Soil water is an important composition of water recycle in the soil-plant-atmosphere continuum.However, intense water exchange between soil-plant and soil-atmosphere interfaces only occurs in a certain layer of the so...Soil water is an important composition of water recycle in the soil-plant-atmosphere continuum.However, intense water exchange between soil-plant and soil-atmosphere interfaces only occurs in a certain layer of the soil profile. For deep insight into water active layer(WAL, defined as the soil layer with a coefficient of variation in soil water content >10% in a given time domain) in the Loess Plateau of China,we measured soil water content(SWC) in the 0.0–5.0 m soil profile from 86 sampling sites along an approximately 860-km long south-north transect during the period 2013–2016. Moreover, a dataset contained four climatic factors(mean annual precipitation, mean annual evaporation, annual mean temperature and mean annual dryness index) and five local factors(altitude, slope gradient, land use, clay content and soil organic carbon) of each sampling site was obtained. In this study, three WAL indices(WALT(the thickness of WAL), WAL-CV(the mean coefficient of variation in SWC within WAL) and WALSWC(the mean SWC within WAL)) were used to evaluate the characteristics of WAL. The results showed that with increasing latitude, WAL-T and WAL-CV increased firstly and then decreased. WAL-SWC showed an opposite distribution pattern along the south-north transect compared with WAL-T and WAL-CV.Average WAL-T of the transect was 2.0 m, suggesting intense soil water exchange in the 0.0–2.0 m soil layer in the study area. Soil water exchange was deeper and more intense in the middle region than in the southern and northern regions, with the values of WAL-CV and WAL-T being 27.3% and 4.3 m in the middle region,respectively. Both climatic(10.1%) and local(4.9%) factors influenced the indices of WAL, with climatic factors having a more dominant effect. Compared with multiple linear regressions, pedotransfer functions(PTFs) from arti?cial neural network can better estimate the WAL indices. PTFs developed by artificial neural network respectively explained 86%, 81% and 64% of the total variations in WAL-T, WAL-SWC and WAL-CV. Knowledge of WAL is crucial for understanding the regional water budget and evaluating the stable soil water reserve, regional water characteristics and eco-hydrological processes in the Loess Plateau of China.展开更多
为应对传统水网系统面临的运行效率低、安全风险高、人力成本大等挑战,借鉴无人驾驶汽车(autonomous vehicle,AV)在感知、决策、控制和系统架构方面的先进理念及等级划分标准,提出一种自主运行智慧水网(autonomous water network,AWN)...为应对传统水网系统面临的运行效率低、安全风险高、人力成本大等挑战,借鉴无人驾驶汽车(autonomous vehicle,AV)在感知、决策、控制和系统架构方面的先进理念及等级划分标准,提出一种自主运行智慧水网(autonomous water network,AWN)的架构设计,并参照SAE J3016标准提出水网智能化等级(WN-L0至WN-L5)划分方案。继而引入类似AV的域控制器概念,构建水网调度域、水环境安全域、水机设备控制域、水工程设施设备安全域等核心功能域。阐述实现AWN所需的关键技术环节,并探讨端到端与模块化技术路线的可行性。通过展望AWN的未来发展趋势与面临挑战,旨在为未来智慧水网的建设提供理论框架、等级标准和技术路径参考。展开更多
基金supported by the National Natural Science Foundation of China (41530854, 41571130081)the National Key Project for Research and Development (2016YFC0501605)the Youth Innovation Promotion Association of Chinese Academy of Sciences (2017076)
文摘Soil water is an important composition of water recycle in the soil-plant-atmosphere continuum.However, intense water exchange between soil-plant and soil-atmosphere interfaces only occurs in a certain layer of the soil profile. For deep insight into water active layer(WAL, defined as the soil layer with a coefficient of variation in soil water content >10% in a given time domain) in the Loess Plateau of China,we measured soil water content(SWC) in the 0.0–5.0 m soil profile from 86 sampling sites along an approximately 860-km long south-north transect during the period 2013–2016. Moreover, a dataset contained four climatic factors(mean annual precipitation, mean annual evaporation, annual mean temperature and mean annual dryness index) and five local factors(altitude, slope gradient, land use, clay content and soil organic carbon) of each sampling site was obtained. In this study, three WAL indices(WALT(the thickness of WAL), WAL-CV(the mean coefficient of variation in SWC within WAL) and WALSWC(the mean SWC within WAL)) were used to evaluate the characteristics of WAL. The results showed that with increasing latitude, WAL-T and WAL-CV increased firstly and then decreased. WAL-SWC showed an opposite distribution pattern along the south-north transect compared with WAL-T and WAL-CV.Average WAL-T of the transect was 2.0 m, suggesting intense soil water exchange in the 0.0–2.0 m soil layer in the study area. Soil water exchange was deeper and more intense in the middle region than in the southern and northern regions, with the values of WAL-CV and WAL-T being 27.3% and 4.3 m in the middle region,respectively. Both climatic(10.1%) and local(4.9%) factors influenced the indices of WAL, with climatic factors having a more dominant effect. Compared with multiple linear regressions, pedotransfer functions(PTFs) from arti?cial neural network can better estimate the WAL indices. PTFs developed by artificial neural network respectively explained 86%, 81% and 64% of the total variations in WAL-T, WAL-SWC and WAL-CV. Knowledge of WAL is crucial for understanding the regional water budget and evaluating the stable soil water reserve, regional water characteristics and eco-hydrological processes in the Loess Plateau of China.
文摘为应对传统水网系统面临的运行效率低、安全风险高、人力成本大等挑战,借鉴无人驾驶汽车(autonomous vehicle,AV)在感知、决策、控制和系统架构方面的先进理念及等级划分标准,提出一种自主运行智慧水网(autonomous water network,AWN)的架构设计,并参照SAE J3016标准提出水网智能化等级(WN-L0至WN-L5)划分方案。继而引入类似AV的域控制器概念,构建水网调度域、水环境安全域、水机设备控制域、水工程设施设备安全域等核心功能域。阐述实现AWN所需的关键技术环节,并探讨端到端与模块化技术路线的可行性。通过展望AWN的未来发展趋势与面临挑战,旨在为未来智慧水网的建设提供理论框架、等级标准和技术路径参考。
