Water Cycle Process Research: Experiments and Observations

The evaluation of methods and instrumentation for measuring water cycle parameters and for monitoring the status of hydrological process will assist governmental personnel, researchers, and water resources practitioners in determining strategies for field and laboratory measurements. This chapter aims to specify the instruments and techniques developed during the long-term monitoring phase of field experimental stations and the establishment phase of indoor experimental laboratory in the Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The two field experimental stations, Dongtaigou and Chongling, have been initiated to observe and quantify the water cycle process for more than 10 years, which has formed a complete set of observing and experimental methods in watershed. The experimental laboratory is a new integrated water cycle experiment platform, based on the new technology integrated control, measurement, sensors, and information processing. It includes artificial rainfall system, experimental sink of runoff and erosion, river simulation system, and transformation dynamical processes experimental device among precipitation, vegetation water, surface water, soil water, and groundwater. The continued instrumentation development and advanced experimental strategies will serve as a first port of call for professionals studying the behavior of water footprint

Experimental Variant Slope Soil Tank for Measurements of Runoff and Soil Erosion

Rainfall-runoff processes and the related soil erosion are pivotal research regions in hydrology, soil science, and environment science. Thus, physics model experiments in laboratory scale on the aspect of measuring runoff and soil are one of the best tools in this field. This chapter aims to specify the experimental variant slope soil tank at home and in the USA. The developing of experimental soil tank of variant slopes with artificial simulating rainfall system will assist to understand soil water motivation, runoff yield, and nonpoint source pollution

Field scale interaction and nutrient exchange between surface water and shallow groundwater in the Baiyang Lake region, North China Plain

Fertilizer input for agricultural food production, as well as the discharge of domestic and industrial water pollutants, increases pressures on locally scarce and vulnerable water resources in the North China Plain. In order to: (a) understand pollutant exchange between surface water and groundwater, (b) quantify nutrient loadings, and (c) identify major nutrient removal pathways by using qualitative and quantitative methods, including the geochemical model PHREEQC) a one-year study at a wheat (Triticum ciestiu um L.) and maize (Zea mays L.) double cropping system in the Baiyang Lake area in Hebei Province, China, was undertaken. The study showed a high influence of low-quality surface water on the shallow aquifer. Major inflowing pollutants into the aquifer were ammonium and nitrate via inflow from the adjacent Fu River (up to 29.8 mg/L NH4-N and 6.8 mg/L NO3-N), as well as nitrate via vertical transport from the field surface (up to 134.8 mg/L NO3-N in soil water). Results from a conceptual model show an excess nitrogen input of about 320 kg/ha/a. Nevertheless, both nitrogen species were only detected at low concentrations in shallow groundwater, averaging at 3.6 mg/L NH4-N and 1.8 mg/L NO3-N. Measurement results supported by PHREEQC-modeling indicated cation exchange, denitrification, and anaerobic ammonium oxidation coupled with partial denitrification as major nitrogen removal pathways. Despite the current removal capacity, the excessive nitrogen fertilization may pose a future threat to groundwater quality. Surface water quality improvements are therefore recommended in conjunction with simultaneous monitoring of nitrate in the aquifer, and reduced agricultural N-inputs should be considered. (C) 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V

A δ2H offset correction method for quantifying root water uptake of riparian trees

Root water uptake plays an important role in water cycle in Groundwater-Soil-Plant-Atmosphere-Continuum. Stable isotopes (δ2H and δ18O) are effective tools to quantify the use of different water sources by plant roots. However, the widespread δ2H offsets of stem water from its potential sources due to δ2H fractionation during root water uptake result in conflicting interpretations of water utilization using stable isotopes. In this study, a potential water source line (PWL), i.e., a linear regression line between δ18O and δ2H data of both soil water at different depths and groundwater, was proposed to correct δ2H offsets of stem water. The PWL-corrected δ2H was determined by subtracting the deviation between δ2H in stem water and PWL from the original value. The MixSIAR model coupled with seven types of input data (i.e. various combinations of single or dual isotopes with uncorrected or corrected δ2H data by PWL or soil water line (SWL)) were used to determine seasonal variations in water uptake patterns of riparian tree of Salix babylonica (L.) along the Jian and Chaobai River in Beijing, China. These methods were evaluated via three criteria including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and root mean square error (RMSE). Results showed that different types of input data led to considerable differences in the contributions of soil water at upper 30 cm (9.9–57.6%) and below 80 cm depths (29.0–76.4%). Seasonal water uptake patterns were significantly different especially when δ2H offset was pronounced (p < 0.05). The dual-isotopes method with uncorrected δ2H underestimated the contributions of soil water in the 0–30 cm layer (by 30.4%) and groundwater (by 56.3%) compared to that with PWL-corrected δ2H. The PWL correction method obtained a higher groundwater contribution (mean of 29.5%) than that estimated by the SWL correction method (mean of 24.5%). The MixSIAR model using dual-isotopes with PWL-corrected δ2H produced the smallest AIC (94.1), BIC (91.9) and RMSE values (4.8%) than other methods (94.9–101.7, 92.6–99.5 and 5.3–12.4%, respectively), which underlined the best performance of PWL correction method. The present study provides crucial insights into quantifying accurate root water uptake sources even if δ2H offset exists

Claudin-1/4 as directly target gene of HIF-1α can feedback regulating HIF-1α by PI3K-AKT-mTOR and impact the proliferation of esophageal squamous cell though Rho GTPase and p-JNK pathway

Immunohistochemical microarray comprising 80 patients with esophageal squamous cell carcinoma (ESCC) and discovered that the expression of CLDN1 and CLDN4 were significantly higher in cancer tissues compared to para-cancerous tissues. Furthermore, CLDN4 significantly affected the overall survival of cancer patients. When two ESCC cell lines (TE1, KYSE410) were exposed to hypoxia (0.1% O2), CLDN1/4 was shown to influence the occurrence and development of esophageal cancer. Compared with the control culture group, the cancer cells cultured under hypoxic conditions exhibited obvious changes in CLDN1 and CLDN4 expression at both the mRNA and protein levels. Through genetic intervention and Chip, we found that HIF-1α could directly regulate the expression of CLDN1 and CLDN4 in cancer cells. Hypoxia can affect the proliferation and apoptosis of cancer cells by regulating the PI3K-Akt-mTOR pathway. Molecular analysis further revealed that CLDN1 and CLDN4 can participate in the regulation process and had a feedback regulatory effect on HIF-1α expression in cancer cells. In vitro cellular experiments and vivo experiments in nude mice further revealed that changes in CLDN4 expression in cancer cells could affect the proliferation of cancer cells via regulation of Rho GTP and p-JNK pathway. Whether CLDN4 can be target for the treatment of ESCC needs further research

Task-driven data fusion for additive manufacturing: framework, approaches, and case studies

Additive manufacturing (AM) has been envisioned as a critical technology for the next industrial revolution. Due to the advances in data sensing and collection technologies, a large amount of data, generated from multiple sources in AM production, becomes available for relevant analytics to improve process reliability, repeatability, and part quality. However, AM processes occur over a wide range of spatial and temporal scales where the data generally involves different types, dimensions and structures, leading to difficulties when integrating and then analysing. Hence, in what way and how to integrate the heterogeneous data or merge the spatial and temporal information lead to significant challenges in data analytics for AM systems. This paper proposed a task-driven data fusion framework that enables the integration of heterogeneous data from different sources and modalities based on tasks to support decision-making activities. In this framework, the data analytics activities involved in the task are identified in the first place. Then, the data required for the task is identified, collected, and characterised. Finally, data fusion techniques are employed and applied based on the characteristics of the data for integration to support data analytics. The fusion techniques that best fit the task requirements are selected as the final fusion approach. Case studies on different research directions of AM, including AM energy consumption prediction, mechanical properties prediction of additively manufactured lattice structures, and investigation of remelting process on part density, were carried out to demonstrate the feasibility and effectiveness of the proposed framework and approaches