Influence of Landscape Structures on Water Quality at Multiple Temporal and Spatial Scales: A Case Study of Wujiang River Watershed in Guizhou

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.

Assessing Anthropogenic Impacts on Chemical and Biochemical Oxygen Demand in Different Spatial Scales with Bayesian Networks

“…The larger transportation capacity carried more pollution into rivers, including contaminates reserved in the former dry season and produced by agricultural activities in the current wet seasons. The transformed "sink" and "source" processes in farmland were a potential reason for the phenomenon that COD and BOD loads were both higher in wet seasons than those in dry seasons, which was similar to several previous studies [29,72,73]. Since the farmland had the largest proportion in the HRB and played an important role in water degradation, it is urgent to take measures to reduce organic matters transported from farmland, such as controlling the use of agricultural fertilizers, establishing a "field-ditch-pond" structure and constructing wetland detention ponds near riparian areas [65,74,75].…”

“…The chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were selected to be typical water quality indicators to analyze, while COD is an indicator of the mass of oxygen consumed by organic pollutants and BOD is the amount of oxygen needed by aerobic biological organisms breaking down organic matters. The two parameters reflect the levels of oxygen-consuming organic pollution in the water body and are used as the main criteria for aquatic ecosystem resources assessment [29][30][31]. Moreover, COD was the most severe water pollution in the HRB based on previous studies [32,33].…”

Characterizing the heterogeneous correlations between the landscape patterns and seasonal variations of total nitrogen and total phosphorus in a peri-urban watershed

“…CON had negative effects on TP in both wet and dry seasons (Fig. 6b ), consistent with reports by Xu et al ( 2019b ) and Guo et al ( 2018 ).…”

“…Statistical analyses such as correlation analysis, principal component analysis, and conventional linear regression can be used to analyze the effects of landscape patterns on river water pollution. In addition, multivariate stepwise regression and redundancy analysis can also be used to evaluate the factors that cause seasonal variations in river water quality (Yu et al 2016 ; Wu and Lu 2019 ; Xu et al 2019b ). However, all these methods are based on global statistics so that spatial changes in influential factors are difficult to consider.…”

 the Impact of Watershed Land Use on River Water Quality: Implication for Planning in the Case of Kebena River Watershed, Addis Ababa, Ethiopia

“…In line with this, it is necessary to remove polluting land uses, incorporate open ditches, small streams, and roads with environmentally suitable storm water management network, and establish adequate river buffer while reconnecting green patches within the catchment. Furthermore, it is crucial to incorporate water pollution mitigation schemes into urban planning decisions through provision of compact development approach, and brown eld development approaches to reduce natural landscape composition fragmentation (Xu et al, 2019), and riparian buffers development to control further increment of impervious surfaces (Xu et al, 2019;Chouli, Aftias, and Deutsch., 2007;Mander et al, 1997).…”