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Soil carbon dioxide (CO2) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO2 fluxes (Rs) with soil temperature (Ts) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on Rs using the measured high-frequency data. Rs trend was tested by Mann–Kendall and Sen Estimator. Predicted Rss under different climate scenarios were compared using Parallel-line Analysis. The findings indicated that daily Rs exponentially increased with Ts constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between Rs and Ts, but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, Ts × θ) significantly impacted Rs with linear regression Rs0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in Rss and relationships among Rs and Ts and θ in different years. Predicted yearly Rss, root Rss and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the Rss significantly increased as temperature rose by 1°C or higher. Predicted yearly Rss, root Rss and yield reduced with precipitation scenario increase, and the root Rss and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly Rs from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield Rs and evaluating sensitivities of different cropland Rss to temperature changes.

Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian neural network (BNN) ensembling method, which combines climate models within a Bayesian model averaging framework, to improve the predictive capability of model ensembles. Our proposed BNN approach calculates spatiotemporally varying model weights and biases by leveraging individual models' simulation skill, calibrates the ensemble prediction against observations by considering observation data uncertainty, and quantifies epistemic uncertainty when extrapolating to new conditions. More importantly, the BNN method provides interpretability about which climate model contributes more to the ensemble prediction at which locations and times. Thus, beyond its predictive capability, the method also brings insights and understanding of the models to guide further model and data development. In this study, we design experiments using an ensemble of CMIP6 climate model simulations to illustrate the BNN ensembling method's capability with respect to prediction accuracy, interpretability, and uncertainty quantification (UQ). We demonstrate that BNN can correctly assign larger weights to the regions and seasons where the individual model fits the observation better. Moreover, its offered interpretability is consistent with our understanding of localized climate model performance. Additionally, BNN shows an increasing uncertainty when the prediction is farther away from the period with constrained data, which appropriately reflects our trustworthiness of the models in the changing climate.

This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12 km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth-generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions. First, how well are the 3 and 24 h precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) 4 km Stage IV and Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data on 3 and 24 h timescales, respectively. Our analysis suggests that the 12 km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3 and 24 h precipitation maximum over the CONUS, in contrast to ERA5, which generally underestimates these quantities mainly over the eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3 and 24 h annual and seasonal maximum precipitation. WRF exhibits seasonally dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics but is susceptible to common regional climate model biases. Consequently, if used as input data for domain-specific models, we suggest moderate bias correction be applied to the dynamically downscaled product.

Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian neural network (BNN) ensembling method, which combines climate models within a Bayesian model averaging framework, to improve the predictive capability of model ensembles. Our proposed BNN approach calculates spatiotemporally varying model weights and biases by leveraging individual models' simulation skill, calibrates the ensemble prediction against observations by considering observation data uncertainty, and quantifies epistemic uncertainty when extrapolating to new conditions. More importantly, the BNN method provides interpretability about which climate model contributes more to the ensemble prediction at which locations and times. Thus, beyond its predictive capability, the method also brings insights and understanding of the models to guide further model and data development. In this study, we apply the BNN weighting scheme to an ensemble of CMIP6 climate models for monthly precipitation prediction over the conterminous United States. In both synthetic and real case studies, we demonstrate that BNN produces predictions of monthly precipitation with higher accuracy than three baseline ensembling methods. BNN can correctly assign a larger weight to the regions and seasons where the individual model fits the observation better. Moreover, its offered interpretability is consistent with our understanding of localized climate model performance. Additionally, BNN shows an increasing uncertainty when the prediction is farther away from the period with constrained data, which appropriately reflects our predictive confidence and trustworthiness of the models in the changing climate.

An intensification of hydroclimate extremes in response to increase in radiative forcing has the potential to cause severe and widespread socioeconomic damages. Therefore, a comprehensive evaluation of projected changes in the characteristics of these extremes in a warming climate is necessary for emergency preparedness and planning. While the intensity and frequency of these extremes have been thoroughly investigated, the efforts on understanding their spatial characteristics are still limited. To this end, we use an ensemble of high-resolution regional climate simulations to investigate the spatial characteristics of daily-scale precipitation events across the United States, in addition to other features. The simulations cover 1966–2005 in the historical period and 2011–2050 in the future period under Representative Concentration Pathway 8.5 (RCP 8.5) scenario. The simulated ensemble compares well with observations in the historical period, and project further intensification of widespread extremes in the near future. Further, our results demonstrate that the projected changes in the characteristics of precipitation events are associated with more frequent occurrences of extreme years where contributions from intense and widespread events to the annual precipitation is unprecedently high. These findings highlight the need for more rigorous investigations of changes in the spatial characteristics of extremes to prepare for potential future changes and associated risks.

Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

This study uses integrated hydrometeorological simulations over the Alabama-Coosa-Tallapoosa (ACT) River Basin in the southeastern United States to understand the impact of climate change on probable maximum precipitation.

The magnitude and frequency of hydrometeorological extremes are expected to increase in the conterminous United States (CONUS) over the rest of this century, and their increase will significantly impact water resource management. In this study, we evaluated the large-scale climate change effects on extreme hydrological events and their implications for reservoir inflows in 138 headwater subbasins located upstream of reservoirs across CONUS using the Variable Infiltration Capacity (VIC) hydrologic model. The VIC model was forced with a 10-member ensemble of global circulation models under the Representative Concentration Pathway 8.5 that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24° grid cell resolution. Four commonly used indices, including mean annual flow, annual center timing, 100-year daily high streamflow, and 10-year 7-day average low streamflow were used for evaluation. The results projected an increase in the high streamflow by 44% for a majority of subbasins upstream of flood control reservoirs in the central United States (US) and a decrease in the low streamflow by 11% for subbasins upstream of hydropower reservoirs across the western US. In the eastern US, frequencies of both high and low streamflow were projected to increase in the majority of subbasins upstream of both hydropower and flood control reservoirs. Increased frequencies of both high and low streamflow events can potentially make reservoirs across CONUS more vulnerable to future climate conditions. This study estimates reservoir inflow changes over the next several decades, which can be used to optimize water supply management downstream.

Post-partum haemorrhage (PPH) is the most common cause of maternal mortality in India. Uterine artery embolization (UAE) for treating PPH is a recent developed minimally invasive method. It has high efficacy and low complication rate. Uterine necrosis is a rare and life threatening complication of UAE. Imaging features on ultrasound include distended endometrial cavity with dirty acoustic shadowing and thinning of myometrium. On CT, hypo to non-enhancing myometrium with or without air foci is seen. Contrast enhanced MRI is a key diagnostic imaging which has a high soft tissue resolution. Thinning of myometrium with non-enhancing myometrium is a key feature to the diagnosis of uterine necrosis. We present a rare case report of uterine necrosis post UAE.

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.

This study introduces a mixed integer linear fractional programming (MILFP) method to optimize conjunctive use of future surface water and groundwater resources under projected climate change scenarios. The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. Future inflows to the reservoirs were estimated from the future runoffs projected through hydroclimate modeling considering the Variable Infiltration Capacity model, and 11 sets of downscaled Coupled Model Intercomparison Project phase 5 global climate model projections. Bayesian model averaging was adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. Optimized conjunctive management solutions were investigated for a water supply network in northern Louisiana which includes the Sparta aquifer. Runoff projections under climate change scenarios indicate that runoff will likely decrease in winter and increase in other seasons. Results from the developed conjunctive management model with MILFP indicate that the future reservoir water, even at 2.5% low inflow cumulative probability level, could counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraints.

We present high-resolution near-term ensemble projections of hydro-climatic changes over the contiguous U.S. using a regional climate model (RegCM4) that dynamically downscales 11 Global Climate Models from the 5th phase of Coupled Model Inter-comparison Project at 18km horizontal grid spacing. All model integrations span 41 years in the historical period (1965 – 2005) and 41 years in the near-term future period (2010 – 2050) under Representative Concentration Pathway 8.5 and cover a domain that includes the contiguous U.S. and parts of Canada and Mexico. Should emissions continue to rise, surface temperatures in every region within the U.S. will reach a new climate norm well before mid 21st century regardless of the magnitudes of regional warming. Significant warming will likely intensify the regional hydrological cycle through the acceleration of the historical trends in cold, warm and wet extremes. The future temperature response will be partly regulated by changes in snow hydrology over the regions that historically receive a major portion of cold season precipitation in the form of snow. Our results indicate the existence of the Clausius-Clapeyron scaling at regional scales where per degree centigrade rise in surface temperature will lead to a 7.4% increase in precipitation from extremes. More importantly,more » both winter (snow) and summer (liquid) extremes are projected to increase across the U.S. These changes in precipitation characteristics will be driven by a shift towards shorter and wetter seasons. Altogether, projected changes in the regional hydro-climate can have substantial impacts on the natural and human systems across the U.S.« less

Despite the fact that Global Climate Model (GCM) outputs have been used to project hydrologic impacts of climate change using off-line hydrologic models for two decades, many of these efforts have been disjointed — applications or at least calibrations have been focused on individual river basins and using a few of the available GCMs. This study improves upon earlier attempts by systematically projecting hydrologic impacts for the entire conterminous United States (US), using outputs from ten GCMs from the latest Coupled Model Intercomparison Project phase 5 (CMIP5) archive, with seamless hydrologic model calibration and validation techniques to produce a spatially and temporally consistent set of current hydrologic projections. The Variable Infiltration Capacity (VIC) model was forced with ten-member ensemble projections of precipitation and air temperature that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24° (~ 4 km) grid resolution for the baseline (1966–2005) and future (2011–2050) periods under the Representative Concentration Pathway 8.5. Based on regional analysis, the VIC model projections indicate an increase in winter and spring total runoff due to increases in winter precipitation of up to 20% in most regions of the US. However, decreases in snow water equivalent (SWE) and snow-covered days will lead to significant decreases in summer runoff with more pronounced shifts in the time of occurrence of annual peak runoff projected over the eastern and western US. In contrast, the central US will experience year-round increases in total runoff, mostly associated with increases in both extreme high and low runoff. The projected hydrological changes described in this study have implications for various aspects of future water resource management, including water supply, flood and drought preparation, and reservoir operation.

We show that 21st century increase in radiative forcing does not significantly impact the frequency of South Asian summer monsoon depressions (MDs) or their trajectories in the Coupled Model Intercomparison Project Phase 5 general circulation models (GCMs). A significant relationship exists between the climatological occurrences of MDs and the strength of the background upper (lower) tropospheric meridional (zonal) winds and tropospheric moisture in the core genesis region of MDs. Likewise, there is a strong relationship between the strength of the meridional tropospheric temperature gradient in the GCMs and the trajectories of MDs over land. While monsoon dynamics progressively weakens in the future, atmospheric moisture exhibits a strong increase, limiting the impact of changes in dynamics on the frequency of MDs. Moreover, the weakening of meridional tropospheric temperature gradient in the future is substantially weaker than its inherent underestimation in the GCMs. Our results also indicate that future increases in the extreme wet events are dominated by nondepression day occurrences, which may render the monsoon extremes less predictable in the future. Plain Language Summary Monsoon depressions (MDs) are one of the most important synoptic scale transient weather systems that transport large amount of moisture over the South Asian landmass and contribute significantly to the total precipitation. In this study, we investigate representation of MDs in the Coupled Model Intercomparison Project Phase 5 general circulation models, their simulated responses to increase in radiative forcing during the 21st century under Representative Concentration Pathway 8.5, and the resulting changes in precipitation characteristics at intraseasonal time scales. We show that 21st century increase in radiative forcing does not significantly impact the frequency of South Asian summer monsoon depressions or their trajectories in the general circulation models. Our results indicate a significant relationship between the climatological occurrences of MDs and the background upper (lower) tropospheric meridional (zonal) winds and tropospheric moisture in the core genesis region of MDs. While monsoon dynamics progressively weakens in the future, atmospheric moisture exhibits a strong increase, limiting the impact of changes in dynamics on the frequency of MDs. Our results also indicate that future increase in the extreme wet events is dominated by nondepression day extremes, which may render the monsoon extremes less predictable in the future.

This paper analyzes a suite of global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archives to understand the mechanisms behind a net increase in the South Asian summer monsoon precipitation in response to enhanced radiative forcing during the twenty-first century. An increase in radiative forcing fuels an increase in the atmospheric moisture content through warmer temperatures, which overwhelms the weakening of monsoon circulation and results in an increase of moisture convergence and therefore summer monsoon precipitation over South Asia. Moisture source analysis suggests that both regional (local recycling, the Arabian Sea, the Bay of Bengal) and remote (including the south Indian Ocean) sources contribute to the moisture supply for precipitation over South Asia during the summer season that is facilitated by the monsoon dynamics. For regional moisture sources, the effect of excessive atmospheric moisture is offset by weaker monsoon circulation and uncertainty in the response of the evapotranspiration over land, so anomalies in their contribution to the total moisture supply are either mixed or muted. In contrast, weakening of the monsoon dynamics has less influence on the moisture supply from remote sources that not only is a dominant moisture contributor in the historical period but is also the net driver of the positive summer monsoon precipitation response in the twenty-first century. The results also indicate that historic measures of the monsoon dynamics may not be well suited to predict the nonstationary moisture-driven South Asian summer monsoon precipitation response in the twenty-first century.

Goitre has ceased to be a major problem in many developed countries but still it continuous to be a serious health problem in many third world countries. Thyroid cancers have a harder consistency than benign thyroid nodules; therefore, real-time elastography (RTE) has been recently proposed as a new diagnostic tool able to differentiate malignant from benign lesions by providing an estimation of tissue stiffness. Elastography is a technique that uses ultrasound to analyze the stiffness of a nodule by measuring the amount of distortion that occurs when the nodule is subjected to external pressure. Utilization of ultrasound elastography could reduce the rate of thyroid biopsies because of its high elasticity being highly associated with benign cytology.

Probable maximum precipitation (PMP), defined as the largest rainfall depth that could physically occur under a series of adverse atmospheric conditions, has been an important design criterion for critical infrastructures such as dams and nuclear power plants. To understand how PMP may respond to projected future climate forcings, we used a physics-based numerical weather simulation model to estimate PMP across various durations and areas over the Alabama-Coosa-Tallapoosa (ACT) river basin in the southeastern United States. Six sets of Weather Research and Forecasting (WRF) model experiments driven by both reanalysis and global climate model projections, with a total of 120 storms, were conducted. The depth-area-duration relationship was derived for each set of WRF simulations and compared with the conventional PMP estimates. Our results showed that PMP driven by projected future climate forcings is higher than 1981–2010 baseline values by around 20% in the 2021–2050 near-future and 44% in the 2071–2100 far-future periods. The additional sensitivity simulations of background air temperature warming also showed an enhancement of PMP, suggesting that atmospheric warming could be one important factor controlling the increase in PMP. In light of the projected increase in precipitation extremes under a warming environment, the reasonableness and role of PMP deserves more in-depth examination.

Additional tables, figures, methods and discussion detailing effects of immunity, temperature, precipitation, winter freezes and drought on human West Nile virus cases across the United States

We develop a highly-resolved ensemble of climate simulations and empirical relationships between weather and household energy consumption to provide one of the most detailed estimates to date for potential climate-driven changes in the United States residential energy demand under the highest greenhouse gas emissions pathway. Our results indicate that more intense and prolonged warm conditions will drive an increase in electricity demand while a shorter and milder cold season will reduce natural gas demand by the mid 21st century. The environmental conditions that favor more cooling degree days in summer and reduced heating degree days in winter are driven by changes in daily maximum temperatures and daily minimum temperatures in the respective seasons. Our results also indicate that climate-driven change can potentially reverse impacts of a projected decrease in rural population on residential energy demand. These projected changes in climate-driven energy demand have implications for future energy planning and management.

Ovarian cysts are the most frequent type of neonatal abdominal tumours with an estimated incidence of more than 30%. 1 Cyst in fetal-neonatal ovary may undergo complications such as hemorrhage, rupture or torsion among which torsion is the most common complication and may lead to loss of ovarian tissue. Thus, it warrants immediate attention and decision to intervene is taken when there is slightest doubt of complication. We present an interesting case of postnatally diagnosed ovarian cyst with torsion.

The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We found that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. These results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases.