Compressive Sensing (CS) is a rapidly developing field of research that has revolutionized the way signals are acquired, processed, and stored. CS enables the reconstruction of signals from a small number of measurements, which can result in significant computation time and resource savings. Compressive Sensing for Water Systems examines how the CS techniques can be utilized to optimize various applications in hydrology and environmental science. It discusses the use of adaptive compressive sensing (ACS) techniques as a solution to reduce the amount of data that needs to be transmitted and processed, enabling more efficient monitoring and management of water systems. It includes the case studies and practical considerations, such as examples of sensor placement, network topology, and how ACS can improve the reliability, accuracy, and efficiency of water management systems and smart networks.

• Examines best practices for water quality monitoring and water resource management.
• Discusses how CS can aid in studying atmospheric water vapor distribution and its impact on weather and climate.
• Applying CS as a transformative approach for remote flood monitoring from sensor networks and remote-sensing platforms.
• Investigates practical implementations of CS in weather radar application.
• Synergy of CS and mixture modeling facilitates probabilistic classification, robust reconstruction, and enhanced differentiation between snow, clouds, and other features