Water quality data for Fall Creek,New York,USA: 1972–1995

The 33 086 ha mixed land use Fall Creek watershed in upstate New York is part of the Great Lakes drainage system. Results from more than 3500 water samples are available in a data set that compiles flow data and measurements of various water quality analytes collected between 1972 and 1995 in all seasons and under all flow regimes in Fall Creek and its tributaries. Data is freely accessible at https://ecommons.cornell.edu/handle/1813/8148 and includes measurements of suspended solids, pH, alkalinity, calcium, magnesium, potassium, sodium, chloride, nitrate nitrogen (NO3-N), sulphate sulphur (SO4-S), phosphorus (P) fractions molybdate reactive P (MRP) and total dissolved P (TDP), percent P in sediment, and ammonium nitrogen (NH4-N). Methods, sub-watershed areas, and coordinates for sampling sites are also included. The work represented in this data set has made important scientific contributions to understanding of hydrological and biogeochemical processes that influence loading in mixed use watersheds and that have an impact on algal productivity in receiving water bodies. In addition, the work has been foundational for important regulatory and management decisions in the region.     

Wetland hydroperiod classification in the western prairies using multitemporal synthetic aperture radar

Wetlands represent one of the world's most biodiverse and threatened ecosystem types and were diminished globally by about two‐thirds in the 20th century. There is continuing decline in wetland quantity and function due to infilling and other human activities. In addition, with climate change, warmer temperatures and changes in precipitation and evapotranspiration are reducing wetland surface and groundwater supplies, further altering wetland hydrology and vegetation. There is a need to automate inventory and monitoring of wetlands, and as a study system, we investigated the Shepard Slough wetlands complex, which includes numerous wetlands in urban, suburban, and agricultural zones in the prairie pothole region of southern Alberta, Canada. Here, wetlands are generally confined to depressions in the undulating terrain, challenging wetlands inventory and monitoring. This study applied threshold and frequency analysis routines for high‐resolution, single‐polarization (HH) RADARSAT‐2, synthetic aperture radar mapping. This enabled a growing season surface water extent hyroperiod‐based wetland classification, which can support water and wetland resource monitoring. This 3‐year study demonstrated synthetic aperture radar‐derived multitemporal open‐water masks provided an effective index of wetland permanence class, with overall accuracies of 89% to 95% compared with optical validation data, and RMSE between 0.2 and 0.7 m between model and field validation data. This allowed for characterizing the distribution and dynamics of 4 marsh wetlands hydroperiod classes, temporary, seasonal, semipermanent, and permanent, and mapping of the sequential vegetation bands that included emergent, obligate wetland, facultative wetland, and upland plant communities. Hydroperiod variation and surface water extent were found to be influenced by short‐term rainfall events in both wet and dry years. Seasonal hydroperiods in wetlands were particularly variable if there was a decrease in the temporary or semipermanent hydroperiod classes. In years with extreme rain events, the temporary wetlands especially increased relative to longer lasting wetlands (84% in 2015 with significant rainfall events, compared with 42% otherwise).     

Wetlands and low-gradient topography are associated with longer hydrologic transit times in Precambrian Shield headwater catchments

The estimation of hydrologic transit times in a catchment provides insights into the integrated effects of water storage, mixing dynamics, and runoff generation processes. There has been limited effort to estimate transit times in southern boreal Precambrian Shield landscapes, which are characteristically heterogeneous with surface cover including till, thin soils, bedrock outcrops, and depressional wetland features that play contrasting hydrologic roles. This study presents approximately 3.5 years of precipitation and streamflow water isotope data and estimates mean transit times (MTTs) and the young water fraction (p_y) across six small catchments in the Muskoka-Haliburton region of south-central Ontario. The main objectives were to define a typical range of MTTs for headwater catchments in this region and to identify landscape variables that best explain differences in MTTs/p_y using airborne light detection and ranging and digital terrain analysis. Of the transit time distributions, the two parallel linear reservoir and gamma distributions best describe the hydrology of these catchments, particularly because of their ability to capture more extreme changes related to events such as snowmelt. The estimated MTTs, regardless of the modelling approach or distribution used, are positively associated with the percent wetland area and negatively with mean slope in the catchments. In this landscape, low-gradient features such as wetlands increase catchment scale water storage when antecedent conditions are dryer and decrease transit times when there is a moisture surplus, which plausibly explains the increases in MTTs and mean annual runoff from catchments with significant coverage of these landscape features.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

基于噪声谱比法的内蒙古西部地区台站场地响应研究

利用内蒙古西部12个台站的地脉动噪声数据，采用噪声谱比法研究台站的场地响应情况。分析表明，台站场地响应按曲线形态可分为3类，且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现，场地响应曲线形态基本一致，只有极少数台站存在明显差异。结果表明，内蒙古西部区域大部分台站的台基状况总体较好，场地响应曲线较为平坦，无明显频率放大点。     

用C#.Net实现软件系统的动态更新

为了便于大型软件系统的后续开发和用户软件系统的升级，介绍了软件系统在Internet/Intranet上更新功能或数据的原理和方法，并用C#．Net进行了编程实现。     

MATLAB在运用系统建模处理南海气象数据过程中应用

介绍了MATLAB语言特点和系统建模方法的基本理论.根据南海气象数据的实际建模处理过程,给出了建模的详细步骤及其MATLAB实现过程以及MATLABTM的主要实现程序.试验讨论和结果表明利用MATLAB语言可以方便地对南海气象数据用系统建模方法进行建模和处理,MATLAB在运用系统建模法处理南海气象数据方面具有明显的优越性.     

影响鸢乌贼手钓上钩率的因子探讨

根据印度洋西北部鸢乌贼（Sym plectoteuthis oualaniensis）作业渔场的现场调查数据．分析了作业水深、作业时段、摄食等级和月相等因子对手钓上钩率的影响。统计表明．晚上、午夜和凌晨3个不同时段和不同作业水深对手钓上钩率均有显著性影响。3个时段中，其摄食等级呈先低后高再低的现象。午夜和凌展，上钩率的变化趋势与摄食等级的变化趋势呈显著的负相关。在月相的影响下．离朔日三四天之前，手钓产量呈上升趋势；离望日五六天之前。手钓产量呈明显的下降趋势。