Many ecological functions of wetlands are influenced by wet-periods, i.e., the time interval when groundwater table (GWT) is continuously near the land surface. Hence, there is a crucial need to understand the controls on interannual variations of wet-periods. Given the scarcity of long-term measurements of GWT in wetlands, understanding variations in wet-periods using a measurement approach alone is challenging. Here we used a physically based, fully distributed hydrologic model, in synergy with publicly available hydrologic data, to simulate long-term wet-period variations in 10 inland forested wetlands in a southeastern US watershed. A Bayesian regression and variable selection framework was then implemented to (a) evaluate the extent to which the simulated wet-periods can be estimated and predicted by precipitation (Ppt) and potential evapotranspiration (PET) and (b) infer the relative roles of seasonal Ppt and PET. Our results indicate that wet-period start date and duration could vary by more than 6 months during the 32 year simulation period. Remarkably, 60–90% of these variations could be captured using regressions based on seasonal Ppt and PET in most wetlands. Effects of seasonal meteorological conditions on wet-period variations were found to be nonuniform, which indicate that the annual variables may not explain interannual variations in wet-periods. The Bayesian framework was able to predict wet-period variations with errors smaller than 1 month at a 90% confidence level. The presented framework provides a minimalistic approach for estimating and predicting wet-period variations in wetlands and may be used to understand the future responses of associated ecological functions in wetlands.
Liu, Y. and Kumar, M. (2016): Role of meteorological controls on interannual variations in wet‐period characteristics of wetlands. Water Resources Research 52(7): 5056-5074. DOI: 10.1002/2015WR018493
This Paper/Book acknowledges NSF CZO grant support.