Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Xiaobin Zhang,Kefeng Zheng,Li Sheng,Baoliang Xie,Weidong Lou,Hao Hu,Qing Gu,Yihang Zhu,Ligang Ma

doi:10.1007/s11269-022-03090-8

Abstract

Better characterizing the spatio-temporal pattern of water quality would increase the ability to effectively manage water resources. This study applied the concept of temporal stability analysis (TSA) to explore the temporal characteristics of spatial variability in surface water quality. Measurement data from 41 monitoring stations in Qiantang River, China during 2017–2019 were used for analysis. The data included four water quality indicators: dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and ammonia nitrogen (NH3–N). A Spearman’s rank correlation for each pair of monitoring times was performed to characterize the spatial pattern of water quality. A temporal analysis of relative differences was applied to examine the temporal stability of the sampling sites. The rank correlation analysis suggests that the spatial pattern of water quality was maintained for a specific period of time and the TP concentration was most temporally stable compared with the other three indicators across the study area. The standard deviation of the relative difference (SDRD) and index of temporal stability (ITS) were found to be better for identifying the stable sites compared to the mean absolute bias error (MABE) and root mean square error (RMSE) in this study. A correlation analysis between the temporal stability indices and potential influencing factors showed that land use proportions (forest, built-up land, and agricultural land), and socio-economic indicators (gross domestic product [GDP] and population density) were closely associated with the temporal stability of water quality. The results showed evidence that the TSA method was feasible and effective in identifying the temporal stability of surface water quality and optimizing the water quality monitoring program. This study’s method and findings can help improve surface water quality monitoring strategies and water resource management.

Highlights

Monitoring surface water quality is critical for regulating and managing water resources.The water monitoring network is important for evaluating, preserving, and remediating water quality (Ouyang, 2005)
Many surface water quality monitoring networks have been developed without a deliberate design (Strobl and Robillard, 2008), which may lead to incorrect decisions and wasted resources
The mean dissolved oxygen (DO) concentrations increased from September 2017 to February 2018

Summary

Introduction

Monitoring surface water quality is critical for regulating and managing water resources. The water monitoring network is important for evaluating, preserving, and remediating water quality (Ouyang, 2005). Recent significant advances in communication and sensor technology have greatly improved the ability to monitor surface water quality (Glasgow et al., 2004), and the real-time remote monitoring network for surface water quality has become essential for water resource management (Nasirudin et al, 2011; Quinn et al, 2010; Storey et al, 2011). Many surface water quality monitoring networks have been developed without a deliberate design (Strobl and Robillard, 2008), which may lead to incorrect decisions and wasted resources. Designing monitoring systems is vital for monitoring water quality and managing water resources

Methods

Results

Conclusion