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Catchment stream water total nitrogen (TN) (a) and phosphorus (TP) (b) concentrations are higher in the dry season than in the wet season, and the upstream 2000 ha are a hot zone for TN (c) and TP pollutions (d). Note: Red shading indicates stream water TN and TP concentrations saturation, with the threshold for saturation being stream water TN and TP removal efficiency below the first-order process. [Display omitted] • Stream water TN and TP concentrations were higher in dry season than in wet season. • Underlying surface conditions determines stream water TN and TP levels (67–90%). • Catchment area was greatest determinant of stream water TN and TP levels (32–69%). • Upstream were hotspots for N and P pollutions in an area of 2,000 ha. Catchment underlying surface conditions influence stream water nitrogen (N) and phosphorus (P) concentrations, but it is not known how much this characteristic affects N and P concentrations at different spatial and temporal scales, thus limiting the effectiveness of N and P controls at key spatial and temporal scales. Here, the area, elevation, mean slope gradient, and land use in eight subtropical hydrologically nested catchments were investigated, and the stream water total-N (TN) and total-P (TP) concentrations were observed from 2011 to 2017 to quantify the extent to which the underlying surface conditions of the catchment influenced stream water N and P levels. The stream water TN and TP concentrations were significantly higher in the dry season than in the wet season (2.79 vs. 2.35 mg N L-1 and 0.16 vs. 0.14 mg P L-1), indicating significant seasonal variation in the stream water N and P levels. The underlying surface conditions of the catchment, including area, elevation, mean slope gradient, and land use, were significantly correlated with the stream water N and P concentrations (p < 0.05) and explained 67.1%-90.3% of the total variation in the seasonal stream water N and P concentrations. The catchment area was the greatest determinant of stream water N and P levels, independently explaining 32.2–69.3% of the variation. Interestingly, the N and P concentrations were higher in the 2000 ha upstream catchments and decreased rapidly with increasing catchment area during both wet and dry seasons. This indicates that the saturation threshold for N and P concentrations in subtropical agricultural catchments was within the upstream 2000 ha. Therefore, an upstream catchment area of 2000 ha was likely the optimal spatial scale for implementing pollution control practices in agricultural catchments. [ABSTRACT FROM AUTHOR]

Multidimensional (coupled land use, soil properties, and topography) landscape effects on stream water nitrogen (N) are complex and scale-dependent. However, studies that identify critical buffer zones that explain large variations in riverine N, and estimate specific thresholds of multidimensional landscape patterns at the class level, result in a sudden changes in riverine N pollution, are still limited. Here, a new multidimensional landscape metric that combined land use, soil properties, and topography effects was applied to various riparian buffer zones and sub-watershed scales, and their relationships to riverine N levels were investigated. We used stream water ammonium−N, nitrate−N, and total−N concentrations datasets, from 2010 to 2017, in the nine subtropical sub-watersheds in China. The results of model selection and model averaging in ordinary least squares regressions, indicated that the riparian buffer zone with widths of 400 m, had more pronounced influence on water NH 4 –N and TN levels than at other scales. Within the 400 m buffer zone, the key landscape metrics for NH 4 –N, NO 3 –N and TN concentrations in stream water were different, and explained up to 43.35%–76.55% (adjusted R2) of the total variation in river N levels. When ENN_MN Class17 below 39–56 m, PD Class8 above 4.63–6.55 n/km2, PLAND Class27 above 23–29%, and CONTIG_MN Class42 below 0.35–0.37% within the 400 m buffer zone, riverine NH 4 –N and TN would be abruptly increased. This study provided practical ideas for regulation regarding landscape management linked to watershed structure, and identified reference thresholds for multidimensional landscape metrics, which should help reduce riverine N pollution in subtropical China. • Multidimensional landscape patterns affect riverine N at watershed outlets. • The 400 m riparian buffer zone has the strongest influence on riverine NH 4 –N and TN concentrations. • The ENN_MN Class17 , PD Class8 , PLAND Class27 , and CONTIG_MN Class42 are important metrics to characterize riverine N. • There exist change points in riverine N along a gradient of multidimensional landscape metrics. [ABSTRACT FROM AUTHOR]

• Unique natural processes and anthropogenic activities induced moderate riverine P pollution in subtropical agricultural catchments. • Decreasing trend of riverine P levels was mainly caused by reduced catchment livestock density. • Landscape configuration and livestock density were major determinants of increasing riverine P. • Landscape and livestock planning need to occur simultaneously to ensure effective water quality improvements. Natural and anthropogenic determinants of riverine phosphorus (P) pollution strongly vary with different ecological and socio-economic conditions and represent a substantial challenge to aquatic environment protection and ecological restoration in agricultural catchments. Riverine water flux and total P (TP) and dissolved P (DP) concentrations were observed, and major catchment geometric, landscape and anthropogenic characteristics were investigated in one forest and ten agricultural catchments in subtropical China during 2011–2017 to identify the key determinants of the catchment riverine P levels and loadings and to develop prediction functions for these P levels and loadings. The results suggested that 39% of the collected stream water samples were determined to have TP pollution, indicating moderate riverine P pollution in the catchments. The catchment riverine P levels illustrated an increasing P trend during 2011–2016 but a decreasing P trend in 2017 through the Hodrick-Prescott (HP) filter analysis, and the decreasing trend was mainly caused by reduced catchment livestock density. Landscape configuration (cohesion index (COHESION), aggregation index (AI), mean shape index (SHMN), and patch density (PD)) and anthropogenic activities (livestock density) were identified as the major determinants of riverine P concentrations and loadings through partial least squares regression (PLSR) analysis, of which cohesion index and livestock density were the only two variables that had a variable importance for the projection (VIP) value greater than 1.0; this result confirmed the substantial effects of these variables on riverine P. The multiple linear regression prediction functions showed generally acceptable performances (R2 ≥ 0.40) for riverine P but relatively poor performance in predicting P loadings (R2 = 0.40–0.46) in comparison to predicting P concentrations (R2 = 0.51–0.54), probably due to the variations in the hydrological process in subtropical China. Therefore, the results provide useful information for identifying the natural and anthropogenic determinants of riverine P to control this type of P in subtropical agricultural catchments. [ABSTRACT FROM AUTHOR]