Research Question: To assess how sandbar size along the Upper Escambia River has changed over time, particularly before and after a major tributary avulsion and subsequent restoration project.

Background: The Escambia River in northwest Florida features dynamic alluvial sandbars that play key roles in habitat provisioning, sediment transport, and channel morphology. In 1978, Big Escambia Creek, a tributary to the Escambia, avulsed into nearby sand and gravel mining pits, greatly increasing sediment delivery to the river. This event caused substantial wetland loss and disrupted aquatic ecosystems. In 2005, a multi-million dollar restoration project was implemented to reroute the creek and reduce sediment input. The morphology of sandbars in this system reflects both hydrologic dynamics and human impacts, making them ideal indicators for assessing long-term changes in river function.

Figure 1. Study area map of the Escambia River, showing the USGS gauge at Century, FL, river kilometer markers, flowline, and elevation. The inset map highlights the Escambia River basin and its major tributaries.

Methods: To evaluate changes in sandbar size and volume, 28 sandbars were digitized from aerial imagery between 1940 and 2021 using ArcGIS Pro. Imagery was grouped by comparable flow conditions into paired low- and high-flow periods. Sandbar areas were measured and compared across time and between upstream and downstream sections relative to the avulsion site. Additional analysis included regression of sandbar area against river stage and visualization of residuals to detect unusual trends. Lidar-based elevation data from 2006 and 2017 were used to calculate volumetric changes for 13 of the largest sandbars.

Results: At low flows, total sandbar area increased by 9.1 hectares from 1940 to 1999 but decreased by 4.1 hectares from 1999 to 2013. During higher flows, sandbar areas increased by 5.6 hectares from 1978 to 2015, despite expected submergence. Upstream sandbars were consistently larger, with their average area increasing initially after the avulsion but decreasing slightly below pre-avulsion levels by 2013. Downstream bars exhibited a steady increase in size throughout the time period. These results can be seen in figures 2 and 3. Paired t-tests indicated that some of these changes, particularly the downstream increases during the early period and high-flow increases for both regions were statistically significant. Regression analysis (Figure 4) showed a moderate negative correlation between stage height and sandbar area, as expected due to submergence at higher flows. However, 2009 emerged as an outlier year, with significantly larger-than-expected sandbar areas following a major flood. The hydrograph for the 2009 aerial photography can be seen in Figure 5, showing the major flood. Lidar analysis confirmed that all 13 sandbars studied experienced increases in both volume and elevation between 2006 and 2017, despite higher stage levels in 2017. This suggests widespread sediment deposition across the river system.

Figure 2: Plot of the 10 upstream sandbars studied on the Escambia River for two different paired imagery sets, distinguished by water level percentiles.

Figure 3: Plot of the 18 downstream sandbars studied on the Escambia River for two different paired imagery sets, distinguished by water level percentiles.

Figure 4. Regression plot for the sandbar at River KM 71.6, showing a negative linear trend—higher river stage levels correspond to reduced visible sandbar area due to submersion.

Figure 5. Hydrograph of daily discharge measurements from USGS for 2009, with the aerial photography date marked by an orange triangle.

Discussion: The results both confirmed and complicated expectations. The avulsion event in 1978 clearly contributed to an increase in downstream sediment deposition, reflected in growing sandbar sizes. However, the effect of the 2005 restoration project appeared more nuanced. While some evidence suggested stabilization of downstream bars, upstream bars actually decreased in area post-restoration, perhaps due to sediment depletion from unknown upstream sources or infrastructure such as dams beyond the study area. These patterns emphasize that sediment inputs, once disrupted, can have wide-ranging and long-lasting consequences, and restoration may not uniformly reverse those effects.

The 2009 anomaly highlighted the importance of flood events in sediment delivery and bar formation. The flood that year was followed by widespread positive residuals, meaning sandbars were much larger than expected for the stage, a likely result of flood-driven sediment mobilization. This illustrates that timing and intensity of floods, in combination with sediment availability, strongly influence bar morphology.

Importantly, lidar-derived volumetric increases on all 13 bars studied reveal that sandbar growth is not just a function of visual area but of true vertical accretion, underscoring the dynamic nature of sediment storage in this system. The uniform increase in volume even during higher stage conditions in 2017 indicates ongoing sediment accumulation, suggesting that sources likely including legacy sediment from the 1978 avulsion continue to contribute to riverbed change.

Background: Hurricane Ian began as a tropical storm on September 23, 2022, and rapidly intensified into a Category 5 hurricane within days. One key factor in its rapid development was the presence of warm ocean waters in the Gulf of Mexico, which supplied the storm with heat and moisture, fueling its growth. While this process is well understood, another well-documented but less widely recognized phenomenon is the cooling of sea surface temperatures (SST) following a hurricane's passage. This cooling occurs due to both heat transfer from the ocean to the storm system and the upwelling of cooler subsurface waters. The drop in SST after a hurricane can have significant implications, including influencing the development of future storms.

This project examines how Hurricane Ian influenced SST in the eastern Gulf of Mexico, with the primary objective of quantifying and visualizing this cooling effect using geospatial analysis techniques in ArcGIS Pro.

Geographic Data: The data analyzed in this study included a shapefile of Hurricane Ian's track from the National Hurricane Center (NHC) and sea surface temperature (SST) data from NOAA’s Physical Science Laboratory in NetCDF format. The SST dataset included both daily mean SST values and SST anomalies, representing deviations from average temperature conditions.

Geographic Methods: The hurricane track shapefile was uploaded to ArcGIS Pro and overlaid on a base map. SST data was processed using the "Make NetCDF Raster Layer" tool, where longitude and latitude were set as the x- and y-dimension variables, respectively. The raster variable was assigned as either SST mean or SST anomaly, and the band dimension was set to time. For analysis, SST data was extracted for two key dates: September 26, 2022 (before Hurricane Ian’s passage) and September 29, 2022 (after its passage). This produced four raster layers: an SST daily mean raster and an SST anomaly raster for each date, which were used to create maps visualizing SST changes before and after the hurricane.

To quantify temperature changes, the Raster Calculator tool in ArcGIS Pro was used to detect SST differences before and after Hurricane Ian’s passage. A simple expression subtracting the SST raster from September 26 from the SST raster on September 29 produced a difference raster, where negative values indicated cooling and positive values indicated warming. This raster was then reclassified, and a blue-to-red color ramp was applied for visualization, with blue representing cooling areas and red indicating warming.

An area near Hurricane Ian’s landfall exhibited widespread SST cooling. To further analyze the degree of cooling, a polygon was drawn to define this region of interest. The Zonal Statistics tool was applied to calculate mean, maximum, and minimum temperature changes within this area, providing a quantitative measure of SST differences before and after landfall.

To statistically identify regions of significant SST cooling, a Hot Spot Analysis (Getis-Ord Gi)* was performed. The temperature difference raster created in the previous step was first converted to point features using the Raster to Point tool. These points were then used as input for the Hot Spot Analysis tool, with a fixed distance band of 50 km. The resulting layer identified statistically significant cooling hot spots (shown in blue) and warming areas (shown in red), allowing for a spatial assessment of temperature change patterns.

Summary of Key Results: Prior to Hurricane Ian’s passage on September 26, 2022, sea surface temperatures (SSTs) were notably elevated in the Gulf of Mexico, particularly southwest of Florida. The SST anomaly data indicated that these areas were warmer than the climatological average, providing the necessary heat energy that contributed to Ian’s intensification. Following the hurricane’s landfall on September 29, 2022, a significant cooling effect was observed in the SST means data. The Raster Calculation (Change Detection) confirmed that SSTs decreased up to 2.5°C in the gulf regions surrounding Florida, with greater cooling near the hurricane track and even larger decreases closer to landfall (Figure 1). Zonal Statistics further quantified the SST drop within a defined area (see Figures 2-3) off the coast near landfall. The results indicate a clear cooling trend, with the mean SST dropping by 1.2°C over the selected zone. Additionally, the Hot Spot Analysis (Getis-Ord Gi)* revealed that cold spots (areas of statistically significant cooling) aligned with the visually observed SST decreases, further reinforcing the robustness of the temperature drop (Figure 4). The clustering of these cold spots near Ian’s track and landfall location supports the conclusion that the hurricane’s passage was directly responsible for the cooling effect.

Figure 1: SST Difference Map using Raster Calculator

SST’s in °C Before Ian’s Passage (Sept. 26, 2022):

SST’s in °C After Ian’s Passage (Sept. 29, 2022):

Figures 2 & 3: Maps showing Mean SST Values before landfall (09/26/2022) and after landfall (09/29/2022) with a defined area of interest used in Zonal Statistic Calculations.

Figure 4: SST Difference Map shown in Figure 1 with Hotspot Analysis Points overlain

Background: Atmospheric rivers (ARs) are long, narrow corridors of water vapor transport in the atmosphere, responsible for much of the poleward moisture flux in the mid-latitudes. While essential for delivering beneficial precipitation to the U.S. West Coast, ARs also contribute to hazardous flooding and infrastructure damage when they are particularly intense. Their impacts vary seasonally due to changes in atmospheric dynamics, storm structure, and local terrain interactions. This review explores the structure of ARs, how they are identified and measured, and the seasonal variability in their precipitation effects—particularly along the Pacific coast. The analysis includes a review of key literature addressing the meteorological structure of ARs, the tools used for their classification, and case-specific data to illustrate the impacts of winter ARs. Special attention is given to the winter season, when ARs become more frequent and intense, amplifying the region’s hydroclimatic extremes.

Paper 1: Neiman, P. J., Ralph, F. M., Wick, G. A., Lundquist, J. D., & Dettinger, M. D. (2008). “Meteorological Characteristics and Overland Precipitation Impacts of Atmospheric Rivers Affecting the West Coast of North America Based on Eight Years of SSM/I Satellite Observations”

This study provides a comprehensive meteorological analysis of ARs using satellite and reanalysis data. It details the structural alignment of ARs with warm conveyor belts and low-level jets in extratropical cyclones, especially on the warm side of the cold front. The paper highlights stark seasonal differences: winter ARs are associated with stronger IVT magnitudes, deeper moisture profiles, and enhanced ascent—factors that contribute to heavier and more widespread precipitation. The authors use vertical profiles and composite maps to demonstrate how wintertime ARs tend to produce significantly more rainfall than their summer counterparts. Their findings underscore how synoptic-scale processes and topography interact differently by season, making winter ARs especially potent in coastal regions such as California, Oregon, and Washington.

Paper 2: Ralph, F. M., Rutz, J. J., Cordeira, J. M., Dettinger, M., Anderson, M., Reynolds, D., et al. (2019). “A Scale to Characterize the Strength and Impacts of Atmospheric Rivers.”

This paper introduces a five-category scale classifying AR events based on IVT magnitude and duration, ranging from weak (Category 1) to exceptional (Category 5). The authors emphasize that most Category 4 and 5 ARs occur in winter, consistent with stronger jet streams and synoptic support. This scale offers a way to communicate not only the strength of an AR but also its likely impact, whether beneficial (e.g., filling reservoirs) or hazardous (e.g., triggering floods). By analyzing past events, the study demonstrates that even ARs of similar IVT can have very different impacts depending on season, soil moisture, and antecedent weather. This classification scheme was instrumental in the case study of a December 2022 AR, which was rated Category 4 due to its high IVT but had mostly beneficial outcomes due to short duration and favorable antecedent conditions.

Paper 3: Gershunov, A., Shulgina, T., Clemesha, R. E. S., Guirguis, K., Pierce, D. W., Dettinger, M. D., et al. (2019). “Precipitation regime change in Western North America: The role of Atmospheric Rivers.”

Focusing on long-term climate trends, this paper discusses the role of ARs in shaping California's hydroclimate. ARs contributed significantly to ending the prolonged 2012–2016 drought and were responsible for the extremely wet conditions in the 2016–2017 water year. This study points out that California experiences the largest interannual variability in precipitation of any U.S. state, and ARs are a key driver of that variability. The seasonal clustering of ARs—particularly during winter months—can both relieve drought and create flood emergencies. The authors argue that while ARs are vital for the region’s water supply, their increasing intensity in a warming climate will require better monitoring and flexible infrastructure planning.

Paper 4: Slinskey, E. A., Loikith, P. C., Waliser, D. E., Guan, B., & Martin, A. (2020). “A Climatology of Atmospheric Rivers and Associated Precipitation for the Seven U.S. National Climate Assessment Regions.”

This climatological study examines AR frequency and distribution across all four seasons in the U.S., using data from 1981 to 2016. The study confirms that the majority of ARs affecting the West Coast occur during winter, especially from December through February, with significantly lower frequencies in spring, summer, and fall. The researchers emphasize that this seasonal pattern is tied to synoptic-scale processes like extratropical cyclone activity and jet stream dynamics. AR frequency maps included in the paper visually reinforce these seasonal patterns. By breaking the U.S. into climate assessment regions, the study also reveals spatial differences in AR frequency that may help tailor region-specific forecasting and planning efforts.