Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract

This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline and future (2011-2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. this study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.A changing climate is projected to intensify regional and global hydrologic cycles 1,2 . These alterations in hydrologic cycles will potentially increase the frequency and magnitudes of hydroclimate extremes such as floods and droughts, and potentially impact water resource availability due to changes in the seasonality of streamflow and runoff 3-5 . The future hydrologic projections are, therefore, important to inform mitigation and adaptation strategies aimed at addressing impacts of climate change in addition to increasing water demands. Moreover, reliable estimates of hydroclimate extreme trends can ensure better preparedness of society and infrastructure from threats arising from extreme events and their socioeconomic impacts 6 .Studies assessing climate change impacts on future hydrology at regional or catchment scales often adapt a standard procedure involving the use of a hierarchical hydro-meteorological framework (hereinafter "modeling framework"), including selection of the following key elements: (a) greenhouse gas emission scenario, (b) global climate model (GCM), (c) downscaling method (statistical or dynamical), (d) bias correction of downscaled data (if required), and (e) hydrologic model 7-10 . These hydrologic projections are inevitably associated with uncertainties introduced at each stage of the modeling framework. In addition to external factors such as natural variability and the choice of emission scenarios, many uncertainties are model-related, such as model assumptions, structures, accuracy, initial conditions, calibration procedures, training datasets,...

Ubiquitous increases in flood magnitude in the Columbia River basin under climate change

“…Although in a broad hydrologic sense a flood is a flood regardless of what time of year it occurs, there are potentially significant ecological differences depending on time of year; for example, scouring the river bottom causes significant loss of salmon eggs (Goode et al, 2013). Moreover, water management policies are strongly linked to the calendar year (see Discussion).…”

Uncertainties in coastal flood risk assessments in small island developing states

“…To further improve the presented methodology, a Monte Carlo analysis that considers a pdf for each uncertain input to estimate the pdf of the expected damages could be performed, although the computational effort is prohibitive. To avoid the compu-tational burden of a Monte Carlo analysis, an ANOVA (analysis of variance) may be performed, as shown for example by Gangrade et al (2020).…”

“…Compound flooding events (e.g. coastal and riverine) can significantly increase the damages more than single events only (Ganguli and Merz, 2019;Kumbier et al, 2018;Wahl et al, 2015;Ward et al, 2017), and further research could estimate the added uncertainty. Moreover, the interdependency between different ESL components has been neglected, although tide and sea level changes are often correlated, adding further uncertainty in the analysis (Devlin et al, 2017).…”

Assessing Climate Change-Induced Flood Risk in the Conasauga River Watershed: An Application of Ensemble Hydrodynamic Inundation Modeling

“…The ensemble streamflow projections were generated by a hierarchical modeling framework, which started with regional climate downscaling followed by hydrologic modeling (Gangrade et al, 2020). The climate projections were generated by dynamically downscaling of 11 GCMs from the Coupled Model Intercomparison Project Phase-5 (CMIP5) data archive.…”

“…The following technique was adopted in this study. First, we generated streamflow projection by utilizing an ensemble of simulated streamflow hydrographs driven by both historical observations and downscaled climate projections (Gangrade et al, 2020) as inputs for hydrodynamic inundation modeling as presented in section 2.2. Then, we set up and calibrated a 2D hydrodynamic inundation model, Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON; Morales-Hernández et al, 2020b), in our study area which is presented in section 2.3.…”