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Predicting effects of climate change on stream ecosystems in the conterminous United States: results from a pilot study in California Charles P. Hawkins Jiming Jin, David Tarboton, Ryan Hill & John Olson 1 USEPA-STAR funder project: Predicting effects of climate change on stream biodiversity in the USA Maximum Annual Air Temperature (1971-2000) (~ 0- 30 C) 2 Annual Mean Precipitation (1971-2000) (<10 – 400 cm / year) 3 How will climate change affect stream invertebrate faunas at both reference and previously stressed sites? Will effects of climate change confound biological assessments? Are certain taxa and types of streams more vulnerable to climate change than others? 4 National Wadeable Stream Assessment Reference Sites 5 Climate Change Greenhouse gas forcing GCM then RCM 6 hr 20 km resolution USGS Streamflow Statistical downscaler Physically based hydrologic model Physically based change predictions DEM, land cover, Soils, geology Statistical streamflow regime model PCA factors Macroinvertebrate composition and assemblage structure Streamflow classification WSA Probabilistic Sites 7 V California as a pilot study 1999 variation in mean annual temperature (oC) (PRISM data) 8 9 Probabilities of detection (PD): A foundation for estimating biodiversity and calculating biological indices PD are associated with both abundance and richness: – MMIs: • Abundance-based metrics • Richness metrics – O/E = ∑O/∑PD 10 Hypothetical Changes in PD & Taxa Richness Taxon A B C D E etc. NTaxa 1999 0.87 0.01 0.92 0.74 0.16 0.60 3.30 1900 0.85 0.03 0.88 0.75 0.20 0.64 3.35 2040 0.41 0.20 0.71 0.77 0.05 0.50 2.64 2090 2090-1999 0.22 -0.65 0.36 +0.35 0.58 -0.34 0.74 0.00 0.00 -0.16 0.31 -0.29 2.21 -1.09 11 NCAR PRISM 1999-2009 CCSM Temperature Stream A2 Precipitation Benthic (4 km) Invertebrate (150 km) Data Downscaled Climate RIVPACS Predictions Model 1999-2008 Predicted 1900-1909 Taxon2040-2049 Catchment Specific PD 2090-2999 Data 12 Estimating O/E 1999 calibration: ∑O/∑pd 1900, 2040, 2090 predictions: ∑pd(year) / ∑pd(1999) 13 14 The California reference sites: 340 taxa. 327 sites classified into 14 groups for model building. 12 site classes Group 13 15 Group Group Group 2.5 14 13 1 16 14 12 9 10 11 8 7 6 5 4 3 2 13 12 11 0 9 10 10 8 20 7 Group 6 ● 5 40 4 60 2 0 3 50 1 5 2 10 Basin Area (log km2) ● Conductivity (log uS/cm) 14 13 20 1 30 14 12 9 10 11 8 7 6 5 4 3 2 1 MAAT (C) 15 13 12 11 9 10 8 7 6 5 1.0 4 ● 3 2.5 2 2.0 Elev Range (SQRT m) Group 13: warmest driest highest TDS 1 14 1.5 13 12 9 10 11 8 7 6 5 4 3 2 1 Annual PPT (mm) 5 Predictors 25 4 3 ● 0 Group 3.0 ● 2.0 1.5 1.0 30 20 MAAT Predicted changes in climate-sensitive predictors 10 0 3.0 2.5 1900 1999 2040 2090 Decade 2.0 logCond logPPT 2.5 1.5 2.0 1.5 1.0 1900 1999 2040 2090 Decade 1.0 1900 1999 2040 2090 Decade 17 A2 Climate Change Scenario (CCSM 250 -> 4 km empirically downscaled predictions) Mean Annual Temperature (oC) 1900 backcast 1999 modeled 2090 forecast A2 Climate Change Scenario (CCSM 250 -> 4 km empirically downscaled predictions) Mean Monthly Precipitation (mm) 1900 backcast 1999 modeled 2090 forecast 2090-1999 comparisons 20 Changes in climate-sensitive predictors (2090-1999) across 327 reference sites Precipitation Statistic (mm) Mean -26 Minimum -61 Maximum +5 Temp (C) 2.4 1.4 4.6 Cond (μS/cm) 48 -30 180 21 22 Individual taxa • Average changes in PD – 172 decreasers (8 ≤ 0.1) – 168 increasers (1 ≥ 0.1) • Many predicted local extinctions (rare taxa). • No predicted regional extinctions. 23 Most Sensitive Taxa (mean ∆PD) -0.16 +0.10 -0.14 +0.08 -0.13 +0.07 24 What is the consequence for reference site taxa richness and O/E? 25 ~10% loss in mean site richness by 2090 No loss in regional richness 26 Mean ∆O/E = -0.12 40% -1.0 -0.5 12% 0.0 0.5 1.0 OE5_2090_1999 Change in O/E (2090-1999) 27 28 Predictors of Site Vulnerability (Δ O/E) (PRE = 0.24) -0.12 (n=327) MAAT > 9.8 MAP < 58 mm -0.16 -0.04 (260) (67) -0.23 Coldest Driest -0.06 MAAT < 16.5 (150) (110) -0.26 0.18 (139) (11) Warmest Artifact associated with end-member predictions? 29 How realistic are these predictions? • Climate predictions? – Accuracy of CCSM? – Accuracy of downscaling? • Biota predictions? – General accuracy of RIVPACS model? – End-member problem? 30 Related Issues and Work • Downscaling – some caveats – Empirical versus dynamic – Yearly/seasonally versus regime • Invertebrate-environment relationships – Stream temperature models – Flow models 31 A2 2090 Temperature CCSM Original (150 km) o ( C) CCSM Downscaled (4km) Stream Temperature Modeling Pilot Study N = 455 50 validation Modeling (MLR) XTEMP Model Geography Climate-WS Predictors elevation latitude WS area longitude elevation mean air temperature latitude WS area # of frost-free days others R2 0.75 RMSE 1.3o 0.86 1.0o Geography model SD = 1.5 Climate-WS model SD = 1.0 XTEMP Residuals Variable XTEMP SUMMER WINTER Predictors R2 RMSE elevation mean air temperature latitude # of frost-free days WS area 0.86 1.0o 0.73 2.2o 0.75 1.7o maximum air temperature # of frost-free days latitude soil bulk density WS area mean air temperature minimum air temperature soil permeability % granitic geology depth to water table Do modeled stream temperatures improve predictions of biota? 7 classes of CO reference sites n = 132 How well did 2 RIVPACS-type models predict invertebrate composition in Colorado reference streams? Model Predictors Both Day of year Long-term precipitation Previous year precipitation Local topographic relief Latitude Elevation WS area SUMMER WINTER Geography Predicted Temperature % correct by model Groups Surrogates 3 Temps 1 47 47 2 73 82 3 73 73 4 64 80 5 8 33 6 33 60 7 53 53 Total 54 63 Change 0 +9 0 +16 +25 +27 0 9 Expanded stream temperature modeling EPA-STAR grant & USGS/NAWQA collaboration 41 Stream temperature candidate reference sites Stream flow modeling by David Tarboton and students 43 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Magnitude Predictability Flood duration Seasonality Flashiness Baseflow Zerodays Magnitude Predictability Flood duration Seasonality Flashiness Baseflow 6 Zerodays 6 1, 160 6 4 4 2 2 0 0 -2 -2 -4 -4 3, 113 6 4 4 2 2 0 0 -2 -2 -4 -4 2, 140 4, 130 1 0.1 0.01 10 1 0.1 0 1 0.1 Months 0.01 8, Small flashy streams 3 Monthly mean flow m s 7, Small unpredictable streams 100 0.01 Centroid sites 50th quantile 5th - 95th range 6, Big seasonal streams 0.01 0.1 Dec Feb Apr Jun AugOct Dec Feb Apr Jun 1 10 Months 0.01 0.1 0 1 10 Months 100 5, Baseflow dominated streams 3 Monthly mean flow m s Months 10 Months 100 10 3 Monthly mean flow m s 100 10 1 0.1 0.01 100 3 Monthly mean flow m s 4, Big streams with low predictability Oct 100 2, Smaller predictable 3, Mid-size perennial intermittent streams with low baseflow streams with low seasonality 1, Seasonal streams Aug Oct Dec Feb Apr Jun Aug Modeled stream flows and temperatures improve predictions of stream invertebrates Model Null Flow (7 factors) Temperature (3 variables) Flow + Temperature O/E 10th % 0.58 0.73 0.67 0.80 47 Flow and temperature modeling sites