Influence Of Watershed Landscape Characteristics On River Water Quality And Research On Reduction Of Nitrogen In Riparian In Liu Xi River

In the case of point source pollution getting better control, the optimization of land use composition and space layout can effectively control the non-point source pollution in water environment management. Thus, water quality response to the temporal and spatial variation of watershed landscape characteristics and its mechanism became the hotspot in controlling water pollution for basic research. Nitrogen is one of the important pollutants for terrestrial water, Nitrogen is from adjacent highlands going through the riparian zone into water by surface and underground runoff. Riparian zone, as an important buffer zone between land and water, is the last barrier to stop land pollutant getting into the water. It plays an important role in controlling nonpoint source. So reaching on riparian zone has great theoretic and practical significance in pollutants removal, water quality and watershed ecosystem health.Combining with the field sampling survey, this paper had analyzed the influence of watershed landscape characteristics on water quality by using GIS, linear multivariate and nonlinear statistical method, and got the typical pollutant of water quality. On this basis, another purpose is to show the important role of riparian zone ecosystem.Main results from study are as follows:(1) To explore the relationship between water quality and landscape characteristic at the Liuxi River watershed, based on collected water quality data, Partial redundancy analysis(PRDA) and Random forests regression(RFR) analysis were integrated to identify the key landscape factors and to determine the important landscape characteristic variable group at the watershed scale. The results showed that between water quality and landscape characteristic variables there was a significant correlation. On the whole, landscape characteristics from upstream to downstream enhanced the gradient of urbanization along with the water quality deterioration. Thereinto, percentage of bare land area had a most important negative effect on water quality, then was percentage of construction land area. However, percentage of forest land area had a certain positive effect on water quality. There were no obvious relations between the percentage of watershed area, orchard area and water quality. Out of the explained variation of water quality, landscape composition(20.7% of 0.657; that is 0.136/0.657) was the major contributions at the watershed scale. The result indicated the importance of the landscape composition for the water quality of the watershed. Specifically, the percentage of bare land area and construction land area were the most important factors to the concentration of total phosphorus and ammonia nitrogen, COD in the river; Meanwhile, because redundancy analysis combined with random forest regression, they could have higher dimension reduction effect, it proved that a combination of both was one of the effective ways to study the relationship between landscape characteristic and water quality.(2) Combing the scale dependence and change-point analysis to discuss the relation between the landscape and the water quality is important to provide valuable information for the restoration of riparian buffer and river management. Liu Xi River watershed in Guangzhou city was as a typical case study area, at the scales of sub-basin and 30 m, 61 m, 100 m, 200 m, 300 m buffer zones, redundancy analysis(RDA) was used to screen the best buffer scale in that the landscape variables had more explaining power for water quality spatial variation, and to gain the best landscape variable group. Change-point analysis was applied to determine the optimum landscape factor for explaining every water quality indicator and to estimate the speci?c locations along a gradient of landscape metric that result in a sudden change in the water quality variable by the Classification and regression tree(CART) model. The results showed that: the water quality might to be better correlated with the buffer landscape metrics than with the sub-basin scale landscape indices, the landscape pattern at the200 m buffer was believed to have the greatest impact on the water quality; The area percentage of bare land(BAR%), the area percentage of construction land(CON%) and the large patch index of construction land(LPIcon) were recognized respectively as the dominant variable influencing the TP, COD and NH4+ for a 200 m buffer zone. The result of change-point analysis indicated the key interval values of the tree landscape variables within the 200 m buffer zone. When the BAR% was >0.8~1.4%, CON% was >15~23% and LPIcon was >5.0%~6.6%, the water quality might increase opportunity for pollutant reduction.(3) Taking the second-order tributaries of Liu Xi River in Guangzhou city as an example, the effect of multi-scale catchment landscape features on the soil physical and chemical properties of riparian zone soils were analyzed. The objectivity of this research is to explore and identify the main factors that influence soil properties at different spatial scales and provide scientific data for riparian zone management and restoration. The results suggest that along the longitudinal direction of the riparian zone, topographic index(TI), land slope, land elevation and land use types are the governing factors on the spatial heterogeneity of soil water content(SWC), soil textures and p H; along the transversal direction, the distance of a landscape from riverside and the vegetation coverage are the factors affecting the spatial heterogeneity of soil dissolved organic carbon(DOC), nitrate nitrogen(NO3--N) and bulk density(BD); along the vertical profile of the riparian zone, the intensity of human disturbance on soil, the root system distribution in soil profile and the groundwater tables are the major factors affecting the spatial heterogeneity of TOC, TN and NH4-N in riparian zones.(4)Soil denitrification enzyme activity was measured by a hierarchical sampling approach within the riparian zone of the Liu Xi River tributary of Guangzhou city, and explored and identified multi-scale factors that influenced soil denitrification. The results showed that soil denitrification potential—represented by soil denitrification enzyme activity(DEA)—showed more heterogeneity on a profile-scale than on a crosssectional scale and landscape-scale in riparian zones; multi-scale factors controlled the degree of spatial variation in soil denitrification. The profile-scale factors, including soil TOC(total organic carbon), TN(total nitrogen) and NO 3--N(nitrate nitrogen), were the major direct regulators of the spatial distribution of DEA. The cross-sectional scale factors, including landscape position and vegetation density, and the landscape-scale factors, including TI, elevation and land-use types, indirectly regulated the distribution of DEA. At the profile scale, the highest DEA occurred on the upper soil, but dramatically declined from the soil surface to the deeper layers. At the cross-sectional scale, the highest DEA was observed at the landscape position, with good landscape connectivity and high-density vegetation within the middle part of the riparian zone. At the watershed scale, DEA showed an increasing trend from upstream to downstream except for the sites in urbanized areas.(5)We examined associations between shalow groundwater of the riparian zone and nitrogen removal efficiency in the secondary tributary of the Liuxi River to determine the main mechanism and main influencing factors. Results showed that the riparian zone had a great cutting effect on nitrate from the high groundwater, so it had better protective effect on river water quality. Denitrification was the main remove mechanism, which directly affected by the nitrogen in the groundwater, carbon source, or the supply of oxygen. And some factors had indirect effects on this mechanism, such as aquifer thickness, land use, et al in vertical landscape scale and landscape position, riparian zone width, et al in horizontal scale. The three sections of both the width of riparian zone showed apparent effect on removing nitrate of the groundwater. D2 had the highest removal rate. The land use type of this sample area is cultivated land, which can provide a rich nitrate. The removal rate of 23 m and 10 m width in D2 were over 90%, significantly higher than other samples.The lowest removal rate was happened in vegetable sample area-D4 which located in the woodland. The removal rate of 23 m riparian zone was only 53%. Without considering the input concentration difference of Nitrate nitrogen, nitrate nitrogen removal rate of 23 m riparian zone was higher than 10 m.