Center for Embedded Network Sensing

This work demonstrates the application of real-time adaptive management principles to the problem of controlling the salinity levels in, and/or protecting groundwater quality beneath, soils undergoing irrigation with relatively saline water (e.g., reclaimed wastewater) under arid/semi- arid conditions. Here, optimal feedback-control scheme known as Receding Horizon Control (RHC) previously applied offline to control soil moisture levels during irrigation (Park et al., 2009) is applied inline during a pilot-scale field test aimed at balancing reclaimed water reuse and soil/groundwater quality in real-time. RHC is supported by sensor measurements, physically-based state prediction models, and optimization algorithms to drive field conditions to a desired environmental state. A simulation model including a one-dimensional (vertical) form of the Richards equation coupled to energy and solute transport equations is employed as a state estimator to provide predicted soil moisture, temperature, and salinity data. Vertical multi-sensor arrays installed in the soil provide initial conditions and continuous feedback to the control scheme. An optimization algorithm determines the optimal irrigation rate and frequency based on the imposed salinity constraints while forced by the requirement to maximize water reuse. The small-scale field test demonstrated that the RHC scheme was capable of maintaining specified salt levels at a prescribed soil depth autonomously. This finding suggests that, given an adequately structured and trained simulation model, sensor networks, prediction models, and optimization algorithms can be incorporated in the context of RHC to achieve water reuse and agricultural objectives while minimizing negative impacts on environmental quality autonomously.