Flood hydrologic response is influenced by rainfall structure (i.e., variability in space and time). How this structure shapes flood frequency is unknown, and flood frequency analyses typically neglect or simplify potentially important aspects of rainfall variability. This study seeks to understand how rainfall structure impacts flood frequency. We use stochastic storm transposition combined with a 15‐year record of hourly, 4‐km2 radar rainfall to generate 10,000 synthetic extreme rain events. These events are resampled into four “scenarios” with differing spatial and temporal resolutions, which are used as input to a distributed hydrologic model. Analysis of variance is used to identify the proportions of total flood peak variability attributable to spatial and to temporal rainfall variability under two antecedent soil moisture conditions. We simulate peak discharges for recurrence intervals of 2 to 500 years for 1,343 subwatersheds ranging in size from 16 to 4,400 km2 in Turkey River in the Midwestern United States, which is situated in a typically humid continental climactic region. Antecedent soil moisture modulates the role of rainfall structure in simulated flood response, particularly for more frequent events and large watershed scales. Rainfall spatial structure is more important than temporal structure for drainage areas larger than approximately 2,000 km2 (approximately 200 km2) for wet (dry) initial soil conditions, especially when soils are dry, while the reverse is true for smaller subwatersheds. The results appear to be related to the differing propensities for surface and subsurface runoff production as a function of basin scale, event magnitude, and soil saturation. Our results suggest that hydrologic model‐based flood frequency analyses, and particularly efforts attempting to spanning a range of scales, must carefully consider rainfall structure.

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

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Brachial plexus root avulsion (BPRA) is a type of injury that leads to motor function loss as a result of motoneurons (MNs) degeneration. Here we identified that the reduced expression of rat miR-137-3p in the ventral horn of spinal cord was associated with MNs death. However, the pathophysiological role of miR-137-3p in root avulsion remains poorly understood. We demonstrated that the calcium-activated neutral protease-2 (calpain-2) was a direct target gene of miR-137-3p with miR-137-3p binding to the 3'-untranslated region of calpain-2. Silencing of calpain-2 suppressed the expression of neuronal nitric oxide synthase (nNOS), a primary source of nitric oxide (NO). After avulsion 2 weeks, up-regulation of miR-137-3p in the spinal cord reduced calpain-2 levels and nNOS expression inside spinal MNs, resulting in an amelioration of the MNs death. These events provide new insight into the mechanism by which upregulation of miR-137-3p can impair MN survival in the BPRA.

With the advancement of urbanization, the harm caused to human health by PM2.5 pollution has been receiving increasing attention worldwide. In order to increase public awareness and understanding of the damage caused by PM2.5 in the air and gain the attention of relevant management departments, Changsha City is used as the research object, and the environmental quality data and public health data of Changsha City from 2013 to 2017 are used. All-cause death, respiratory death, cardiovascular death, chronic bronchitis, and asthma were selected as the endpoints of PM2.5 pollution health effects, according to an exposure–response coefficient, Poisson regression model, and health-impact-assessment-related methods (the Human Capital Approach, the Willingness to Pay Approach, and the Cost of Illness Approach), assessing the health loss and economic loss associated with PM2.5. The results show that the pollution of PM2.5 in Changsha City is serious, which has resulted in extensive health hazards and economic losses to local residents. From 2013 to 2017, when annual average PM2.5 concentrations fell to 10 μg/m3, the total annual losses from the five health-effect endpoints were $2788.41 million, $2123.18 million, $1657.29 million, $1402.90 million, and $1419.92 million, respectively. The proportion of Gross Domestic Product (GDP) in the current year was 2.69%, 1.87%, 1.34%, 1.04% and 0.93%, respectively. Furthermore, when the concentration of PM2.5 in Changsha City drops to the safety threshold of 10 μg/m3, the number of affected populations and health economic losses can far exceed the situation when it falls to 35 μg/m3, as stipulated by the national secondary standard. From 2013 to 2017, the total loss under the former situation was 1.48 times, 1.54 times, 1.86 times, 2.25 times, and 2.33 times that of the latter, respectively. Among them, all-cause death and cardiovascular death are the main sources of health loss. Taking 2017 as an example, when the annual average concentration dropped to 10 μg/m3, the health loss caused by deaths from all-cause death and cardiovascular disease was 49.16% of the total loss and 35.73%, respectively. Additionally, deaths as a result of respiratory disease, asthma, and chronic bronchitis contributed to 7.31%, 7.29%, and 0.51% of the total loss, respectively. The research results can provide a reference for the formulation of air pollution control policies based on health effects, which is of great significance for controlling air pollution and protecting people’s health.

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In order to provide urban flood early warning effectively, two support vector machine (SVM) models, using a numerical model as data producer, were developed to forecast the flood alert and the maximum flood depth, respectively. An application in the urban area of Jinlong River Basin, Hangzhou, China, showed the superiority of the proposed models. Statistical results based on the comparison between the results from SVM models and numerical model, proved that the SVM models could provide accurate forecasts for estimating the urban flood. For all the rainfall events tested with an identical desktop, the SVM models only took 2.1 milliseconds while the numerical model took 25 hours. Therefore, the SVM model demonstrates its potential as a valuable tool to improve emergency responses to alleviate the loss of lives and property due to urban flood.