Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models

Abstract

Future climate forcing data at the temporal and spatial scales needed to drive hydrologic models are not readily available. Simple methods to derive these data from historical data or General Circulation Model (GCM) results may not adequately capture future hydrological variability. This study assessed streamflow response to daily future climate forcing data produced by a new method using subsets of multi-model GCM ensembles for the mid-21st century period in northeast Kansas. Daily timeseries of precipitation and temperature were developed for six future climate scenarios: stationary, uniform 10% changes in precipitation; shifts based on a 15-GCM ensemble-mean; and shifts based on three seasonally-consistent subsets of GCMs representing Spring-Summer combinations that were wetter or drier than the historical period. The analysis of daily streamflow and hydrologic index statistics were conducted. Stationary 10% precipitation shifts generally bounded the monthly mean streamflow projections of the other scenarios, and the 15-GCM ensemble-mean captured non-stationary effects of annual and seasonal hydrological response, but did not identify important intra-annual shifts in drought and flood characteristics. The seasonally-consistent subset ensembles produced a range of distinct monthly streamflow trends, particularly for extreme low-flow and high-flow events. Meaningful water management and planning for the future will require hydrological impact simulations that reflect the range of possible future climates. Use of GCM ensemble-mean climate forcing data without consideration of the range of seasonal patterns among models was demonstrated to remove important seasonal hydrologic patterns that were retained in the subset ensemble-mean approach.

Streamflow response to potential changes in climate in the Upper Rio Grande Basin

“…The use of multiple GCMs and downscaling techniques is recommended when representing future climate projections because of uncertainty in model predictions associated with individual GCMs and downscaling techniques (Wootten and others, 2014). An ensemble of downscaled GCMs better represent a consensus of forcing data to assess hydrologic response at the local scale than the use of one specific GCM does (Sheshukov and others, 2011;Winkler and others, 2011;Sheshukov and Douglas-Mankin, 2017).…”

“…The ensemble of these data was used and the analysis was based on the entire range of output from these different GCMs and downscaling methods in order to reduce dependence on a singular data source. An ensemble of downscaled GCMs better represents a plausible range of climatological forcing on hydrology that can be used to assess hydrologic response at the local scale than does the use of one specific GCM (Sheshukov and others, 2011;Winkler and others, 2011;Sheshukov and Douglas-Mankin, 2017). In addition, the current study also incorporated three RCPs, thereby representing a range of scenarios that could occur as a result of human action.…”

Research Trends of Hydrological Drought: A Systematic Review

“…Hydrological indices and low flow analysis have been used extensively and are a suitable method at the planning level of water resources development and management [53]. Some researchers have been developing a hydrological indices method that provides a specific spatial and temporal comparison of hydrological requirements [54][55][56][57][58][59][60][61][62]. Drought indices are significant features of monitoring and evaluation of drought as they simplify interrelationships between many climate-related parameters in terms of their intensity, duration, frequency and spatial extent [63].…”