Topography Impacts Hydrology in the Sub-Humid Ethiopian Highlands

Understanding the relationship between topography, hydrological processes, and runoff source areas is essential in engineering design, such as predicting floods and implementing effective watershed management practices. This relationship is not well defined in the highlands with a monsoon climate and needs further study. The objective of this study is to relate topographic position and hydrological response in tropical highlands. The research was conducted in the Debre Mawi watershed in the northwest sub-humid Ethiopian highlands. In the monsoon rain phase of 2017 and 2018, groundwater depth, infiltration rate, and surface runoff were monitored at the upslope, midslope, and downslope positions. Surface runoff rates were measured in farmer fields through distributed V-notch weirs as estimates of positional runoff. Average water table depths were 30 cm deep in the downslope regions and 95 cm in the upslope position. The water table depth affected the steady-state infiltration rate in the rain phase. It was high upslope (350 mm h−1), low midslope (49 mm h−1), and zero downslope. In 2017, the average runoff coefficients were 0.29 for the upslope and midslope and 0.73 downslope. Thus, topographic position affects all aspects of the watershed hydrology in the humid highlands and is critical in determining runoff response.

Influence of Neighborhood Competition on Douglas-Fir Growth Is Not Altered by Local Environmental Conditions and Climate

To evaluate impacts of competitive conditions in tree neighborhoods on growth responses as influenced by moisture availability and local environmental conditions, we sampled 102 codominant 40- to 70-year-old coastal Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in regions with relatively high and low moisture availability. We quantified local environmental conditions (topographic position index, heat load, and soil depth), and the annual basal area increment and climate moisture deficits during two growth periods: a five-year period prior to commercial thinning and years 6 to 10 after thinning. In both regions and growth periods, tree growth was higher for trees growing in local neighborhoods with lower competition. The density/growth relationships differed by region and by growth period in the moist regions, but they were not influenced by climate moisture deficit. Furthermore, including topographic position index, heat load, or soil depth did not improve model support. Our results highlight the importance of managing local competition and indicate that environmental factors such as soil depth, heat load, and topography may be less likely to warrant consideration when developing thinning prescriptions. This could allow foresters to accommodate other ecosystem services when designing density management treatments, at least within typical growing conditions. Study Implications: Concerns about climate change have led to questions whether existing management practices, such as current thinning prescriptions, need to be modified to ensure sustainable provision of ecosystem services. Our results highlight the prominent role of local competitive conditions and indicate that fine-scale differences in topography and soils within our study region are not useful criteria for modifying thinning prescriptions to alter how trees responds to climate conditions, at least under typical growing conditions. Thus, foresters can focus their prescriptions on other aspects when developing thinning or other partial harvesting operations, such as timber production or wildlife habitat.

A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

Evolution of Habitat Quality and Its Topographic Gradient Effect in Northwest Hubei Province from 2000 to 2020 Based on the InVEST Model

Regional land use change and ecological security are important fields and have been popular issues in global change research in recent years. Regional habitat quality is also an important embodiment of the service function and health of ecosystems. Taking Shiyan City of Hubei Province as an example, the spatiotemporal differences in habitat quality in Shiyan City were evaluated using the habitat quality module of the InVEST model and GIS spatial analysis method based on DEM and land use data from 2000, 2005, 2010, 2015, and 2020. According to the habitat quality index values, the habitats were divided into four levels indicating habitat quality: I (very bad), II (bad), III (good), and IV (excellent), and the topographic gradient effect of habitat quality was studied using the topographic position index. The results show the following. (1) The habitat quality of Shiyan City showed relatively high and obvious spatial heterogeneity overall and, more specifically, was high in the northwest and southwest, moderate in the center, and low in the northeast. The higher quality habitats (levels III, IV) were mainly distributed in mountain and hill areas and water areas, while those with lower quality habitats (levels I, II) were mainly distributed in agricultural urban areas. (2) From 2000 to 2020, the overall average habitat quality of Shiyan City first increased, then decreased, and then increased again. Additionally, the habitat area increased with an improvement in the level. There was a trend in habitat transformation moving from low to high quality level, showing a spatial pattern of “rising in the southwest and falling in the northeast”. (3) The habitat quality in the water area and woodland area was the highest, followed by grassland, and that of cultivated land was the lowest. From 2000 to 2020, the habitat quality of cultivated land, woodland, and grassland decreased slightly, while the habitat quality of water increased significantly. (4) The higher the level of the topographic position index, the smaller the change range of land use types with time. The terrain gradient effect of habitat quality was significant. With the increase in terrain level, the average habitat quality correspondingly improved, but the increasing range became smaller and smaller. These results are helpful in revealing the spatiotemporal evolution of habitat quality caused by land use changes in Shiyan City and can provide a scientific basis for the optimization of regional ecosystem patterns and land use planning and management, and they are of great significance for planning the rational and sustainable use of land resources and the construction of an ecological civilization.

Topographic Position, Land Use and Soil Management Effects on Soil Organic Carbon (Vineyard Region of Niš, Serbia)

Spatial distribution of soil organic carbon (SOC) is the result of a combination of various factors related to both the natural environment and anthropogenic activities. The aim of this study was to examine (i) the state of SOC in topsoil and subsoil of vineyards compared to the nearest forest, (ii) the influence of soil management on SOC, (iii) the variation in SOC content with topographic position, (iv) the intensity of soil erosion in order to estimate the leaching of SOC from upper to lower topographic positions, and (v) the significance of SOC for the reduction of soil’s susceptibility to compaction. The study area was the vineyard region of Niš, which represents a medium-sized vineyard region in Serbia. About 32% of the total land area is affected, to some degree, by soil erosion. However, according to the mean annual soil loss rate, the total area is classified as having tolerable erosion risk. Land use was shown to be an important factor that controls SOC content. The vineyards contained less SOC than forest land. The SOC content was affected by topographic position. The interactive effect of topographic position and land use on SOC was significant. The SOC of forest land was significantly higher at the upper position than at the middle and lower positions. Spatial distribution of organic carbon in vineyards was not influenced by altitude, but occurred as a consequence of different soil management practices. The deep tillage at 60–80 cm, along with application of organic amendments, showed the potential to preserve SOC in the subsoil and prevent carbon loss from the surface layer. Penetrometric resistance values indicated optimum soil compaction in the surface layer of the soil, while low permeability was observed in deeper layers. Increases in SOC content reduce soil compaction and thus the risk of erosion and landslides. Knowledge of soil carbon distribution as a function of topographic position, land use and soil management is important for sustainable production and climate change mitigation.

Morphometric analysis and mapping: ways to apply the new global catalog of Mercury’s craters

&lt;p&gt;Morphometric parameters allow us to categorize relief features and create maps of geological and geomorphological formations on Earth and other celestial bodies. Catalogs of impact craters can be extremely useful for these purposes, since diameter, shape and other characteristics of craters should be taken into account in most cases when morphometric parameters are calculated.&lt;/p&gt;&lt;p&gt;We work on automation of geomorphological analysis and mapping. To achieve it we used supervised classification method and MESSENGER&amp;#8217;s data &amp;#8211; global mosaic of Mercury, images and several DEMs [1, 2]. Supervised classification method implies training samples which are necessary to find ranges of values, associated to a certain relief form, and define boundaries between the different types of surface, which training samples represent: smooth plains, hummocky inter-crater plains, etc.&lt;/p&gt;&lt;p&gt;In order to analyze and zone the surface at the global level, we calculated the following morphometric parameters:&lt;br&gt;1. Interquartile range of the second derivative of heights [3]. This parameter gives us the global patterns of planetary relief &amp;#8211; distribution of smooth and rough areas.&lt;br&gt;2. Relative topographic position (RTP) [4]. This parameter is suitable for automatic detection of concave/convex objects.&lt;br&gt;3. Vertical curvature. It is a measure of relative deceleration and acceleration of gravity-driven flows. Maps of vertical curvature show terraces and scarps [5].&lt;/p&gt;&lt;p&gt;Additionally we studied craters included in the catalog. We calculated various morphometric parameters for all of them, such as: depth, relative depth (the ratio of depth to diameter of craters), rim&amp;#8217;s volume to bowl&amp;#8217;s volume ratio and steepness of craters&amp;#8217; slopes.&lt;/p&gt;&lt;p&gt;As result we created thematic maps based on all of these parameters. At the detailed level, craters with complex structure (terraces and central peaks), craters located next to unusual textures [6] and multi-ringed basins were selected as objects of mapping. At the global level, we show regional differences in density of different categories of craters (with various degrees of their preservation).&lt;/p&gt;&lt;p&gt;Zharkova A.Yu., Kokhanov A.A., Kolenkina M.M., Kozlova N.A. and Zavyalov I.Yu. were supported by Russian Foundation for Basic Research (RFBR), project No 20-35-70019.&lt;/p&gt;&lt;p&gt;[1] Becker K. J., Robinson M. S., Becker T. L., Weller L. A., Edmundson K. L., Neumann G. A., Perry, M. E., Solomon, S. C. First Global Digital&lt;br&gt;Elevation Model of Mercury. 47th Lunar and Planetary Science Conference, 2016, LPI Contribution No. 1903, p.2959.&lt;br&gt;[2] Preusker F., Oberst J., Stark A., Matz K-D., Gwinner K., Roatsch T., 2017 High-Resolution Topography from MESSENGER Orbital Stereo Imaging &amp;#8211; The Southern hemispehre. EPSC Abstracts, Vol. 11, EPSC2017-591.&lt;br&gt;[3] Kokhanov, A.A., Bystrov, A.Y., Kreslavsky, M.A., Matveev, E.V., Karachevtseva, I.P., 2016. Automation of morphometric measurements for planetary surface analysis and cartography. In Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 431-433. doi.org/10.5194/isprs-archives-XLI-B4-431-2016.&lt;br&gt;[4] Jenness, J., 2006. Topographic Position Index (TPI) v. 1.3a, Jenness Enterprices. url: http://www.jennessent.com/arcview/tpi.htm&lt;br&gt;[5] Florinsky, I.V. An illustrated introduction to general geomorphometry. Progress in Physical Geography, 2017, 41: 723&amp;#8211;752. https://journals.sagepub.com/doi/10.1177/0309133317733667&amp;#160;&lt;br&gt;[6] Zharkova A.Yu., Kreslavsky M.A., Head J.W., Kokhanov A.A. Regolith textures on Mercury: Comparison with the Moon. Icarus, Volume 351, 2020, 113945, ISSN 0019-1035, https://doi.org/10.1016/j.icarus.2020.113945&lt;/p&gt;