Statistical and dynamical downscaling impact on projected hydrologic assessment in arid environment: A case study from Bill Williams River basin and Alamo Lake, Arizona

Abstract

A study was conducted to assess the projected impact of future climate on Alamo Lake and the Bill Williams River basin. We analyzed simulations of three-selected Representative Concentration Pathways 8.5 Global Climate Models (GCM) (i.e. HadGEM2-ES, MPI-ESM-LR and GFDL-ESM2M). These GCMs which, were part of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, were selected as well performing GCMs that represent the historic climatology and prevailing precipitation bearing synoptic conditions in the southwest US. An analysis of both statistically and dynamically downscaled simulations projected increase in the frequency of dry winters during the mid-21st century (2020–2059) in two out of the three selected GCMs. For summer precipitation, the statistically downscaled simulations are inconclusive whereas, the dynamically downscaled simulations showed significant but contradicting future projections.In order to assess the impact of the projected climate on the hydrologic cycle at the Bill Williams River basins, we developed a modeling framework that includes the following components: 1) a weather generator that produces realizations of likely hourly precipitation events over the basin; 2) a hydrologic model that is based on the Colorado Basin River Forecast Center (CBRFC), National Weather Service modeling configuration that predicts flow at ten internal points and inflow into Alamo Lake; and 3) a lake model with the existing operation rules to simulates the lake outflow and levels.Using the above-described modeling framework, the impact of the projected mid 21-century climate on Alamo Lake was examined with respect to the total outflow from the dam, the frequency of large outflow events, and the frequency of high and low lake levels. The results show that dynamic downscaling provides a larger range of impacts than those provided by statistical downscaling. The results also indicate a wide range of impact scenarios with contradicting trends among the selected climate projections for mid-21st Century. These results imply increasing challenges in operating the Lake at its target level. This modeling framework can potentially be used to examine various future scenarios and to develop recommendations for a sustainable management scheme for the Alamo Lake. Keywords: Bill Williams River, Alamo Lake, Statistical downscaling, Dynamical downscaling, Hydrologic impact assessment, Arid Hydrology