The Relevance of Soil Moisture by Remote Sensing and Hydrological Modelling:12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future

AbstractAccurate soil moisture information is critically important for hydrological modelling and natural hazards (landslide & debris flow). However, its effective utilisation in those areas is still in a state of infancy. This paper focuses on exploring the advances and potential issues in current application of satellite soil moisture observations in hydrological modelling. It has proposed that hydrological application of soil moisture data requires two inter-connected components: 1) soil moisture data relevant to hydrology, and 2) appropriate hydrological model structure compatible with such data. In order to meet these two requirements, the following three research tasks are suggested: the first is to carry out comprehensive evaluations of satellite soil moisture observations for hydrological modelling; the second is that the soil moisture representations in hydrological models may need to be modified so that they are more compatible with the real field soil moisture variations; and the third is that a soil moisture product (i.e., soil moisture deficit) directly applicable to hydrological modelling should be developed

Comparative study of microwave radiation-induced magnetoresistive oscillations induced by circularly- and linearly- polarized photo-excitation

A comparative study of the radiation-induced magnetoresistance oscillations in the high mobility GaAs/AlGaAs heterostructure two dimensional electron system (2DES) under linearly- and circularlypolarized microwave excitation indicates a profound difference in the response observed upon rotating the microwave launcher for the two cases, although circularly polarized microwave radiation induced magnetoresistance oscillations observed at low magnetic fields are similar to the oscillations observed with linearly polarized radiation. For the linearly polarized radiation, the magnetoresistive response is a strong sinusoidal function of the launcher rotation (or linear polarization) angle, {\theta}. For circularly polarized radiation, the oscillatory magnetoresistive response is hardly sensitive to {\theta}

The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }

Evaluation of SMOS soil moisture retrievals over the central United States for hydro-meteorological application

Soil moisture has been widely recognized as a key variable in hydro-meteorological processes and plays an important role in hydrological modelling. Remote sensing techniques have improved the availability of soil moisture data, however, most previous studies have only focused on the evaluation of retrieved data against point-based observations using only one overpass (i.e., the ascending orbit). Recently, the global Level-3 soil moisture dataset generated from Soil Moisture and Ocean Salinity (SMOS) observations was released by the Barcelona Expert Center. To address the aforementioned issues, this study is particularly focused on a basin scale evaluation in which the soil moisture deficit is derived from a three-layer Xinanjiang model used as a hydrological benchmark for all comparisons. In addition, both ascending and descending overpasses were analyzed for a more comprehensive comparison. It was interesting to find that the SMOS soil moisture accuracy did not improve with time as we would have expected. Furthermore, none of the overpasses provided reliable soil moisture estimates during the frozen season, especially for the ascending orbit. When frozen periods were removed, both overpasses showed significant improvements (i.e., the correlations increased from r = −0.53 to r = −0.65 and from r = −0.62 to r = −0.70 for the ascending and descending overpasses, respectively). In addition, it was noted that the SMOS retrievals from the descending overpass consistently were approximately 11.7% wetter than the ascending retrievals by volume. The overall assessment demonstrated that the descending orbit outperformed the ascending orbit, which was unexpected and enriched our knowledge in this area. Finally, the potential reasons were discussed

Models of preconception care implementation in selected countries.

Globally, maternal and child health faces diverse challenges depending on the status of the development of the country. Some countries have introduced or explored preconception care for various reasons. Falling birth rates and increasing knowledge about risk factors for adverse pregnancy outcomes led to the introduction of preconception care in Hong Kong in 1998, and South Korea in 2004. In Hong Kong, comprehensive preconception care including laboratory tests are provided to over 4000 women each year at a cost of $75 per person. In Korea, about 60$12) for preconception health care services. These case studies illustrate programmatic feasibility of preconception care services to address maternal and child health and other public health challenges in developed and emerging economies

Soil moisture sensor network design for hydrological applications

Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration, and runoff, hence a vital state variable in hydrological modelling. However, due to the heterogeneity of soil moisture in space, most existing in situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on principal component analysis (PCA) for the investigation of the network redundancy degree and K-means cluster analysis (CA) and a selection of statistical criteria for the determination of the optimal sensor number and placements. Furthermore, the long-term (10-year) 5 km surface soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia-Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale surface soil moisture (i.e. with NSE and r at 0.995 and 0.999, respectively, for the areal mean estimation; and 0.973 and 0.990, respectively, for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22 124 km2 catchment is needed, and in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme, which are also discussed in the paper