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

Abstract

This 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.

Estimating source regions for snowmelt runoff in a Rocky Mountain basin: tests of a data‐based conceptual modeling approach

“…Other studies have identified the need for a SWE volume control in the SRM structure (Bavera et al ., ), and similar to the approach presented in Furey et al . (), we use SWE as the primary state variable for the snowmelt portion of the model. The basin area is divided into elevation zones, each assigned input precipitation and temperature values for each time step.…”

“…The model structure is similar to the SRM (Martinec et al, 2008), except it is driven directly by changes in snow water equivalent (SWE) rather than changes in snow-covered area. Other studies have identified the need for a SWE volume control in the SRM structure (Bavera et al, 2012), and similar to the approach presented in Furey et al (2012), we use SWE as the primary state variable for the snowmelt portion of the model. The basin area is divided into elevation zones, each assigned input precipitation and temperature values for each time step.…”

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

“…Spatiotemporal changes in the mountain snowpack of the western U.S. are well documented (Clow, 2010;Fritze et al, 2011;Harpold et al, 2012;Regonda et al, 2005;Stewart et al, 2005) and both data-based and modeling studies suggesting that streamflow generation is sensitive to loss of snow on multiple time scales (Barnhart et al, 2016;Berghuijs et al, 2014;Clow, 2010;Furey et al, 2012;Jefferson, 2011;Regonda et al, 2005;Stewart et al, 2004Stewart et al, , 2005. Areas conducive to snowfall are forecast to shrink considerably (Klos et al, 2014;Luce et al, 2014), and recent analyses across catchments in the U.S. suggest that a shift in precipitation (P) from snow to rain leads to a decrease in annual streamflow across all climate types (Berghuijs et al, 2014).…”

Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

“…Incorporating site-specific constant LAI from field measurements or remotely sensed data may have improved model performance, especially during spring green-up and fall senescence, and is recommended for future site-specific studies. The water balance in hydrologic models can be highly sensitive to the method chosen to represent root uptake and plant water use (Gerten et al, 2004), and hydrologic models generally poorly capture or replicate the interactions between soil, vegetation, and atmospheric properties that combine to control plant water use (Gómez-Plaza et al, 2001;Gerten et al, 2004;Zeng et al, 2005). In addition, we did not allow for frozen soils in our simulations, but this can be a strong influence on soil input partitioning in places where snow depth was < 50 cm and incapable of insulating the soil (Slater et al, 2017).…”