4.1. Key landscape indicators affecting water quality variation
As can be seen from table 6, the landscape indicators affected water quality parameters at different spatial scales were heterogeneity. In the dry season, agricultural land (PLADJ) (B =-0.615) had the most significant negative effect on pH, as agricultural activities produce large amounts of amines that make the soil saline and alkaline, thereby affecting the pH of the river. Forest land (PLAND) (B =-0.802) had a stronger effect on EC at the watershed scale, as forestland intercept and retain dissolved substances in the hills and gullies, preventing their transport along hydrological channels, thus attenuating land use effects and reducing solutes in rivers, ultimately resulting in lower conductivity levels (Walsh et a., 2009). During the wet season, agricultural land (PLAND) significantly affected EC values; urban land (PLAND) (B =-0.668) had the most significant effect on DO, as the waste produced by urban residents increases, the organic waste load and consumes large amounts of oxygen through oxidation, resulting in lower DO values. In contrast, during the wet season, forestland (AI) (B =-0.631) had a more prominent effect than urban land, as rainfall events over large areas of forestland wash soil or fallen leaves into the river, increasing the organic matter in it, and again reducing the DO values (Ding et al., 2016; Bo et al., 2017). In the dry season, the change of COD affected by agricultural land (PLAND) (B = 0.541) is positive, during this period, the application of fertilizers causes organic matter to flow into the river, increasing the consumption of oxygen in the water. In the wet season, COD was positively correlated with urban land (PLAND) (B = 0.667), as the discharge of urban sewage and wastewater increases the load on this parameter (Lee et al., 2009; Bu et al.,2014; Yu et al., 2016; Cheng et al., 2018). NH3-N was positively correlated with water (LSI) (B = 0.446) in the dry season and with cities (PLAND) (B = 0.347) in the wet season. In general, the flow characteristics and self-purification capacity of the water bodies will degrade the pollutants in this process, thus the rivers are negatively correlated with NH3-N (Liu et al., 2018), but it has also been suggested that wetlands close to water bodies absorb pollutants such as NH3-N, and the relationship between this compound and water bodies is complex (Shen et al., 2015). At the same time, wastewater treatment plants discharge a certain amount of pollutants into water bodies, causing NH3-N to be positively correlated with towns and cities land; TN was positively influenced by arable land (PLAND) (B = 0.603) and (LSI) (B = 0.449), due to the high use of urea in agricultural fertilizers, which has a high nitrogen content; and agricultural land use affected the TN input into the river. TP was significantly influenced by cities (PLADJ) (B = 0.547) in the dry season, and its concentration in rivers was affected by urban sewage and other reasons; in essence, TP concentration in rivers increases with the urbanization level. In contrast, TP was positively influenced by arable land (PLADJ) (B = 0.614) in the wet season, and application of chemical fertilizer were the key factor in the variation of nitrogen and phosphorus contents.
Table 6 The relative importance of the significant predictors in the best models
Parameter In(x+1)
|
Reach Scale
|
Riparian Scale
|
Sub-basin Scale
|
Significant Predictors(B)
|
Adj.R2
|
VIF
|
Significant Predictors(B)
|
Adj.R2
|
VIF
|
Significant Predictors(B)
|
Adj.R2
|
VIF
|
Dry season
|
pH
|
LSI6(0.539)
|
0.657
|
1
|
PLAND2(0.533)
|
0.251
|
1
|
PLADJ1(-0.615)
|
0.35
|
1
|
EC
|
LSI2(0.207)
|
0.225
|
1.112
|
AI1(0.356)
|
0.301
|
1.011
|
PLAND2(-0.802)
|
0.67
|
1.201
|
DO
|
PLAND5(-0.567)、 LSI1(-0.775)
|
0.618
|
1.266
|
PLAND5(-0.668)、
AI6(-0.643)
|
0.787
|
1
|
LSI6(-0.59)
|
0.319
|
1
|
PLADJ2(-0.415)、 PLADJ4(0.441)
|
CODMn
|
PLAND5(-0.511)
|
0.456
|
1
|
PLAND2(-0.823)、 AI3(-0.612)
|
0.602
|
1.311
|
AI2(-0.461)
|
0.415
|
1.029
|
NH3-N
|
PLAND2(-0.455)
|
0.489
|
1.2
|
PLAND5(0.498)、 PLAND6(0.529)
|
0.535
|
1.048
|
PLAND1(0.487)、 PLAND6(0.515)
|
0.792
|
1.013
|
TN
|
PLAND1( 0.603)
|
0.819
|
1.367
|
PLAND2(-0.616)
|
0.351
|
1.647
|
PLAND1(0.677)
|
0.434
|
1
|
TP
|
PLADJ5(0.547)
|
0.566
|
1
|
PLAND3(-0.535)
|
0.254
|
1.731
|
PLAND2(-0.467)、 PLAND3(-0.817)
|
0.741
|
1.026
|
COD
|
PLAND5(-0.373)、 PLADJ4(0.455)
|
0.341
|
1.027
|
PLAND2(0.514)、 LSI5(-0.366)
|
0.455
|
0.905
|
PLAND5(0.673)、 PLADJ1(-0.863)
|
0.358
|
1.835
|
TDS
|
LSI1(0.337)
|
0.287
|
1.085
|
PLAND6(0.755)、 AI2(0.421)
|
0.444
|
1.779
|
PLAND6(0.588)
|
0.316
|
1
|
NO2-N
|
AI2(-0.455)
|
0.401
|
1.099
|
LSI3(-0.41)、AI6(0.588)
|
0.378
|
1.651
|
PLAND4(0.398)
PLADJ6(0.559)
|
0.307
|
1.057
|
Wet season
|
|
|
|
|
|
|
|
|
|
pH
|
LSI3(0.437)
|
0.153
|
1
|
PLADJ3(0.432)
|
0.649
|
1
|
-
|
0.167
|
1
|
EC
|
PLAND2(-0.512)
|
0.398
|
1.211
|
AI2(-0.354)、AI3(-0.298)
|
0.366
|
1.304
|
PLAND1(0.785)
|
0.809
|
1.001
|
DO
|
PLAND5(0.451)、
AI2(-0.631)
|
0.766
|
1
|
LSI5(0.557)
|
0.279
|
1
|
PLAND2(-0.410)
|
0.13
|
1
|
Parameter In(x+1)
|
Reach Scale
|
Riparian Scale
|
Sub-basin Scale
|
Significant Predictors(B)
|
Adj.R2
|
VIF
|
Significant Predictors(B)
|
Adj.R2
|
VIF
|
Significant Predictors(B)
|
Adj.R2
|
VIF
|
CODMn
|
PLAND5(0.59)
|
0.467
|
1.011
|
PLAND2(-0.83)、 AI3(0.31)
|
0.689
|
1.128
|
PLADJ6(0.648)
|
0.394
|
1
|
NH3-N
|
PLAND1(-1.180)、 LSI1(1.147)
|
0.494
|
1.069
|
PLAND5(0.347)、 PLAND6(0.74)
|
0.687
|
1.008
|
PLAND6(0.637)
|
0.379
|
1
|
LSI5(0.562)
|
TN
|
PLAND2(-0.501)
|
0.445
|
1.088
|
PLADJ5(0.413)
|
0.133
|
1
|
LSI1(0.449)、 SHDI(0.785)
|
0.696
|
1.185
|
TP
|
AI2(0.412)
|
0.347
|
1
|
PLAND2(-0.411)、 PLAND3(-0.502)
|
0.289
|
1.011
|
PLADJ1(0.614)
|
0.651
|
1.344
|
COD
|
PLAND5(0.667)
|
0.585
|
1.223
|
PLAND2(0.434)
|
0.151
|
1
|
PLAND6(1.038)、 LSI6(-0.772)
|
0.331
|
2.797
|
TDS
|
PLAND2(-0.412)
|
0.387
|
1.092
|
PLAND6(0.678)、 AI2(0.426)
|
0.353
|
1.192
|
PLAND6(0.472)
|
0.187
|
1
|
NO2-N
|
PLADJ1(0.331)
|
0.401
|
1.055
|
PLAND5(0.2)、 LSI2(-0.34)、
|
0.611
|
1.127
|
PLAND4(0.475)、 PLAND6(0.604)
|
0.351
|
1.113
|
AI2(0.449)、 AI6(0.64)
|
Note: Unlisted indexes fail to pass the p<0.05 test, * means p=0.05 is significantly correlated, * * means p=0.01 is significantly correlated.
4.2. Analysis on the influence mechanism of spatial scale change of land use on basin water quality
Table 1 shows that, as a reference, the riparian zone scale is better than the sub-basin scale in both dry and wet seasons, because it better explains the overall variation of river water quality, indicating that water quality pollution prevention and control depend to some extent on regional management, and the research conclusion is consistent with the results of other scholars (Buck et al., 2004; Gove et al., 2001; King et al., 2005). The MLR model revealed that different water quality parameters did not respond to the same extent to large and local scale factors, while some parameters varied consistently at different scales in different seasons such as EC and CODMn. The sub-basin scale could better explain the changes in EC, while the riparian zone scale could explain the changes in CODMn. In the wet season, the sub-basin scale better reflected the transformation processes of pollutants such as TN, TP, and NO2-N, indicating that, at large scales, the main sources of these compounds are nonpoint sources, and that surface runoff and erosion play a key role in the loss of organic matter and hydrological transformation processes in the basin (Allen 2004; Dodds et al 2008). At the riparian zone scale, the variation of NH3-N can be better explained in the wet season, during which the riparian zone can effectively immobilize N and exert an inverse effect on it in the river water. In contrast, the dry season can reflect the variability of DO and TDS. In summary, as different biogeochemical processes occur under different conditions (Strayer et al., 2003), the optimal scales for each water quality indicator are therefore different. These complex scale effects also highlight the fact that selecting an optimal scale to control water quality changes is highly challenging, and water environment control and protect with land use planning should be carried out using a multi-scale perspective.
4.3. Effect analysis of slope change on basin water quality
Topographic features, which determine the flow of pollutants from nonpoint sources to rivers, have been identified in previous studies as important factors affecting how land use influences basin water quality (Wang et al., 2015). The studies have shown that the change of slope size on the water quality of the basin was also affected by seasonal variation. In the WRB, the correlation with water quality was stronger during the dry season, with forestland, grassland, and urban land being the dominant land uses. In the dry season, the pathways of pollutant discharge represent a key factor in the significant correlation observed between urbanization and water quality, and urban land is generally considered to be the main source pollution. Precipitation possibly diluted the discharged wastewater to some extent, leading to the weaker correlation between urban land and water quality observed in the wet season.
The greater the slope at the basin scale, the stronger the correlation and water quality variables. Slope can be used as a parameter for the rate of water flow over the surface, and usually water quality is positively correlated with the slope coefficient. In addition, as the slope increases, the rate of water flow increases as well, leading to soil erosion exceeding the rate of pollutants and further increasing the risk of water quality degradation. The forest slope coefficient, on the other hand, is negatively correlated with water quality variables and, at lower slopes, forestland can act as a sink and intercept pollutants. In contrast, at the riparian zone scale, water quality variables are significantly correlated with lower slope coefficients, because the land type in flat areas is generally urban or agricultural land, and steeper forestland is more likely to accumulate and discharged pollutants, leading to water quality degradation. The deviations in slope coefficients at the two scales have different effects on water quality variables, mainly because the water flow at the watershed scale is in contact with vegetation for a longer period of time, thus increasing the effectiveness of filtered nutrients in the runoff. Therefore, attention needs to be paid to the degree of influence of slope on basin water quality.
4.4. Prevention and control of regional pollution in river basins
The study area, object of the present research, is wide, its topography is complex, and the basic conditions of the three sub-basins are different. For water quality protection and control, the scale effect of space and season should be considered comprehensively. The WRB, with its dense urban agglomerations, high degree of river development, large area of agricultural land. In the wet season, chemical fertilizer application in agricultural activities is the main source of pollution; while in the dry season the pollution source are the discharge of industrial wastewater and domestic sewage from urban agglomerations, with the most significant impact visible at the riparian zone scale. Therefore, it is suggested that the basin water quality management in the WRB should pay attention to the spatial scale planning, and the water quality protection measures in the riparian zone should be taken as the focus, such as increasing vegetation cover near agricultural activities and river channels and planting riparian vegetation, as well as ensuring that the discharge of sewage and wastewater meets the appropriate discharge standards.
The BLR and JHR are located in the Loess Plateau erosion area, and the vegetation cover in their basins mitigates the impact of land use on water quality to some extent. The size, aggregation, and landscape shape of forestlands and grasslands will reduce the surface runoff carrying nutrient salts into the river caused by rainfall flushing. At the same time, slope is also important factors affecting the influence of land use on basin water quality. The terrain of the BLR and JHR basins is complex, and it comprises both plains and mountains. In the mountainous regions of the basins, it is possible to reduce nutrient and organic concentration and alleviate the impact of soil erosion on rivers by maintaining a certain extent of grassland and forest area, whereas in the plain areas, the development of cluster agriculture, rational fertilization, and the use of vegetation buffers along riverbanks can contribute to the improvement of basin water quality to a certain extent.
In summary, to preserve water quality in the WRB, which is subjected to rapid urbanization, it is necessary to focus on the sewage and wastewater discharge. In regions with high agricultural aggregation, the adoption of modern agricultural methods and rational fertilization represent management measures that can reduce non-point source pollution, whereas in mountainous areas affected by serious soil erosion, increasing vegetation coverage can effectively reduce the negative impact on water quality. In essence, the water quality management of the WRB should focus on the landscape planning of riparian zones, which can better develop the river and build a healthy watershed ecosystem.