Seasonal connections between meteoric water and streamflow generation along a mountain headwater stream

In snowmelt-driven mountain watersheds, the hydrologic connectivity between meteoric waters and stream flow generation varies strongly with the season, reflecting variable connection to soil and groundwater storage within the watershed. This variable connectivity regulates how streamflow generation mechanisms transform the seasonal and elevational variation in oxygen and hydrogen isotopic composition (δ¹⁸O and δD) of meteoric precipitation. Thus, water isotopes in stream flow can signal immediate connectivity or more prolonged mixing, especially in high-relief mountainous catchments. We characterized δ¹⁸O and δD values in stream water along an elevational gradient in a mountain headwater catchment in southwestern Montana. Stream water isotopic compositions related most strongly to elevation between February and March, exhibiting higher δ¹⁸O and δD values with decreasing elevation. These elevational isotopic lapse rates likely reflect increased connection between stream flow and proximal snow-derived water sources heavily subject to elevational isotopic effects. These patterns disappeared during summer sampling, when consistently lower δ¹⁸O and δD values of stream water reflected contributions from snowmelt or colder rainfall, despite much higher δ¹⁸O and δD values expected in warmer seasonal rainfall. The consistently low isotopic values and absence of a trend with elevation during summer suggest lower connectivity between summer precipitation and stream flow generation as a consequence of drier soils and greater transpiration. As further evidence of intermittent seasonal connectivity between the stream and adjacent groundwaters, we observed a late-winter flush of nitrate into the stream at higher elevations, consistent with increased connection to accumulating mineralized nitrogen in riparian wetlands. This pattern was distinct from mid-summer patterns of nitrate loading at lower elevations that suggested heightened human recreational activity along the stream corridor. These observations provide insights linking stream flow generation and seasonal water storage in high elevation mountainous watersheds. Greater understanding of the connections between surface water, soil water and groundwater in these environments will help predict how the quality and quantity of mountain runoff will respond to changing climate and allow better informed water management decisions.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

A modified numerical model for moisture-salt transport in unsaturated sandy soil under evaporation

Soil salinization, caused by salt migration and accumulation underneath the soil surface, will corrode structures. To analyze the moisture-salt migration and salt precipitation in soil under evaporation conditions, a mathematical model consisting of a series of theoretical equations is briefly presented. The filling effect of precipitated salts on tortuosity factor and evaporation rate are taken into account in relevant equations. Besides, a transition equation to link the solute transport equation before and after salt precipitation is proposed. Meanwhile, a new relative humidity equation deduced from Pitzer ions model is used to modify the vapor transport flux equation. The results show that the calculated values are in good agreement with the published experimental data, especially for the simulation of volume water content and evaporation rate of Toyoura sand, which confirm the reliability and applicability of the proposed model.     

自然和自然贡献情景模型研究综述

本文回顾了自然和自然贡献情景模型发展的背景、历史和内容,概括总结了自然和自然贡献情景模型的发展进程以及联合国生物多样性与生态系统服务政府间科学—政策平台(IPBES)情景模型的概念框架,讨论了自然和自然贡献情景模型存在的问题和发展方向。为了在全球层面解决现有综合集成模型存在的问题,根据地球表层建模基本定理和生态环境曲面建模基本定理,提出了具有中国原创特点的自然与自然贡献情景模型概念框架。     

景观生态分类与制图浅议

本文在查阅分析大量文献和前人研究的基础上 ,对目前景观生态分类和景观制图作了详细的对比分析 ,认为景观分类需要结合实际区域现状 ,采用逐级分类的方法 ;同时利用 ETM遥感影像为数据源 ,以天山北麓为示范区.研制其土地利用土地覆盖变化的景观类型图。     

平衡剖面的制作流程及其地质意义

平衡剖面技术是地质思维和计算机技术的结晶,使对断层构造的研究提高到定量阶段,其依据是在垂直构造走向的剖面上,地层长度和面积(2D)或体积(3D)是均衡的。在此原理基础上利用数学手段对盆地的构造发育史进行正演和反演模拟,直观地再现地下构造的原始几何形态,迅速提供地震剖面的构造解释方案,并对解释结果进行检验(不平衡的剖面其解释一般有问题),为深刻认识构造发育史、分析油气运移及聚集规律提供依据,提高了工作效率。其结果也为盆地模拟、油藏模拟、定量计算构造伸缩量等地质研究打下了坚实的基础[1]。     

Laboratory kinetics of C2H radical reactions with ethane, propane, and n-butane at T = 96-296 K: implications for Titan

The kinetics of the reactions of C₂H radical with ethane (k₁), propane (k₂), and n-butane (k₃) are studied over the temperature range of T = 96-296 K with a pulsed Laval nozzle apparatus that utilizes a pulsed laser photolysis-chemiluminescence technique. The C₂H decay profiles in the presence of both the alkane reactant and O₂ are monitored by the CH(A²Δ) chemiluminescence tracer method. The results, together with available literature data, yield the following Arrhenius expressions: k₁(T) = (0.51 ± 0.06) × 10⁻¹⁰ exp[(−76 ± 30)K/T] cm³ molecule⁻¹ s⁻¹ (T = 96-800 K), k₂(T) = (0.98 ± 0.32) × 10⁻¹⁰exp[(−71 ± 60)K/T] cm³ molecule⁻¹ s⁻¹ (T = 96-361 K), and k₃(T) = (1.23 ± 0.26) × 10⁻¹⁰ cm³ molecule⁻¹ s⁻¹ (T = 96-297 K). At T = 296 K, k₁ is measured as a function of total pressure and has little or no pressure dependence. The results from this work support a direct hydrogen abstraction mechanism for the title reactions. Implications to the atmospheric chemistry of Titan are discussed.