No abstract

Water quality is highly influenced by the composition and configuration of landscape structure, and regulated by various spatiotemporal factors. Using the Wujiang river watershed as a case study, this research assesses the influence of landscape metrics-including composition and spatial configuration-on river water quality. An understanding of the relationship between landscape metrics and water quality can be used to improve water contamination predictability and provide restoration and management strategies. For this study, eight water quality variables were collected from 32 sampling sites from 2014 through 2017. Water quality variables included nutrient pollutant indicators ammoniacal nitrogen (NH 3 -N), nitrogen (NO 3 − ), and total phosphate (TP), as well as oxygen-consuming organic matter indicators COD (chemical oxygen demand), biochemical oxygen demand (BOD 5 ), dissolved oxygen (DO), and potassium permanganate index (COD Mn ). Partial least squares (PLS) regression was used to quantitatively analyze the influence of landscape metrics on water quality at five buffer zone scales (extending 3, 6, 9, 12, and 15 km from the sample site) in the Wujiang river watershed. Results revealed that water quality is affected by landscape composition, landscape configuration, and precipitation. During the dry season, landscape metrics at both landscape and class levels predicted organic matter at the five buffer zone scales. During the wet season, only class-level landscape metrics predicted water contaminants, including organic matter and nutrients, at the middle three of five buffer scales. We identified the following important indicators of water quality degradation: percent of landscape, edge density, and aggregation index for built-up land; aggregation index for water; CONTAGION; COHESION; and landscape shape index. These results suggest that pollution can be mitigated by reducing natural landscape composition fragmentation, increasing the connectedness of region rivers, and minimizing human disturbance of landscape structures in the watershed area.

The dynamic responses of wetlands to upstream water conservancy projects are becoming increasingly crucial for watershed management. Poyang Lake is a dynamic wetland system of critical ecological importance and connected with the Yangtze river. However, in the context of disturbed water regime in Poyang Lake resulting from human activities and climate change, the responses of vegetation dynamics to the Three Gorges Dam (TGD) have not been investigated. We addressed this knowledge gap by using daily water level data and Landsat images from 1987 to 2018. Landsat images were acquired between October and December to ensure similar phenological conditions. Object-oriented Artificial Neural Network Regression for wetland classification was developed based on abundant training and validation samples. Interactions between vegetation coverage and water regimes pre and post the operation of the TGD were compared using classification and regression trees and the random forest model. Since the implementation of the TGD in 2003, Poyang Lake has become drier, especially during the dry season. A more rapid plant growth rate was observed post TGD (44.74 km 2 year −1) compared to that of the entire study period (12.9 km 2 year −1). Average water level for the antecedent 20 days most significantly affected vegetation before 2003, whereas average water level for the antecedent 5 or 10 days was more important after 2003. The impoundment of the TGD after the flood season accelerated the drawdown processes of Poyang Lake, and the rapidly exposed wetlands accelerated vegetation expansion during the dry seasons, resulting in shrinkage and degradation of the lake area. This study deepens our knowledge of the influences of newly developed dams on lakes and rivers.