Impact of climate change and land cover dynamics on nitrate transport to surface waters.

The study investigated the impact of climate and land cover change on water quality. The novel contribution of the study was to investigate the individual and combined impacts of climate and land cover change on water quality with high spatial and temporal resolution in a basin in Turkey. The global circulation model MPI-ESM-MR was dynamically downscaled to 10-km resolution under the RCP8.5 emission scenario. The Soil and Water Assessment Tool (SWAT) was used to model stream flow and nitrate loads. The land cover model outputs that were produced by the Land Change Modeler (LCM) were used for these simulation studies. Results revealed that decreasing precipitation intensity driven by climate change could significantly reduce nitrate transport to surface waters. In the 2075-2100 period, nitrate-nitrogen (NO3-N) loads transported to surface water decreased by more than 75%. Furthermore, the transition predominantly from forestry to pastoral farming systems increased loads by about 6%. The study results indicated that fine-resolution land use and climate data lead to better model performance. Environmental managers can also benefit greatly from the LCM-based forecast of land use changes and the SWAT model's attribution of changes in water quality to land use changes.

Entropy-assisted approach to determine priorities in water quality monitoring process.

Effective determination of water quality and water pollution assessment is crucial and challenging processes. Evaluating water quality in rivers, researchers have referred to various statistical, probabilistic and stochastic methods to obtain efficient information from the monitoring network. As data are greatly random, the information content can be obtained by utilizing various methods including but not limited to the "entropy." Monitoring is a difficult process due to high measurement costs, while it is also difficult to optimize the network in terms of time, space, and especially the variable to be monitored. In the presented study, it is aimed to create an effective approach to be used in optimizing the monitoring network by determining the "prior" variables by entropy that measures the uncertainty by using all the data without time difference. The presented study proposes an alternative method to define the water quality variables that should be monitored much more frequently. Study is exemplified for demonstrating its potential use in a case study level, Grand River in Canada, by assessing water quality data obtained from 15 water quality monitoring stations. Results showed that BOD, Cl, and NO2-N among examined 8 different variables are as the "prior" variables should be monitored. It is being proven that the prior variable that should be monitored for optimization of the network can be easily determined with the information obtained from the data statistically evaluated with entropy, and it can be stated as an effective method for managers to use in the decision-making process.