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Valuing year‐to‐go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada
Author(s) -
Rheinheimer David E.,
Bales Roger C.,
Oroza Carlos A.,
Lund Jay R.,
Viers Joshua H.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr018295
Subject(s) - hydropower , inflow , revenue , snow , environmental science , water storage , range (aeronautics) , meteorology , hydrology (agriculture) , regression analysis , hydrological modelling , regression , climatology , econometrics , economics , statistics , geography , engineering , mathematics , geology , mechanical engineering , geotechnical engineering , accounting , aerospace engineering , electrical engineering , inlet
We assessed the potential value of hydrologic forecasting improvements for a snow‐dominated high‐elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest‐of‐year inflows from regression‐based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by $0.14 to $1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness‐to‐pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression‐based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.