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Lawn irrigation is by far the largest outdoor user of municipal water supply in the US. Existing estimates indicate that the land area cultivated with turf grass is up to three times larger than that of corn, the largest irrigated crop in the US. With urbanization and climate change, stormwater management is an increasingly large challenge for most cities. In this research, we develop, test, validate, and demonstrate a prototype cyber-physical system, referred to as i2Water, that utilizes weather-soil-vegetation modeling, rainwater harvesting and control, environmental sensing, and medium-range ensemble forecasting of precipitation and temperature for integrated control of rainwater harvesting and lawn irrigation. This research is expected to demonstrate how cyber-enabled integration of advanced sensing and joint optimal control of lawn irrigation and rainwater harvesting can transform the current approaches and practices of water conservation and stormwater management in large urban areas. While our research is limited to DFW, the methodology and the techniques are expected to be transferable to other regions.
The objective of this project is to develop a prototype cyber-physical system for integrated control of lawn irrigation and rainwater harvesting for water conservation and stormwater reduction, and to assess and demonstrate the potential impact and value of the system in DFW. While a great deal of research has been carried out on these topics individually at scales ranging from a house to a subdivision, limited knowledge currently exists on how the impact and potential benefits may scale when lawn irrigation and rainwater harvesting are controlled jointly for areas with population of less than 10,000 to over 100,000. The premise of this research is that integrated control of lawn irrigation and rainwater harvesting enabled by advanced sensing, communications, computing, control, and medium-range weather forecasting holds large potential in large urban areas such as DFW for water conservation and stormwater reduction, and can transform the current approaches and practices.
In this research, we develop, test, validate, and demonstrate a prototype cyber-physical system, referred to as i2Water, that utilizes weather-soil-vegetation (WSV) modeling, rainwater harvesting and control, environmental sensing, and medium-range ensemble precipitation and temperature forecasts (EQPF, EQTF) for integrated control of rainwater harvesting and lawn irrigation. For site evaluation, we deploy the prototype i2Water at the UTA Community Garden and carry out a set of real-time experiments in a controlled environment for testing and validation of the cyber-physical system for a summer season. Once the prototype system is validated and the best-performing decision criterion is selected, we apply the system in a simulation mode to areas of varying sizes and degrees of urbanization in DFW, and carry out quantitative assessment of the potential impact and benefits via multi-year hindcasting using high-resolution quantitative precipitation estimates (QPE) from NEXRAD and Collaborative Adaptive Sensing of the Atmosphere (CASA), and medium-range EQPF and EQTF from the Global Ensemble Forecast System (GEFS) reforecast dataset as bias-corrected via the NWS Hydrologic Ensemble Forecast Service (HEFS).
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This research supported by the NSF under Grant No. CyberSEES-1442735. This support is gratefully acknowledged.
