Reliable future climate projections for sustainable hydro-meteorological assessments in the Western Lake Erie Basin

Authors: MEHAN, SUSHANT - Purdue University; GITAU, MARGARET - Purdue University; Flanagan, Dennis

Submitted to: Water
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/16/2019
Publication Date: 3/20/2019
Citation: Mehan, S., Gitau, M.W., Flanagan, D.C. 2019. Reliable future climate projections for sustainable hydro-meteorological assessments in the Western Lake Erie Basin. Water. 11(3):581. https://doi.org/10.3390/w11030581.
DOI: https://doi.org/10.3390/w11030581

Interpretive Summary: Weather is the most critical factor affecting many processes on the land surface. Temperatures, storm amounts and rainfall intensities determine whether precipitation falls as rain or snow, how much runoff occurs, if there will be flooding or droughts, how much crops will grow and yield, how much soil erosion by water occurs, and the loss and transport of pollutants in water. In this study we wanted to develop a reliable set of baseline and projected future climate data for use in hydrologic and natural resource applications and modeling. We tested how well two different climate predictors reproduce baseline climate similar to observed temperatures and precipitation for three weather stations in the Maumee River watershed in northwestern Ohio, northeastern Indiana, and southern Michigan. We found that the Multivariate Adaptive Constructed Analogs (MACA) statistical downscaling method had substantially less bias in its baseline period predictions for 1966-2005, so MACA was further tested with additional approaches to reduce bias for the 3 stations. The best bias correction was applied with MACA projections for 8 total stations in the western Lake Erie Basin, with future climate projections there to 2099. These data will be provided in a public website that is currently under development at Purdue University. This research impacts scientists, university faculty, graduate students, natural resource agency personnel, and others who use climate information and future climate forecasts in their work. The procedures developed here could be extended to other locations.

Technical Abstract: Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data; inaccuracies in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate (precipitation and temperature) database for the Western Lake Erie Basin (WLEB) for the 21st century. Two statistically downscaled bias-corrected sources of climate projections (GDO and MACA) were tested for their effectiveness in simulating historic climate (1966-2005) using ground-based station data from the National Climatic Data Center (NCDC). MACA was found to have less bias than GDO and was better in simulating certain climate indices, thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, maximum one-day precipitation could vary between 120 and 650 mm across the basin, while the number of days with no precipitation could reduce by 5-15 % under the RCP 4.5 and RCP 8.5. The number of wet sequences could increase up to 9 times and the conditional probability of having a wet day followed by wet day could decrease by 25%. The maximum and minimum daily air temperatures could increase by 2-12 % while the annual number of days for optimal corn growth could decrease by 0-10 days. The resulting climate database will be made accessible through an open-access platform.