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Climate Change Impact on Seasonal Runoff Predictability in Sierra Nevada Watersheds
Minxue (Kevin) He*, Mitchel Russo*, Michael Anderson** *DWR/Hydrology Branch/River Forecasting Section **DWR/State Climatologist
[email protected] November 3, 2016
Acknowledgements
Stephen Nemeth, John King, Hyun-Min Shin, Angelique Fabbiani-Leon
Outline
2/22
Introduction (slide 3)
Method (slides 4-5)
Results (slides 6-20)
Summary (slides 21-22)
Introduction
3
Why do we need seasonal streamflow prediction? Water supply (State Water Project: allocation quota)
Reservoir operations (drought/flood/water quality management, hydropower etc.)
Farming (what to plant well before the growing season starts)
What is the current forecasting practice? Regression equations seasonal streamflow forecasts (~1930)
Predictand (dependent): April-July streamflow runoff volume [AJ FNF]
Predictors (independent): snow (April 1st [A1 SWE (#1)]), precipitation (Oct.-March [OM Ppt (#2)],April-June [AJun Ppt (#3)]), runoff ([OM FNF (#4)])
Is the relationship between them stationary in a changing climate?
What will this presentation cover? What changed (in a hydroclimatic sense) by Clim. Change? How is the AJ runoff predictability responding to Clim. Change?
Method: Study Area
4
1
2 3 4
5 6 7 8
9 10
11 12
Sacramento (Hydrologic Region) 4 Basins: 1. Bend Bridge [SBB], 2. Feather [FTO], 3. Yuba [YRS], 4. American [AMF]
San Joaquin 4 Basins: 5. Stanislaus [SNS], 6.Tuolumne [TLG], 7. Merced [MRC], 8. San Joaquin [SJF]
Tulare 4 Basins: 9. Kings [KGF], 10. Kaweah [KWT], 11. Tule [SCC], 12. Kern [KRI]
Study Basins Basin Characteristics
Method
5/22
Study Period: Water Year 1930-2015 (86 years) Study Dataset (basin-scale)
Annual A1 SWE (DWR California Data Exchange Center [CDEC])
Monthly observed/calculated Full Natural Flow (FNF) (CDEC)
Monthly precipitation, average, max, min temperature (PRISM)
Study Metrics Correlation (AJ runoff and predictors) predictability
How correlation evolves how predictability changes Trend slope changing rate
“Rank-based” linear regression (References)
Significance level α = 0.05 (5%) References S. Yue and C.Y. Wang (2002), Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test, Water Resources Research, 38 (6), 1068
M. He and M. Gautam (2016), Variability and Trends in Precipitation, Temperature and Drought Indices in the State of California, Hydrology, 3 (2), 14
Example Plots
6
Example Time Series and Trend Line
Basin A
Basin B
Basin C
Slope of Trend Line
Water Year
WY1930-2015 α = 5%
Y = 0.015X + C1
Y = C2
Y = -0.01X + C3
Trend Slope
Basin ID
0.015
-0.01
0
Results: Hydroclimatic Trend
7
Minimum temperature: trend slope [ºC/year; 1930-2015;α=5%]
In 86-yrs: +2.19 ºC +2.2 ºC +2.16 ºC +1.74 ºC +2.60 ºC
Trend slope
Basin ID Temporal scale Average
0.025 ºC/year
0.026 ºC/year
0.025 ºC/year
0.02 ºC/year
0.03 ºC/year
Results: Hydroclimatic Trend
8
Average temperature: trend slope [ºC/year; 1930-2015;α=5%]
Increasing at annual scale and in Summer; Fall: 7/12 basins; Winter:11; Spring:6
Basin ID
Trend slope
Temporal scale Average
0.015 ºC/year (1.3 ºC in 86 yrs)
Average 0.018 ºC/year (1.5 ºC in 86 yrs)
Results: Hydroclimatic Trend
9
Maximum temperature: trend slope [ºC/year; 1930-2015;α=5%]
No significant trends for Sac and San Joaquin basins; no trends in Fall
Basin ID
Trend slope
Temporal scale
Results: Hydroclimatic Trend
10/22
Precipitation: trend slope [inch/year; 1930-2015;α=5%]
Only trend in SCC (Tule River) at annual scale: -0.007 inch/yr (-0.7 inch/century)
Basin ID
Trend slope
Temporal scale
Results: Hydroclimatic Trend
11
Predictand and predictors: trend slope [1930-2015;α=5%] Basin ID
Trend slope
Predictand Predictors
No significant trend at any basin
R
End Year of the 20-year Moving Window
Example Calculation and Plot
12
Correlation predictability
Water Year 1930 1931 1932 1933 1934 …. 1949 1950 1951 … 2015
AJ Volume (TAF) 225 102 507 293 113 … 832 965 495 … 256
Precipitation (inch) 3.7 3.6 3.1 1.9 5.5 … 2.9 1.5 4.2 … 1.2
Correlation 0.16 0.2 0.24 .. 0.58 1930-1949
1931-1950
1932-1951
1996-2015
Example calculation of moving 20-year correlation (R)
Example plot of the moving 20-year correlation (R)
1949-2015 (67yrs)
Results: Predictability
13
Basin scale moving 20-yr R: AJ runoff & predictors
Variation pattern similar; Major predictors: A1 SWE and OM Ppt
R
R
Results: Predictability
14
Regional scale moving 20-yr R: AJ runoff & predictors
Predictability weaker for Sacramento Region; Increasing: AJun Ppt
R
R
Example Plots
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Example Time Series and Trend Line Slope of
Trend Line
End Year of the 20-year Moving Window
1949-2015 (67yrs) α = 5%
Basin A
Basin B
Basin C
R
R
R
Y = 0.015X + C1
Y = C2
Y = -0.01X + C3
Trend Slope
Basin ID
0.015
0
-0.01
Results: Predictability Trend
16
Trend slope of predictability from A1 (April 1) SWE
Mostly increasing except for FTO (Feather) and SCC (Tule) Increasing most significant for SBB (Sac. @ Bend Bridge)
Trend Slope (/decade)
Basin ID
Unit: []/decade; 1949-2015;α=5%
Results: Predictability Trend
17
Trend slope of the predictability from OM (10-3) FNF
Increasing at eight basins (4 Sac; 3 San Joaquin; 1 Tulare) Decreasing at SCC (Tulare)
Trend Slope (/decade)
Basin ID
Unit: []/decade; 1949-2015;α=5%
Results: Predictability Trend
18
Trend slope of the predictability from OM (10-3) Ppt
Increasing at Sac and San Joaquin basins Decreasing at SCC (Tule River)
Trend Slope (/decade)
Basin ID
Unit: []/decade; 1949-2015;α=5%
Results: Predictability Trend
19
Trend slope of the predictability from AJun (4-6) Ppt
Increasing across all study basins
Trend Slope (/decade)
Basin ID
Unit: []/decade; 1949-2015;α=5%
Results: Predictability Trend
20
Trend slope in predictability from predictors (Regional)
Sac and San Joaquin: Predictability from all predictors - increasing Tulare: from A1 SWE & AJun Ppt - increasing; OM Ppt – decreasing Increasing rate highest: Preidctor AJun Ppt; Region Sac
Trend Slope (/decade)
Predictors
Unit: []/decade; 1949-2015;α=5%
Summary
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What have been changed by Clim. Change (CC)? Minimum and Average temperatures increasing (Summer: highest)
Precipitation, runoff (FNF) & snowpack (A1 SWE): no change
How resilient is AJ runoff predictability to CC? Predictability more from A1 SWE & OM Ppt (v.s. OM FNF & AJun Ppt) Predictability trend: basin-dependent, predictor-dependent
from A1 SWE & AJun Ppt: increasing from OM FNF & OM Ppt: decreasing at a Tulare basin (Tule River) increasing most significant: Sacramento region
Implication/value of this work: Don’t cry wolf (yet)
Current regression equations valid/usable in a changing climate Adapting to dance with the wolf
Adjust the weight of each predictor Focus on basins with relatively lower predictability
T H A N K
Y O U !
22/22
1. Reference:
M. He, M. Russo and M. Anderson (2016), “Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada”, Climate (under review)
2. What makes a crystal ball (for seasonal runoff forecasting)?
Model Forecaster (“Good Feel”)
Data