ALBERT

Description: Cave dripwater hydrochemistry responds to environmental changes, both within and outside of the cave, and thereby conveys this information to any stalagmite fed by the drips. As stalagmites are important archives of climate proxy information, understanding how dripwater hydrochemistry responds to environmental forcing is critical. However, despite the large number of speleothems in SW China, the response of dripwater to regional climate variability is not yet adequately understood. A 3-year study of three drip sites in Xueyu Cave, Chongqing Municipality, SW China, revealed the most important mechanisms controlling dripwater chemical variability. The principal chemical indices (pH, specific conductivity, Ca 2+ , Mg 2+ , Sr 2+ and ) in collected dripwaters and the local climate data were analysed in this study. The principal controls on the hydrochemistry were found to be the external climate and its changes, groundwater residence time, cave ventilation and prior calcite precipitation (PCP) processes. Dripwater hydrochemistry showed strongly coherent seasonal patterns despite the fact that all sites are Ca–HCO 3 type waters and supersaturated with calcite. Seasonal changes in dripwater hydrochemistry were influenced by the soil and vadose zone CO 2 content as well as groundwater residence time in the upper karst zone. Cave-air CO 2 seasonal variations were consistent with changes in dripwater P CO 2 and cave ventilation. Trace element ratios (Mg/Ca and Sr/Ca) of dripwater were controlled by PCP processes. Seasonal variations in dripwater Mg/Ca and Sr/Ca ratios in Xueyu Cave showed inverse changes with the Asia Monsoon Index during the monitoring period, reflecting the seasonal climate changes that may have been recorded in the speleothems. Based on a linear regression of P CO 2 and the Ca 2+ data in the cold–dry winter season, a 130-ppm shift in cave-air P CO 2 results in a 1-ppm shift in dripwater Ca 2+ concentration in Xueyu Cave. This study illustrates the importance of understanding factors controlling the changes in the composition of dripwater before it reaches the speleothem.

Description: The Returning Farmland to Forest Program (RFFP) was implemented in China in 1999 with the goal of supporting environmental restoration by returning significant areas of cultivated land to forest. While afforestation supports long-term ecosystem services like carbon sequestration and the reduction of soil and water loss, it also reduces the amount of available arable land, putting financial pressure on those who depend on it for their livelihoods. In an effort to balance both ecological and economic benefits, regional governments offer financial compensation to farmers to offset these pressures in the form of a dollar amount per hectare of reforested land. The current study explores participants’ willingness to accept pay (WTA), along with the difference between the offered per hectare compensation and the amount deemed acceptable by RFFP participants in the region. To this end, 92 households from the representative afforestation area were surveyed in Huining County, Gansu Province, China - an area of strategic ecological importance in the Loess Plateau. The results showed 12.0% of the surveyed respondents to be satisfied with the current compensation policy, while 88.0% of respondents were not. The respondents’ lower and upper WTA limits were $221/ha/year and $1331/ha/year, respectively, with an average WTA of $777/ha/year. The compensation that respondents would be most willing to accept was distributed in the $444–888/ha/year and the $889–1331/ha/year ranges, accounting for 37.0% and 31.5% of the total responses, respectively. Gender, age, and education were found to be the main factors influencing a respondents’ WTA. Results of the survey suggest that the actual compensation amount ($355/ha/year) is much lower than respondents’ WTA, and that compensation measures and policies should be improved to guarantee a basic income.

Description: Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA).

Description: The thermal and dynamical causes of boundary layer low-level jets (LLJs) over the southeast coast of China (a very complex terrain) are explored using a very high-resolution diagnostic model CALMET along with the advanced weather research and forecasting (WRF-ARW) model, combined with observations. Both diurnal and seasonal variations in LLJs are investigated using simulations during two observational periods and four months in 2011. Two configurations, with different vertical and horizontal resolutions, are compared. The results show that the use of higher vertical and horizontal resolutions (including land cover/use) in the diagnostic model CALMET leads to large improvements in simulating boundary layer LLJs over complex terrain as compared with using lower vertical and horizontal resolutions in both WRF-ARW and CALMET models. The simulations using the diagnostic model CALMET better reproduced the observations, in that both LLJ events occur in the night (nocturnal LLJs) and in the afternoon (afternoon LLJs) are noticed. Compared to the nocturnal LLJs, the afternoon LLJs have larger wind speeds and occur at lower heights. The afternoon LLJ characteristics are closely associated with local thermodynamic circulations including the mountain–valley breeze and land–ocean breeze, which are regulated by the thermal contrasts between the ocean and mountains, and diurnal cycle of boundary layer friction. A comprehensive analysis of an afternoon LLJ and nocturnal LLJ indicates that there are large differences in wind field, vertical motions, and water vapor distributions between them. The local thermodynamic circulations also strongly affect vertical motions; even under a relatively stable atmosphere, the vertical motions during LLJs are stronger than the monthly average. Afternoon LLJ events, associated with the southeasterly land–ocean breeze in summer, occur more frequently than in winter; in contrast, nocturnal LLJ events, occur in summer less frequently than in winter. The seasonal variation analysis shows that land–ocean breezes have significant effects on the wind speed, wind direction and heights of the afternoon LLJs; local thermal contrast forcing is believed to be the main factor that affects the LLJs during the daytime especially in warm seasons.

Description: Globally, reanalysis datasets are widely used in assessing climate change, validating numerical models, and understanding the interactions between the components of a climate system. However, due to the relatively coarse resolution, most global reanalysis datasets are not suitable to apply at the local and regional scales directly with the inadequate descriptions of mesoscale systems and climatic extreme incidents such as mesoscale convective systems (MCS), squall lines, tropical cyclones, regional droughts and heat waves. In this study, by using a data assimilation system of Gridpoint Statistical Interpolation (GSI), and a mesoscale atmospheric model of WRF (Weather Research and Forecast model), we build a regional reanalysis system. This is preliminary and the first experimental attempt to construct a high-resolution reanalysis for China main land. Four regional testbed datasets are generated for year 2013 via three widely used methods (classical dynamical downscaling, spectral nudging, data assimilation) and a hybrid method with data assimilation coupled with spectral nudging. Temperature at 2m, precipitation and upper-level atmospheric variables are evaluated by comparing against observations for one-year-long tests. It can be concluded that the regional reanalysis with assimilation and nudging methods can better produce the atmospheric variables from surface to upper levels, and regional extreme events such as heat waves, than the classical dynamical downscaling. Compared to the ERA-Interim global reanalysis, the hybrid nudging method performs slightly better in reproducing upper-level temperature and low-level moisture over China, which improves regional reanalysis data quality.

Description: Northeast China is China’s primary grain production base. A large amount of crop straw is incinerated every spring and autumn, which greatly impacts air quality. To study the degree of influence of straw burning on urban pollutant concentrations, this study used The Moderate-Resolution Imaging Spectroradiometer/Terra Thermal Anomalies & Fire Daily L3 Global 1 km V006 (MOD14A1) and The Moderate-Resolution Imaging Spectroradiometer/Aqua Thermal Anomalies and Fire Daily L3 Global 1 km V006 (MYD14A1) data from 2015 to 2017 to extract fire spot data on arable land burning and to study the spatial distribution characteristics of straw burning on urban pollutant concentrations, temporal variation characteristics and impact thresholds. The results show that straw burning in Northeast China is concentrated in spring and autumn; the seasonal spatial distributions of PM2.5, PM10 andAir Quality Index (AQI) in 41 cities or regions in Northeast China correspond to the seasonal variation of fire spots; and pollutants appear in the peak periods of fire spots. In areas where the concentration coefficient of rice or corn is greater than 1, the number of fire spots has a strong correlation with the urban pollution index. The correlation coefficient R between the number of burned fire spots and the pollutant concentration has a certain relationship with the urban distribution. Cities are aggregated in geospatial space with different R values.

Description: The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

Description: China's worst flooding since 1998 occurred over the Yangtze River Valley from 30 June to 6 July 2016. This study investigated the event using a new method – the spectral nudging and update cycle (SN + UIC) – in the regional Weather Research and Forecasting model, fuller use of small-scale features by using multi-scale blending. The SN + UIC was found to be successful in improving the prediction of this persistent severe rainfall event; and the larger the magnitude and longer the lead time, the more obvious the improvement. It was also found that the use of this new method decreased the root-mean-square error for related meteorological variables.

Description: The Andaman Sea (AS) is a poorly observed basin, where even the fundamental physical characteristics have not been fully documented. Here, the seasonal variations of the upper ocean structure and the air-sea interactions in the central AS were studied using a moored surface buoy. The seasonal double-peak pattern of the sea surface temperature (SST) was identified with the corresponding mixed layer variations. Compared with the buoys in the Bay of Bengal (BOB), the thermal stratification in the central AS was much stronger in the winter to spring, when a shallower isothermal layer and a thinner barrier layer were sustained. The temperature inversion was strongest from June to July because of substantial surface heat loss and subsurface prewarming. The heat budget analysis of the mixed layer showed that the net surface heat fluxes dominated the seasonal SST cycle. Vertical entrainment was significant from April to July. It had a strong cooling effect from April to May and a striking warming effect from June to July. A sensitivity experiment highlighted the importance of salinity. The AS warmer surface water in the winter was associated with weak heat loss caused by weaker longwave radiation and latent heat losses. However, the AS latent heat loss was larger than the BOB in summer due to its lower relative humidity.

Description: Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).