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.
Highlights
Scientific centers around the world have developed General Circulation Models (GCMs) for prediction of future climate trends
Since the resolution of GCMs is generally coarse for adequate estimation of the impacts of climate projections on hydrologic processes in a watershed, and in consideration of known GCM output uncertainty, methods to downscale or bias-correct the model outputs have been developed [6,7,8,9]
An analysis of climate projections, including a continuation of baseline historical climate, stationary climate shifts of 10% greater or less precipitation than baseline, and non-stationary shifts created using various ensembles of 15 GCMs, was conducted for the mid-21st century (2046−2065)
Summary
Scientific centers around the world have developed General Circulation Models (GCMs) for prediction of future climate trends. Each GCM accounts for sea, atmosphere, and land interactions and simulates climate projections on grids with resolution from 2500 km to 200,000 km2 [1,2]. Since the resolution of GCMs is generally coarse for adequate estimation of the impacts of climate projections on hydrologic processes in a watershed, and in consideration of known GCM output uncertainty, methods to downscale or bias-correct the model outputs have been developed [6,7,8,9]. One family of downscaling methods involves a statistical approach of applying smaller-scale climatic observations to bias-correct the larger-scale GCM data [7,10,11].
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