Correction to: Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-13406-7     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

A particle swarm optimization methodology to design an effective air quality monitoring network

Environment, Development and Sustainability - Good air quality is highly essential to the well-being of mankind, all living organisms and the environment. The quality of air is degrading at a...     

A multivariate statistical approach for the evaluation of spatial and temporal dynamics of surface water quality from the small reservoir located in the drought-prone area of South-West India: a case study of Tiru reservoir (India)

Environmental Science and Pollution Research - With the use of different multivariate statistical analysis methods, spatio-temporal fluctuations in the water parameters of Tiru reservoir located at...     

The deflectors influence on flow complexity,bed morphology,sediment transport and water quality of urban lotic waters - A laboratory study

In this laboratory study different combinations of bed (sand, pebble gravel [gravel], and a mix of sand and gravel) and flow (typical and overtopping) were experimented with to investigate the impact of porous deflectors in flow diversity, water quality, and fish performance in prismatic open channels. Deflectors changed the gradually varied flow to a rapidly varied flow, as a sudden change in the water depth was observed at the deflectors, and this change was large for smooth beds. With the presence of gravel, the scouring near the downstream deflector was almost twice that of the sand bed, and with the scouring at its own upstream deflector, irrespective of whether the flow was typical or overtopping. This behavior was a result of sand mobilization due to shear stress and sand mobilization aided gravel transport. The mixed bed showed less gravel movement compared to the gravel-only bed. The percentage of sediment washed out was minor for all bed scenarios, indicating that sediment transport was local. Relative to the sand bed without deflectors (representing a typical urban canal), deflectors resulted in reduced and improved water quality (in terms of sediment load) for sand, and mixed bed, respectively. The fishes found refuge and were comfortable in the pool areas created by deflectors unlike in channels without deflectors where they showed exhaustion.     

Impact of floodplain and Stage 0 stream restoration on flood attenuation and floodplain exchange during small frequent storms

River flooding impacts human life and infrastructure, yet provides habitat and ecosystem services. Traditional flood control (e.g., levees, dams) reduces habitat and ecosystem services, and exacerbates flooding elsewhere. Floodplain restoration (i.e., bankfull floodplain reconnection and Stage 0) can also provide flood management, but has not been sufficiently evaluated for small frequent storms. We used 1D unsteady Hydrologic Engineering Center's River Analysis System to simulate small storms in a 5 km-long, second-order generic stream from the Chesapeake Bay watershed, and varied % channel restored (starting at the upstream end), restoration location, restoration bank height (distinguishes bankfull from Stage 0 restoration), and floodplain width/Manning's n. Stream restoration decreased (attenuated) peak flow up to 37% and increased floodplain exchange by up to 46%. Floodplain width and % channel restored had the largest impact on flood attenuation. The incremental effects of new restoration projects on flood attenuation were greatest when little prior restoration had occurred. By contrast, incremental effects on floodplain exchange were greatest in the presence of substantial prior restoration, setting up a tradeoff. A similar tradeoff was revealed between attenuation and exchange for project location, but not bank height or floodplain width. In particular, attenuation and exchange were always greater for Stage 0 than for bankfull floodplain restoration. Stage 0 thus may counteract human impacts such as urbanization.     

Selective separation of cobalt versus nickel by split-phosphinate complexation using a phosphonium-based ionic liquid

Environmental Chemistry Letters - The rising demand of metals for batteries is calling for advanced methods to separate metallic mixtures in mineral ores and electronic waste. In particular, the...     

Analyzing temporal–spatial characteristics of drought events in the northern part of Cyprus

Drought in the northern part of Cyprus has become a recurrent phenomenon. In the last few decades, Cyprus has experienced significantly severe drought events occurring periodically, and this trend is now continuing. With rainfall distribution varying considerably across the region and frequent drought conditions, the water resources, agriculture, economy and the environment have been adversely affected. This study aims to investigate spatial–temporal characteristic of drought using Standardized Precipitation Index (SPI) at multiple timescales (3, 6 and 12 months). Monthly time series of 36 years (1977–2013) rainfall data from nine weather stations are used to derive SPI values. Based on different drought categories, this study focuses on propagation of drought from one timescale to another and estimating critical rainfall values during moderate, severe and extreme drought conditions. The analysis revealed that there is a strong correlation among different timescales in detecting drought events. On average, 79 and 78% of 3-month timescale drought propagated into 6- and 12-month drought events, respectively, while 90% of 6-month timescale drought events propagated into 12-month drought events. The derived critical rainfall value for extreme droughts over a 12-month timescale was less than 255 mm/year in the town of Alsancak, while for Guzelyurt, a major citrus growing city, this figure was less than 135 mm/year. The results are validated through drought events detected at various regions of the Mediterranean basin and local flood occurrences during the wet periods and decline in water tables at drought seasons.     

Supervisor expediency to employee expediency: The moderating role of leader–member exchange and the mediating role of employee unethical tolerance

We utilize social learning theory to test the role‐modeling effect of supervisor expediency (i.e., a supervisor's use of unethical practices to expedite work for self‐serving purposes). In particular, we examine the relationship between supervisor expediency and employee expediency, as moderated by leader–member exchange (LMX) and mediated by employee unethical tolerance. We predict that employees are more likely to model their supervisors' expedient behaviors when their relationship is characterized by high‐LMX (a high‐quality exchange relationship that is rich in socioemotional support). Furthermore, we argue that supervisor expediency, especially when LMX is high, influences employees' attitudes of unethical tolerance, which then affects employees' expedient behaviors. Across 2 multisource field studies and a third time‐lagged field study, we found general support for our theoretical predictions. Theoretical and practical implications are discussed.