Detecting trends that are nonlinear and asymmetric on diurnal and seasonal time scales

Trends in climate time series are often nonlinear and temporally-asymmetric, i.e. the trend is different for different seasons and/or hours of the day. Here a method is developed that allows the nonlinearity and temporal asymmetry of a trend to be investigated simultaneously. First, nonlinear trend components are extracted from a univariate time series, by adapting a nonparametric dimension-reduction method. Then, the nonlinear trend components are substituted into a regression model in which the periodic mean component and the periodic variation in the amplitude of the nonlinear trend are modeled using harmonic functions of the seasonal and diurnal periods. Third, trend patterns in the positive and negative anomalies are investigated, by extending the nonlinear trend model using indicator variables. Fourth, a non-local inferential test is developed to test the statistical significance of the trend patterns. The nonlinear trend model is applied to a simulated time series, as well as to long-term high-resolution temperature records from five Southern Hemisphere sites: Lucas Heights, Sydney Airport, Cape Grim, Macquarie Island and Law Dome. Our method should be generally useful for identifying the effect of both climate-related factors and observation/site-related factors on seasonal and diurnal trends in meteorological data series.     

Does the Social Capital in Networks of “Fish and Fire” Scientists and Managers Suggest Learning?

Patterns of social interaction influence how knowledge is generated, communicated, and applied. Theories of social capital and organizational learning suggest that interactions within disciplinary or functional groups foster communication of knowledge, whereas interactions across groups foster generation of new knowledge. We used social network analysis to examine patterns of social interaction reported in survey data from scientists and managers who work on fish and fire issues. We found that few fish and fire scientists and managers interact with one another, suggesting low bridging social capital and thus, limited opportunity for generation of new knowledge. We also found that although interaction occurs among scientists—suggesting modest bonding social capital—few managers interact with other managers, indicating limited opportunity for communication of scientific knowledge for the purposes of application. We discuss constraints and opportunities for organizational learning evident in these patterns of social interaction among fish and fire scientists and managers.     

A case study on the correlation of micro-contaminants and potassium in the Leine River (Germany)

River monitoring with focus on the correlation of 41 micro-contaminants with K⁺ and its temporal and spatial variation was carried out in the Leine River (Germany). A positive correlation of concentrations of wastewater-related micro-contaminants and K⁺ is to be expected in receiving waters of wastewater treatment plant (WWTP) effluent since urine is a significant source of K⁺ in WWTP effluent. This correlation was found for compounds, which meet the following criteria: (1) WWTP effluent is the dominating source of the compound, (2) variability of its mass flux in the WWTP is negligible, and (3) the compound is persistent in WWTPs and in the environment. The excellent positive correlation of carbamazepine with K⁺ resulted in the fitting of a universal linear equation to the summer and winter data. For almost all other correlating compounds (1H-benzotriazole, citalopram, diclofenac, metoprolol, sotalol, sulfamethoxazole and tolyltriazole), slopes of the line fittings were steeper in winter (x-axis: K⁺, y-axis: respective micro-contaminant). This has been attributed to a presumed lower degree of degradation and attenuation within WWTPs and in the environment due to low temperatures as well as an increase in consumption of these compounds in the winter months. As part of this research, a sampling event along the entire flow path of the Leine River (∼280 km) was conducted to identify compounds demonstrating stable ratios of various respective micro-contaminants with K⁺. Among other compounds, carbamazepine, sulfamethoxazole, and tolyltriazole demonstrated the best correlations with R² > 0.89. Potassium-equivalents of the individual micro-contaminants depended on land use and population structure of the investigated river section.     

卡斯特地区的公路设计

１场地考察第一步是利用现有岩土工程资料,如州和政府的资料、航空照片、卫星图像和设计单位的数据库。这些对考察卡斯特地区有用的资料包括：（１）详细地图（这些地图不全发表）,一些州的调查实际上包含有碳酸盐岩石详细地图,从中可了解到已知的或可疑落水洞地区。（...     

Roadway design in karst

To minimize costs in conventional roadway design, as much low or valley areas as possible are utilized. In many areas of the eastern United States, these valleys are filled with carbonate rocks. Excavation is used to minimize grades—this removes protective overburden or rock cover over cavities; fill also is used to minimize grades—this can increase loads on marginally stable soil arches or rock cavity roofs. Surface water runoff is directed toward low areas—the low areas are likely zones of weakness or solutioning, thereby increasing the potential for sinkhole development and providing an opportunity for ground—water contamination, and remediation usually consists of blindly filling rock cavities, thus either channeling the still-contaminated surface flows someplace else or perhaps eliminating useful ground water recharge conduits. The authors suggest that the key to proper design, construction, and remediation for roadways planned in karst is to understand the geologic and hydrogeologic setting of the route(s) or locale, perform true geotechnical engineering design, and remediate with an understanding of the overall engineering geologic, hydrogeologic, and environmental picture.     

In memoriam: Professor Arthur Getis (July 6, 1934–May 13, 2022)

Journal of Geographical Systems -     

Non-stationary response of structures excited by random seismic processes with time variable frequency content

Seismic random processes are characterized by high non-stationarity and, in most cases, by a marked variability of frequency content. The hypothesis modeling seismic signal as a simple product of a stationary signal and a deterministic modulation function, consequently, is hardly ever applicable. Several mathematical models aimed at expressing the recorded process by means of a system of stationary random processes and deterministic amplitude and frequency modulations are proposed. Models oriented into the frequency domain with subsequent response analysis based on integral spectral resolution and models oriented into the time domain based on the multicomponent resolution are investigated. The resolution into individual components (non-stationary signals) is carried out by three methods. The resolution into intrinsic mode functions seems to possess the best characteristics and yields results almost not differing from the results obtained by stochastic simulation. An example of the seismic response of an existing bridge obtained by two older models and three variants of multicomponent resolution is given.     

The causes of land-use and land-cover change: moving beyond the myths

Common understanding of the causes of land-use and land-cover change is dominated by simplifications which, in turn, underlie many environment-development policies. This article tracks some of the major myths on driving forces of land-cover change and proposes alternative pathways of change that are better supported by case study evidence. Cases reviewed support the conclusion that neither population nor poverty alone constitute the sole and major underlying causes of land-cover change worldwide. Rather, peoples’ responses to economic opportunities, as mediated by institutional factors, drive land-cover changes. Opportunities and constraints for new land uses are created by local as well as national markets and policies. Global forces become the main determinants of land-use change, as they amplify or attenuate local factors.     

A distribution-free ordinal classification of floods based on moments

ABSTRACT

Classification of floods is often based on return periods of their peaks estimated from probability distributions and hence depends on assumptions. The choice of an appropriate distribution function and parameter estimation are often connected with high uncertainties. In addition, limited length of data series and the stochastic characteristic of the occurrence of extreme events add further uncertainty. Here, a distribution-free classification approach is proposed based on statistical moments. By using robust estimators the sampling effects are reduced and time series of different lengths can be analysed together. With a developed optimization procedure, locally and regionally consistent flood categories can be defined. In application, it is shown that the resulting flood categories can be used to assess the spatial extent of extreme floods and their coincidences. Moreover, groups of gauges, where simultaneous events belong to the same classes, are indicators for homogeneous groups of gauges in regionalization.