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There is a renewed focus on the design of infrastructure resilient to extreme hydrometeorological events. While precipitation‐based intensity‐duration‐frequency (IDF) curves are commonly used as part of infrastructure design, a large percentage of peak runoff events in snow‐dominated regions are caused by snowmelt, particularly during rain‐on‐snow (ROS) events. In these regions, precipitation‐based IDF curves may lead to substantial overestimation/underestimation of design basis events and subsequent overdesign/underdesign of infrastructure. To overcome this deficiency, we proposed next‐generation IDF (NG‐IDF) curves, which characterize the actual water reaching the land surface. We compared NG‐IDF curves to standard precipitation‐based IDF curves for estimates of extreme events at 376 Snowpack Telemetry (SNOTEL) stations across the western United States that each had at least 30 years of high‐quality records. We found standard precipitation‐based IDF curves at 45% of the stations were subject to underdesign, many with significant underestimation of 100 year extreme events, for which the precipitation‐based IDF curves can underestimate water potentially available for runoff by as much as 125% due to snowmelt and ROS events. The regions with the greatest potential for underdesign were in the Pacific Northwest, the Sierra Nevada Mountains, and the Middle and Southern Rockies. We also found the potential for overdesign at 20% of the stations, primarily in the Middle Rockies and Arizona mountains. These results demonstrate the need to consider snow processes in the development of IDF curves, and they suggest use of the more robust NG‐IDF curves for hydrologic design in snow‐dominated environments.

Abstract:Demodex has been considered to be related with multiple skin disorders, but controversy persists. In this case-control study, a survey was conducted with 860 dermatosis patients aged 12 to 84 years in Xi'an, China to identify the association between facial dermatosis and Demodex. Amongst the patients, 539 suffered from facial dermatosis and 321 suffered from non-facial dermatosis. Demodex mites were sampled and examined using the skin pressurization method. Multivariate regression analysis was applied to analyze the association between facial dermatosis and Demodex infestation, and to identify the risk factors of Demodex infestation. The results showed that total detection rate of Demodex was 43.0%. Patients aged above 30 years had higher odds of Demodex infestation than those under 30 years. Compared to patients with neutral skin, patients with mixed, oily, or dry skin were more likely to be infested with Demodex (odds ratios (ORs) were 2.5, 2.4, and 1.6, respectively). Moreover, Demodex infestation was found to be statistically associated with rosacea (OR=8.1), steroid-induced dermatitis (OR=2.7), seborrheic dermatitis (OR=2.2), and primary irritation dermatitis (OR=2.1). In particular, ORs calculated from the severe infestation (≥5 mites/cm 2 ) rate were significantly higher than those of the total rate. Therefore, we concluded that Demodex is associated with rosacea, steroid-induced dermatitis, seborrheic dermatitis, and primary irritation dermatitis. The rate of severe infestation is found to be more correlated with various dermatosis than the total infestation rate. The risk factors of Demodex infestation, age, and skin types were identified. Our study also suggested that good hygiene practice might reduce the chances of demodicosis and Demodex infestation.

Our study demonstrates that bakuchiol is comparable with retinol in its ability to improve photoageing and is better tolerated than retinol. Bakuchiol is promising as a more tolerable alternative to retinol.

In snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains.

Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX).A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that:(1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study.

Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.Index Terms-Data assimilation (DA), hydrologic measurements, remote sensing, satellite applications.

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initial condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.