22Field-scale studies have shown that beneficial management practices (BMPs), such as 23 nutrient management plans and grass buffers, can reduce the downstream transport of non-24 point source contaminants. This study presents a novel method for evaluating the 25 effectiveness of BMPs using in situ data. From 2005 to 2012, hydrometric monitoring and 26 water quality monitoring were carried out at the outlet and along two main branches of a 27 micro-watershed (236 ha) with a high proportion of cultivated land. The method was based 28 on evaluating the uncertainty associated with the determination of water flow and 29 agricultural contaminant loads, with the latter being based on statistical distributions of 30 nutrient or sediment concentrations. Distribution of loads (i.e. April -November) was 31 estimated in order to assess the cumulative effectiveness of all implemented BMPs with an 32 emphasis in riparian buffers established on the micro-watershed under study at different 33 spatio-temporal scales. Results showed the concentrations and loads of total nitrogen (TN), 34 total phosphorus (TP), nitrate-nitrogen (NO 3 − − N) and particulate phosphorus (PP) were 35 significantly lower following riparian buffer implementation. A significant decrease in 36 nitrite-nitrogen (NO 2 − − N) and ammonium nitrogen (NH 4 + − N) in the loads also occurred 37 after riparian buffers were established. Spatially, a ratio approach based on comparing an 38 export fraction [loads (kg) to nutrient balances (kg)] downstream from riparian buffers with 39 that at the outlet of the same stream, showed a significant reduction in the ratio downstream 40 from the riparian buffer for TN and TP in 2009, with no significant reduction in 2010, 2011 41 and 2012. Ratios calculated on a seasonal basis showed the riparian buffers were less 42 effective in the spring, as well as during seasons marked by one or more intense rainfall 43 events. 44 3
This study is part of a project aimed at developing an automated algorithm for algal bloom detection and quantification in inland water bodies using Moderate resolution imaging spectroradiometer (MODIS) imagery. An important step is to adequately detect and exclude clouds and haze because their presence affects chlorophyll-a (chl-a) estimations. Currently available cloud masking products appear to be ineffective in turbid coastal waters. The purpose of this study is to develop a cloud masking algorithm based on a probabilistic algorithm (Linear Discriminant Analysis) and designed for water bodies by using MODIS images downscaled at a 250 m spatial resolution (MODIS-D-250). Confusion matrix shows that the new cloud mask algorithm yields very satisfactory results, enabling water classification for heavy turbid conditions with a mean kappa coefficient ( (of 0.982 and a 95% confidence interval ranging from 0.979 to 0.986. The model also shows a very low commission error (sensitive to the presence of haze) which is essential for accurate water quality monitoring, knowing that the presence of clouds/haze/aerosols leads to major issues in the estimation of water quality parameters. The cloud mask model applied on MODIS-D-250 images improves the sensitivity to haze and the classification of turbid waters located at the edge of urban areas better than the operational MODIS products, and it clearly shows an improvement of the spatial resolution (250 m spatial resolution) compared to other cloud mask algorithms (500 m or 1 km spatial resolution) leading to an increase in exploitable data for water quality studies.
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