Real-time estimation of TP load in a Mississippi Delta stream using a dynamic data driven application system

Elevated phosphorus (P) in surface waters can cause eutrophication of aquatic ecosystems and can impair water for drinking, industry, agriculture, and recreation. Currently, no effort has been devoted to estimating real-time variation and load of total P (TP) in surface waters due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health, drinking water supply, and rapidly developing events such as algal blooms, the availability of timely P information is very critical. In this study, we developed a new approach in the form of a dynamic data driven application system (DDDAS) for monitoring the real-time variation and load of TP in surface water. This DDDAS consisted of the following three major components: (1) a User Control that interacts with Schedule Run to implement the DDDAS with starting and ending times; (2) a Schedule Run that activates the Hydstra model; and (3) a Hydstra model that downloads the real-time data from a US Geological Survey (USGS) website that is updated every 15 min with data from USGS monitoring stations, predicts real-time variation and load of TP, graphs the variables in real-time on a computer screen, and sends email alerts when the TP exceeds a certain value. The DDDAS was applied to monitor real-time variation and load of TP for 30 days in Deer Creek, a stream located east of Leland, Mississippi, USA. Results showed that the TP concentrations in the stream ranged from 0.24 to 0.48 mg L−1 with an average of 0.30 mg L−1 for a 30-day monitoring period, whereas the cumulative load of TP from the stream was about 2.8 kg for the same monitoring period. Our study suggests that the DDDAS developed in this study was useful for estimating the real-time variation and load of TP in surface water ecosystems.

► We presented a new approach for real-time monitoring of total phosphorus load in a stream.
► We designed a Dynamic Data Driven Application System for total phosphorus forecasting.
► We applied the HYDSTRA model for real-time acquisition of USGS data.
► No studies have been devoted to predicting the real-time variation nutrients in surface water priori to our work.