A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

Gourab Kumer Saha; Farshid Rahmani; Chaopeng Shen; Li Li; Raj Cibin

doi:10.1016/j.scitotenv.2023.162930

A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

Sci Total Environ. 2023 Jun 20:878:162930. doi: 10.1016/j.scitotenv.2023.162930. Epub 2023 Mar 18.

Authors

Gourab Kumer Saha¹, Farshid Rahmani², Chaopeng Shen², Li Li², Raj Cibin³

Affiliations

¹ Department of Agricultural and Biological Engineering, The Pennsylvania State University, United States of America.
² Department of Civil and Environmental Engineering, The Pennsylvania State University, United States of America.
³ Department of Agricultural and Biological Engineering, The Pennsylvania State University, United States of America; Department of Civil and Environmental Engineering, The Pennsylvania State University, United States of America. Electronic address: craj@psu.edu.

PMID: 36934914
DOI: 10.1016/j.scitotenv.2023.162930

Abstract

High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5-60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE > 0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.

Keywords: C-Q relationship; Deep learning; LSTM; Machine learning; Stream nitrate modeling; Water quality.