Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland

Abstract

Modeling nitrate fate and transport in water sources is an essential component of predictive water quality management. Both mechanistic and data-driven models are currently in use. Mechanistic models, such as SWAT, simulate daily nitrate loads based on the results of simulating water flow. Data-driven models allow one to simulate nitrate loads and water flow independently. Performance of SWAT and deep learning model was evaluated in cases when deep learning model is used in (a) independent simulations of flow series and nitrate concentration series, and (b) in both flow rate and concentration simulations to obtain nitrate load values. The data were collected at the Tuckahoe Creek watershed in Maryland, United States. The data-driven deep learning model was built using long-short-term-memory (LSTM) and three-dimensional convolutional networks (3D Convolutional Networks) to simulate flow rate and nitrate concentration using weather data and imagery to derive leaf area index according to land use. Models were calibrated with data over training period 2014-2017 and validated with data over testing period. SWAT Nash-Sutcliffe efficiency (NSE) was 0.31 and 0.40 for flow rate and-0.26 and-0.18 for the nitrate load rate over training and testing periods, respectively. Three data- driven modeling scenarios were implemented: (1) using the observed flow rate and simulated nitrate concentration, (2) using the simulated flow rate and observed nitrate concentration, and (3) using the simulated flow rate and nitrate concentration. The deep learning model performed better than SWAT in all three scenarios with NSE from 0.49 to 0.58 for training and from 0.28 to 0.80 for testing periods with scenario 1 showing the best results. The difference in performance was most pronounced in fall and winter seasons. The deep learning modeling can be an efficient alternative to mechanistic watershed-scale water quality models provided the regular high-frequency data collection is implemented.