Modeling environmental data plays a crucial role in explaining environmental phenomena. In some cases, well-known distributions, e.g., Weibull, inverse Weibull, and Gumbel distributions, cannot model environmental events adequately. Therefore, many authors tried to find new statistical distributions to represent environmental phenomena more accurately. In this paper, an alpha-monotone generalized log-Moyal (alpha-GlogM) distribution is introduced and some statistical properties such as cumulative distribution function, hazard rate function (hrf), scale-mixture representation, and moments are derived. The hrf of the alpha-GlogM distribution can form a variety of shapes including the bathtub shape. The alpha-GlogM distribution converges to generalized half-normal (GHN) and inverse GHN distributions. It reduces to slash GHN and alpha-monotone inverse GHN distributions for certain parameter settings. Environmental data sets are used to show implementations of the alpha-GlogM distribution and also to compare its modeling performance with its rivals. The comparisons are carried out using well-known information criteria and goodness-of-fit statistics. The comparison results show that the alpha-GlogM distribution is preferable over its rivals in terms of the modeling capability.