Silicon Concentrations and Stoichiometry in Two Agricultural Watersheds: Implications for Management and Downstream Water Quality

Agriculture alters the biogeochemical cycling of nutrients such as nitrogen (N), phosphorus (P), and silicon (Si) which contributes to the stoichiometric imbalance among these nutrients in aquatic systems. Limitation of Si relative to N and P can facilitate the growth of non-siliceous, potentially harmful, algal taxa which has severe environmental and economic impacts. Planting winter cover crops can retain N and P on the landscape, yet their effect on Si concentrations and stoichiometry is unknown. We analyzed three years of biweekly concentrations and loads of dissolved N, P, and Si from subsurface tile drains and stream water in two agricultural watersheds in northern Indiana. Intra-annual patterns in Si concentrations and stoichiometry showed that cover crop vegetation growth did not reduce in-stream Si concentrations as expected, although, compared to fallow conditions, winter cover crops increased Si:N ratios to conditions more favorable for diatom growth. To assess the risk of non-siliceous algal growth, we calculated a stoichiometric index to quantify biomass growth facilitated by excess N and P relative to Si. Index values showed a divergence between predicted algal growth and what we observed in the streams, indicating other factors influence algal community composition. The stoichiometric imbalance was more pronounced at high flows, suggesting increased risk of harmful blooms as environmental change increases the frequency and intensity of precipitation in the midwestern U.S. Our data include some of the first measurements of Si within small agricultural watersheds and provide the groundwork for understanding the role of agriculture on Si export and stoichiometry.

Relative Contribution of Climate Change and Anthropogenic Activities to Streamflow Alterations in Illinois

Rainfed agricultural systems have become more vulnerable to climate change due to their significant dependence on natural precipitation. Drastic changes in precipitation, superimposed with anthropogenic activities, including land use land cover change, can modify the hydrologic response, especially in agricultural watersheds. In this study, Fisher Information and cumulative sum charts (CUMSUM) methods were applied to detect the hydrologic regime shifts in six watersheds in Illinois, USA. The regime shift analysis identified shifts in streamflow regime in three agricultural watersheds, mainly around the 1970s; whereas, no significant change in streamflow was observed for forest-dominated watersheds. Furthermore, the Budyko framework was used to determine the relative contribution in streamflow alterations (i.e., regime shifts in streamflow) for the agricultural watersheds, which evidenced significant shifts in streamflow. The Budyko analysis inferred that alterations in streamflow could be primarily attributed to anthropogenic activities with a comparatively lower contribution from climate in agricultural watersheds. The relative contribution from anthropogenic activities were 71.66%, 81.46%, and 74.04%; whereas, the relative contribution from climate were 28.34%, 18.54%, and 25.96% for the Sangamon, Vermillion, and Skillet agricultural watersheds, respectively. The techniques used and the results obtained from the study would be helpful for future research in assessing the vulnerability and impact of management practices in a highly managed agricultural watershed.

Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse

Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quality over traditional methods. In this study, the performance of nine different ML algorithms is evaluated to predict nitrate and phosphorus concentration for five different watersheds with different land-use practices. The land-use distribution affects the model performance for all methods. In urban watersheds, the regular and predictable nature of nitrate concentration from wastewater treatment plants results in more accurate estimates. For the nitrate prediction, ANN outperforms other ML models for the urban and agricultural watersheds, while RT-BO performs well for the forested Grand watershed. For the total phosphorus prediction, ensemble-BO and M-SVM outperform other ML models for the agricultural and forested watershed, while the ANN performs better than other ML models for the urban Cuyahoga watershed. In predicting phosphorus concentration, the model predictability is better for agricultural and forested watersheds. Regarding consistency, Bayesian optimized RT, ensemble, and GPR consistently yielded good performance for all watersheds. The methodology and results outlined in this study will assist policymakers in accurately predicting nitrate and phosphorus concentration which will be instrumental in drafting a proper plan to deal with the problem of water pollution.

The quality of bottom sediments in small water reservoirs located in agricultural watersheds

Silting of water reservoirs by sediments detached through soil erosion is a serious water management problem. One of the solutions is direct application of sediments to the soil. The aim of the paper is to monitor the quality of bottom sediments taken from selected water reservoirs in the eastern Slovakia (Hervartov, Nižný Žipov, Byšta) and to evaluate their quality according to legislation. The results showed that the concentrations of total nitrogen, phosphorus and potassium in sediments are higher than in soils taken from the vicinity of reservoirs.Simultaneously, it was confirmed that the sediment in the evaluated reservoirs meets the physicochemical parameters according to the Act No. 188/2003 Coll. for direct application to the soil.