Presenter: Dr. Guoshuai Zhang
Co-author(s): Dr. Yan Chen, Mr. Zhonghua Li, Mr. Shunxing Qin
Organisation: Chinese Academy of Environmental Planning
Sub-theme
6. Innovation for Water Governance and Management
Topic
6-4. Integrated river basin management
Body
Assessing annual nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. While several studies have investigated the uncertainty in nutrient load estimations resulting from different calculation methodologies and sampling frequencies, the impact of load estimating algorithms on sample strategy optimization has not been thoroughly investigated. Therefore, this study aimed to assess the uncertainty in seasonal and monthly total nitrogen and phosphorus load estimates using several widely-used load estimation algorithms in the Yiluo River Basin, a tributary of the Yellow River in China. To accomplish this, the study conducted a bootstrapping process on the 2019 nitrogen and phosphorus datasets from Yiluo River basin to assess the reliability of nine load calculation algorithms at four different sampling frequencies (3 daily, weekly, biweekly, and 4 weekly). The results revealed a relative error (RE) in monthly TP flux estimates, with means ranging from -13.2% and standard deviation 36.1% (once per three days) to means of -19.6% and standard deviation 60.4% (monthly sampling). Meanwhile, the RE in monthly TN flux estimates ranged from means of -10.9% and standard deviation 34.7% (once per three days) to means of -19.3% and standard deviation 58.1% (monthly sampling). Based on the results, biweekly and weekly sampling routines using methods M1 and M4 for total phosphorus and methods M2 and M5 for total nitrogen were found to be the optimal sampling program. These findings highlight the importance of carefully selecting appropriate methods for estimating nutrient loads to avoid significant errors, especially when concentration data is scarce. Furthermore, the study explored the effects of temporal fluctuations in data availability on load estimating algorithms' impact on sampling strategy optimization, which is crucial for establishing monitoring programs in watersheds with similar characteristics. In conclusion, this study provides valuable insights into the uncertainty in nutrient load estimates and the impact of load estimating algorithms on sampling strategy optimization. It underscores the importance of selecting appropriate methods for estimating nutrient loads to minimize errors and improve the effectiveness of aquatic ecosystem management and monitoring. The findings of this study can provide decision-makers with valuable insights into the uncertainty in nutrient load estimates and the impact of load estimating algorithms on sampling strategy optimization and establish effective monitoring programs and develop informed strategies for reducing nutrient loads, contributing to the protection and conservation of the water resources in the watershed.