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Purpose: The Low-Energy X-ray telescope (LE) is a main instrument of the Insight-HXMT mission and consists of 96 Swept Charge Devices (SCD) covering the 1-10 keV energy band. The energy gain and resolution are continuously calibrated by analysing Cassiopeia A (Cas A) and blank sky data, while the effective areas are also calibrated with the observations of the Crab Nebula. In this paper, we present the evolution of the in-orbit performances of LE in the first 5 years since launch. Methods: The Insight-HXMT Data Analysis Software package (HXMTDAS) is utilized to extract the spectra of Cas A, blank sky, and Crab Nebula using different Good Time Interval (GTI) selections. We fit a model with a power-law continuum and several Gaussian lines to different ranges of Cas A and blank sky spectra to get peak energies of their lines through xspec. After updating the energy gain calibration in CALibration DataBase (CALDB), we rerun the Cas A data to obtain the energy resolution. An empirical function is used to modify the simulated effective areas so that the background-subtracted spectrum of the Crab Nebula can best match the standard model of the Crab Nebula. Results: The energy gain, resolution, and effective areas are calibrated every month. The corresponding calibration results are duly updated in CALDB, which can be downloaded and used for the analysis of Insight-HXMT data. Simultaneous observations with NuSTAR and NICER can also be used to verify our derived results. Conclusion: LE is a well calibrated X-ray telescope working in 1-10 keV band. The uncertainty of LE gain is less than 20 eV in 2-9 keV band and the uncertainty of LE resolution is less than 15eV. The systematic errors of LE, compared to the model of the Crab Nebula, are lower than 1.5% in 1-10 keV., 20 pages, 10 figures, submitted

This paper takes B2C platform enterprises as the main subject of evaluation and considers the construction of e-supply chains among complementary roles of the platform as the research object. It considers distributors-retailers-consumers as each ring of e-supply chains and uses supply chain flexibility, supply chain performance, supply chain risk, supply chain credit, and supply chain competitiveness as indicators to comprehensively evaluate multiple e-supply chains on the same type of platform enterprises. Innovations: (a) It focuses on the micro-environment of the e-supply chain where the platform enterprises and complementary players are located. (b) Combining the entropy method “from cause to effect” and the fuzzy comprehensive evaluation method “from cause to effect,” combining objective and subjective empowerment and dividing the expert interviewees into roles in the supply chain. (c) Unlike the traditional evaluation mechanism, where the analysis target is independent of the supply chain, this paper focuses on analyzing the overall and comprehensive performance of the e-supply chain built by complementary platform roles in various dimensions.

Climate change impacts on low streamflows provide a comprehensive picture of the state of surface and groundwater resources, particularly in arid and semi-arid regions. The objective of this study was to assess climate change impacts on low streamflow variations by detecting long-term spatio-temporal changes in climatic variables of rainfall and temperature, and their associations with low streamflow fluctuations. Seasonal variations in low streamflows (summer and winter) were examined at 18 hydrometric stations located in the Namak Lake basin, Iran, over 1970-2015, using the modified Mann-Kendall and Sen’s slope estimator methods. Seasonal low streamflow demonstrated a significant diminishing trend (more than 55% of the stations), while summer low streamflow showed a more drastic decreasing trend. Long-term changes in seasonal and annual rainfall/temperature also revealed a dominant decreasing trend in winter and spring rainfall (82% and 58% of all stations, respectively) and a dominant increasing trend in all temperature time scales (90% of all stations). The effects of climate variations on low streamflow were quantified by the Spearman’s rank correlation and Cross-SampEn methods. The results revealed that winter rainfall, and annual and summer temperatures have the strongest association with seasonal low streamflows, especially according to the entropy method. Although the relationships between low streamflows and temperature/rainfall are related to the different processes generating streamflows, particularly in heterogeneous locations, the results show that rainfall has a stronger influence on low streamflows in the study region than temperature does. The research findings indicate low streamflows are more nonlinearly related to climatic parameters, and Cross-SampEn robustness reflects degree of asynchrony for complex and non-stationary time series.

Exposed surface defect-enriched Co3O4 catalysts derived from metal organic framework (MOF) were fabricated by the promotion of surface Mn species for toluene oxidation. The incorporation of Mn species into Co3O4 surface lattice could give rise to the local lattice distortion in spinel structure, resulting in highly exposed surface defect rather than bulk defect. More Co3+ species were also exposed on the surface of MnOx/Co3O4 samples owing to the electron transfer from Co to Mn species by the occupation of surface Mn in octahedral Co3+ sites. Accordingly, the low-temperature reducibility and high mobility of lattice oxygen were significantly improved in virtue of the highly exposed surface defect and predominately surface Co3+ sites, thus promoting the catalytic activity and stability for toluene oxidation. Moreover, the toluene conversion decreased with the increase of weight hourly space velocity (WHSV). In situ DRIFTS results confirmed the continuous oxidation process for toluene degradation, and the conversion of benzoate into maleic anhydride should be the rate-controlling step.

A novel extended cohesive damage element method is used to develop a numerical snowpack model to study the fundamental damage mechanisms of snowpack under external drivers and to investigate multiple mixed mode damage propagation within snowpack. A new mixed mode damage criterion is introduced to account for tensile and shear fractures as well as compressive crushing together with shear crack for approximating the mixed mode damage initiation and propagation in the weak layer in snowpack. A propagation saw test (PST) is considered to understand basic damage involution in snowpack under self-weight related bending. The nonlinear fracture modelling prediction agrees with the PST sample well. This paper provides an alternative approach as a predictive method using the extended cohesive damage element for potentially forecasting slab avalanches in snow terrain according to weather forecast and planned human activities in the future.

Texts are the result of engravings in different social contexts, and they are culturally activated in different ways. The image of wuxia is formed by various accompanying texts sharing and participating in constructing a collection of wuxia narratives. By means of text-naming methods, authors, serialization of novels, film and television adaptations, criticism, and so forth, the image of wuxia operates in a continuous cycle that flows throughout texts that are gradually accumulated in the literary corpus. On this basis, it condenses and expresses a series of ideological discourses centred on topics such as gender and nation. These accompanying texts have diverse identities and play multiple roles in the cause of wuxia coming to be viewed as public heroes. They not only supply us with frameworks and positioning of wuxia novels but also provide cultural observations of society so as to participate in the construction of wuxia myths.

Catalyst support is crucial to modulating and immobilizing the active metal phases and the catalytic performance can be optimized by the rational design of support. Herein, the hierarchical alumina microspheres with abundant coordinatively unsaturated pentacoordinated Al3+ (Al3+penta) sites were constructed by a facile solvothermal method and then employed as the support materials to anchor Pd species. XPS and NMR results revealed that Pd species were prone to the adsorption, dispersion and stabilization over Al3+penta sites by strong interaction in the form of Pd4+ species. During the catalytic oxidation process, the crystal structure and morphology of Pd/Al2O3 catalysts remained stable, but the surficial lattice oxygen and cationic Pd4+ species presented a distinct increase. In comparison with the fresh catalyst, the catalytic performance of the used Pd/Al2O3 catalysts exhibited a significant improvement and still kept unchanged even after the multiple consecutive catalytic process. The used Pd/Al2O3 catalyst also possessed the excellent water tolerance and almost 1000 ppm toluene at a weight hourly space velocity of 60 000 mL gcat−1 h−1 could be converted into nontoxic CO2 and H2O over the catalyst surface.

Internationalisation facilitated through ‘sister’ relationships establishes transnational, cooperative partnerships and cultural exchanges between institutions within and between geographic locatio...

We explore post-processing methods that can reduce biases in simulated flow in a hydrological model (HYMOD). Here, three bias-correction methods are compared using a set of calibrated parameters as a baseline (Cases 1 and 5). The proposed bias-correction methods are based on a flow duration curve (Case 2), an autoregressive model based on residuals obtained from simulated flows (Case 3), and a rating curve (Case 4). A clear seasonality representing a more substantial variability in winter than summer was evident in all cases. The extended range of residuals was usually observed in winter, indicating that the HYMOD may not reproduce high flows appropriately. This study confirmed that bias-corrected flows are more effective than the baseline model in terms of correcting a systematic error in the simulated flow. Moreover, a comparison of root mean square error over different flow regimes demonstrates that Case 3 is the most effective at correcting systematic biases over the entire flow regime. Finally, monthly water balances for all cases are evaluated and compared during both calibration and validation periods. The water balance in Case 3 is also closer to the observed values. The effects of different post-processing approaches on the performance of bias-correction are examined and discussed. HIGHLIGHTS We explore three bias-correction methods that can reduce biases in simulated flow.; This is the first attempt to combine calibration and bias correction in the runoff simulation.; This study confirmed that bias correction approaches are more effective in terms of correcting a systematic error.; The monthly water balance estimated from the proposed approach was close to that of the observed value.

Water quality monitoring is essential to understanding the complex dynamics of water ecosystems, the impact of human infrastructure on them and to ensure the safe use of water resources for drinking, recreation and transport. High frequency in-situ monitoring systems are being increasingly employed in water quality monitoring schemes due to their much finer temporal measurement scales possible and reduced cost associated with manual sampling, manpower and time needed to process results compared to traditional grab-sampling.Modelling water quality data at higher frequency reduces uncertainty and allows for the capture of transient events, although due to potential constraints of data storage, inducement of noise, and power conservation it is worthwhile not using an excessively high sampling frequency.This study explores the issue of frequency optimisation of water quality monitoring schemes by applying three different statistical approaches for determining the optimum sampling frequency.The proposed approaches are tested utilising a high frequency dataset built from recording continuous physical and chemical water quality parameters (temperature, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), turbidity and conductivity) with multiparameter sondes at 3 sites in Bristol’s Floating Harbour.As a result, this analysis provides practical tools to understand how different sampling frequencies are representative of the water quality changes. Furthermore, it helps determine the minimum frequency required to communicate periodic fluctuations in water quality and investigate the additional benefit of recording data at a frequency higher than the minimum required.

Early hydrological hazard warning demands precise weather forecasts to accurately predict the timing and the location of intense precipitation events which can cause severe floods/landslides and present risks to urban and natural environments. Extrapolation of precipitation by radar rainfall products at high space and time scales with short lead times outperforms forecasts of numerical weather prediction. Therefore, developing and improving of rainfall nowcasts systems are essential. Rainfall nowcasting is the process of forecasting precipitation field movement and evolution at high spatial and temporal resolutions with short lead times( Keywords: radar rainfall nowcasting; deep learning; optical flow; extrapolation; rainnet; rainymotion

In recent decades, WRF has been widely used in regional rainfall simulations, and many studies have shown it has good performance in reproducing rainfall distribution. However, the WRF simulated rainfall amounts are often significantly underestimated which might be due to insufficient consideration of aerosol-cloud-precipitation-meteorology interaction in its mdoelling. WRF-chem as a meteorology-chemistry coupling model is expected to improve such a shortcoming. In this study, we carry out a series of WRF and WRF-chem simulations of a large-scale extreme rainfall event (occurred from October 11th to 15th, 2018) over the UK to explore whether different aerosol effects could help improve the rainfall simulation performance. To compare and evaluate the influences of different aerosol effects, four types of simulations using WRF and WRF-chem were conducted. The baseline simulation (called WF_B) was simulated by WRF without any emission data and chemical boundary conditions. The sensitivity simulation (WC_NE) was simulated by WRF-chem with emission data and chemical boundary conditions as well as used a chemical mechanism. But it turned off aerosol direct and indirect effects. The other two sensitivity simulations WC_DE and WC_DAIE were conducted by turning on the direct aerosol effect and turning on all (direct and indirect) aerosol effects, respectively. All simulations used the same domain configurations, physical schemes, and meteorological boundary conditions. Through comparing the difference between the four simulated rainfall distributions and amounts, the impact of aerosol direct effect, indirect effect, and net (direct + indirect) effect on extreme rainfall simulation were estimated. The simulation results were compared with UK radar observations. The sensitivity study shows that the rainfall intensity performance greatly improved with the inclusion of the aerosol-cloud interaction in the modelling (indirect effect). However, aerosol-radiation feedback (direct effect) does not have a significant impact on rainfall intensity estimations. One of the reasons was because the aerosol indirect effect has a great influence on droplet/particle concentration, precipitation efficiency and cloud life in nature. Statistics show that there are 115 grids in radar observation with rainfall greater than 100 mm, while WF_B, WC_NE, WC_DE and WC_DAIE simulations have respectively 44, 44, 44 and 117 grids with rainfall greater than 100 mm. In addition, the Root Mean Square Error of WF_B, WC_NE, WC_DE and WC_DAIE accumulated rainfall is 2.501, 2.501, 2.484 and 0.779 respectively. On the other hand, the rainfall spatial performances of the four simulations are relatively close, which were not improved obviously with the inclusion of aerosol effects. Their probability of detection (POD), frequency bias index (FBI), critical success index (CSI), and false alarm ratio (FAR) performances were averaged at 0.941, 0.946, 0.936, and 0.006, respectively. Finally, using the chemical mechanism and chemical data but turning off aerosol effects resulted in similar rainfall estimations of WRF-chem and original WRF. In summary, it is highly recommended to turn on WRF aerosol effects, especially the indirect aerosol effects in extreme rainfall simulations.

In the twenty-first century, extreme weather events leading to flooding and heat waves, have become one of the most severe challenges in urban areas, especially under the circumstances of local climate change and rapid urbanisation. In the future, cities are going to encounter more severe natural disaster risks and understanding how these could combine with modification of the urban environment (for example through adoption of green infrastructure) is critical for decisions relating to mitigation and adaptation to climate change. Green infrastructure is a subset of resilient infrastructure, which may mitigate the adverse effects caused by extreme weather and contribute to regulating urban climate. In addition, high-performing green spaces bring additional benefits for society in terms of health and wellbeing. The Weather Research and Forecasting (WRF) model is a numerical weather prediction system supporting both atmospheric research and operational forecasting. Within this modelling system, there is the possibility to modify parameters according to various urban areas within the WRF-Urban configuration. In this study, Newcastle upon Tyne (a UK city with the benefit of a lot of observational sensor data) is selected as an initial target city for identifying the optimal WRF configuration by varying the model resolution, domain size and nesting strategy. Future work will explore the influence of implementing green infrastructure in the context of climate change and urbanisation, then extending this analysis to London.

Flood events are expected to become increasingly common with the global increases in weather extremes. The present state of the technologies for flood risk mapping is typically tested on small geographical regions due to limitation of flood inundation observations, which hinders the implementation of flood risk management activities. Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of weather conditions and the time of day or night. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time of global, operational SAR data freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits all available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions.

Hydrological models serve as useful tools to describe current conditions and to predict future conditions in a catchment. However, the errors from input data including precipitation and potential evapotranspiration (PET) and model parameterization can lead to huge uncertainties on the model outputs. Although it is challenging to quantify the effect from individual sections due to the high non-linearity of hydrological processes, the potential compensations among different inputs and model parameters may provide valuable insights into the input data correction and the model calibration. In this study, we aim to improve the understanding of the adaptation mechanisms between model parameters and the quality of inputs during the hydrological simulation. The objectives are to investigate: (1) the most effective metrics needed to characterize the hydrological applicability of input sources; (2) the hydrological model adaptivity to input sources of varied quality; (3) the compensating interaction of different inputs on the hydrological modelling. We demonstrate our approach to the widely used conceptual Xin’anjiang (XAJ) hydrological model. Rainfall estimations from multiple sources are collected for a headwater catchment in the Southern United States and the Brue catchment in Southwest England, from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting (WRF) model dynamic downscaling. Results suggest that: (1) The total water balance is a poor indicator of rainfall data quality for hydrological simulations. Instead, the event-based water balance shows a stronger influence on representing the differences in hydrological applicability, especially for heavy storm events; (2) A high compensation relation exists among the quality of rainfall data, the model’s initial soil moisture state, and water balance-related parameters in the XAJ model, allowing the poor WRF rainfall datasets to generate good streamflow simulations; (3) A new hydrological proxy (term), called Compensating Interaction Angle (CIA) between different inputs is diagnosed to quantitatively measure the trade-off between their quality in producing satisfactory hydrological performance. The proposed CIA is recommended to apply in other regions and hydrological models to validate if a general pattern exists and how it varies regionally.

Flood estimation in ungauged basins is important for flood design, and for improving our understanding of the sensitivity of flood magnitude to changes in climate and land cover. Flood estimates by current methods (e.g. statistical regression, unit hydrograph) have high uncertainty, even in places with dense observing networks (e.g. +/- 50-100% in the UK). Reductions in this uncertainty are being sought by using alternative methods, such as continuous simulation using hydrological models (spatially-distributed or lumped), and event-scale derived distribution approaches. The very significant challenges for reliable application of continuous simulation models in ungauged catchments are well described in the literature.The research reported here is part of a larger project to estimate the probability distribution of flood peak magnitude in ungauged catchments, using an event-based derived distribution method. The derived distribution approach at the event scale typically combines the following elements: a stochastic rainfall model, an event-scale rainfall-runoff model (usually with “losses” and a “baseflow” component), and a runoff routing model. In principle, every element of this approach may be considered as a (seasonally varying) random variable, though previous research has typically considered only the rainfall as stochastic. The flood peak distribution is obtained by integrating over joint distributions of the model elements.One of the novel aspects of the proposed approach is that, in place of an explicit routing method, we estimate the flood peak magnitude as the ratio of the event runoff depth (mm) to a characteristic timescale of the hydrograph (hours). The event runoff depth is the product of rainfall depth and event runoff coefficient, which in turn depends on both antecedent conditions and event rainfall. The characteristic timescale of the hydrograph is a second temporal moment (temporal “width” of the hydrograph). Although a comprehensive theory exists for space-time influences on this hydrograph time scale, research to date suggests that it depends, to first order, on time scales associated with rainfall and catchment response.Here we report on extensive (many events, many catchments) testing in the UK of (i) whether the temporal standard deviation of the flow hydrograph is a good choice for the characteristic time scale of the hydrograph in the context of predicting the flood peak (Viglione et al 2010, Journal of Hydrology) (ii) whether the temporal standard deviation of the hydrograph can be predicted from time scales associated with rainfall and catchment response, as proposed by Woods and Sivapalan (1999, Water Resources Research) and Gaál et al (2012, Water Resources Research).

In this study, a comparative analysis of three satellite precipitation products including the Tropical Rainfall Measuring Mission (TRMM-3B43 V7), the Precipitation Estimation from Remotely Sensed I...

The importance of soil moisture conditions in the initiation of landslides has been widely recognized. This study takes advantage of the distributed hydrological model to derive the soil wetness index. The derived soil wetness index is then used to determine soil wetness thresholds for landslide occurrences. In order to predict landslides based on alert zones, a zone threshold is introduced together with the soil wetness threshold to constitute the integrated threshold. We evaluate the prediction performance of the integrated thresholds with the use of skill scores and the receiver operating characteristic (ROC) curves. This study is carried out in a sub-region of the Emilia-Romagna region, Northern Italy. Results show that the derived soil wetness index could account for the hydrological process that is controlled by meteorological conditions and topographic properties. The proposed integrated threshold shows a better predictive capability than the rainfall threshold, demonstrating the effectiveness of applying the soil wetness index in landslide predictions. The optimal threshold is also determined by compromising the correct predictions and incorrect predictions, it is found that the optimal integrated threshold is more advantageous in reducing false alarms compared with the optimal rainfall threshold. This study highlights the potential of applying hydrological simulations in landslide prediction studies and provides a new way to make use of high-resolution data in zone-based landslide predictions.

Ischiofemoral impingement (IFI) syndrome is considered the narrowing of the ischiofemoral space (IFS), leading to pathological changes in the quadratus femoris and sciatic nerve, causing posterior hip and sciatica-like pain. Open or arthroscopic resection of the lesser trochanter to enlarge the IFS is the main surgical procedure. However, there is a lack of research on isolated IFI, and currently known surgical procedures are at risk of weakening the flexion strength of the hip joint. In this study, four patients, who were diagnosed with isolated IFI and had undergone arthroscopic treatment with partial resection of the lesser trochanter, debridement of the quadratus femoris, and decompression of the sciatic nerve, were reviewed. To the best of our knowledge, this is the first study to describe the management of IFI using a series of surgical procedures

Accurate spatial distribution information of rainfall is essential to rainfall-induced hazard predictions and statistical interpolation methods may serve as a useful tool to produce a detailed distribution from coarse data sources. Although numerous comparison studies about different interpolation methods have been conducted on irregular rain-gauge networks, there is a need to perform such work on the increasingly available gridded rainfall data. Carried out in the Emilia-Romagna region (Italy) from 2008 to 2018, this study aims to examine accurate and appropriate interpolation methods to produce finer rainfall surface maps based on the 0.25° × 0.25° ERA5 gridded precipitation datasets. Five interpolation techniques, namely Thiessen polygons, Inverse Distance Weighting (IDW), Thin Plate Spline (TPS), Ordinary Kriging (OK), and ordinary Co-Kriging (CoK), have been selected and compared at different time scales (annual, monthly, and annual maximum daily precipitation). To assess the accuracy, the leave-one-out-cross-validation（LOOCV）test was used by using the indexes of Bias, Correlation coefficient (CC), the Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE) and the Kling-Gupta Efficiency (KGE). Additionally, visual inspections are employed to evaluate the plausibility of the interpolated maps. Results show: (1) All five interpolation methods have certain capabilities to improve spatial resolution, but they fail on accuracy at the daily scale. The OK generally outperforms the other four methods, while TPS shows better performance through the visual inspection at the monthly scale; (2) Unlike in the case of the interpolation using conventional point-based rain-gauge data, the multivariate method CoK is inferior to the univariate ones and the p-power of the IDW also differs; (3) Winter and spring results are better than those of summer and autumn. This study has provided useful guidance on choosing suitable rainfall interpolation methods for gridded datasets, which can be expanded in other regions and data sources to explore the generality of the conclusions.

With the development of remote sensing technology, satellite-based soil moisture estimates become more and more available, and the potential of using satellite soil moisture products in landslide hazard assessment has been widely recognized. However, to our knowledge, there is a lack of studies exploring the performance difference of various satellite soil moisture products for such an application. Therefore, this study aims to compare several state-of-the-art satellite soil moisture products on their potentials in landslide applications. The selected products include the ESA CCI soil moisture dataset, the SMAP Level-3 (L3), enhanced Level-3 (L3), Level-4 (L4) surface, and Level-4 (L4) root zone soil moisture datasets. Specifically, the completeness of different datasets is calculated to assess their applicability in practical applications. To investigate the relationship between the soil moisture and the commonly used rainfall information in landslide predictions, the correlation study of the satellite soil moisture with the antecedent cumulated rainfall is also carried out. In addition, to explore whether the satellite soil moisture can provide valuable information for landslide hazard assessment, infiltration events are identified based on the time series of satellite soil moisture, and the significance of event characteristics (such as event duration, soil moisture change, etc.) in landslide occurrence is then investigated with Bayesian analysis. This study is carried out in a landslide-prone area, the Emilia-Romagna region in northern Italy. Results show that the SMAP L4 product does not have any missing values, beneficial to the continuous monitoring of landslides. As for the correlation relationship between soil moisture and antecedent cumulated rainfall, the SMAP L4 product also has more rational spatial distribution of the Pearson correlation coefficients compared with other datasets, which can be better explained by the distribution of slope and TWI (topographic wetness index). Bayesian analysis on the infiltration events shows that our prior knowledge of the probability of landslide occurrence is better improved by using the ‘SMAP L4 root zone soil moisture’-derived infiltration events, indicating its greater potential to be used for landslide hazard assessment in the study region.

Here we report the spectral-timing results of the black hole X-ray binary 4U 1630–47 during its 2021 outburst using observations from the Hard X-ray Modulation Telescope (Insight-HXMT). Type C quasi-periodic oscillations (QPOs) in ∼1.6–4.2 Hz and quasi-regular modulation (QRM) near 60 mHz are detected during the outburst. The mHz QRM has a fractional rms of ∼10%–16% in the 8–35 keV energy band with a Q factor (frequency/width) of ∼2–4. Benefiting from the broad energy band of Insight-HXMT, we study the energy dependence of the ∼60 mHz QRM in 1–100 keV for the first time. We find that the fractional rms of the mHz QRM increases with photon energy, while the time lags of the mHz QRM are soft and decrease with photon energy. Fast recurrence of the mHz QRM, in a timescale of less than 1 hr, has been observed during the outburst. During this period, the corresponding energy spectra moderately change when the source transitions from the QRM state to the non-QRM state. The QRM phenomenon also shows a dependence with the accretion rate. We suggest that the QRM could be caused by an unknown accretion instability aroused from the corona.

Wind drift has a significant influence on the rainfall-runoff relationship in urban high-rise building areas since the oblique rainfall caused by the wind drift can interact with the building walls. However, the impact of the rainfall inclination angle on the rainfall-runoff process in urban high-rise building areas has not been studied. In this study, the relationship between wind and the rainfall-runoff process in such areas was explored. A theoretical framework was developed to describe their relationship, including a computational fluid dynamics (CFD) method to obtain the relationship between wind speed and rainfall inclination and a newly derived equation to describe the relationship between rainfall inclination and the runoff coefficient. Subsequently, a laboratory scale model experiment was conducted to verify the proposed framework. The main results are that (1) the runoff coefficient calculated by the proposed theoretical framework is highly consistent with that obtained from the laboratory experiment; (2) the runoff coefficient of urban high-rise building areas increases with wind speed; (3) the change of the runoff coefficient for the experiment with larger raindrop is 0.047 when the wind speed increases from 0 to 5.9 m/s while that for the experiment with smaller raindrop is 0.064, which means that the rainfall with larger droplets is less influenced by the wind.

The Weather Research and Forecasting (WRF) model can help to improve our understanding of analysing and forecasting hydrometeorological disasters. Especially for some regions like the Nile Delta, which faces growing climate hazards but has inadequate in situ rainfall observations. However, identifying an optimal configuration to run the WRF model is often a challenge. In this study, the WRF model was used to simulate extreme rainfall events at high spatial and temporal resolutions centered around Alexandria, in northern Egypt. In particular, a progressive multimetric configuration optimisation (PMCO) method is proposed to identify the possible optimal configurations of WRF in the aspect of domain size, numbers of vertical levels, nesting ratio, spin-up times, and physical parameterization schemes (microphysics, planetary boundary layer, and cumulus), based on 48 specifically designed experiments. The simulation performances are quantified and sorted by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). All WRF simulations use the ERA5 reanalysis dataset as boundary conditions and the WRF results are verified against the Integrated Multi-satellitE Retrievals for GPM (IMERG). The results show that the rainfall distribution and magnitude are most sensitive to the spin-up time and physical parameterization schemes. It is also observed that the improvement of WRF's reproducibility of rainfall intensity is usually accompanied by a decrease in the reproducibility of rainfall distribution. The best model configuration for the study area comprises of three-level nesting (D01 80x80; D02 112x112; D03 88X88), 58 vertical levels, 1:3:3 downscaling ratio, 48h spin-up time, WRF Single-Moment 6-class microphysics scheme, Mellor-Yamada-Janjic planetary boundary layer scheme, and Grell-Freitas cumulus. The stability of this configuration is also verified with the other three extreme rainfall events over Egypt. The results show that there exists a common WRF configuration set in Egypt that produces the relatively good simulations for extreme rainfall events.

Aiming at the rate and causes of the incision variations in the upper Himalaya Orogenic belt of Tibet, a new method of incision rate estimation based on the analysis of the evolution of Zhangmu landslide and its spatial and temporal relationship with the surrounding gullies is applied. The research results are: (1) The Zhangmu landslide was an ancient huge landslide, occurred at 24.2 ± 2.4 ka BP, consisting of an ancient bedrock landslide and subsequent deposit landslides, with an area of 1.23 km2, and remaining volume of 23.4 × 106 m3. (2) The fluvial incision rate shows variations in Poiqu and its branch, which may be controlled by the discharge and decreases from the Poiqu (11.5 ± 1.2 mm/a) to the tributaries, from 4.8 ± 0.5 mm/a in the Bangcundong gully to 2.4 ± 0.2 mm/a in Zhangmu gully. (3) The fluvial incision rate also shows temporal variations in the Poiqu. There are two periods of low rate and high rate incision processes in the Poiqu, while the section shape transforms from a gentle slope to a cliff. In the low rate period from 24.2 ka BP to 10.0 ka BP in the Poiqu valley, the incision rate was 10.4 ± 1.0 mm/a, while the high fluvial incision rate since 10.0 ka BP was 13.0 ± 1.3 mm/a; (4) The fluvial incision promotes the landscape evolution and triggers the potential landslide in thousands of years.

Soil moisture plays a key role in land-atmosphere interaction systems. Although it can be estimated through in situ measurements, satellite remote sensing, and hydrological modelling, using indicators to index soil moisture conditions is another useful way. In this study, one of these indicators, the antecedent precipitation index (API), is explored. Modifications were proposed to the conventional version of API by introducing two parameters to make it more in line with the physical process. First, the recession coefficient is allowed to vary with the change of air temperature, which could take into account the variation of the evapotranspiration process. Second, the API value is restricted by the maximum value of API, accounting for the maximum water holding capacity of the soil. The modified API was then calibrated and validated by comparing with the in situ measured soil moisture. The better correlation between these two datasets demonstrates that the modified API could better indicate soil moisture conditions, compared with the conventional API. The capability of the modified API to index soil moisture conditions was further explored by applying it to landslide predictions in the Emilia-Romagna region, northern Italy. Here, the recent 3-day rainfall vs the antecedent soil wetness thresholds (RS thresholds) were constructed, in which the soil wetness is indexed by the modified API. The validation of RS thresholds was carried out with the use of the contingency matrix and receiver operating characteristic (ROC) curves. By comparing the prediction performance between RS thresholds and rainfall thresholds, it is found that RS threshold could provide better prediction capabilities in terms of higher hit rate and lower false alarm rate. The positive results indicate that the modified API could provide superior performance of indexing soil moisture conditions, demonstrating the effectiveness of the proposed modifications.

After a long-standing debate of pluralism in heritage conservation, the global practice has just started to broaden its view from material to people and even to nature, leading to the potential of a more comprehensive understanding and harmony between these spheres. Notwithstanding that the shift from material to people and then to nature seemingly looks like the only path in the modern heritage conservation movement to achieve the foregoing goals, in fact, there exist some regional cultures that originally featured particular views on human&ndash, nature harmony. This paper hence highlights the regional difference in heritage with a focus on China of ancient times, which unfolds the particular perspective emphasising the unity of human and nature. With a case study of Huaqing Palace of the Tang Dynasty (618&ndash, 907 CE), the research is expected to be the first attempt to rediscover that the four schools of thought, Buddhism, Taoism, Confucianism and I Ching, had jointly formed a &ldquo, wisdom&rdquo, system of the ancient Han Chinese in shaping the idea of cultural heritage, as well as the idea of heritage conservation, which were inherited by modern Chinese without knowing and recognising it. The paper, therefore, argues that without understanding and acknowledging the significance of the ancient Han Chinese&rsquo, s particular view on nature and the universe formed by the four schools of thought behind the material, it is not likely to protect and promote comprehensively their heritage value, such that the importance of cultural diversity will be just rhetoric.

This study aims to explore the spatio-temporal characteristics of meteorological and agricultural droughts using the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI), respectively, as well as their relationships over the past three decades (1986-2016) in South Korea. The SSI shows less frequent droughts and longer drought duration compared to the SPI, due to the gradual decrease in the autocorrelation functions of the SSI. The strongest cross-correlations are observed at a 1-month lag between the SPI and SSI for most stations. Thus, the SPI could be more appropriate for defining the onset of a drought, whereas the SSI appears to be more effective for describing drought persistence. Moreover, the transition from meteorological to agricultural droughts is significantly dependent on the season, indicating that the transition between them is highly correlated with antecedent moisture conditions. The copula-based Multivariate Standardized Drought Index (MSDI) is introduced to explicitly postulate interdependence between the SPI and SSI in the context of a multivariate probability distribution. We employ a hierarchical agglomerative clustering approach along with a quantile regression model to better understand the spatio-temporal pattern of the MSDI. More drought episodes under moderate to severe drought conditions are observed along the southern coast of South Korea. Additionally, persistent droughts with higher severity are observed in the northern part of South Korea, which may be attributed to a significant decreasing trend (or increasing drought risk).

A giant soil landslide named Boli landslide occurred on the right bank of Taozi Gully, a branch of the Jiami River in Taozi town, Sichuan Province, China, at approximately 5:40 am local time on 19 July 2018. The landslide destroyed 186 houses and nearly 300 acres of agricultural land in Boli village. Real-time evacuation was successfully implemented to avoid casualties of 281 people among 65 settlements due to the effective warning. This study revealed the typical characteristics, the cause, and the failure mechanism of Boli landslide on the basis of multi-temporal remote sensing images, field investigation, and analysis of the landslide soils. A simple, effective, and scalable community-based early warning system was also proposed in the study on the basis of real-time evacuation in response to landslide hazards in mountain in near future. Moreover, the experience and the effective risk avoidance mode of this study can be used by the concern parties to improve the disaster prevention and countermeasures of poor mountainous areas.

Dimethyl disulfide (DMDS) is an alternative fumigant to methyl bromide that was phased out globally due to its stratospheric ozone-depleting properties. Covering the surface of the soil with a plastic tarpaulin or 'barrier film' when using a soil fumigant is typically used to retain fumigants in the soil and to reduce emissions. Emission levels depend on the film's permeability, which varies mainly according to the film's material, the type of fumigant and the environmental conditions. We used specialized laboratory equipment to test the permeability of four films to DMDS under similar temperature and relative humidity (RH) conditions present in the field: polyethylene (PE), polyvinyl chloride (PVC), polyvinylidene chloride (PVDC) and ethylene vinyl alcohol copolymer (EVOH). This report presents evidence that the influence of temperature and relative humidity on the permeability of four films to the fumigant DMDS: PE，PVC,PVDC, EVOH. This research confirmed that PE and PVC films are relatively permeable to DMDS and PVC was more unstable to a range of environmental condition than other three films; PVDC and EVOH films are relatively impermeable to the fumigant DMDS and the permeability of PVDC was more stable to a range of environmental conditions than EVOH. The cumulative emissions of DMDS from soil covered with PE, PVC, PVDC or EVOH were 21.38%, 27.51%, 1.59% and 1.52%, respectively. As the permeability of PVDC was more stable to a range of environmental conditions than EVOH, PVDC shows potential for use in the field with a volatile fumigant such as DMDS.

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Flood estimation in ungauged basins is important for flood design, and for improving our understanding of the sensitivity of flood magnitude to changes in climate and land cover. Flood estimates by current methods (e.g. statistical regression, unit hydrograph) have high uncertainty, even in places with dense observing networks (e.g. +/- 50-100% in the UK). Reductions in this uncertainty are being sought by using alternative methods, such as continuous simulation using hydrological models (spatially-distributed or lumped), and event-scale derived distribution approaches. The very significant challenges for reliable application of continuous simulation models in ungauged catchments are well described in the literature.The event-scale derived distribution approach also has challenges, which we explore below. The derived distribution approach at the event scale typically combines the following elements: a stochastic rainfall model, an event-scale rainfall-runoff model (including “losses” and a “baseflow” component), and a runoff routing model. In principle, every element of this approach may be considered as a (seasonally varying) random variable. The flood peak distribution is obtained by integrating over joint distributions of the model elements.First challenge: what is the physical basis for estimating the event runoff coefficient? In the 1970s, this was addressed using infiltration theory, but other runoff generation mechanisms are often more important. How do we connect our knowledge of seasonal water balance and runoff generation processes to the probability distribution of event runoff coefficients, and its seasonal variation? We suggest (i) begin with locations which are dominated by a small number of runoff generation mechanisms (ii) make use of existing theory on links between climate, catchment characteristics and seasonal water balance (iii) adapt relevant simple concepts of runoff generation which link seasonal water balance to runoff generation.Second challenge: how do we parsimoniously quantify the impacts of within-storm temporal rainfall patterns on the flood hydrograph? Existing approaches use stochastic rainfall models to explicitly generate (hourly) time series of rainfall; since catchments damp out high frequency forcing, we suggest that these rainfall series often contain excessive temporal detail and obscure the most informative interactions between rainfall and catchment response. We propose that we use stochastic models that can generate hydrologically relevant attributes of rainfall events (e.g. intensity/depth/duration, spatial and temporal moments), and then apply rainfall-runoff transformations which operate on rainfall moments, and do not require excess detail in temporal (or spatial) patterns of rainfall.Third challenge: What is an event? This is no problem for theoretical models, but it is hard as a data analysis question, and we need data analysis to implement and evaluate the derived distribution method. The event identification methods of engineering hydrology are subjective, require manual intervention and are poorly suited for large sample hydrology! We suggest the answer lies in the catchment’s response time.The underlying conceptual framework to link seasonal climate and hydrology to floods is already available (Sivapalan et al, 2005). What these challenges require is that we integrate and apply more of our existing hydrological concepts and knowledge to implement the process-based theory of flood frequency.

Determination of peak flow or flow hydrograph in ungauged basins can be affected by considerable degree of uncertainty. Despite the considerable efforts to overcome this challenge, current methods provide design flood estimates that are still highly uncertain in ungauged catchments, even in the UK where the gauged network is relatively dense. A possible solution may be found in stochastic approaches and more specifically in the Derived Flood Frequency method, which gives the possibility to decompose runoff response effects dictated by the dominant hydrological processes for a catchment under study. Data scarcity can be then circumvented by application of UK-specific stochastic models, from which rainfall events and their relevant features are sampled. In this work, the latter rainfall model will be presented as a joint distribution function of spatial and temporal moments of catchment rainfall, along with their Intensity and Total Depth. The marginal distributions for each rainfall characteristic are studied through the L-moment method, which was previously developed for regional frequency analysis. The multivariate distribution of these rainfall characteristics will be described through the Vine Copula method, which can account for dependence very flexibly among several variables. Parameterisation procedures still require more development to allow application over ungauged case of studies.