Stream hydrological and ecological responses to climate change assessed with an artificial neural network

Abstract

An artificial neural network (ANN) was used to evaluate the hydrological responses of two streams in the northeastern U.S. having different hydroclimatologies (rainfall and snow+rain) to hypothetical changes in precipitation and thermal regimes associated with climate change. For each stream, historic precipitation and temperature data were used as input to an ANN, which generated a synthetic daily hydrograph with high goodness-of-fit (r2 > 0.80). Four scenarios of climate change were used to evaluate stream responses to climate change: +25% precipitation, -25% precipitation, 2 x the coefficient of variation in precipitation regime, and +3°C average temperature. Responses were expressed in hydrological terms of ecological relevance, including flow variability, baseflow conditions, and frequency and predictability of floods. Increased average precipitation induced elevated runoff and more frequent high flow events, while decreased precipitation had the opposite effect. Elevated temperature reduced average runoff. Doubled precipitation variability had a large effect on many variables, including average runoff, variability of flow, flooding frequency, and baseflow stability. In general, the rainfall-dominated stream exhibited greater relative response to climate change scenarios than did the snowmelt stream.