资源环境科技发展态势分析平台

In spring 2019, a catastrophic flood occurred along the Missouri and Mississippi River basins in the United States, which was characterized as the longest lasting flood since the Great Flood of 1927. The 2019 flooding resulted in extremely wet soils for 3-4 months over the Great Plains. Using rawinsonde-derived atmospheric boundary layer depths (BLDs) and in situ soil moisture (SM) data sets at 10 sites located meridionally across the two river-valleys, we investigated the SM controls on regional-scale BLDs during spring 2019. The impact of spring flooding on atmospheric boundary layer dynamics is reported via regression analyses between daily SM and BLDs yielding statistically significant negative r (p < 0.0012) with substantial spatial variability (r: -0.25 to -0.70). Results suggest (1) the strengthening of the negative SM-BLD relationship in the wake of extreme flooding and (2) positive SM anomalies of 0.05-0.12 m(3) m(-3) resulted in negative BLD anomalies (-100 to -400 m) compared to 8-year means, confirming the impact of perturbed land atmosphere feedback processes (LAFP). These results offer a test bed for developing better numerical models with advanced representations of LAFP.

Plain Language Summary In spring 2019, a significant flood occurred along the Missouri and Mississippi River basins in the United States, causing the soil to remain very wet and saturated at some places for more than 3 months. In the present study, we investigated how the height of Earth's atmospheric boundary layer, which is an important parameter used in weather forecast and air pollution models, varied because of the wet soils in this region. We performed this study using a network of observations obtained from instrumented weather balloons, as well as highly accurate measurements of soil moisture from within the region. We found that the wet soils resulted in smaller boundary layer heights when compared with how boundary layer heights typically behave over the region. The knowledge gained from this work will be used to help us better represent extreme events in weather forecasting models.