Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used. The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited. © 2007 Elsevier B.V. All rights reserved

Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map

This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.Parameter calibration and sensitivity analysis are usually not straightforward tasks for distributed hydrological models, owing to the complexity of model and large number of parameters. A two-step sensitivity analysis approach is proposed for analyzing the hydrological signatures based on the Distributed Hydrology-Soil-Vegetation Model in Jinhua River Basin, East China. A preliminary sensitivity analysis is conducted to obtain influential parameters via Analysis of Variance. These parameters are further analyzed through a variance-based global sensitivity analysis method to achieve robust rankings and parameter contributions. Parallel computing is designed to reduce computational burden. The results reveal that only a few parameters are significantly sensitive and the interactions between parameters could not be ignored. When analyzing hydrological signatures, it is found that water yield was simulated very well for most samples. Small and medium floods are simulated very well while slight underestimations happen to large floods.This work was supported by National Natural Science Foundation of China (91547106 and 51379183), Zhejiang Provincial Natural Science Foundation of China (LR14E090001), and National Key Research and Development Plan "Inter-governmental Cooperation in International Scientific and Technological Innovation"(2016YFE0122100)

Rapid identification of the medicinal plant Taraxacum formosanum and distinguishing of this plant from its adulterants by ribosomal DNA internal transcribed spacer (ITS) based DNA barcode

Original identification of medicinal plants is essential for quality control. In this study, the internal transcribed spacer 2 (ITS2) nuclear ribosomal DNA served as a DNA barcode and was amplified by allele-specific PCR. This approach was exploited to differentiate Taraxacum formosanum from five related adulterants. Using a set of designed PCR primers, a highly specific 223 bp PCR product of T. formosanum was successfully amplified by PCR. However, no similar DNA fragment was amplified from any of the other adulterants. This indicates that, our allele specific primers have high specificity and can accurately discriminate T. formosanum from its adulterant plants.Key words: Medicinal plant, polymerase chain reaction (PCR), authentication, Taraxacum formosanum, traditional Chinese medicinal, internal transcribed spacers 2 (ITS2)

Clinical and biochemical effects of a combination botanical product (ClearGuard™) for allergy: a pilot randomized double-blind placebo-controlled trial

<p>Abstract</p> <p>Background</p> <p>Botanical products are frequently used for treatment of nasal allergy. Three of these substances, <it>Cinnamomum zeylanicum</it>, <it>Malpighia glabra</it>, and <it>Bidens pilosa</it>, have been shown to have a number of anti-allergic properties <it>in-vitro</it>. The current study was conducted to determine the effects of these combined ingredients upon the nasal response to allergen challenge in patients with seasonal allergic rhinitis.</p> <p>Methods</p> <p>Twenty subjects were randomized to receive the combination botanical product, (CBP) 2 tablets three times a day, loratadine, 10 mg once a day in the morning, or placebo, using a randomized, double-blinded crossover design. Following 2 days of each treatment and during the third day of treatment, subjects underwent a nasal allergen challenge (NAC), in which nasal symptoms were assessed after each challenge dose and every 2 hours for 8 hours. Nasal lavage fluid was assessed for tryptase, prostaglandin D2, and leukotriene E4 concentrations and inflammatory cells.</p> <p>Results</p> <p>Loratadine significantly reduced the total nasal symptom score during the NAC compared with placebo (P = 0.04) while the CBP did not. During the 8 hour period following NAC, loratadine and the CBP both reduced NSS compared with placebo (P = 0.034 and P = 0.029, respectively). Analysis of nasal lavage fluid demonstrated that the CBP prevented the increase in prostaglandin D2 release following NAC, while neither loratadine nor placebo had this effect. None of the treatments significantly affected tryptase or leukotriene E4 release or inflammatory cell infiltration.</p> <p>Conclusion</p> <p>The CBP significantly reduced NSS during the 8 hours following NAC and marginally inhibited the release of prostaglandin D2 into nasal lavage fluid, suggesting potential clinical utility in patients with allergic rhinitis.</p

Structure, chemistry, and charge transfer resistance of the interface between Li7La3Zr2O12 electrolyte and LiCoO2 cathode

All-solid-state batteries promise significant safety and energy density advantages over liquid-electrolyte batteries. The interface between the cathode and the solid electrolyte is an important contributor to charge transfer resistance. Strong bonding of solid oxide electrolytes and cathodes requires sintering at elevated temperatures. Knowledge of the temperature dependence of the composition and charge transfer properties of this interface is important for determining the ideal sintering conditions. To understand the interfacial decomposition processes and their onset temperatures, model systems of LiCoO2 (LCO) thin films deposited on cubic Al-doped Li7La3Zr2O12 (LLZO) pellets were studied as a function of temperature using interface-sensitive techniques. X-ray photoelectron spectroscopy (XPS), secondary ion mass spectroscopy (SIMS), and energy-dispersive X-ray spectroscopy (EDS) data indicated significant cation interdiffusion and structural changes starting at temperatures as low as 300°C. La2Zr2O7 and Li2CO3 were identified as decomposition products after annealing at 500°C by synchrotron X-ray diffraction (XRD). X-ray absorption spectroscopy (XAS) results indicate the presence of also LaCoO3, in addition to La2Zr2O7 and Li2CO3. Based on electrochemical impedance spectroscopy, and depth profiling of the Li distribution upon potentiostatic hold experiments on symmetric LCO|LLZO|LCO cells, the interfaces exhibited significantly increased impedance, up to 8 times that of the as-deposited samples after annealing at 500°C. Our results indicate that lower-temperature processing conditions, shorter annealing time scales, and CO2-free environments are desirable for obtaining ceramic cathode-electrolyte interfaces that enable fast Li transfer and high capacity

GLAST: Understanding the High Energy Gamma-Ray Sky

We discuss the ability of the GLAST Large Area Telescope (LAT) to identify, resolve, and study the high energy gamma-ray sky. Compared to previous instruments the telescope will have greatly improved sensitivity and ability to localize gamma-ray point sources. The ability to resolve the location and identity of EGRET unidentified sources is described. We summarize the current knowledge of the high energy gamma-ray sky and discuss the astrophysics of known and some prospective classes of gamma-ray emitters. In addition, we also describe the potential of GLAST to resolve old puzzles and to discover new classes of sources.Comment: To appear in Cosmic Gamma Ray Sources, Kluwer ASSL Series, Edited by K.S. Cheng and G.E. Romer