A Stochastic Conceptual Modeling Approach for Examining the Effects of Climate Change on Streamflows in Mountain Basins

Abstract

his study presents a modeling approach for examining how changes in climate affect streamflow in mesoscale mountain basins dominated by snowmelt runoff. A conceptual snowmelt-runoff model was developed that is forced by daily time series of temperature and precipitation. The model can be run using either observed climate data or artificial climate data generated from a GCM or a stochastic model. The model was applied to a case-study basin, the north fork of the Clearwater River in Idaho, using stochastically generated climate scenarios. Climate scenarios were generated using a contemporaneous auto-regressive integrated moving average (CARIMA) model for temperature and a precipitation model based on a two-state first-order Markov process. A baseline climate scenario was developed that represents recently observed temperature and precipitation conditions and then 15 additional climate scenarios that represent shifts in recent conditions. For each scenario, model application produced an ensemble of 50 streamflow traces each spanning 30 yr. Results show that an increase in temperature among scenarios leads to a decrease in streamflow and vice versa. Decreases in temperature shift the basin runoff to fully snowmelt dominated, whereas increases in temperature increase the frequency of midwinter runoff events. Increasing precipitation leads to increased runoff in cases where the temperature remains the same as the observed record, but not in cases where the temperature increases. The modeling approach presented here can be used by water managers to examine which types of climate change could require modifications in water planning and operations.