Incorporating probabilistic seasonal climate forecasts into river management using a risk‐based framework. Issue 8 (19th August 2013) - Towler, Erin; Roberts, Mike; Rajagopalan, Balaji; Sojda, Richard S.
Abstract : [1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk‐based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision‐relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season‐ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end‐to‐end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very "sharp"). Synthetic forecasts show that a modest "sharpening" can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement. Key Points: Connects climate forecasts with sustainable ecosystem management General Linear Models used to predict decision‐relevant streamflow attributes Presents end‐to‐end risk‐based framework to support proactive planning