Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation

Area of Science:

• Hydrology
• Remote Sensing
• Agricultural Science

Background:

• Hydrologic extremes, like floods, involve complex interactions and impacts, such as soil saturation and reduced vegetation growth.
• Conceptual hydrological models struggle to accurately represent these interconnected processes and their triggers.
• The 2019 flood in the Northern Mississippi and Missouri Basins exemplifies such a complex event with significant agricultural consequences.

Purpose of the Study:

• To demonstrate the value of integrating remote sensing data into hydrologic modeling systems.
• To improve the representation of processes leading to extreme flood events and their agricultural impacts.
• To assess the accuracy improvements in soil moisture, snowmelt, and vegetation anomalies.

Main Methods:

• Utilized remote sensing data within a hydrologic modeling system.
• Analyzed the 2019 flood event in the Northern Mississippi and Missouri Basins.
• Compared model outputs with and without remote sensing data infusion.

Main Results:

• Remote sensing data integration substantially improved the representation of flood-related processes and agricultural disturbances.
• Accuracy of soil moisture estimates increased by up to 16%.
• Accuracy of snowmelt estimates increased by up to 24%, with improved vegetation anomaly representation.

Conclusions:

• Incorporating remote sensing data enhances the accuracy and capability of hydrologic models for extreme events.
• This approach provides a more robust understanding of the interplay between hydrologic extremes and agricultural impacts.
• The findings support the use of remote sensing for better flood prediction and agricultural impact assessment.