Future Shift of the Relative Roles of Precipitation and Temperature in Controlling Annual Runoff in the Conterminous United States

Transcription

1 Future Shift of the Relative Roles of Precipitation and Temperature in Controlling Annual Runoff in the Conterminous United States Kai Duan 1, Ge Sun 2, Steven McNulty 2, Peter Caldwell 3, Erika Cohen 2, Heather Aldridge 1, and Yang Zhang 1 1 Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 2 Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Raleigh, NC 3 Coweeta Hydrologic Laboratory, USDA Forest Service, Otto, NC November 14, 2016, AWRA conference, Orlando

2 Background and motivation Precipitation (P) variability has been the major factor controlling annual runoff (R) in recent decades Watershed scale: (Karl and Riebsame, 1989), Regional scale (Gupta et al., 2015; Ryberg et al., 2014) Continental scale (McCabe and Wolock, 2011) Temperature (T) becomes increasingly important, but varies spatially Upper Colorado (Woodhouse et al., 2016) Central Rocky Mountains (Sospedra-Alfonso, 2015)

3 Questions and objectives How will the relative roles of P and T shift if the warming continues as projected by current climate models, instead of following recent trends? How will the relative roles of P and T vary spatially due to the various background climate, land-cover, topography, soil property?

4 Methodology Hydrological modeling with historic and future climates GCMs Downscaling Hydrological model Separate P and T effects on runoff (R) ΔR = ΔR P + ΔR T + ΔR P&T Quantify contributions of changing P and T Relative weights of each component in ΔR

5 Water Supply Stress Index Model (WaSSI) Snow model (McCabe and Markstrom, 2007) [Rainfall, Snowfall] = f (T, P, Elevation, Latitude) Evapotranspiration model (Sun et al., 2011) PAET = f (P, PET, LAI, Landcover) Soil water accounting model (Caldwell et al., 2012) [ET, Runoff] = f (PAET, Rainfall, Snowfall, Soil moisture, Landcover)

6 Datasets Dataset Source Original resolution Time period Historic Climate PRISM Climate Group 4 km Future Climate MACA downscaled projections derived from 20 CMIP5 GCMs ~6 km (1/16 deg) Leaf Area Index Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km Land-cover Distribution Soil Properties 2006 National Land Cover Database for the conterminous US STATSGO-based Sacramento Soil Moisture Accounting Model Soil Parameters 30 m km N/A Elevation USGS National Elevation Dataset 30 m N/A

7 Model setup Temporal resolution: Monthly Spatial resolution: 12-digit HUC (~100 km 2 ) Domain: Conterminous U.S. (CONUS) Time periods: Historical: , PRISM datasets Future: , MACA datasets including data downscaled from 20 CMIP5 GCMs

8 Historical vs. future T 2010s: s: s: s: s: s: 12.3

9 Historical vs. future P

10 Historical vs. future R

11 Methodology Hydrological modeling with historic and future climates GCMs Downscaling Hydrological model Separate P and T effects on runoff (R) ΔR = ΔR P + ΔR T + ΔR P&T Quantify contributions of changing P and T Relative weights of each component in ΔR

12 Separating individual effects ΔR = ΔR P + ΔR T + ΔR P&T ΔR: total effect of P and T, ΔR = R{P(t 2 ),T(t 2 )}-R{P(t 1 ),T(t 1 )} ΔR P : independent effect of P, ΔR P = R{P(t 2 ),T(t 1 )}-R{P(t 1 ),T(t 1 )} ΔR T : independent effect of T, ΔR T = R{P(t 1 ),T(t 2 )}-R{P(t 1 ),T(t 1 )} ΔR P&T : effect of interactions between changes in P and T, ΔR P&T = ΔR - ΔR P - ΔR T Quantifying contributions Sensitivity test method The three components contributions to runoff change are quantified by their weights. For example, P contribution is calculated as: Con (P) = 100% ΔR P / ( ΔR P + ΔR T + ΔR P&T )

13 Modeling experiments Historical changes From (pre-change) to (post-change) Future changes (against ) S1: RCP4.5/2030s, (near future) under RCP4.5 S2: RCP4.5/2080s, (far future) under RCP4.5 S3: RCP8.5/2030s, (near future) under RCP8.5 S4: RCP8.5/2080s, (far future) under RCP8.5

14 Effects on overall runoff RCP4.5/2030s RCP4.5/2080s RCP8.5/2030s RCP8.5/2080s ΔR (Total effect) = ΔR P + ΔR T + ΔR P&T Medians: ΔR P : 6% ~ 12% ΔR T : -10% ~ -27% ΔR: -3% ~ -11%

15 Contributions to runoff change RCP4.5/2030s RCP4.5/2080s RCP8.5/2030s RCP8.5/2080s Medians: Con(P) : 31% ~ 39% Con(T) : 58% ~ 65% Con(P&T): 3% ~ 6%

16 Contributions by 18 Water Resource Regions (WRRs) Historical: Con(P) > 99% Future: Con(T) > Con(P) in most cases Con(P) > Con(T) in WRR#1 (S1~S3), #12 (S1), #18 (S1~S4)

17 Dominant drivers of runoff change RCP4.5/2030s RCP4.5/2080s RCP8.5/2030s RCP8.5/2080s Indicator of dominance: 1) Contribution > 50% 2) Significant by Wilcoxon signed-rank test *MCP: multi-model mean contribution of P MCT: multi-model mean contribution of T

18 Cross comparison of the areal proportions (%) Scenario S1 S2 S3 S4 P-dominant R R Total T-dominant R R Total Non-dominant (MCP > MCT) R R Total Non-dominant (MCT > MCP) R R Total

19 Summary Despite the increasing P, R is projected to decrease Increasing T is likely to outweigh P variability in controlling annual runoff Increasing T and P tends to dominate runoff-decreasing and runoff-increasing watersheds, respectively Water resources planning needs to prepare different management strategies for P-dominant and T-dominant areas that have contrasting future hydrological conditions

20 Thank you! Kai Duan