Assimilating Low-Cost High-Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

Han, Feng1,2; Hu, Zhaoping1; Chen, Nengwang3; Wang, Yao3; Jiang, Jiping1,2; Zheng, Yi1,2

2023-04-01

10.1029/2022WR033673

WATER RESOURCES RESEARCH   影响因子和分区

0043-1397

1944-7973

Uncertainty reduction in watershed water quality (WWQ) modeling remains a major challenge. One important reason is the lack of sufficient available water quality observations because traditional laboratory analysis of water samples has high labor, financial and time costs. Low-cost high-frequency water quality data from in-situ sensors provide an opportunity to solve this problem. However, long-term sensing in complex natural environments usually suffers more significant errors. This study aimed to develop a novel method to utilize in-situ sensor data in WWQ modeling, namely, the Bayesian calibration using multisource observations (BCMSO), which can simultaneously assimilate laboratory-based observations and in-situ sensor data. Both synthetic and real-world cases of nitrate modeling were used to demonstrate the methodology, and the Soil and Water Assessment Tool was employed as the WWQ model. The results indicated that direct assimilation of sensor data using traditional Bayesian calibration generated obvious deviations in parameter inference and model simulation, which could consequently bias future predictions and affect management decision correctness. However, after proper treatment of errors in sensor data, the BCMSO method could extract meaningful information from sensor data and prevent negative impacts of errors. The modeling uncertainty was also greatly reduced. In the real-world case, with 1 yr of subhourly electrical conductivity sensor data incorporated, the modeling uncertainty of nitrate concentration and management cost of controlling nitrate pollution were reduced by 70%. The BCMSO method provides a flexible framework to accommodate nonconventional observations in environmental modeling and can be easily extended to other modeling fields.

sensor data water quality Bayesian calibration uncertainty SWAT nitrate

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China (NSFC)[51961125203] ; Shenzhen Science and Technology Innovation Commission[KCXFZ202002011006491]

Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources

Environmental Sciences ; Limnology ; Water Resources

WOS:000975381200001

AMER GEOPHYSICAL UNION

20231814034472

Bayesian networks ; Calibration ; Cost benefit analysis ; Errors ; Nitrates ; Quality control ; Uncertainty analysis ; Water pollution ; Water quality ; Watersheds

Waterways:407.2 ; Surface Water:444.1 ; Water Analysis:445.2 ; Water Pollution:453 ; Inorganic Compounds:804.2 ; Cost and Value Engineering; Industrial Economics:911 ; Management:912.2 ; Quality Assurance and Control:913.3 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Probability Theory:922.1

ENVIRONMENT/ECOLOGY

Web of Science

1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Municipal Engn Lab Environm IoT Technol, Shenzhen, Peoples R China
3.Xiamen Univ, Coll Environm & Ecol, Fujian Prov Key Lab Coastal Ecol & Environm Studie, Xiamen, Peoples R China

Han, Feng,Hu, Zhaoping,Chen, Nengwang,et al. Assimilating Low-Cost High-Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach[J]. WATER RESOURCES RESEARCH,2023,59(4).

Han, Feng,Hu, Zhaoping,Chen, Nengwang,Wang, Yao,Jiang, Jiping,&Zheng, Yi.(2023).Assimilating Low-Cost High-Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach.WATER RESOURCES RESEARCH,59(4).

Han, Feng,et al."Assimilating Low-Cost High-Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach".WATER RESOURCES RESEARCH 59.4(2023).