How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus

Investigating water–land–climate interactions is critical for urban development and watershed management. This study examined this nexus by elasticity and statistical approaches through the lens of three watersheds: The Yukon, Mekong and Murray. Here, this study reports the fundamental characteristics, explanations and ecological and management implications of terrestrial determinant influence on the response of water quality to climate drivers. The stability of the response, measured by climate elasticity of water quality (CEWQ), is highly dependent on terrestrial determinants, with strong impacts from anthropogenic biomes and low impacts from surficial geology. Compared to temperature elasticity, precipitation elasticity of water quality is more unstable due to its possible linkages with many terrestrial determinants. Correlation and linear models were developed for the interaction system, which uncovered many interesting scenarios. The results implied that watersheds with a higher ratio of rangeland biomes have a lower risk of instability as compared to watersheds with a higher proportion of dense settlement, cropland and forested biomes. This study discusses some of the most essential pathways where instability might adversely affect CEWQ parameters and recommends suggestions for policy makers to alleviate the instability impacts to bring sustainability to the water environment

Application of positive matrix factorization to identify potential sources of water quality deterioration of Huaihe River, China

Abstract Identification of nonpoint source (NPS) pollution is essential for effective water management. In this study we used a combined approach of hierarchal cluster analysis (HCA) and positive matrix factorization (PMF) to identify NPS pollution for Huaihe River basin in China. NH3-N, COD, DO and pH were regularly monitored weekly over 2 years (2011–2012) from 27 monitoring stations subjected to high anthropogenic and natural changes. As identified by multiple correspondence analyses, the monitoring stations #3, #9 and #21 are located away from the rest of sites. HCA classified all the stations into 4 groups. PMF identified four factors on each group and each season. They were associated with the major causes of Huaihe River water quality deterioration resulted by discharges inputs from urban, agricultural and industrial land uses. Seasonal NPS pollution variation was found, and it is possibly linked with natural processes, for instance hydrological regime. This research work demonstrates the usefulness of PMF model for the identification of NPS pollution in surface waters. Furthermore, our study also shows that urban, agricultural and industrial land uses were the main factors impairing surface water quality, and limiting NPS pollution would be critical for enhancing surface water quality in the study area