Linking Land Use Metrics Measured in Aquatic–Terrestrial Interfaces to Water Quality of Reservoir-Based Water Sources in Eastern China

Abstract

The effects of anthropogenic land use on the water quality of reservoir-based water sources are understudied. We trained a self-organizing map (SOM) to measure the spatial pattern of nutrients over the course of a year in 61 reservoirs located in eastern China. In the linear regression model and one-way analyses of variance, we found that water quality was influenced by period of the year (flood, low, and normal periods based on rainfall conditions) and reservoir altitude (plains vs. mountains). Our results indicated that land use metrics measured in aquatic–terrestrial interfaces significantly influenced the water quality of reservoirs. The land use intensity (LUI) and the proportion of construction land had a positive correlation with ammonia nitrogen (NH3-N) and chemical oxygen demand (CODMn) concentrations, and redundancy analysis indicated that the percent of landscape (PLAND) represented by construction land was positively correlated with CODMn, NH3-N, total phosphorus (TP), and total nitrogen (TN) concentrations. The proportion of cropland was not correlated with any water quality property except for CODMn concentration. The total explained variance for water quality was highest when the scale was large (the area defined by a 1500 m radius around the reservoir), indicating that management which ensures water safety should be carried out at this scale.

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

“…As areas with weak synergies were mostly located in a region with a relatively developed economy and intensive human activities, food production and regulating services were both at a relatively low level (Figures 2 and 4). Therefore, for these areas, on the one hand, it is necessary to strengthen the management of land-use types, especially at the county scale, set up ecological red lines [76], implement the Grain for Green Project (GFGP) [77], improve the supply capacity of stock, carbon sequestration, water conservation, and flood mitigation services, and alleviate the strong trade-off between services. On the other hand, the government could improve the utilization rate of water and soil resources through scientific irrigation and farming methods, aiming to achieve the high yield, high quality, and high efficiency of crops.…”

The Spatiotemporal Eutrophication Status and Trends in the Paldang Reservoir, Republic of Korea

“…The regional and global impacts of climate change on reservoirs have become highly important. Reservoirs are critical resources in freshwater ecosystems that perform various functions, such as providing drinking water, regulating climate, maintaining biodiversity, and nitrate cycling [2]. However, pressure and competition for sustainable water management in reservoirs are increasingly exacerbated by human-induced factors, such as agricultural practices, urbanization, and industrialization [3].…”

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

“…It is generated from surface water bodies, which provide drinking water, preserve biodiversity, control climate, and maintain phosphorus and nitrate cycling [1]. However, in recent decades, the quality of these water bodies has been threatened by anthropogenic activities (e.g., urbanization, agriculture, water extraction, sewage discharge) [2][3][4][5]. Therefore, in the management plan of pollution control at a watershed scale, a worthwhile initial step is the identification of the pollution sources.…”

“…On its side, the SOM has the advantages of providing a suitable representation of non-linear processes, as well as a large number of parallel distributed structures, and is capable of learning and induction. Ding et al [4] used the SOM training to improve linear techniques to identify the temporal and spatial patterns of several water quality variables. The authors found that the sampling-site elevation affected the water quality throughout different seasons.…”