Using spatially explicit indicators to investigate watershed characteristics and stream temperature relationships

Land Cover Effects on Selected Nutrient Compounds in Small Lowland Agricultural Catchments

The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO3 and P-PO4 concentrations and their landscape dependence was documented in the Świder River catchment in central Poland. From April 2019 to March 2020, water samples were collected from fourteen streams in the monthly timescale and the concentrations of N-NO3 and P-PO4 were correlated with land cover metrics based on the Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets. It was documented that agricultural lands and forests have a clear seasonal impact on N-NO3 concentrations, whereas the effect of meadows was weak and its direction was dependent on the dataset. The application of buffer zones metrics increased the correlation performance, whereas Euclidean distance scaling improved correlation mainly for forest datasets. The concentration of P-PO4 was not significantly related with land cover metrics, as their dynamics were driven mainly by hydrological conditions. The obtained results provided a new insight into landscape–water quality relationships in lowland agricultural landscape, with a special focus on evaluating the predictive performance of different land cover metrics and datasets.

Assessing land use and landscape factors as determinants of water quality trends in Nyong River basin, Cameroon

Changes in LULC and landscape factors impact water quality at spatial and temporal scales. In this study, we investigated the current status in water quality for sub-watersheds of the southern portion of the Nyong River basin of Cameroon from 1994 to 2014 using the WHO guideline. The trends in the water quality parameters were explored using Mann-Kendall test, and their relationship with changes in LULC and landscape factors were analysed using multiple linear regression and Pearson correlation. The current status in water quality did not exceed the WHO guideline limits for drinking water despite a 16% decrease in forest cover and 10% increase in agricultural areas during the period of record. The concentration and changes in water quality trends varied significantly among the sub-watersheds. The concentration of Ca²⁺, Mg²⁺, Na⁺, SiO_2, K⁺, DOC, SPM and WT showed significantly increasing trends in the Nsimi small sub-watershed, while only Ca²⁺, Mg²⁺, Na⁺ and NO₃^- showed significantly increasing trends in the large sub-watersheds of Mbalmayo and Olama. A combination of one to five LULC and landscape factors, including changes in urban cover, young secondary forest, slope, elevation and population explained 10 to 70% of the changes in water quality trends at watershed scale. Although the interaction of LULC and landscape factors seems to have low impact on the water quality so far, maintaining greater than 70% forest cover and appropriate fallow farming system is invaluable to protecting water quality in the Nyong River basin in the Congo basin and in other forest-rich regions.

Patterns and predictions of drinking water nitrate violations across the conterminous United States

Excess nitrate in drinking water is a human health concern, especially for young children. Public drinking water systems in violation of the 10 mg nitrate-N/L maximum contaminant level (MCL) must be reported in EPA's Safe Drinking Water Information System (SDWIS). We used SDWIS data with random forest modeling to examine the drivers of nitrate violations across the conterminous U.S. and to predict where public water systems are at risk of exceeding the nitrate MCL. As explanatory variables, we used land cover, nitrogen inputs, soil/hydrogeology, and climate variables. While we looked at the role of nitrate treatment in separate analyses, we did not include treatment as a factor in the final models, due to incomplete information in SDWIS. For groundwater (GW) systems, a classification model correctly classified 79% of catchments in violation and a regression model explained 43% of the variation in nitrate concentrations above the MCL. The most important variables in the GW classification model were % cropland, agricultural drainage, irrigation-to-precipitation ratio, nitrogen surplus, and surplus precipitation. Regions predicted to have risk for nitrate violations in GW were the Central California Valley, parts of Washington, Idaho, the Great Plains, Piedmont of Pennsylvania and Coastal Plains of Delaware, and regions of Wisconsin, Iowa, and Minnesota. For surface water (SW) systems, a classification model correctly classified 90% of catchments and a regression model explained 52% of the variation in nitrate concentration. The variables most important for the SW classification model were largely hydroclimatic variables including surplus precipitation, irrigation-to-precipitation ratio, and % shrubland. Areas at greatest risk for SW nitrate violations were generally in the non-mountainous west and southwest. Identifying the areas with possible risk for future violations and potential drivers of nitrate violations across U.S. can inform decisions on how source water protection and other management options could best protect drinking water.

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

Assessing landscape and contaminant point-sources as spatial determinants of water quality in the Vermilion River System, Ontario, Canada

The Vermilion River and major tributaries (VRMT) are located in the Vermilion watershed (4272 km²) in north-central Ontario, Canada. This watershed not only is dominated by natural land-cover but also has a legacy of mining and other development activities. The VRMT receive various point (e.g., sewage effluent) and non-point (e.g., mining activity runoff) inputs, in addition to flow regulation features. Further development in the Vermilion watershed has been proposed, raising concerns about cumulative impacts to ecosystem health in the VRMT. Due to the lack of historical assessments on riverine-health in the VRMT, a comprehensive suite of water quality parameters was collected monthly at 28 sites during the ice-free period of 2013 and 2014. Canadian water quality guidelines and objectives were not met by an assortment of water quality parameters, including nutrients and metals. This demonstrates that the VRMT is an impacted system with several pollution hotspots, particularly downstream of wastewater treatment facilities. Water quality throughout the river system appeared to be influenced by three distinct land-cover categories: forest, barren, and agriculture. Three spatial pathway models (geographical, topographical, and river network) were employed to assess the complex interactions between spatial pathways, stressors, and water quality condition. Topographical landscape analyses were performed at five different scales, where the strongest relationships between water quality and land-use occurred at the catchment scale. Sites on the main stem of Junction Creek, a tributary impacted by industrial and urban development, had above average concentrations for the majority of water quality parameters measured, including metals and nitrogen. The river network pathway (i.e., asymmetric eigenvector map (AEM)) and topographical feature (i.e., catchment land-use) models explained most of the variation in water quality (62.2%), indicating that they may be useful tools in assessing the spatial determinants of water quality decline.