Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

Abstract

Abstract. Assessing 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 for the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties into the simulated hydrologic variables. Here, a modeling framework was 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), were coupled with the Hillslope River Routing model to simulate surface/subsurface runoff and streamflow. A case study conducted in Santa Barbara County, California, reveals increased surface runoff in February and March but decreased runoff in other months, a delayed (3 d, median) and shortened (6 d, median) wet season, and increased daily discharge especially for the extremes (e.g., 100-year flood discharge, Q100). The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %. For projected changes in runoff and discharge, general circulation models (GCMs) and emission scenarios are two major uncertainty sources, accounting for about half of the total uncertainty. For the changes in seasonality, GCMs and hydrologic models are two major uncertainty contributors (∼35 %). In contrast, the contribution of hydrologic model parameters to the total uncertainty of changes in these hydrologic variables is relatively small (<6 %), limiting the impacts of hydrologic model parameter equifinality in climate change impact analysis. This study provides useful information for practices associated with water resources, risk control, and ecosystem conservation and for studies related to hydrologic model evaluation and climate change impact analysis for the study region as well as other Mediterranean regions.

The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

“…The hydrologic model used is a modified version of the Hillslope River Routing (HRR) model [1,3], which now derives surface and subsurface flow using the Variable Infiltration Capacity (VIC) model formulation equations [27]; it is termed the HRR-VIC model. HRR is a topography-based routing model that integrates surface and subsurface runoff laterally into river channels and routes discharges throughout the river network based on the Muskingum-Cunge method, resulting in discharge estimates for each river reach.…”

“…Each model unit consists of a river reach and its associated catchment boundaries that become approximated by two planes draining laterally into the main river channel. This effort builds on previous studies that coupled the HRR and VIC models to estimate streamflow [1,30]. The resulting modeled discharge estimates from HRR-VIC will be referred to as Qm.…”

“…Hydrologic and hydrodynamic models are useful for assessing climate change impacts [1], predicting flood characteristics, forecasting applications [2], and understanding the transfer and storage of water and energy globally [3]. Satellite remote sensing has been utilized to quantify and assess water security [4], and measurements can be coupled with hydrologic models to improve overall discharge estimation [5][6][7][8][9].…”

Future climate impacts on the hydrology of headwater streams in the Amazon River Basin: Implications for migratory goliath catfishes

“…Such relationships may be revealed when more land surface data are available. Furthermore, the hydrologic model parameters play a negligible role in contributing to the uncertainty of estimated discharge compared to other factors (e.g., climate forcings of precipitation and temperature) (Feng & Beighley, 2020). This makes assumptions regarding spatially uniform hydrologic parameters less impactful to the results and conclusions of this study.…”

Quantifying the impact of land cover changes on hydrological responses in India

“…This is known 70 as equifinality and is considered as one of the main sources of uncertainty in hydrological modelling (Her et al, 2019). Recent climate change studies have acknowledged the uncertainties stemming from model parameters, and therefore they take into account these uncertainties while predicting the hydrological responses due to climate change (Chaney et al, 2015;Feng and Beighley, 2020;Her et al, 2019;Huang and Liang, 2006;Joseph et al, 75 2018; Mockler et al, 2016;Singh et al, 2014). However, little is known about the contributions of model parameter uncertainties to the land use change impacts and thus, very few studies exist (Breuer et al, 2006;Chen et al, 2019b) which reported that uncertainties associated with the model parameters could significantly influence land cover change impacts and hence should not be overlooked while modelling hydrologic responses to LULC change.…”