Anthropogenic activities are altering flood frequency-magnitude distributions along many of the world’s large rivers, yet isolating the impact of any single factor amongst the multitudes of competing anthropogenic drivers is a persistent, yet important challenge if we are to mitigate their negative consequences. The Usumacinta River in southeastern Mexico provides an ideal opportunity to study an anthropogenic driver in isolation: tropical forest conversion. This article employs a novel approach to disentangle the anthropogenic signal from climate variability, and provides valuable insights into the impact of forest conversion on flood severity. Here we analyse continuous daily time series of precipitation, temperature, and discharge to identify long-term trends, and compare ratios of catchment-wide precipitation totals to daily discharges in order to account for climatic variability. We also identify an anthropogenic signature of tropical forest conversion at the intra-annual scale, successfully reproduce this signal using a distributed hydrological model (VMOD), and demonstrate that the continued conversion of tropical forest to agricultural land use will further exacerbate large-scale flooding. We find statistically significant increasing trends in annual minimum, mean, and maximum discharges that are not evident in either precipitation or temperature records. We also find that mean monthly discharges have increased between 7% and 75% in the past decade, in contrast to mean monthly precipitation, which shows no statistically significant trend. Model results demonstrate that forest cover loss is responsible for raising the 10-year return peak discharge by 25%, while the total conversion of forest to agricultural use would result in an additional 18% rise. These findings highlight the need for a holistic approach to catchment-wide land management in tropical regions that weights the benefits of agricultural expansion against the consequences of increased flood prevalence, and the economic and social costs that they incur.
