Climate change and human activities are two major driving forces affecting the hydrologic cycle, which further influence the stationarity of the hydrologic regime. Hydrological drought is a ...substantial negative deviation from the normal hydrologic conditions affected by these two phenomena. In this study, we propose a framework for quantifying the effects of climate change and human activities on hydrological drought. First, trend analysis and change‐point test are performed to determine variations of hydrological variables. After that, the fixed runoff threshold level method (TLM) and the standardized runoff index (SRI) are used to verify whether the traditional assessment methods for hydrological drought are applicable in a changing environment. Finally, two improved drought assessment methods, the variable TLM and the SRI based on parameter transplantation are employed to quantify the impacts of climate change and human activities on hydrological drought based on the reconstructed natural runoff series obtained using the variable infiltration capacity hydrologic model. The results of a case study on the typical semiarid Laohahe basin in North China show that the stationarity of the hydrological processes in the basin is destroyed by human activities (an obvious change‐point for runoff series is identified in 1979). The traditional hydrological drought assessment methods can no longer be applied to the period of 1980–2015. In contrast, the proposed separation framework is able to quantify the contributions of climate change and human activities to hydrological drought during the above period. Their ranges of contributions to hydrological drought calculated by the variable TLM method are 20.6–41.2% and 58.8–79.4%, and the results determined by the SRI based on parameter transplantation method are 15.3–45.3% and 54.7–84.7%, respectively. It is concluded that human activities have a dominant effect on hydrological drought in the study region. The novelty of the study is twofold. First, the proposed method is demonstrated to be efficient in quantifying the effects of climate change and human activities on hydrological drought. Second, the findings of this study can be used for hydrological drought assessment and water resource management in water‐stressed regions under nonstationary conditions.
Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and ...societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI) and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings.
Detection of nanoscale objects is highly desirable in various fields such as early‐stage disease diagnosis, environmental monitoring and homeland security. Optical microcavity sensors are renowned ...for ultrahigh sensitivities due to strongly enhanced light‐matter interaction. This review focuses on single nanoparticle detection using optical whispering gallery microcavities and photonic crystal microcavities, both of which have been developing rapidly over the past few years. The reactive and dissipative sensing methods, characterized by light‐analyte interactions, are explained explicitly. The sensitivity and the detection limit are essentially determined by the cavity properties, and are limited by the various noise sources in the measurements. On the one hand, recent advances include significant sensitivity enhancement using techniques to construct novel microcavity structures with reduced mode volumes, to localize the mode field, or to introduce optical gain. On the other hand, researchers attempt to lower the detection limit by improving the spectral resolution, which can be implemented by suppressing the experimental noises. We also review the methods of achieving a better temporal resolution by employing mode locking techniques or cavity ring up spectroscopy. In conclusion, outlooks on the possible ways to implement microcavity‐based sensing devices and potential applications are provided.
Single nanoparticle detection is of critical importance in various fields from fundamental research to practical applications. Optical microcavities are excellent candidates to be employed in ultra‐sensitive sensing due to significantly enhanced light‐matter interaction. The sensing performance can be improved by obtaining better spectral resolution and temporal resolution, and techniques can be applied to realize practical and portable sensors using microcavities.
While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local ...hydro‐climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP‐SVR, ASFP‐ELM, and ASFP‐RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1‐ and 3‐month lead times. Results show that the ASFP‐ELM model can effectively predict space‐time evolutions of drought events with satisfactory skills, outperforming the ASFP‐SVR and ASFP‐RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.
Key Points
Antecedent sea surface temperature fluctuations are introduced as predictors for meteorological drought prediction
Extreme learning machine predicts droughts more effectively than support vector regression and random forest
The new approach allows for ensemble, probability, and deterministic drought predictions
The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental ...observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario.
The stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels of daily and subdaily extreme precipitation in most areas of China compared with non‐stationary GEV distribution under the high‐emission scenarios.
Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition ...to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state‐of‐the‐art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow‐characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.
Key Points
The correction of modeled precipitation‐temperature correlations can improve the accuracy of hydrological simulations
The advantages of using multivariate bias correction methods in hydrological simulations are weakened when it comes to the validation period
The benefits of correcting precipitation‐temperature correlations in hydrological simulations are climate regime dependent
Large dams and reservoirs alter not only the natural flow regimes of streams and rivers but also their flooding cycles and flood magnitudes. Although the effect of dams and reservoirs has been ...reported for some vulnerable locations, the understanding of the inner‐basin variation with respect to the effects remains limited. In this study, we analyse the Three Gorges Dam (TGD) built on the Changjiang mainstream (Yangtze River) to investigate the dam effect variations in the system of interconnected water bodies located downstream. We investigated the effect of flow alterations along the downstream river network using discharge time series at different gauging stations. The river–lake interactions (referring to the interactions between the Changjiang mainstream and its tributary lakes i.e. the Dongting and Poyang lakes) and their roles in modifying the TGD effect intensity were also investigated in the large‐scale river–lake system. The results show that the water storage of the tributary lakes decreased after the activation of the TGD. Severe droughts occurred in the lakes, weakening their ability to recharge the Changjiang mainstream. As a consequence, the effect of the TGD on the Changjiang flow increase during the dry season diminished quickly downstream of the dam, whereas its impact on the flow decrease during the wet season gradually exacerbated along the mainstream, especially at sites located downstream of the lake outlets. Therefore, when assessing dam‐induced hydrological changes, special attention should be paid to the changes in the storage of tributary lakes and the associated effects in the mainstream. This is of high importance for managing the water resource trade‐offs between different water bodies in dam‐affected riverine systems.
The effect of the TGD on the flow of the Changjiang (Yangtze River) during the dry season diminished quickly along the mainstream, whereas its decreasing effect on the wet‐season Changjiang flow intensified gradually.
Periodic year‐round droughts of the tributary lakes were the primary driving force of the inner‐basin variations of the effects of the TGD.
The impounding of the TGD during the wet season played an important role in severe lake droughts.
Thousands of genes have been well demonstrated to play important roles in cancer progression. As genes do not function in isolation, they can be grouped into "networks" based on their interactions. ...In this study, we discover a network regulating Claudin-4 in gastric cancer. We observe that Claudin-4 is up-regulated in gastric cancer and is associated with poor prognosis. Claudin-4 reinforce proliferation, invasion, and EMT in AGS, HGC-27, and SGC-7901 cells, which could be reversed by miR-596 and miR-3620-3p. In addition, lncRNA-KRTAP5-AS1 and lncRNA-TUBB2A could act as competing endogenous RNAs to affect the function of Claudin-4. Our results suggest that non-coding RNAs play important roles in the regulatory network of Claudin-4. As such, non-coding RNAs should be considered as potential biomarkers and therapeutic targets against gastric cancer.Non-coding RNAs can modify the expression of proteins in cancer networks. Here the authors reveal a regulatory network in gastric cancer whereby claudin-4 expression is reduced by specific miRNAs, which are in turn bound by specific lncRNAs acting as competing endogenous RNAs (ceRNAs), resulting in increased claudin-4 expression.
Noble metal-based nanomaterials have shown promise as potential enzyme mimetics, but the facet effect and underlying molecular mechanisms are largely unknown. Herein, with a combined experimental and ...theoretical approach, we unveil that palladium (Pd) nanocrystals exhibit facet-dependent oxidase and peroxidase-like activities that endow them with excellent antibacterial properties via generation of reactive oxygen species. The antibacterial efficiency of Pd nanocrystals against Gram-positive bacteria is consistent with the extent of their enzyme-like activity, that is {100}-faceted Pd cubes with higher activities kill bacteria more effectively than {111}-faceted Pd octahedrons. Surprisingly, a reverse trend of antibacterial activity is observed against Gram-negative bacteria, with Pd octahedrons displaying stronger penetration into bacterial membranes than Pd nanocubes, thereby exerting higher antibacterial activity than the latter. Our findings provide a deeper understanding of facet-dependent enzyme-like activities and might advance the development of noble metal-based nanomaterials with both enhanced and targeted antibacterial activities.
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