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Experimental Inflow and Storage Forecasts Portal Harminder Singh Department of Civil and Environmental Engineering State Climate Office of NC 1.

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Presentation on theme: "Experimental Inflow and Storage Forecasts Portal Harminder Singh Department of Civil and Environmental Engineering State Climate Office of NC 1."— Presentation transcript:

1 Experimental Inflow and Storage Forecasts Portal Harminder Singh Department of Civil and Environmental Engineering State Climate Office of NC 1

2 Presentation Outline 1.Introduction and Objectives 2.Inflow Forecasting Model 3.Storage Forecasting Model 4.Inflow and Storage Portal - Overview 5.Inflow Forecasts (Monthly and Seasonal) 6.Storage Forecasts (Monthly and Seasonal) 7.Conclusion and Future Work Falls Jordan Philpott Kerr Scott Rocky Creek SF Catawba 2

3 Presentation Outline 3

4 Need for Inflow and Storage Forecasts Need of Inflow and Storage Forecasts – Recent Increase in Demand – Urbanization – Demand induced droughts even under normal inflow variability Streamflow Forecasts and Water Management – Daily to Weekly time scale – Flooding, Peak power generation – Monthly to Seasonal Time Scales – Allocation and Firm Power Provide Inflow and Storage forecasts for Reservoirs in the Southeast US Inflow and Storage Forecasts Portal – Portal Overview and Skill Assessment 4

5 Inflow Forecasts - Challenges Monthly to Seasonal Inflows – Monthly to Seasonal Climate over the watershed – Current Basin Storage – Soil Moisture and Ground water Challenges in Seasonal Streamflow Forecasting – Climate Forecasts – Needs to be downscaled – Limited Basin Storage Data Streamflow Forecasts and Reservoir Management – Streamflow Forecasts needed at the reservoir site – Interest on net-inflows = Total streamflow - Evaporation – Inflow forecasts needs to be probabilistic 5

6 Need for Storage Forecasts Reservoirs in the east are within-year – Humid basins – Fill it up by April 1 st – Winter is the critical filling period – good skill Why we need storage forecasts? – Inflow forecasts related to storage projections – Initial conditions in the reservoirs also influence Issues in developing storage forecasts – Need to consider end of the month/season target storage – Varies depending on the user-defined releases – Probabilistic information in meeting the target storage 6

7 Presentation Outline 7

8 Inflow Forecasting Model - Overview Precipitation Forecasts (P t ) from GCMs IRI Data Library Predictand Model Observed Streamflow (Q t-1 ) Statistical Downscaling Model (PCR) Forecasted Streamflow (Q t ) Training Period : Data up to previous year? Archived Forecasts : 1990-till date Predictors State Climate Office of NC Portal automatically downloads Updated Monthly/Seasonal Precipitation Forecasts from GCMs between 15-18 of each month Use for Storage Forecast (Reservoir Model) Climate Data (GCMs): ECHAM 4.5 Observed Streamflow: USACE Site 8

9 Inflow Forecasts – Statistical Downscaling Statistical Downscaling -Precipitation forecasts from ECHAM4.5 -forced with constructed analogue SST forecasts -Precipitation from the GCMs is spatially correlated -Principal component Regression -Principal Component Analysis (PCA) is used to reduce the data -PCA is applied on the predictors -Streamflow and ECHAM4.5 precipitation forecasts -Principal component regression to obtain Inflow forecasts. -Inflow Forecasts are provides as Ensembles 9

10 Presentation Outline 10

11 Storage Forecasting Model Net-Inflows Forecast: q tk ; t=1…,T; k=1,…,N Continuity Equation: t=1,2, …, T User will prescribe the releases or use observed releases Critical variable: End of the season target storage – Initial storage can provide water for entire forecasting period Simulation Model estimates P(S T L < S T < S T U ) – Probability of having the storage within the conservation pool 11

12 Presentation Outline 12

13 Spatial and Temporal Extent Inflow and Storage Forecasts – Spatial Extent – Neuse - Falls Lake – Fully Automated – Cape Fear - Jordan Lake – Fully Automated – Yadkin – Scott Keer Reservoir – Roanoke – Philpott – Catawba – South Fork and Rocky Creek Inflow and Storage Forecasts – Temporal Extent – Monthly (at 1, 2 or 3 month lead time) and seasonal – Available from 1990 to present, updated month – Individual year forecasts or Retrospective forecasts 13

14 Inflow Forecasting Models Inflow Forecasts Models – Statistical Downscaling (PCR) IRI Climate Forecasts – ECHAM4.5, Multimodel – Monthly/Seasonal Climatology (No Forecasts) – Land Surface Models (Under Integration) NASA’s Land Information System – NOAH 3.2 Variable Infiltration Capacity Model Forecast Skills – Deterministic forecasts or as ensembles – Retrospective skill summary 14

15 Experimental Inflow and Storage Forecasts Portal (http://www.nc-climate.ncsu.edu/inflowforecast) 15

16 Inflow Forecasts (Individual Year) 16

17 Inflow Forecasts (Individual Year) 17

18 Forecast skill Evaluation Month/Season Observed Inflow (CFS) Forecast 50th Percentile (CFS) Relative RMSE MSSSRPSS January '9013107540.425-0.227-0.313 Categorical Forecasts Climatological Percentile Percentile Values (CFS)Model Probabilities January '90 <10%< 1580.040 10-33%158 - 7000.426 33-50%368 - 7000.092 50-67%857 - 12420.158 67-90%1242 - 19310.858 >90%> 19310.142 Inflow Forecasts (Individual Year) 18

19 Storage Forecasts (Individual Year) 19

20 Storage Forecasts (Individual Year) Stage Level (ft above MSL): Model Percentiles: Below conservation pool:<236.50.00% Within limits of conservation pool: 236.5 - 251.579.40% Within limits of flood control pool:251.5 - 264.819.00% Above flood control pool:>264.81.60% 20

21 Inflow Forecasts (Retrospective) 21

22 Inflow Forecasts (Retrospective) 22

23 January Forecast Skill Evaluation Relative RMSE RPSSCorrelationMSSS 0.418-0.1480.6630.156 January Forecast Probability Distribution Year Percentiles Observed Inflow (CFS) <33%33-67%>67% Percentile Flow Values 700 - 1242> 1242-- 19900.4660.2500.2841310 19910.3420.2560.4022084 19920.2900.2500.4601174 19930.3220.2560.4221868 19940.4040.2580.338784 19950.3640.2580.378843 19960.5060.2420.2521682 19970.4500.2540.2961151 19980.1720.1940.6343006 19990.5860.2180.1961598 20000.5520.2280.2201127 Inflow Forecasts (Retrospective) This is the percentile range which was predicted to be the most likely to occur The observed flow falls between the percentiles indicated by the column for each year The observed flow was forecasted correctly by the model for this yea 23

24 Storage Forecasts (Retrospective) 24

25 Storage Forecasts (Retrospective) Year Observed Outflow (cfs) Observed Inflow (cfs) Median Forecasted Inflow (cfs) Start-of-Month Elevation End-of- Month Elevation 199012911310756250.54250.2 199121252084999245.57250.11 199250111731138250.23250.05 1993187318671048246.42250.2 1994121783864247.31250.62 1995367843946250.39250.17 199616161682690250.64250.45 199711261150780249.88250.73 1998116530061709247.36257.65 19998861598577251.98251.09 20006931127625249.86254.44 Reservoir Information for January: Storage Range Probabilities: Year Below Conservation Pool Within Conservation Pool Within Flood Control Pool Spilling Over Flood Control Pool Observed Storage (acre-ft) Median Forecasted Storage (acre-ft) 19900.00%77.80%20.40%1.80%11602087057 199164.00%26.00%8.20%1.80%1149752370 19920.00%31.60%61.60%6.80%114278155522 199348.00%38.20%11.60%2.20%11602027816 19940.00%49.40%47.40%3.20%120895132051 19950.00%28.80%66.40%4.80%115671153849 19960.20%85.60%13.00%1.20%11892264217 19970.00%75.80%22.40%1.80%12217291101 19980.80%53.40%34.80%11.00%219366120241 19990.00%63.60%35.00%1.40%126431118481 20000.00%70.40%28.20%1.40%170804108012 25

26 Presentation Outline 26

27 Seasonal Inflow Forecasts - JFM 27

28 Seasonal Inflow Forecasts - AMJ 28

29 Seasonal Inflow Forecasts - JAS 29

30 Seasonal Inflow Forecasts - OND 30

31 Seasonal Inflow (Climatology) 31

32 Seasonal Inflow Forecasts – Skill Summary Falls Lake - Season Jan - MarApr - JunJul - SepOct - Dec R-RMSE0.7030.9591.5632.465 RPSS0.230.1-0.130.1 Correlation0.8610.5470.420.413 MSSS0.4080.1460.1590.076 Relative-RMSE : A good forecast is expected to have R-RMSE closer to zero RPSS : If RPSS is positive, then the forecast skill exceeds that of the climatological probabilities. Correlation: A good forecast is expected to have a correlation around one. MSSS : A good forecast is expected to have MSSS be closer to one. 32

33 Seasonal Inflow Forecasts - PCR vs. Climatology Falls Lake Year Percentiles (Climatology) <33%33-67%>67% 19900.3140.3880.298 19910.3140.3880.298 19920.3140.3880.298 19930.3140.3880.298 19940.3140.3880.298 19950.3140.3880.298 19960.3140.3880.298 19970.3140.3880.298 19980.3140.3880.298 19990.3140.3880.298 20000.3140.3880.298 20010.3140.3880.298 20020.3140.3880.298 20030.3140.3880.298 20040.3140.3880.298 20050.3140.3880.298 20060.3140.3880.298 20070.3140.3880.298 Percentiles (PCR Model) <33%33-67%>67% 0.3640.3220.314 0.1600.2700.570 0.2600.3280.412 0.3220.266 0.3060.3280.366 0.2260.3000.474 0.2040.2660.530 0.4580.2840.258 0.3680.3220.310 0.2100.2480.542 0.5740.2640.162 0.6240.2440.132 0.3240.3320.344 0.3340.3320.334 0.2880.3180.394 0.5320.2900.178 0.3700.3240.306 0.4020.3280.270 Model Predicted Percentile Observed Flow Percentile Model Predicted and Observed Flow Percentile 33

34 Seasonal Storage Forecasts - JFM 34

35 Seasonal Storage Forecasts – JFM Falls Lake Year Below Conservation Pool Within Conservation Pool Within Flood Control Pool Spilling Over Flood Control Pool 200030.90%33.50%23.20%12.40% 20010.00%17.30%62.20%20.50% 20020.00%18.10%62.50%19.40% 200318.60%26.00%30.50%24.90% 20040.00%18.60%57.10%24.30% 20057.50%34.80%40.20%17.50% 20060.00%12.40%66.60%21.00% 20074.80%28.20%43.20%23.80% 20080.00%34.90%52.60%12.50% 20096.10%36.80%39.00%18.10% 201020.50%24.20%30.30%25.00% 20110.00%13.30%68.90%17.80% 20120.00%7.50%64.10%28.40% Model Predicted Percentile Observed Storage Percentile Model Predicted and Observed Storage Percentile 35

36 Seasonal Storage Forecasts: Climatology – JFM 36

37 Monthly Inflow Forecasts - 6/2007 (3 Month Lead) Climatological Percentile Percentile Values (CFS)Model Probabilities June '07 <10%< 00.518 10-33%0 - 670.036 33-50%55 - 670.032 50-67%130 - 3250.098 67-90%325 - 10930.93 >90%> 10930.07 37

38 Monthly Inflow Forecasts - 7/2007 (3-Month Lead) Climatological Percentile Percentile Values (CFS)Model Probabilities July '07 <10%< 20.262 10-33%2 - 340.034 33-50%31 - 340.058 50-67%83 - 2320.19 67-90%232 - 7560.962 >90%> 7560.038 38

39 Monthly Inflow Forecasts - 8/2007 (3-month Lead) Climatological Percentile Percentile Values (CFS)Model Probabilities August '07 <10%< 00.400 10-33%0 - 280.032 33-50%21 - 280.13 50-67%139 - 2910.166 67-90%291 - 7950.978 >90%> 7950.022 39

40 Monthly Storage Forecasts – JJA 2007- 3-Month Lead 40

41 Monthly Inflow Forecasts: Sep 1996 41

42 Inflow Forecasts- Sep 2012 – 1-Month lead 42

43 Inflow Forecasts - Oct 2012 – 2 Month Lead 43

44 Inflow Forecasts - Nov 2012 – 3 Month Lead 44

45 Storage Forecasts - Nov 2012 – 3 Month Lead 45

46 Storage Forecasts - Nov 2012 – 3-Month Lead Stage Level (ft above MSL):Model Percentiles: Below conservation pool:<236.510.60% Within limits of conservation pool:236.5 - 251.524.80% Within limits of flood control pool:251.5 - 264.857.80% Above flood control pool:>264.86.80% 46

47 Overview of Forecasting Individual Year or Retrospective Forecasts Seasonal and Monthly Forecast – Inflow forecast with skills summary – Storage forecast User defined outflow or observed outflow New Inflow and Storage Monthly Forecast – Available at the middle of month – Various lead times – Requires user defined outflows 47

48 Presentation Outline 48

49 Conclusion, Future Work Add Land Surface Models for Inflow Forecasts Develop Multimodel Inflow Forecasts Provide Inflow and Storage Forecasts for other Reservoirs in the Southeast US Available at the State Climate Office Website (http://www.nc-climate.ncsu.edu/inflowforecast) 49

50 Acknowledgements Project Funded by: Water Resources Research Institute (WRRI) and NC Urban Water Consortium (NC UWC) Dr. Sankar Arumugam – Associate Professor - NC State University Dr. Ryan Boyles - State Climatologist and Director - State Climate Office of North Carolina Dr. Tushar Sinha - Postdoctoral Research Scientist - NC State University Simon Mason - Research Scientist - IRI Andrew McNamara - Graduate Student - NC State University Thomas Petersen - Prospective Graduate Student 50

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