Features of the Clinical Course of Arterial Hypotension in Pregnant Women

A study was carried out of women with arterial hypotension against the background of the development of the pathological state of the fetus and newborns born to women living in two different climatic regions of Osh city and in the highlands of Chon-Alay district of Osh region. In the pathogenesis of pregnancy complications caused by arterial hypotension, the leading importance is attached to vascular disorders and microcirculation disorders, leading to systemic hemodynamic changes in the body of a pregnant woman. It was found that in mountainous terrain, arterial hypotension and exogenous hypoxia affect the “mother–placenta–fetus–newborn” system, increasing the load on the respiratory, circulatory and hematopoietic organs of the mother, and also leads to impaired placental function.

Z badań nad Biłgorajem. Weryfikacja i dopełnienie

The article presents the state of research into the toponym Biłgoraj: initially incorrectly interpreted as a topographic name consisting of the segment Biel- “swamp” (>Ukrainian Bił-) and the morpheme -goraj “mountainous terrain”. Another interpretation of this toponym says that this name is memorial and physiographic in nature, with its first physiographic part coming from the local adjective *bieły (in general Polish biały “white”), shortened to Bieł- (>Ukrainian Bił-), referring to the first part of the name of the river *Bieła Łada < Biała Łada, which Biłgoraj is located on, and the second morpheme – the memorial one taken from the name of a nearby settlement Goraj, which was the seat of the ancestors of Adam Gorajski, the founder of Biłgoraj, a settlement also located on the river Biała Łada.

A seasonal algorithm of the snow-covered area fraction for mountainous terrain

The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.

The Harvest Operability Index (HOI): A Decision Support Tool for Mechanized Timber Harvesting in Mountainous Terrain

Forest operations have become increasingly reliant on mechanized harvesting equipment due to their increased production capacities in competitive markets. However, operating heavy machinery in mountainous terrain poses numerous operational and accessibility challenges from steep slopes, erosion risk, and poor road access. Geographic Information Systems (GIS) have effectively been used in various studies to identify areas in mountainous landscapes that pose no or reduced constraints for harvesting equipment operation. This study introduces the Harvest Operability Index (HOI), which rates a landscape for wheel-based equipment suitability (i.e., operability) and assesses its application in 13,118 ha of the Jocassee Gorges Natural Resource Area, situated on the Southern Blue Ridge Escarpment in Northwestern South Carolina, USA. The HOI incorporated slope, distance from roads, cost distance from major highways, primary Streamside Management Zones (SMZ), stand age, and soil suitability ratings for harvesting equipment operation. Upon reclassification to a 5-tier suitability scale, the HOI revealed 60% (7824 ha) of the case study area was in a Slope Exclusion Zone, or land area inoperable for wheel-based equipment due to steep slopes. Values of Very Poor and Poor Operability occupied less than 1% (213 ha) of land area whereas Moderate Operability values were 9% of the land area (1257 ha). Values of Good Operability occupied 18% (2442 ha) of the study area and values of Very Good Operability occupied 10% (1381 ha). These results reflected the challenges of mechanized harvesting in the study area due to a preponderance of steep slopes and poorly suited soil. Our model delineated areas of high equipment operability in two locations in the study area, despite a lack of recent logging activity around them. Results of the HOI analysis offer an accessible way for forest managers to better prioritize logging operations in areas that are highly operable and therefore more likely to possess lower overall harvesting costs, for wheel-based harvesting systems. The HOI can also be used as an asset for other forest management priorities, such as identifying highly operable areas that can use timber harvesting for fuel reduction and ecological restoration in fire-dependent forests. This model can be applied to various other regions where mountainous terrain poses a limitation to wheel-based harvesting equipment operation- and where wheel-based equipment is essential to advance the pace and scale of harvesting for ecological restoration.

Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

Topographic and Landcover Influence on Lower Atmospheric Profiles Measured by Small Unoccupied Aerial Systems (sUAS)

Small unoccupied aerial systems (sUASs) are increasingly being used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors, low cost, and precise maneuverability and navigation make them a versatile tool for a field researcher. Procedures and instrumentation for sUASs are largely undefined, especially for atmospheric and hydrologic applications. The sUAS’s ability to collect atmospheric data for characterizing land–atmosphere interactions was examined at three distinct locations: Costa Rican rainforest, mountainous terrain in Georgia, USA, and land surfaces surrounding a lake in Florida, USA. This study aims to give further insight on rapid, sub-hourly changes in the planetary boundary layer and how land development alters land–atmosphere interactions. The methodology of using an sUAS for land–atmospheric remote sensing and data collection was developed and refined by considering sUAS wind downdraft influence and executing systematic flight patterns throughout the day. The sUAS was successful in gathering temperature and dew point data, including rapid variations due to changing weather conditions, at high spatial and temporal resolution over various land types, including water, forest, mountainous terrain, agriculture, and impermeable human-made surfaces. The procedure produced reliably consistent vertical profiles over small domains in space and time, validating the general approach. These findings suggest a healthy ability to diagnose land surface atmospheric interactions that influence the dynamic nature of the near-surface boundary layer.

Differences in spatial patterns and driving factors of biomass carbon density between natural coniferous and broad-leaved forests in mountainous terrain, eastern Loess Plateau of China

Background Mountain forests in China are an integral part of the country’s natural vegetation. Understanding the spatial variability and control mechanisms for biomass carbon density of mountain forests is necessary to make full use of the carbon sequestration potential for climate change mitigation. Based on the 9th national forest inventory data in Shanxi Province, which is mountainous terrain, eastern Loess Plateau of China, we characterized the spatial pattern of biomass carbon density for natural coniferous and broad-leaved forests using Local Getis-ord G* and proposed an integrative framework to evaluate the direct and indirect effects of stand, geographical and climatic factors on biomass carbon density for the two types of forests using structural equation modeling. Results There was no significant difference between the mean biomass carbon densities of the natural coniferous and broad-leaved forests. The number of spots with a spatial autocorrelation accounted for 51.6% of all plots of the natural forests. Compared with the broad-leaved forests, the hot spots at the 1% significance level for the coniferous forests were distributed in areas with higher latitude, higher elevation, lower temperature, and lower precipitation. Geographical factors affected biomass carbon density positively and indirectly, via the stand and climatic factors, with larger effects for the natural coniferous than broad-leaved forests. Latitude and elevation are the most crucial driving factors for coniferous forests, but stand age and forest coverage are for broad-leaved forests. Climatic factors had weaker effects than other factors, with negative effects of temperature for coniferous and no effects for broad-leaved forests. Conclusions The effects of stand, geographical and climatic factors on biomass carbon density are different between natural coniferous and broad-leaved forests, respectively. Employing the integrative framework can improve the prediction of the impact of stand, geographical and climatic factors on natural forests in mountainous areas.

Mountain definitions and their consequences

Mountains are rugged structures in the landscape that are difficult to delineate. Given that they host an overproportional fraction of biodiversity of high ecological and conservational value, conventions on what is mountainous and what not are in need. This short communication aims at explaining the differences among various popular mountain definitions. Defining mountainous terrain is key for global assessments of plant species richness in mountains and their likely responses to climatic change, as well as for assessing the human population density in and around mountainous terrain.