ranka P301 Snow-rain(T) Mild Shallow fast TMS 2 P303 Snow-rain(T) Mild Intermediate fast TMI 3 D102 Snow-rain(T) Steep intermediate fast TSI 1 B201 Snow Mild Intermediate fast SMI 6 B203 Snow Mild Shallow fast SMS 5 B204 Snow Mild Intermediate fast SMI 6 T003 snow Steep Deep fast SSD 4 1) Flow prediction accuracy to DEM resolution Q Reff 1 Qsim,i 2 obs,i i Qsim,i Qobs 2 i Rlogeff 1 log( Q obs,i ) log( Qsim,i ) 2 i log( Q sim,i PerErr Q i sim Qobs ) log( Qobs) 2 Qobs Total Accuracy Reff Rlogeff (1 PerErr ) 1) Model accuracy for snow-rain transition watersheds are higher sensitive to DEM resol ution than snow-dominated watershed 2) Model accuracy for watersheds with steep slopes are higher sensitive to DEM resoluti on than watersheds with mild slopes 2) The sensitivity of ecohydrologic predictions to vegetation parameters LAI resolution Canopy fraction 2) Impact of canopy fraction map on model accuracy, and model estimates of ecohydrologic response to climate Snow-dominated watershed(B203) Soil parameters Model accuracy Model estimates of Peak SWE, ET and NPP Effect of rooting depth and litter biomass on model estimates of ecohydrologic response to climate? 3) Impact of soil parameter uncertainty on model estimates of ecohhydrologic response to inter-annual climate variability Hypothesis: model estimates for snow-rain transition watershed is more sensitive to soil parameter uncertainty than model estimate for snow-dominated watersheds Predictive uncertainty measure 1 T U (Qt (2 std ) Qt (2 std ) T t • Variation of total accuracy is due to soil parameter uncertainty. • Behavior parameter sets are selected based on total accuracy (>0.3). •Uncertainty boundary is calculated using mean streamflow ±2x standard deviation of streamflow across selected soil parameters sets. • SMS has higher model accuracy than TMS. • SMS has higher predictive uncertainty than TMS. 3) Impact of soil parameter uncertainty on model estimates of ecohhydrologic response to inter-annual climate variability Snow-dominated watershed •The model estimates of ecohydrologic response to interannual climate has high variation across behavior soil parameter sets. •The estimate of summer streamflow has higher variance across soil parameter sets with lower precipitation. •The estimates of annual ET and annual NPP has highest variance across soil parameter sets at intermediate precipitation. 4) Strategic sampling of soil moisture and transpiration Objective: Using strategically sampled soil moisture and vegetation water use data to improve the model prediction and reduce the parameter and predictive uncertainty. CZT4 CZT7 CZT5 CZT8 CZT6 CZT3 4) Strategic sampling of microclimate Objective: Using fine-spatial scale micro-climate data to quantify the effect of including micro-climate variation in model-based climate change impact analysis 1) Topographic gradients 4) Strategic sampling of microclimate 3) Potential cold air pooling area Cold air pooling based on Lundquist et al.(2008) 5) Effect of climate warming on ecohydrologic response of small Sierra Mountain watersheds Three aspects 1) snow-rain transition watershed vs snowdominated watershed 2) Shallow-soil depth vs deep soil depth 3) Scale (small watershed vs large watershed) Inter-annual ecohydrologic variation Seasonal ecohydrologic variation P303 B203 P303 B203 Spatial control on ET •Elevation •Aspect •LAI •Wetness Linear regression model :Pearson correlation coefficient Thanks and any questions?
