Neonicotinoids have been the most commonly used insecticides since the early 1990s. Despite their efficacy in improving crop protection and management, these agrochemicals have gained recent attention for their negative impacts on non-target species such as honeybees and aquatic invertebrates. In recent years, neonicotinoids have been detected in rivers and streams across the world. Determining and predicting the exposure potential of neonicotinoids in surface water requires a thorough understanding of their fate and transport mechanisms. Therefore, our objective was to provide a comprehensive review of neonicotinoids with a focus on their fate and transport mechanisms to and within surface waters and their occurrence in waterways throughout the world. A better understanding of fate and transport mechanisms will enable researchers to accurately predict occurrence and persistence of insecticides entering surface waters and potential exposure to non-target organisms in agricultural intensive regions. This review has direct implications on how neonicotinoids are monitored and degraded in aquatic ecosystems. Further, an improved understanding of the fate and transport of neonicotinoids aide natural resource practitioners in the development and implementation of effective best management practices to reduce the potential impact and exposure of neonicotinoids in waterways and aquatic ecosystems.

In the Great Plains of the central United States, water resources for human and aquatic life rely primarily on surface runoff and local recharge from rangelands that are under rapid transformation to woodland by the encroachment of Eastern redcedar (redcedar; Juniperus virginiana) trees. In this synthesis, the current understanding and impact of redcedar encroachment on the water budget and water resources available for non-ecosystem use are reviewed. Existing studies concluded that the conversion from herbaceous-dominated rangeland to redcedar woodland increases precipitation loss to canopy interception and vegetation transpiration. The decrease of soil moisture, particularly for the subsurface soil layer, is widely documented. The depletion of soil moisture is directly related to the observed decrease in surface runoff, and the potential of deep recharge for redcedar encroached watersheds. Model simulations suggest that complete conversion of the rangelands to redcedar woodland at the watershed and basin scale in the South-central Great Plains would lead to reduced streamflow throughout the year, with the reductions of streamflow between 20 to 40% depending on the aridity of the climate of the watershed. Recommended topics for future studies include: (i) The spatial dynamics of redcedar proliferation and its impact on water budget across a regional hydrologic network; (ii) the temporal dynamics of precipitation interception by the herbaceous canopy; (iii) the impact of redcedar infilling into deciduous forests such as the Cross Timbers and its impact on water budget and water availability for non-ecosystem use; (iv) land surface and climate interaction and cross-scale hydrological modeling and forecasting; (v) impact of redcedar encroachment on sediment production and water quality; and (vi) assessment and efficacy of different redcedar control measures in restoring hydrological functions of watershed.

Data‐driven models used for predicting soil temperature usually have increasing errors with increasing depth. By exploring the integration of knowledge‐based and machine learning approaches, this study used a novel transformation of meteorological variables to increase prediction accuracy of soil temperature with increasing soil depth. Using datasets for two soil textures (silty clay loam and loamy coarse sand) at two locations with different climates, predictive models were developed for five depths (5, 10, 20, 50, and 100 cm) as a function of meteorological features using an adaptive neuro‐fuzzy inference system (ANFIS). For each depth, soil temperature was predicted with nontransformation (NT), autocorrelation (AC), moving average (MA), and a combination of transformations (NT‐ACMA) of meteorological features. Across all depths, the predictive accuracy of NT‐ACMA models was significantly higher than that of NT, AC, and MA models for both soil textures ( R ² = .99, RMSE = 1 °C for silty clay loam; R ² = .99, RMSE = 1.2 °C for loamy coarse sand), with increasing prediction accuracy as soil depth increases. Results for different soil textures and climates in 11 locations across the contiguous United States show that, except for 100‐cm depth, there seems to be significant positive linear relationships between the best moving average and the solar inclination. This makes our NT‐ACMA technique transferable to any location in the contiguous United States irrespective of the location, climate, and soil texture. We conclude that integrating lag times and moving averages of meteorological features can lead to better prediction of soil temperature at different soil depths.