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Complex network (CN) is a graph theory‐based depiction of relation shared by various elements of a complex dynamical system such as the atmosphere. Here we apply the concept of CN to understand the directionality and topological structure of summer extreme precipitation events (SEPEs) over the conterminous United States (CONUS). The SEPEs are calculated based on the 95th percentile daily rainfall at 0.5° × 0.5° spatial resolution for CONUS to investigate the multidimensional characteristics of precipitation extremes. The derived CN coefficients (e.g., betweenness centrality, clustering coefficient, orientation, and network divergence) reveal important structural and dynamical information about the topology of the SEPEs and improve understanding of the dominant meteorological patterns. The initiation and propagation of SEPEs from the source zones to the sink zones are identified. The SEPEs are influenced by topography, dominant wind patterns, and moisture sources in terms of their topological structure and spatial dynamics. Plain Language Summary: Every year, the United States incurs a loss of $3–$4 billion from flooding caused by extreme rainfall events. Under climate change, extreme rainfall events are likely to intensify further in the future as the atmosphere can hold more moisture with warmer temperatures. However, very few studies have explored the inherent topological structure characterizing extreme precipitation. Understanding of this structure and its driving meteorological conditions holds a key to understanding the response of extreme precipitation systems to climate change. In this study, key influencing factors such as topography, dominant wind pattern, and soil moisture controlling the topology of the summer extreme precipitation events in the United States are identified using complex network analysis along with the regions predominantly acting as moisture source or sink. Key Points: Physical control of dominant weather regime, topography, and soil moisture on synchronization of extreme precipitation events is revealed in the United StatesRegions of moisture inflow, moisture source, and moisture convergence associated with summer extreme precipitation events are identifiedSix clusters interacting through moisture exchange emerged in the United States, demonstrating the dynamical synchronization of summer extreme precipitation [ABSTRACT FROM AUTHOR]

• Comparison of Budyko-based methods to separate catchment and climate effects on flow. • Probabilistic nature of catchment parameter and its uncertainty considered. • New, improved Two-Stage Decomposition method proposed for separation. • Proposed method considers uncertainty in evaporative index using bootstrap method. • Proposed method more robust than using any single earlier-practiced method. Streamflow variability is affected by both climate change and direct human impacts within the catchment. Quantification of the role of these two factors have significant implications for water resources. In recent years, Budyko-based analytical methods have been widely used for such quantification due to their simplicity and efficacy. This paper presents a comparison of such methods to attribute changes in streamflow in four Model Parameter Estimation Experiment (MOPEX) catchments in the contiguous United States. Further, a new, improved Two-stage Decomposition method to separate the role of climate and catchment changes on streamflow is proposed, considering the uncertainty in the catchment parameter that further leads to a range of evaporative index values within a catchment. We find that climate and catchment effects are sensitive to the choice of Budyko equation, disaggregation algorithm and approximation technique. Further, the catchment parameter can be expressed probabilistically, to account for the uncertainty because of limited hydro-meteorological records. The proposed approach employs a bootstrap analysis to obtain the stochastic range of the catchment parameter, considering different choices of Budyko equations. We argue that rather than relying on a single Budyko-based approach for separation of climate and catchment effects on streamflow, the proposed method offers a more robust way by considering the entire range of uncertainty of the catchment parameter. [ABSTRACT FROM AUTHOR]

Natural climate solutions provide opportunities to reduce greenhouse gas emissions and the United States is among a growing number of countries promoting storage of carbon in agricultural soils as part of the climate solution. Historical patterns of soil organic carbon (SOC) stock changes provide context about mitigation potential. Therefore, our objective was to quantify the influence of climate-smart soil practices on SOC stock changes in the top 30 cm of mineral soils for croplands in the United States using the DayCent Ecosystem Model. We estimated that SOC stocks increased annually in US croplands from 1995 to 2015, with the largest increase in 1996 of 16.6 Mt C (95% confidence interval ranging from 6.1 to 28.2 Mt CO2 eq.) and the lowest increase in 2015 of 10.6 Mt C (95% confidence interval ranging from − 1.8 to 22.2 Mt C). Most climate-smart soil practices contributed to increases in SOC stocks except for winter cover crops, which had a negligible impact due to a relatively small area with cover crop adoption. Our study suggests that there is potential for enhancing C sinks in cropland soils of the United States although some of the potential has been realized due to past adoption of climate-smart soil practices. [ABSTRACT FROM AUTHOR]

Considering the significant effects of carbon-dioxide (CO2) emissions on the environment, analysis of historical emission levels and prediction of future levels are of huge importance in managing climate change. This study focuses on predicting CO2 emissions in the United States (US), European Union (EU), and China based on the data collected from the World Bank's database. The time series were modelled using the ARIMA methodology, while the analysis was conducted in R. The findings indicate that by 2030, the EU is projected to experience a decline of 23.70% in emissions compared to 2019 levels, while the United States is expected to witness a decrease of 21.95%. However, China's projections are discouraging, with an anticipated increase of 26.83% in emissions. These projections serve as valuable tools to assess the achievement of imposed CO2 emission targets in specific economies, informing the necessary adjustments to existing policies or the implementation of new ones. This study underscores the significance of analysing historical data and forecasting future CO2 emissions to manage climate change and develop energy-related policies effectively. [ABSTRACT FROM AUTHOR]

Climate change impacts on precipitation characteristics will alter the hydrologic characteristics, such as peak flows, time to peak, and erosion potential of watersheds. However, many of the currently available climate change datasets are provided at temporal and spatial resolutions that are inadequate to quantify projected changes in hydrologic characteristics of a watershed. Therefore, it is critical to temporally disaggregate coarse-resolution precipitation data to finer resolutions for studies sensitive to precipitation characteristics. In this study, we generated novel 15-minute precipitation datasets from hourly precipitation datasets obtained from five NA-CORDEX downscaled climate models under RCP 8.5 scenario for the historical (1970–1999) and projected (2030–2059) years over the Southeast United States using a modified version of the stochastic method. The results showed conservation of mass of the precipitation inputs. Furthermore, the probability of zero precipitation, variance of precipitation, and maximum precipitation in the disaggregated data matched well with the observed precipitation characteristics. The generated 15-minute precipitation data can be used in all scientific studies that require precipitation data at that resolution. Measurement(s) Precipitation Technology Type(s) weather station, stochastic Sample Characteristic - Environment Climate system Sample Characteristic - Location Southeast United States [ABSTRACT FROM AUTHOR]

Lakes are primitive water holding geographic structures containing most the fresh water on the Earth's surface, but the recent trends show that climate change can potentially lead to a significant aberration in the Lake water level and its overall pristine state, and therefore, could also threaten the source of freshwater. The ability to forecast the lake water is a paramount decision-making and risk-reduction task, and this is required to retain the sustainability of the natural environment, and to reduce the risk to the local and global food chain, recreation activities, agriculture and ecosystems. In this study, we have designed and evaluated a new hybrid forecasting model, integrating the gravitational search algorithm (GSA), as a heuristic optimization tool, with the Multilayer Perceptron (MLP-GSA) algorithm to forecast water level in Winnipesaukee and Cypress Lakes in the United States of America. The performance of the resulting hybrid MLP-GSA model is benchmarked and compared with the traditional MLP trained with Levenberg-Marquadt back propagation learning algorithm, two other intelligent hybrid models (MLP-PSO and MLP-FFA) and also two stochastic models namely, ARMA and ARIMA models. In this case study, the monthly time scale water level data from 1938 to 2005 and 1942 to 2011 for the Lakes Winnipesaukee and Cypress, respectively, were applied to train and evaluate the MLP-GSA model. The best input combinations of the standalone (MLP) and the hybrid MLP-GSA forecasting models were determined by sensitivity analysis of historical water level training data for 1-month lead forecasting. The hybrid MLP-GSA model was evaluated independently with statistical score metrics: coefficient of correlation, coefficient of efficiency, the root mean square and relative root mean square errors, and the Bayesian Information Criterion. The results showed that the hybrid MLP-GSA4 and MLP-GSA5 model (where the '4 and 5 months' of lagged input combinations of Lake water level data were utilized as the model inputs) performed more accurately than the ARIMA, ARMA, MLP4, MLP-PSO4 and MLP-FFA4 models for the Cypress Lake and ARIMA, ARMA, MLP5, MLP-PSO5 and MLP-FFA5 models for the Winnipesaukee lake, respectively. This study ascertained the robustness of hybrid MLP-GSA over ARMA, ARIMA, MLP, MLP-PSO and MLP-FFA for the forecasting of Lake water level. The high efficacy of the hybrid MLP-GSA model over the other applied models, indicate significant implications of its use in water resources management, decision-making tasks, irrigation management, management of hydrologic structures and sustainable use of water for agriculture and other necessities. [ABSTRACT FROM AUTHOR]

Leishmaniasis, a chronic and persistent intracellular protozoal infection caused by many different species within the genus Leishmania, is an unfamiliar disease to most North American providers. Clinical presentations may include asymptomatic and symptomatic visceral leishmaniasis (so-called Kala-azar), as well as cutaneous or mucosal disease. Although cutaneous leishmaniasis (caused by Leishmania mexicana in the United States) is endemic in some southwest states, other causes for concern include reactivation of imported visceral leishmaniasis remotely in time from the initial infection, and the possible long-term complications of chronic inflammation from asymptomatic infection. Climate change, the identification of competent vectors and reservoirs, a highly mobile populace, significant population groups with proven exposure history, HIV, and widespread use of immunosuppressive medications and organ transplant all create the potential for increased frequency of leishmaniasis in the U.S. Together, these factors could contribute to leishmaniasis emerging as a health threat in the U.S., including the possibility of sustained autochthonous spread of newly introduced visceral disease. We summarize recent data examining the epidemiology and major risk factors for acquisition of cutaneous and visceral leishmaniasis, with a special focus on implications for the United States, as well as discuss key emerging issues affecting the management of visceral leishmaniasis. [ABSTRACT FROM AUTHOR]