Predictability of seasonal streamflow in a changing climate in the Sierra Nevada

Abstract

The goal of this work is to assess climate change and its impact on the predictability of seasonal (i.e., April-July) streamflow in major water supply watersheds in the Sierra Nevada. The specific objective is threefold: (1) to examine the hydroclimatic impact of climate change on precipitation and temperature at the watershed scale, as well as the variability and trends in the predictand (i.e., April-July streamflow runoff) and its operational predictors (including 1 April snow water equivalent, October-March precipitation and runoff, and April-June precipitation) in a changing climate; (2) to detect potential changes in the predictability of April-July streamflow runoff in response to climate change; and (3) to assess the relationship between April-July streamflow runoff and potential new predictors and the corresponding trend. Historical records (water year 1930-2015) of annual peak snow water equivalent, monthly full natural flow, monthly temperature and precipitation data from 12 major watersheds in the west side of the Sierra Nevada in California (which are of great water supply interest) are analyzed. The Mann-Kendall Trend-Free Pre-Whitening procedure is applied in trend analysis. The results indicate that no significant changes in both the predictand and predictors are detected. However, their variabilities tend to be increasing in general. Additionally, the predictability of the April-July runoff contributed from each predictor is generally increasing. The study further shows that standardized precipitation, runoff, and snow indices have higher predictability than their raw data counterparts. These findings are meaningful from both theoretical and practical perspectives, in terms of guiding the development of new forecasting models and enhancing the current operational forecasting model, respectively, for improved seasonal streamflow forecasting.