This study assessed the usefulness of multivariate statistical tools to characterize watershed dynamics and prioritize streams for remediation. Three multiple regression models were developed using water quality data collected from Sinking Creek in the Watauga River watershed in Northeast Tennessee. Model 1 included all water quality parameters, model 2 included parameters identified by stepwise regression, and model 3 was developed using canonical discriminant analysis. Models were evaluated in seven creeks to determine if they correctly classified land use and level of fecal pollution. At the watershed level, the models were statistically significant (p < 0.001) but with low r2 values (Model 1 r2 = 0.02, Model 2 r2 = 0.01, Model 3 r2 = 0.35). Model 3 correctly classified land use in five of seven creeks. These results suggest this approach can be used to set priorities and identify pollution sources, but may be limited when applied across entire watersheds.

Water quality was one of the biggest problems of environment in beginning of 90. of 20th century in the Czech republic. Since 90. the level of water quality was in czech rivers rapidly better. Mainly in big rivers have accomplished great changes. The small rivers haven't notice that rapid changes. The Czech republic, after entrance to the European Union have committed to comply with Directive 91/271/EEC reduce urban waste water and which should have big influence to water quality also in small rivers. During 90. industry technology, agriculture, cleaning technology of waste water etc. reached big development. Therefore the presumption is higher quality of water in the Czech republic since 90. Goal of the thesis is comparison of results from 1999-2000 with results from 2012- 2014 from Mladoticky brook catchment, which is lined up to small river catchments (79,77km2 ). From this comparison there is obvious trend, if conditions of the water quality are really better or not since 90. [33] Since 2012 to 2014 were taken 12 times samples from 11 profiles of water from Mladotice brook catchment and they were consequential chemically analyzed. The samples were evaluated according to ČSN 757221 and compared with results from 1999-2000. From comparison with data 1999-2000 and 2012-2014 was confirmed the...

The present study evaluates the surface water quality in the canals of An Giang province in the period from 2009 to 2016. The results showed that surface water of the canals was contaminated by organic matter and microorganisms which makes it not suitable for water supply and conservation of aquatic life. The water quality parameters such as dissolved oxygen (DO), biological oxygen demand (BOD), total suspended solids (TSS), orthophosphate (P-PO43-) and coliforms levels in the wet season were found to be higher than those in the dry season. The problem of organic and microorganic pollution over a long period of time without solutions leads to declines in water quality and then quantity as well. Agriculture is the main activity contributing to pollution of surface water in interior canals along with the activities of daily life, industry and services. This causes pollution of the surface water on Hau River due to its exchange of water with the connected canals. Good agricultural practices should be implemented to limit the pollution of surface water resources of the Mekong Delta. / Nghiên cứu này nhằm đánh giá diễn biến chất lượng nước mặt trong các kênh rạch nội đồng của tỉnh An Giang trong giai đoạn 2009 – 2016. Kết quả cho thấy nước mặt tại các kênh rạch nội đồng đã ô nhiễm hữu cơ và vi sinh vật. Nguồn nước không phù hợp cho mục đích cấp nước sinh hoạt và bảo tồn thực vật thủy sinh. Các chỉ tiêu như hàm lượng oxy hòa tan (DO), nhu cầu oxy sinh hóa (BOD), tổng chất rắn lơ lửng (TSS), orthophosphate (P-PO43-) và coliforms trong mùa mưa cao hơn mùa khô. Vấn đề ô nhiễm hữu cơ và vi sinh vật diễn ra trong thời gian dài và chưa có giải pháp xử lý làm cho chất lượng nước suy giảm dẫn đến suy giảm về trữ lượng. Nông nghiệp là hoạt động chính góp phần làm ô nhiễm nguồn nước mặt trong các kênh rạch nội đồng bên cạnh các hoạt động sinh hoạt, công nghiệp và dịch vụ. Điều này dẫn đến nước mặt trên sông Hậu cũng có đặt tính ô nhiễm tương tự do trao đổi nước với các kênh rạch nội đồng. Thực hành sản xuất nông nghiệp thân thiện môi trường cần sớm được triển khai để hạn chế ô nhiễm nguồn nước mặt quan trọng của khu vực đồng bằng sông Cửu Long.

<p>Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density.</p><p>In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis.</p><p>Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems.</p><p>However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images.</p><p>Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.</p>

Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density. In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis. Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems. However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images. Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.

<p>Field-edge buffers are a promising way to address nonpoint source pollution from agricultural runoff, but concentrated runoff flow often renders standard fixed-width linear buffers ineffective. AgBufferBuilder (ABB) is a tool within ESRI ArcMap Geographic Information Systems software that designs and evaluates targeted, nonlinear buffers based on hydrologic modeling and other field-specific parameters. We tested ABB on n=45 Areas of Interest (AOIs) stratified based on estimated sediment loading across three sub-watersheds within Michigan’s Saginaw Bay watershed to evaluate the effectiveness of ABB relative to existing practices across a wide range of landscape conditions. We modeled tractor movement around ABB buffer designs to assess more realistic versions of the likely final designs. ABB regularly failed to deliver the desired 75% sediment capture rate using default 9 m x 9 m output raster resolution, with Proposed buffers capturing from 0% to 68.49% of sediment within a given AOI (mean=37.56%). Differences in sediment capture between Proposed and Existing buffers (measured as Proposed – Existing) ranged from -48% to 66.81% of sediment (mean=24.70%). Proposed buffers were estimated to capture more sediment than Existing buffers in 37 of 45 AOIs, representing potential for real improvements over Existing buffers across the wider landscape. In 13 of 45 AOIs, ABB buffers modified for tractor movement captured more sediment than Existing buffers using less total buffer area. We conducted a collaborative design process with three Saginaw Bay watershed farmers to assess their willingness to implement ABB designs. Feedback indicated farmers may prefer in-field erosion control practices like cover cropping and grassed waterways over field-edge ABB designs. More farmer input is needed to better assess farmer perspectives on ABB buffers and to identify preferred data-based design alternatives. Engineered drainage systems with raised ditch berms and upslope catch basins piped underground directly into ditches were encountered several times during site visits. ABB only models surface flow and does not recognize drain output flow entering waterways. Modified ABB functionality that models buffers around drain inlets would greatly improve its functionality on drained sites. This may be accomplishable through modification of user-entered AOI margins but requires further investigation. Unfortunately, the existing tool is built for outdated software and is not widely accessible to non-expert users. We suggest that an update of this tool with additional functionality and user accessibility would be a useful addition in the toolbox of conservation professionals in agricultural landscapes.</p>

<p>Current watershed-scale, nonpoint source (NPS) pollution models do not represent the processes and impacts of agricultural best management practices (BMP) on water quality with sufficient detail. To begin addressing this gap, a novel process-based, watershed-scale, water quality model (WEPP-WQ) was developed based on the Water Erosion Prediction Project (WEPP) and the Soil and Water Assessment Tool (SWAT) models. The proposed model was validated at both hillslope and watershed scales for runoff, sediment, and both soluble and particulate forms of nitrogen and phosphorus. WEPP-WQ is now one of only two models which simulates BMP impacts on water quality in ‘high’ detail, and it is the only one not based on USLE sediment predictions. Model validations indicated that particulate nutrient predictions were better than soluble nutrient predictions for both nitrogen and phosphorus. Predictions of uniform conditions outperformed nonuniform conditions, and calibrated model simulations performed better than uncalibrated model simulations. Applications of these kinds of models in real-world, historical simulations are often limited by a lack of field-scale agricultural management inputs. Therefore, a prototype tool was developed to derive management inputs for hydrologic models from remotely sensed imagery at field-scale resolution. At present, only predictions of crop, cover crop, and tillage practice inference are supported and were validated at annual and average annual time intervals based on data availability for the various management endpoints. Extraction model training and validation were substantially limited by relatively small field areas in the observed management dataset. Both of these efforts contribute to computational modeling research and applications pertaining to agricultural systems and their impacts on the environment.</p>