Development

Urban lawn model development was led by Dr. Migliaccio.

Grass or turf lawns have been estimated to cover 2% of the US land surface and identified as ‘America’s biggest crop’ (Hayden, 2005). This model will provide users with an estimate of irrigation run times needed (minutes) to meet current lawn turf water demand using a simplified approach for automated irrigation systems. This urban lawn model will use FAWN and GAEMN meteorological data to compute a simple, real-time weekly water balance. The simple balance will include the components previously listed (FC, RD, ET, R, MAD) and the irrigation schedule (days and minutes) and irrigation heads (fixed spray, gear driven rotary, or impact sprinklers). Temperatures will also be monitored by the model to determine if they are above the minimum temperature required for crop growth to occur.

An extensive literature review will be conducted to identify these values for the dominant turf varieties in the SE US. This consideration will be integrated into the model so that irrigation is not recommended for dormant urban lawns. There will also be an option to enter irrigation rate (depth/time) if this is known by the user. The user will also indicate their use of a rain sensor with their automated irrigation system. Assumptions regarding crop coefficient (based on a thorough literature search) for turf grass will be used to simplify the water balance approach. The app will provide a suggested irrigation run time for the following week based on the past week’s weather conditions and user inputs. There will be some limitation to this method as rainfall is variable in the SE US and our data are limited to established weather stations previously mentioned. Users will be notified of this limitation and the km (miles) their address is from the weather station. Current and historic weather data will be compared to determine if conditions are ‘wetter’ or ‘dryer’ than usual. The mean of historic data + one standard deviation will be considered normal. A sliding scale with ‘dry’ and ‘wet’ indicators will be used to visual convey this information. A forecast will be provided to help users consider their irrigation schedule using NWS data. The app will also provide an estimate of gallons of water saved by using the app instead of the original timer schedule provided by the user. This app differs from others that are currently available in that it completes a real-time water balance, provides forecasting information, an estimation of water savings achieved through suggested irrigation modifications, and provides a historic perspective of current conditions.