Solis McKenzie (secondradish23)

Excessive nitrate loading from agricultural non-point source is threatening the health of receiving water bodies at the global scale. click here Quantifying the drivers/sources of water and nitrate flux in watersheds and relating them to spatial and temporal land uses is essential for developing effective mitigation strategies. This study investigated the impact of land use on water yield and nitrate loading to surface water in a typical agricultural watershed in Prince Edward Island (PEI), Canada. We used historical streamflow and water quality records to calibrate the comprehensive hydrological model Soil and Water Assessment Tool (SWAT), which was setup with detailed annual land use records. The SWAT model performed well in predicting both daily streamflow and nitrate load. Land use demonstrated little impact on water yield but affected nitrate load significantly. Annual nitrate load ranged from 5.6 to 44.4 kg N ha-1 yr-1 for forest and soybean, respectively. Potato rotated land contributed 84.5% of annual nitrate load to the watershed. Source of water yield demonstrated high variability between the growing season and non-growing season. About 90% of water yield was contributed by groundwater during growing season, while runoff contributed over 60% of water yield during the non-growing season. Groundwater was the dominant source of nitrate loading for both seasons. The watershed estuary faced the highest threats from subbasins in the south western area due to the high nitrate load and proximity to the watershed outlet. Results by the machine learning algorithm random Forest analysis indicated that the climatic variables of temperature and precipitation were the top two factors affecting water yield, with a combined relative importance of 61%. Land use was the dominant factor affecting nitrate load, the relative importance of land use alone was ~50%. The results of this study provided critical insights for watershed management in Atlantic Canada. The accurate quantification of surface heat and water vapor fluxes is significantly essential for understanding water balance dynamics. In this study, 15-m spatial resolution turbulent fluxes (H and LE) in the Zhangye oasis situated the middle reaches of the Heihe River Basin (HRB) were estimated by the remote sensing-based two-source energy balance model (TSEB). The TSEB model uses temperature including land surface temperature (LST) and air temperature (Ta) as the main input variable to compute turbulent fluxes but their spatial resolution is rather limited. To overcome this shortcoming, the 15-m spatial resolution LST and Ta were obtained by using the back-propagation neural network (BPNN). The results indicated that the BPNN was able to obtain finer spatial resolution and LST and Ta; the root mean square error (RMSE) values of LST and Ta are 1.99 K and 0.50 K, respectively. The remotely sensed H and LE predicted by TSEB model utilizing the LST and Ta modeled by BPNN. The results showed that H and LE agreed well with the flux observations from multi-set eddy covariance (EC) systems installed at a number of sites and covering all representative land cover types; particularly for the latent heat flux, its estimates produced mean absolute percent errors (MAPE) of 8.76% for maize, 20.17% for vegetable, 29.06% for residential area, and 16.12% for orchard. This study obtained surface heat and water vapor fluxes at finer spatial resolution than the other flux estimates from the remote sensing models that have been used in the Zhangye oasis. The results produced by combining the TSEB model and BPNN can provide more information for drafting reliable sustainable water resource management schemes and improving the irrigation water use efficiency in arid and semi-arid regions. Based on a large body of evidence asbestos minerals have been classified as carcinogens. Despite the Italian ban on asbestos in 1992 and the subsequent remediation activities, latent sources of