Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins

Tendency of spherically imploding plasma liners formed by merging plasma jets to evolve toward spherical symmetry

Three dimensional hydrodynamic simulations have been performed using smoothed particle hydrodynamics (SPH) in order to study the effects of discrete jets on the processes of plasma liner formation, implosion on vacuum, and expansion. The pressure history of the inner portion of the liner was qualitatively and quantitatively similar from peak compression through the complete stagnation of the liner among simulation results from two one dimensional radiationhydrodynamic codes, 3D SPH with a uniform liner, and 3D SPH with 30 discrete plasma jets. Two dimensional slices of the pressure show that the discrete jet SPH case evolves towards a profile that is almost indistinguishable from the SPH case with a uniform liner, showing that non-uniformities due to discrete jets are smeared out by late stages of the implosion. Liner formation and implosion on vacuum was also shown to be robust to Rayleigh-Taylor instability growth. Interparticle mixing for a liner imploding on vacuum was investigated. The mixing rate was very small until after peak compression for the 30 jet simulation.Comment: 28 pages, 16 figures, submitted to Physics of Plasmas (2012

Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements-A case study in Chile

With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009-2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates

Undetermined states: how to find them and their applications

We investigate the undetermined sets consisting of two-level, multi-partite pure quantum states, whose reduced density matrices give absolutely no information of their original states. Two approached of finding these quantum states are proposed. One is to establish the relation between codewords of the stabilizer quantum error correction codes (SQECCs) and the undetermined states. The other is to study the local complementation rules of the graph states. As an application, the undetermined states can be exploited in the quantum secret sharing scheme. The security is guaranteed by their undetermineness.Comment: 6 pages, no figur

Design and optimization of a nanoprobe comprising amphiphilic chitosan colloids and Au-nanorods: Sensitive detection of human serum albumin in simulated urine

Metallic nanoparticles have been utilized as analytical tools to detect a wide range of organic analytes. In most reports, gold (Au)-based nanosensors have been modified with ligands to introduce selectivity towards a specific target molecule. However, in a recent study a new concept was presented where bare Au-nanorods on self-assembled carboxymethyl-hexanoyl chitosan (CHC) nanocarriers achieved sensitive and selective detection of human serum albumin (HSA) after manipulation of the solution pH. Here this concept was further advanced through optimization of the ratio between Au-nanorods and CHC nanocarriers to create a nanotechnology-based sensor (termed CHC-AuNR nanoprobe) with an outstanding lower detection limit (LDL) for HSA. The CHC-AuNR nanoprobe was evaluated in simulated urine solution and a LDL as low as 1.5 pM was achieved at an estimated AuNR/CHC ratio of 2. Elemental mapping and protein adsorption kinetics over three orders of magnitude in HSA concentration confirmed accumulation of HSA on the nanorods and revealed the adsorption to be completed within 15 min for all investigated concentrations. The results suggest that the CHC-AuNR nanoprobe has potential to be utilized for cost-effective detection of analytes in complex liquids

Holographic dark energy model with non-minimal coupling

We find that holographic dark energy model with non-minimally coupled scalar field gives rise to an accelerating universe by choosing Hubble scale as IR cutoff. We show viable range of a non-minimal coupling parameter in the framework of this model.Comment: 7 pages, no figure, corrected some typos, to be published in Europhys. Let

Cosmic holographic bounds with UV and IR cutoffs

We introduce the cosmic holographic bounds with two UV and IR cutoff scales, to deal with both the inflationary universe in the past and dark energy in the future. To describe quantum fluctuations of inflation on sub-horizon scales, we use the Bekenstein-Hawking energy bound. However, it is not justified that the D-bound is satisfied with the coarse-grained entropy. The Hubble bounds are introduced for classical fluctuations of inflation on super-horizon scales. It turns out that the Hubble entropy bound is satisfied with the entanglement entropy and the Hubble temperature bound leads to a condition for the slow-roll inflation. In order to describe the dark energy, we introduce the holographic energy density which is the one saturating the Bekenstein-Hawking energy bound for a weakly gravitating system. Here the UV (IR) cutoff is given by the Planck scale (future event horizon), respectively. As a result, we find the close connection between quantum and classical fluctuations of inflation, and dark energy.Comment: 15page

Grain Boundary Induced Magneto-Far Infrared Resonances in Superconducting YBa2_2Cu3_3O7−δ_{7-\delta } Thin Films

Spectral features induced by 45$^{\circ }$ in-plane misoriented grains have been observed in the far infrared magneto-transmission of YBa$_2$Cu$_3$O$_{7-\delta }$ thin films. Two strong dispersive features are found at 80 and 160 $cm^{-1}$ and a weaker one at 116 $cm^{-1}$. The data can be well represented by Lorentzian oscillator contributions to the conductivity. Several possible interpretations are discussed. We conclude that the resonances are due to vortex core excitations.Comment: Latex file (14 pages) + 4 Postscript figures, uuencode

Lack of Evidence for a Harmful Effect of Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitors on Fracture Risk among Type 2 Diabetes Patients: A Network and Cumulative Meta-Analysis of Randomized Controlled Trials

Aim To evaluate the comparative effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors on risk of bone fracture in patients with type 2 diabetes mellitus (T2DM). Methods PubMed, EMBASE, CENTRAL and ClinicalTrials.gov were systematically searched from inception to 27 January 2016 to identify randomized controlled trials (RCTs) reporting the outcome of fracture in patients with T2DM treated with SGLT2 inhibitors. Pairwise and network meta-analyses, as well as a cumulative meta-analysis, were performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Results A total of 38 eligible RCTs (10 canagliflozin, 15 dapagliflozin and 13 empagliflozin) involving 30 384 patients, with follow-ups ranging from 24 to 160 weeks, were included. The fracture event rates were 1.59% in the SGLT2 inhibitor groups and 1.56% in the control groups. The incidence of fracture events was similar among these three SGLT2 inhibitor groups. Compared with placebo, canagliflozin (OR 1.15; 95% CI 0.71-1.88), dapagliflozin (OR 0.68; 95% CI 0.37-1.25) and empagliflozin (OR 0.93; 95% CI 0.74-1.18) were not significantly associated with an increased risk of fracture. Our cumulative meta-analysis indicated the robustness of the null findings with regard to SGLT2 inhibitors. Conclusions Our meta-analysis based on available RCT data does not support the harmful effect of SGLT2 inhibitors on fractures, although future safety monitoring from RCTs and real-world data with detailed information on bone health is warranted