The lowermost Mississippi River (LMR) is important to the environment and economy of continental United States. Although bathymetric data have been collected over many decades at numerous cross-sectional sounding points, there has been no consensus on appropriate interpolator for generating bathymetry. Such interpolation is critical to reliable assessments of channel morphology and channel change, which serve for dredging, engineering projects, and mapping of navigation hazards. This study aimed to identify an optimal spatial interpolation for mapping the river bathymetry from cross-sectional sounding measurements. We evaluated a variety of spatial interpolation methods including Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Radial Basis Function (RBF), and Local Polynomial Interpolation (LPI). In addition, we also considered the anisotropic form of IDW (Elliptical IDW as EIDW), that of OK (OKA), and Universal Kriging (UK). Two reaches in the LMR, located between approximately RM (River Miles) 170–140 and RM 60–35, were chosen as the study area. Those interpolators were compared in terms of root-mean-square error (RMSE), mean absolute error (MAE), bias, and coefficient of determination (r²). Our results demonstrate that both of RBF and OKA performed the best in mapping the bathymetry of study reaches. Furthermore, our results also indicate that the addition of anisotropy can significantly reduce RMSE by 5–20%, as compared to isotropic methods. The findings better inform other researchers on selecting a proper interpolation technique for mapping river bathymetry, particularly for other reaches of the Mississippi River.

Alluvial river landscapes of the lower Mississippi and Atchafalaya rivers in the south-central USA are flood prone and have shifted historically in position and form, resulting in interventions for flood reduction, navigation, and water supply. Fisk mapped these landscapes in the middle of the twentieth century as a series of artistic colourful map plates. Selected areas are revisited with modern data sets (LiDAR from 2003, hydrographic surveys from 2006 and 2007) including two sites along the Mississippi River (near the Old River juncture and near Morganza Floodway) and one in the middle Atchafalaya River. By using 2D and 3D geovisualization, we find that the extent, variety, and dimensions of anthropogenic landforms have grown in prominence since Fisk’s mapping. The volumes of the highest positive landforms are quantified to provide some indication of direct and indirect anthropogenic activity in these landscapes.

The lowermost Mississippi River (LMR) is one of the largest deltaic systems in North America and one of the heavily human-manipulated fluvial river systems. Historic hydrographic surveys from the mid-1900s to the early 2010s were used to document the thalweg morphology adjustments, as well as the riffle–pool sequences. Extensive aggradation was observed during 1950s to 1960s, as the Atchafalaya River was enlarging before the completion of the Old River Control Structure (ORCS). Following the completion of the ORCS, reductions in sediment input to the LMR resulted in net degradation of the thalweg profile patterns since the mid-1960s except for the 1992–2004 period. Different flood events that supplied sediment might be the cause of upstream aggradation from 1963–1975 and net aggradation along the entire reach from 1992–2004. Furthermore, the change pattern of thalweg profiles appear to be controlled by backwater effects, as well as the Bonnet Carré spillway opening. Results from riffle–pool sequences reveal that the averaging W_s ratios (length to channel width) are 6–7, similar to numerous previous studies. Temporal variations of the same riffles and pools reveal that aggradation and degradation might be heavily controlled by similar factors to the thalweg variations (i.e., sediment supply, backwater effects). In sum, this study examines decadal-scale geomorphic responses in a low-lying large river system subject to different human interventions, as well as natural flood events. Future management strategies of this and similar river systems should consider recent riverbed changes in dredging, sediment management, and river engineering.

Anti-Asian racism and xenophobia surged on social media during the COVID-19 pandemic, fueling discrimination and violence against Asians and Asian Americans in the United States. This study aims to understand the spatial patterns of Anti-Asian hate speech (AAHS) and identify the socioeconomic and ideological factors that may influence its emergence. Using Geographic Information System (GIS) tools, including hotspot analysis, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR), we analyzed several socioeconomic and ideological factors to understand their impact on the spatial variability of AAHS risk levels. Our models show AAHS hotspots tend to cluster in suburban areas near the metropolitan and micropolitan regions. Our OLS model identifies three significant socioeconomic factors: the poverty level is negatively correlated with the AAHS risk level, but both high education level and diversity index demonstrate positive correlations with the AAHS risk level. The GWR model further explored local spatial patterns of how these factors influence AAHS levels. We suggest that anti-Asian sentiments may be driven by dominant groups fearing the loss of their identity and socioeconomic security. Additionally, in diverse areas, pandemic-related crises can exacerbate existing intergroup tensions, with structural inequalities further deepening divisions. Policymakers can leverage these findings to develop strategies aimed at mitigating the factors contributing to hate speech and effectively combating it. Future studies can expand on our findings to develop more robust theoretical frameworks for understanding these patterns.