Organic matter transport from watershed soil into an aquatic ecosystem plays a key role in the fate of contaminants and lake eutrophication. Special emphasis is needed to understand whether sensitive river indicators can reflect watershed non-point source organic carbon (OC) pollution, in which the accurate assessment of non-point source (NPS) pollution is crucial. This study selected a sub-basin within the Taihu basin, China, as the study site, a typical rural-urban fringe region undergoing rapid urbanization where soil organic carbon (SOC) loss would likely take place due to the integration of agriculture NPS and impervious surface NPS. The seasonal tendency of NPS soil organic carbon (SOC) loads were evaluated by using the integration of SEDD and PLOAD models, which consider the sediment adsorption fraction loads (Sed-OC) and runoff dissolved fraction loads (Dis-OC) together. And then the sensitive water indicators for OC loads were determined by measurements of inflowing river properties and stepwise regression analysis. The results showed that active dissolved carbon fraction loads were the dominant contributors to the total organic carbon loads (Tot-OC) and that Sed-OC loads have more spatial variation. With respect to sensitive river properties, the lignin owned the greatest correlation degree with different OC fraction loads, in which the correlation coefficient between particulate lignin and Sed-OC loads reached 0.782, which is the greatest among the different indicators. In addition, the colored dissolved organic matter (CDOM) was also correlated with Dis-OC loads. However, the particulate organic carbon (POC) was not well related to OC loads. The findings of this study are useful for better understanding the nutrient migration from watershed soil into aquatic ecosystem controlled by watershed NPS pollution.

Glomalin, which sequesters substantial amounts of carbon, plays a critical role in sustaining terrestrial biome functions and contributes to the fate of many pollutants from terrestrial to aquatic ecosystems. Despite having focused on the amount of glomalin produced, very few attempts have been made to understand how landscapes and environmental conditions influence glomalin composition and characteristics. This study focused on glomalin-related soil protein (GRSP) exported as storm runoff including eroded sediment and water that was collected before flowing to surface waters in a peri-urban watershed. GRSP characteristics were assessed by Bradford protein analysis, fluorescence spectroscopy combined with parallel factor analysis (PARAFAC), and the determination of aromaticity based on the specific ultraviolet absorption value (280 nm) and molecular weight. General linear models (GLMs) was established by integrating microbial activity, land cover, water temperature, precipitation, and other solution chemical properties to explain the variations in GRSP characteristics. Results showed that a higher GRSP concentration in agricultural reference sites was produced in the form of specific materials with low molecular weight and aromaticity, as well as high percentage of C1 and C5 components which indicate microbial-processed sources, relative to urbanized and forested sites. Compared with forested land, urbanized land clearly produced runoff GRSP with low molecular weight and aromaticity, as well as more degradation of humic-like materials (C3 component). The highest GLM explaining 89% of the variables, including significant variables (p < 0.05) such as microbial activity, water temperature, and water conductivity, was observed for GRSP characteristics. Therefore, changes in eroded soil GRSP quality can serve as an indicator for improving watershed management and thus protecting aquatic ecosystems.
© 2017, Springer International Publishing Switzerland.

[目的]通过光谱识别太湖流域农用地面源磷流失的土壤主控因子,为简化面源磷流失强度估算提供依据。[方法]通过分析梅梁湾流域耕地和园地中不同面源磷流失强度下的土壤光谱特征,确定影响面源磷流失强度的主要土壤理化性质。[结果]耕地面

We established successfully the Location-weighted landscape Contrast Index (LCI) with three factors of landscape pattern (land-use type, composition and configuration) in the view of the source-sink structures of three organic carbon format loss processes. To reveal the relations between soil organic carbon loss and landscape indicators, we should firstly quantify the variables that denote organic carbon loss status. The sediment delivery distributed model and the Pollution Load (PLOAD) model (R-2 > 0.9; E-NS > 0.9) were integrated to simulate sediment yield, total organic carbon (TOC) loss, adsorbed organic carbon (AOC), and dissolved organic carbon (DOC). Results by Pearson correlation analysis indicate that the dominant factor in the integrated model is the rainfall factor explaining >80% of the variations in both sediment and organic carbon loss. Aside from precipitation, landscape pattern is also a principal factor that correlates with sediment yield and organic carbon loss. Landscape indicators, namely, LCITOC and LCIAOC, are significantly related to the sediment delivery ratio, and they explain 87% and 81% of the variation in sediment yield rate, respectively; however, they do not consider precipitation, with the rainfall factor dropped. LCITOC, LCIAOC, and LCIDOC explain 91%, 89%, and 87% of the variation in the corresponding organic carbon formats, respectively. The landscape indicators show that the landscape pattern is generally unfavorable to AOC loss in most sub-watersheds, whereas it is favorable to DOC and TOC format losses in the whole watershed. These differences among loss formats are considerably attributed to different sink-source compositional structures. Flow length derived from the paddies and rural settlements along river networks is the most unfavorable factor related to spatial configuration of organic carbon source-sink. In this regard, this study proposes the further optimization of the landscape pattern to increase or prevent the flow path of soil and organic carbon from source land-use types to river channels. This goal can be achieved through the development of protection forest belts in hilly areas and around agricultural lands. (C) 2016 Elsevier B.V. All rights reserved.

Abstract Effectively identifying soil properties in relation to non-point source (NPS) phosphorus pollution is important for NPS pollution management. Previous studies have focused on particulate P loads in relation to agricultural non-point source pollution. In areas undergoing rapid urbanization, dissolved P loads may be important with respect to conditions of surface infiltration and rainfall runoff. The present study developed an integrated model for the analysis of both dissolved P and particulate P loads, applied to the Meiliang Bay watershed, Taihu Lake, China. The results showed that NPS P loads up to 15 kg/km² were present, with particulate P loads up to 13 kg/km². The highest loads were concentrated in the southeastern region of the watershed. Although particle P was the main contributor to NPS P loads state, the contribution of dissolved P was significant, especially in sub-basins with significant amount of artificial land cover. The integration of dissolved P and particulate P loads provided more accurate evaluation of NPS P pollution. NPS P loads were found to correspond to specific soil properties. Soil organic matter and total nitrogen were shown to influence dissolved P loads, while total phosphorus and soil particle composition proportion were more closely related to particulate P loads.
© 2015 Elsevier Ltd.

The soil phosphorus (P) assessment is of great significance for agricultural non-point source pollution management in watersheds. Many of current studies provided data with limited accuracy and at relatively small spatial scale, due to the low spatial resolution of available remote sense data. This study used solid pollutants loss equation and high-resolution remote sense data source to determine the spatial characteristics of P loss, and then identified the critical polluted areas and corresponding underlying surface conditions within each land use type. Results showed that non-eroded region and slightly loss region covered more than 65% of the entire area. Although the severely-eroded region only accounted for 12.8% of the area, this region was distributed in the vicinity of downstream of two major inflow drainages. Hence, the harmful effect of this region on the watershed environment should not be neglected. The P loss varied significantly among different land use types, including forest land, arable land and orchard land. The forestland exhibited the lowest loss intensity, while the arable land exhibited the highest loss intensity. And the variation degree of P loss intensity within orchard land was probably greater than the other two land use patterns. Furthermore, this diversity in P loss largely resulted from different underlying surface features including topography, vegetation status, and key soil properties. The result from this study is suitable for practical use in different management strategies for non-point source pollution (NPS) management.
© 2016 American Society of Agricultural and Biological Engineers.

Taihu Lake in China is suffering from severe eutrophication partly due to non-point pollution from the watershed. There is an increasing need to identify the regions within the watershed that most contribute to lake water degradation. The selection of appropriate temporal scales and lake indicators is important to identify sensitive watershed regions. This study selected three eutrophic lake areas, including Meiliang Bay (ML), Zhushan Bay (ZS), and theWestern Coastal region (WC), as well as multiple buffer zones next to the lake boundary as the study sites. Soil erosion intensity was designated as a watershed indicator, and the lake algae area was designated as a lake quality indicator. The sensitive watershed region was identified based on the relationship between these two indicators among different lake divisions for a temporal sequence from 2000 to 2012. The results show that the relationship between soil erosion modulus and lake quality varied among different lake areas. Soil erosion from the two bay areas was more closely correlated with water quality than soil erosion from the WC region. This was most apparent at distances of 5 km to 10 km from the lake, where the r(2) was as high as 0.764. Results indicate that soil erosion could be used as an indicator for identifying key watershed protection areas. Different lake areas need to be considered separately due to differences in geographical features, land use, and the corresponding effects on lake water quality.

土地利用变化是造成流域面源磷流失的重要驱动因素，面源磷载荷研究模型因子繁多、数据获取难度大。因此利用光谱技术，快速甄别影响面源磷流失的关键土壤主控因子对面源污染的合理管控具有重要意义。本基金首先分析太湖流域土地利用变化特征，选取土地利用变化最为剧烈梅梁湾和竹山湾流域为研究区，结合文献调查和野外采样估算流域面源磷载荷。然后进行实验室光谱采集，确定土壤中磷的特征波段，估算不同土地利用类型下土壤磷含量。最后根据野外实测土壤光谱，结合面源磷流失强度，确定不同用地类型下面源磷流失的土壤主控因子及其光谱吸收特征，以达到快速监测面源磷流失的目的:(1)确定了研究区面源磷载荷时空变化特征利用SEDD模型可以准确估算研究区内面源颗粒态磷载荷,R2达到0.9以上，大部分地区面源颗粒态磷载荷呈现出较低的水平，高载荷区主要集中在中部地区，西部和东南部载荷较低，最低值为0 kg/km2,最高值为12.747 kg/km2;(2)探讨了通过光谱直接估算土壤磷含量的可能性直接用所有样本的光谱数据估算土壤磷含量时，建模集R2为0.6,而验证集R2仅为0.5,RMSE为1左右。将样本按照不同的用地类型分类后，茶园土壤磷的R2达到了0.82,RMSE为0.1,但是林地的结果并不理想,R2仅为0.21。土壤磷没有明显的光谱特征，通过光谱直接反演土壤磷含量精度还有待提高。(3)明确了耕地、园地等典型用地类型下面源磷流失的关键主控因子在耕地条件下，面源磷流失强度和光谱指数间的相关系数在650~670nm、1475nm、1680~1695nm处较高，最高达到0.74,确定耕地面源磷流失强度的土壤主控因子是有机质，二者之间呈正相关；在园地条件下，相关系数在685~690nm、710~720nm、1110~1115nm、1150~1155nm和2170nm处较高，确定主控因子是有机质、水分和Fe2+,面源磷流失强度和有机质及Fe2+呈负相关，和水分呈正相关。通过本基金的实施，以第一作者/通讯作者身份在国内外权威学术期刊上累计发表论文8篇，全部被SCI收录，获得软件著作权一项，联合培养硕士研究生6名，超额完成基金立项时的考核指标。