•Evaluate uncertainties of hydrological parameters in WRF-Hydro simulations.•The pedo-transfer function (PTF) based ensemble is used to estimate 3D SHPs.•The 3D soil hydraulic parameters (SHP) are ...important as scaling parameters.
Parameter calibration and uncertainty estimation are crucial for hydrological simulations in the distributed land surface-hydrological model. To investigate soil properties impacting hydrological processes, five conventional pedo-transfer functions (PTFs) are applied to create a 3D soil hydraulic parameter (SHP) ensemble in the Weather Research and Forecasting-Hydrological extension (WRF-Hydro), a distributed, multi-physics land surface hydrological model. The SHPs are generated, based on a high-resolution Chinese soil property dataset, over the heterogeneous Upper Huaihe River basin. The results show that the SHPs can influence the streamflow in WRF-Hydro, which is similar to the impact of the scaling parameters on the streamflow over the study basin. Analyses of the uncertainty in the SHP ensemble reveal that SHPs mainly constrain the peak flow during the flood rise and impact the baseflow during the flood recession. A hydrological Bayesian model average (BMA) method is constructed to postprocess the streamflow ensemble based on the 3D SHPs. Probabilistic streamflow estimations by the BMA method are more skillful than the simulations using the individual 3D SHP ensemble members for all five studied hydrological stations, especially for high flows. Therefore, improved estimation of the uncertainty in the 3D SHPs may enhance the spatial representation of flood processes, resulting in more accurate estimates of the streamflow in the main streams in a heterogeneous basin.
Observations have demonstrated an unstable interannual relationship between El Niño/Southern Oscillation (ENSO) and the East Asian winter monsoon in recent decades due to internal variability. ...However, it remains unclear whether the interannual ENSO‐East Asian winter monsoon relationship (EER) may change with long‐term external forcings, especially when they vary substantially. The results of a set of transient simulations during the last 21,000 years demonstrate the EER is enhanced during the Last Glacial Maximum relative to the Holocene, characterized by a poleward migration of ENSO's signal over the northwest Pacific. The strengthened EER results from the weakened interannual ENSO‐Aleutian Low teleconnection and the northeastward displacement of the climatological Aleutian Low, ultimately forced by the massive North American ice sheets at that time. Our study suggests a changing EER with various external forcings and implies potential uncertainty in applying the present‐day ENSO teleconnections during glacials.
Plain Language Summary
The East Asian winter monsoon (EAWM) exerts significant impacts on the climate over Asia and affects billions of people; thus, a precise EAWM prediction is urgently required. Since the El Niño/South Oscillation (ENSO) provides the majority of skillful predictability, the effect of ENSO on EAWM has received considerable attention. Although recent studies noticed that the ENSO‐EAWM relationship (EER) was unstable during recent decades due to the oceanic oscillations, such as the Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation, it remains unclear whether the EER could change with substantially varied external forcings in the future, such as solar insolation, greenhouse gases, meltwater flux, and ice sheets. Using a set of transient simulations during the last 21,000 years, we found that the EER is significantly strengthened during the Last Glacial Maximum and early deglaciation relative to the Holocene, attributed to the vast North American ice sheets at that time. Therefore, this study will not only have great significance for the paleoclimate research, which usually applied the present‐day ENSO teleconnection to explain past changes in regional climate directly, but also help to project the future EER variation for a better EAWM prediction.
Key Points
The ENSO‐East Asian winter monsoon relationship (EER) is significantly strengthened during the Last Glacial Maximum and early deglaciation
The strengthened EER is characterized by the poleward shift of ENSO's influence over the northwest Pacific
Large North American ice sheets are the ultimate driver of the strengthened EER at that time
This paper proposes a finite-time consensus control algorithm based on nonlinear integral sliding-mode control for second-order multi-agent systems (MASs) with mismatched and matched disturbances. ...Firstly, a nonlinear finite-time disturbance observer is established to estimate the states and mismatched disturbances of the agent. Secondly, a dynamic integral sliding-mode (ISM) surface is designed by employing the estimates of mismatched disturbances. Then, based on the designed ISM and disturbance observer, the discontinuous or continuous campsite control protocols are respectively developed to guarantee the consensus for MASs in finite time with active anti-disturbance control. Finally, numerical simulation results illustrate the effectiveness of the proposed consensus control algorithm.
Objective: To observe the effects of Grifola frondosa polysaccharides (GFP) on proliferation, apoptosis, cell cycle, expression of cyclin and apoptotic proteins of hepatocellular carcinoma HepG2 ...cells of human, and to explore the mechanism of its inhibition on proliferation of HepG2 cells. Methods: The experiment was divided into PGF/control group. Different concentrations of PGF solution were used to interfere with human hepatocellular carcinoma cells HepG2 in vitro. MTT assay was used to detect the effects of different concentrations of PGF on the survival of HepG2 cells. The apoptosis rate and cell cycle distribution of HepG2 cells induced by PGF were detected by flow cytometry. The expressions of Bcl-2, Bax and Caspase3, Cyclin-A1 and Cyclin-B1 were detected by immunohistochemistry. Results: Compared with the control group, PGF could effectively decreased proliferation of human hepatoma HepG2 in a concentration-dependent manner. Cell cycle detection showed that the proportion of S phase in each group was 24.71%, 28.78%, 36.26 and 42.39%, respectively, indicating that cells were blocked in S phase. The immunocytochemical results showed that the expression of Cyclin A1 protein decreased significantly, and the expression of cyclin-B1 was not significantly different before and after treatment. Flow cytometry showed that the apoptosis rates in control group and PGF group were 0, 18.0%, 30.5% and 49.5%, respectively. The difference between PGF group and control group was significant. Immunocytochemical results showed that PGF could significantly inhibit the expression of mitochondrial apoptosis inhibitor Bcl-2, increase the expression of pro-apoptotic factor Bax in a concentration-dependent manner, and up-regulate the percentage of Bcl-2/Bax to induce apoptosis of hepatocellular carcinoma cells. Conclusion: PGF can inhibit the proliferation of human hepatoma HepG2 cells by inducing apoptosis, inhibiting the proliferation of cancer cells, blocking cell cycle, inhibiting the expression of Bcl-2, and upregulating the expression of Bax and Caspase3.
•Three precipitation products are assessed using the Bayesian uncertainty analysis.•Precipitation uncertainty and model errors are modeled jointly with a new error model.•Using multi-satellite ...precipitation ensemble with BMA improves predictive performance.
Global satellite–gauge merged precipitation (SGMP) products combine the advantages of satellite precipitation estimates with rain gauge data, providing great potential to hydrological applications. However, the inaccuracies of the precipitation products together with hydrologic model limitations, could cause great uncertainty in streamflow predictions. Therefore, this study investigates the hydrological value of three mainstream global SGMP products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7 product, the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD), the Global Satellite Mapping of Precipitation (GSMaP) Gauge-calibrated product (GSMaP Gauge). They are used as the precipitation input of the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin in China. To better quantify their effects on parameter calibration and streamflow predictions, a newly developed residual error model accompanied with the Bayesian uncertainty analysis are performed. CMORPH satellite-gauge merged precipitation product, recently developed by the China Meteorological Administration (CMA) (CMORPH CMA), is a high-quality regional precipitation product. Thus, this study applies the CMORPH CMA within the same framework to provide a benchmark. The results show that the parameter uncertainty are influenced significantly by the input of various precipitation products. There is a tradeoff between the deterministic streamflow performance and the probabilistic predictive performance for selecting the best input among the three global precipitation products. The streamflow uncertainty intervals of the three global precipitation products are then merged using the Bayesian Model Averaging (BMA) method. The BMA results show satisfying hydrological performance in terms of deterministic streamflow predictions, with the largest Nash-Sutcliffe coefficient of Efficiency (NSCE) values of 0.86 and 0.64, and the smallest absolute relative error (RE) values of 0% and 10.2% in the calibration and validation periods, respectively. In addition, the BMA results also produce much more reliable probabilistic predictions, which even outperform the outcomes of the high-quality CMORPH CMA. Our study demonstrates the potential uncertainty of various SGMP products for model calibration and streamflow predictions. The hydrologic ensemble using multiple global SGMP products provides a promising and advantageous approach to support water management and decision making, especially in ungauged basins.
Carbon emission reduction is becoming a global issue. Methods of reducing carbon emissions in developing countries have become a hot topic of discussion. Based on the obvious structural ...transformation in developing countries, this paper discusses the logical mechanisms among industrial structure upgrading, green total factor productivity improvements, and carbon emission reduction. In addition, this paper empirically tests these relationships with provincial data from 2000 to 2017 in China. The conclusions are as follows: (1) industrial structure upgrades have a significant impact on carbon emissions. The industrial structure rationalization remains a noteworthy inhibition on carbon emissions. The industrial structure’s advancement has obvious features of development at the current stage, and its effect on carbon emissions shows an inverted “V” trend, which is initially accelerating but then restraining. (2) Upgrades to industrial structures will decrease carbon emissions by raising green total factor productivity. (3) The rise of green total factor productivity in a certain region will have a relatively obvious inhibitory effect on carbon emissions, but it will exhibit a negative spatial spillover effect on the adjacent areas.
A distributed control scheme based on historical information is designed to solve the problem of stable control of multi‐agent systems under denial of service (DoS) attacks in this article. It ...achieves the control objective of bipartite output containment control, that is, the output states of the followers smoothly enter the target area. The control scheme updates the states of followers through historical information in the control protocol when agents are subjected to DoS attacks. A distributed state observer with a storage module is designed to efficiently estimate the state of followers and store the observed information as history information. The historical information of control protocol calls is not necessarily the real state information in the existence of DoS attacks. Consequently, a closed‐loop feedback state compensator is designed. Then, the state compensator is converted from the time domain to the frequency domain for stability analysis using the Nyquist criterion. It is obtained that an upper bound on the amount of historical information can achieve the bipartite output trajectories containment of the controlled system. The output trajectories of the followers converge into two dynamic convex hulls, one of which is surrounded by multiple leaders, and the other is a convex hull with opposite signs of the leaders. Finally, a numerical simulation is used to verify the proposed control scheme, and the operability of the scheme is further demonstrated in a physical experiment.
•A dynamic Bayesian framework is proposed to merge 6-h multi-model soil moisture.•The dynamic Bayesian model averaging (BMA) method proves to be adaptive and robust.•Selecting a subset of soil ...moisture model products as the BMA members is optimal.•The dynamic BMA framework can be used for drought monitoring and prediction.
Accurate estimation of soil moisture (SM) from satellite products and model simulations at sub-daily timescale remains a challenge. This study proposes a general dynamic Bayesian model averaging (BMA) framework for merging sub-daily model products. Compared to the traditional BMA method, this study introduces adaptive weights (dynamically variant with time) for BMA members. Based on the previous evaluation work, a subset of model products is selected from eight model products as BMA members. The dynamic BMA experiment is performed for the surface SM (0–10 cm) model products at sub-daily (6-h) timescale in 2017 over the Yangtze-Huaihe river basin. The results are compared with the automatic SM observations (ASMOs) with unprecedented high spatial and temporal resolution (up to 7 stations within a 104 km2 pixel; hourly). Because weather pattern and model performance change over time, the determination of an optimal training period is critical to obtain adaptive BMA weights for rapid weather regime changes. The sensitivity of training length (days) is then examined, and the optimum data length used in the BMA training period proves to be about 80 days. With deterministic and probabilistic verification metrics, the dynamic BMA estimated SM is comprehensively evaluated against the ASMOs, eight global model products, and the CMA’s (China Meteorological Administration) regional Land Data Assimilation System (CLDAS) product. To better compare the probability distribution of different products, the cumulative distribution function (CDF) consistency histogram and a more objective metric consistency deviation (CD) are proposed to diagnose the consistency of two SM CDFs (e.g., the BMA estimated and the observed CDF). In terms of both the deterministic (the Kling-Gupta efficiency, correlation, system bias, and bias adjusted root-mean square error) and probabilistic verification methods (CD, QQ-plots, and reliability), the dynamic BMA estimated SM outperforms any BMA members and even the benchmark product CLDAS. This study demonstrates that the dynamic BMA framework provides a new solution for merging SM model products. The merged SM and the BMA combined probability distribution can be further used for drought monitoring and prediction.
The cooperative motion of autonomous vehicles (AVs), which communicate through wireless local area networks (WLANs), has been implemented using various technologies, e.g., the Internet of things ...(IoT). However, there are several insecure disturbances that occur during the communication of intelligent entities. These disturbances can compromise the cooperative control of AVs. In this paper, we propose a cooperative protocol for the group motion in IoT that provides anti-disturbance protection for AVs. More specifically, we construct group motion models of AVs (GMM-AVs) with mixed disturbances. The disturbances are derived from matched and mismatched disturbances, where the former (the latter) denotes that the disturbances and the control inputs exist in the same (different) tunnels. We design disturbances sensors to detect the loss of GMM-AVs and propose anti-disturbance measures to compensate for the loss. Subsequently, we establish a distributed cooperative control protocol (DCCP-CP) that provides countermeasure protection by analyzing the group cooperation of AVs. We also achieve distributed consensus of AVs with multiple disturbances by studying the dynamic characterization of GMM-AVs. DCCP-CP may achieve cooperative group motion for AVs under the scenario of various disturbances. Finally, the simulations results verify the validity of GMM-AVs and DCCP-CP.
The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots ...system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite‐time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single‐integrator model and double‐integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite‐time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite‐time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.
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