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Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments

Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments
Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments

We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20–60% of the total forecast value.

Ensemble Streamflow Prediction, Model Predictive Control, Oroville Reservoir (California), reservoir operation, seasonal streamflow forecast, water supply
0043-1397
4209-4225
Anghileri, D.
611ecf6c-55d5-4e63-b051-53e2324a7698
Voisin, N.
e6e1907a-d8f8-49f2-a615-f1558e9413fa
Castelletti, A.
be719c8b-5599-42a5-8404-259074a780d6
Pianosi, F.
45ac34b7-e403-4758-ab54-bcba088f0ab3
Nijssen, B.
386f1ab9-0e33-4f30-8539-e0ec4b96d4cf
Lettenmaier, D. P.
c3ae7db6-9f48-4875-8052-9e16fd099c09
Anghileri, D.
611ecf6c-55d5-4e63-b051-53e2324a7698
Voisin, N.
e6e1907a-d8f8-49f2-a615-f1558e9413fa
Castelletti, A.
be719c8b-5599-42a5-8404-259074a780d6
Pianosi, F.
45ac34b7-e403-4758-ab54-bcba088f0ab3
Nijssen, B.
386f1ab9-0e33-4f30-8539-e0ec4b96d4cf
Lettenmaier, D. P.
c3ae7db6-9f48-4875-8052-9e16fd099c09

Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B. and Lettenmaier, D. P. (2016) Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments. Water Resources Research, 52 (6), 4209-4225. (doi:10.1002/2015WR017864).

Record type: Article

Abstract

We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20–60% of the total forecast value.

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More information

Accepted/In Press date: 6 April 2016
e-pub ahead of print date: 12 April 2016
Published date: 1 June 2016
Keywords: Ensemble Streamflow Prediction, Model Predictive Control, Oroville Reservoir (California), reservoir operation, seasonal streamflow forecast, water supply

Identifiers

Local EPrints ID: 425842
URI: http://eprints.soton.ac.uk/id/eprint/425842
ISSN: 0043-1397
PURE UUID: bbdde3b6-90ce-4eee-ba58-2f700749ac67
ORCID for D. Anghileri: ORCID iD orcid.org/0000-0001-6220-8593

Catalogue record

Date deposited: 05 Nov 2018 17:30
Last modified: 17 Dec 2019 01:23

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