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This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system's capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. [ABSTRACT FROM AUTHOR]

Fog augments the wet deposition of airborne particles entrained in its hydrometeors. This article aims to characterize fog deposition processes around the Barakah nuclear power plant (BNPP), in the United Arab Emirates (UAE), and assess the potential impact of fog on the deposition rate of radionuclides in case of an accidental release. To this end, the microphysics of twelve radiation fog events, typical in such arid climate, were measured during the winter seasons of 2021 and 2022 using a fog monitor that was deployed at the BNPP. The impact of fog deposition on the settling of radionuclides is investigated based on model simulations using the Weather Research Forecasting (WRF) model with the MYJ PBL scheme and FLEXPART. All fog events are found to share a common feature of a bimodal distribution in droplet number concentration (Nc), with modes at 4.5 μ m and 23.16 μ m. It was pointed out that despite the high proportion of smaller droplets in the fog associated with the fine mode, the greatest contribution to the liquid water content (LWC) comes essentially from medium to large droplets between 10 and 35 μ m. The deposition flux of fog water at the site and the fog droplet deposition velocity were estimated using an Eddy Covariance (EC) onsite. Typical mean values for fog droplet deposition velocity are found to range between 2.11 and 7.87 cm s-1. The modeling results show that fog deposition contributed by 30–40 % to the total ground deposition of 137Cs, highlighting the importance of incorporating fog deposition as an additional scavenging mechanism in dispersion modeling under foggy conditions. [ABSTRACT FROM AUTHOR]

The goal of this study is to evaluate the performance of the National Water Model (NWM) in time and space across the contiguous United States. Retrospective streamflow simulations were compared to records from 3260 USGS gauging stations, considering both regulated and natural flow conditions. Statistical metrics, including Kling–Gupta efficiency, Percent Bias, Pearson Correlation Coefficient, Root Mean Squared Error, and Normalized Root Mean Squared Error, were employed to assess the agreement between observed and simulated streamflow. A comparison of historical trends in daily flow data between the model and observed streamflow provided additional insight into the utility of retrospective NWM datasets. Our findings demonstrate a superior agreement between the simulated and observed streamflow for natural flow in comparison to regulated flow. The most favorable agreement between the NWM estimates and observed data was achieved in humid regions during the winter season, whereas a reduced degree of agreement was observed in the Great Plains region. Enhancements to model performance for regulated flow are necessary, and bias correction is crucial for utilizing the NWM retrospective streamflow dataset. The study concludes that the model-agnostic NextGen NWM framework, which accounts for regional performance of the utilized model, could be more suitable for continental-scale hydrologic prediction. [ABSTRACT FROM AUTHOR]