Research about Upper Mississippi River floodplain

USGS2026

More water, more of the time: Spatial changes in flooding over 83 years in the upper Mississippi River floodplain and relationships with streamgage-derived proxies

The hydrologic regime of the upper Mississippi River (UMR) has become wetter, with greater discharges, longer-lasting high-flow conditions, and seasonal shifts in these patterns over the past several decades. How these changes are expressed spatially as floodplain inundation area, frequency, depth, duration, and timing is not well understood. It is also unclear to what degree spatial patterns of submergence are represented by examining discharge data alone. We assessed changes in floodplain inundation characteristics from 1940 to 2022 in navigation pools 3–10 of the UMR using a geospatial model to simulate daily inundation depths. Inundation characteristics shifted significantly across pools, but the direction and magnitude of change varied by pool and metric. Characteristics summarized at the pool scale correlated with streamgage-derived proxies but the strength of the relationship varied. Within pools, variability in inundation trends highlighted the importance of spatially explicit modeling. Our study demonstrates that changes in discharge over 83 years have manifested across the UMR floodplain in ways that may have consequences for ecological patterns and processes. By mapping hydrologically sensitive areas, we can anticipate which areas may be susceptible to additional shifts in river discharge in a climatically uncertain future.

Iowa, Minnesota, Wiscosnin↗

USGS2024

Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories

Reed canarygrass ( Phalaris arundinacea L.) is one of the most common invaders of floodplains and wetlands in North America. In the Upper Mississippi River floodplain, invasion by reed canarygrass in forest understories can inhibit forest regeneration when gaps form in the overstory. Understanding the distribution of reed canarygrass in forest understories is essential for effective management and control. We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). Data from forest inventory study plots with reed canarygrass presence and absence were combined with 10 hypothesized environmental predictors of reed canarygrass invasion. We applied three approaches to better understand and incorporate the influence of spatial autocorrelation among our predictor variables, including random cross-validation, spatial cross-validation, and spatial cross-validation with Euclidean distance fields. Flood frequency, distance to contiguous floodplain, distance to forest edge, and distance to invaded wet meadow were among the most important environmental predictors across the three algorithms. Generally, the mean probability of reed canarygrass presence decreased with increasing flood depth, distance to contiguous floodplain, distance to invaded wet meadow, forest cover, and forest height, while relationships with other predictors were more variable. The ensemble of the three models (i.e., the average prediction) was used to map and summarize potential reed canary grass habitat suitability across the landscape. The maps generated quantified the habitat suitability for reed canarygrass and areas of agreement among the models in forest understories across the floodplain. This information can be used to better understand the extent of invasion, prioritize restoration efforts, and develop further research.

Illinois, Indiana, Iowa, Minnesota, Missouri, Wisc↗