Identifying uncertainties in simulated streamflow from hydrologic model components for climate change impact assessments

Abstract

Abstract. Assessing the impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used in the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties in the simulated hydrologic variables, yet the identification and quantification of such uncertainties is rarely studied. Here, a modeling framework is developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches: runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically-based, infiltration excess) and simple-TOPMODEL (STP, physically-based, saturation excess), are coupled with Hillslope River Routing model to simulate streamflow. A case study conducted in Santa Barbara County, California, reveals that the median changes are 1–10 % increases in mean annual discharge (Qm) and 10–40 % increases in annual maximum daily discharge (Qp) and 100-yr flood discharge (Q100). The Bayesian Model Averaging analysis indicates that the probability of increase in streamflow can be up to 85 %. However, the simulated discharge uncertainties are large (i.e., 230 % for Qm and 330 % for Qp and Q100) with general circulation models (GCMs) and emission scenarios accounting for more than half of the total uncertainty. Hydrologic process models contribute 10–30 % of the total uncertainty, while uncertainty due to hydrologic model parameterization is almost negligible (

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

“…Most rainfall-runoff modelling studies have focused on how parameterization affects model response (Bennett et al 2012, Clark et al 2016, Ahn and Kim 2019, Feng and Beighley 2019, Tesfaye et al 2019. Less attention has been paid to rainfall error propagation and its effects on hydrological simulation results.…”