Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs.
It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006-2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources.
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China. funny@zju.edu.cn.
Qing
Q
Gu
Institution of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310058, China. kwang@zju.edu.cn.
Ke
K
Wang
Institute of Rural Development and Information, Ningbo Academy of Agricultural Sciences, Ningbo 315040, China. vaneljd@163.com.
Jiadan
J
Li
College of Resource and Environmental Science, Xinjiang University, Urumqi 830046, China. mosanzju@126.com.
Ligang
L
Ma
Institution of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310058, China. jsong_deng@zju.edu.cn.|School of Civil Engineering and Environmental Sciences and School of Meteorology, University of Oklahoma, Norman, OK 73019, USA. jsong_deng@zju.edu.cn.
Jinsong
J
Deng
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China. zhengkf@mail.zaas.ac.cn.
Kefeng
K
Zheng
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China. riceipm1@zju.edu.cn.
Xiaobin
X
Zhang
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China. LLANDLL070701@163.com.
Li
L
Sheng
D002681:China; D016000:Cluster Analysis; D060766:Drinking Water; D040362:Geographic Information Systems; D060753:Water Quality; D062066:Water Resources; D014881:Water Supply
D060766:Drinking Water
cluster analysis; reservoir; spatial distribution; temporal variation; water quality