ALBERT

Description: Capacitive deionization (CDI) is an effective desalination technique offering an appropriate route to obtain clean water. In order to obtain excellent CDI performance, a rationally designed structure of electrode materials has been an urgent need for CDI application. In this work, a novel graphene sponge (GS) was proposed as CDI electrode for the first time. The GS was fabricated via directly freeze-drying graphene oxide solution followed by annealing in nitrogen atmosphere. The morphology, structure and electrochemical performance of GS were characterized by scanning electron microscopy, Raman spectroscopy, nitrogen adsorption-desorption, X-ray photoelectron spectroscopy, cyclic voltammetry and electrochemical impedance spectroscopy. The electrosorption performance of GS in NaCl solution was studied and compared with pristine graphene (PG). The results show that due to the unique 3D interconnected porous structure, large accessible surface area and low charge transfer resistance, GS electrode exhibits an ultrahigh electrosorption capacity of 14.9 mg g−1 when the initial NaCl concentration is ~500 mg L−1, which is about 3.2 times of that of PG (4.64 mg g−1), and to our knowledge, it should be the highest value reported for graphene electrodes in similar experimental conditions by now. These results indicate that GS should be a promising candidate for CDI electrode. Scientific Reports 5 doi: 10.1038/srep08458

Description: Soil moisture is a significant state variable in flood forecasting. Nowadays more and more satellite soil moisture products are available, yet their usage in the operational hydrology is still limited. This is because the soil moisture state variables in most operational hydrological models (mostly conceptual models) are over-simplified – resulting in poor compatibility with the satellite soil moisture observations. A case study is provided to discuss this in more detail, with the adoption of the XAJ model and the Soil Moisture and Ocean Salinity (SMOS) level-3 soil moisture observation to illustrate the relevant issues. It is found that there are three distinct deficiencies existed in the XAJ model that could cause the mismatch issues with the SMOS soil moisture observation: i) it is based on runoff generation via the field capacity excess mechanism (interestingly, such a runoff mechanism is called the saturation excess in XAJ while in fact it is clearly a misnomer); ii) evaporation occurs at the potential rate in its upper soil layer until the water storage in the upper layer is exhausted, and then the evapotranspiration process from the lower layers will commence – leading to an abrupt soil water depletion in the upper soil layer; iii) it uses the multi-bucket concept at each soil layer - hence the model has varied soil layers. Therefore, it is a huge challenge to make an operational hydrological model compatible with the satellite soil moisture data. The paper argues that this is possible and some new ideas have been explored and discussed.

Description: Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

Description: Radar-based estimates of rainfall are affected by many sources of uncertainties which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements, but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (〉4 m/s). The multivariate distributed ensemble generator (MDEG) is introduced and established for each subsample. 30 typical events (10 at each wind range) are selected to explore the behaviors of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated and the values of 5 th to 95 th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behavior of the uncertainty band. Based on these evidences, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data. This article is protected by copyright. All rights reserved.

Description: Hydrological models play a significant role in modeling river flow for decision making support in water resource management. In the past decades, many researchers have made a great deal of efforts in calibrating and validating various models, with each study being focused on one or two models. As a result, there is a lack of comparative analysis on the performance of those models to guide hydrologists to choose appropriate models for the individual climate and physical conditions. This paper describes a two-level meta-analysis to develop a matching system between catchment complexity (based on catchment significant features CSFs) and model types. The intention is to use the available CSFs information for choosing the most suitable model type for a given catchment. In this study, the CSFs include the elements of climate, soil type, land cover and catchment scale. Specific choices of model types in small and medium catchments are further explored with all CSFs information obtained. In particular, it is interesting to find that semi-distributed models are the most suitable model type for catchments with the area over 3000 km 2 , regardless of other CSFs. The potential methodology for expanding the matching system between catchment complexity and model complexity is discussed. This article is protected by copyright. All rights reserved.

Description: Author(s): Gengyan Chen, Yi-Cong Yu, Xiao-Lu Zhuo, Yong-Gang Huang, Haoxiang Jiang, Jing-Feng Liu, Chong-Jun Jin, and Xue-Hua Wang We develop a local coupling theory to simultaneously treat the weak and strong interaction between a quantum emitter and photons in arbitrary nanostructures. The local coupling strength proportional to the projected local density of states (PLDOS) for photons is determined by a flexible and efficien... [Phys. Rev. B 87, 195138] Published Tue May 28, 2013

Description: Journal of Chemical & Engineering Data DOI: 10.1021/acs.jced.7b00831

Description: Satellite-based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimise the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in-situ measurements. This study attempts to build the first error distribution model with additional higher order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using SMOS (the Soil Moisture and Ocean Salinity) satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling which is more relevant to the intended data use than the in-situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General Extreme Value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. This article is protected by copyright. All rights reserved.