Urban canopy parameters (UCPs) are essential in order to accurately model the complex interplay between urban areas and their environment. This study compares three different approaches to define the UCPs for Moscow (Russia), using the COSMO numerical weather prediction and climate model coupled to TERRA_URB urban parameterization. In addition to the default urban description based on the global datasets and hard-coded constants (1), we present a protocol to define the required UCPs based on Local Climate Zones (LCZs) (2) and further compare it with a reference UCP dataset, assembled from OpenStreetMap data, recent global land cover data and other satellite imagery (3). The test simulations are conducted for contrasting summer and winter conditions and are evaluated against a dense network of in-situ observations. For the summer period, advanced approaches (2) and (3) show almost similar performance and provide noticeable improvements with respect to default urban description (1). Additional improvements are obtained when using spatially varying urban thermal parameters instead of the hard-coded constants. The LCZ-based approach worsens model performance for winter however, due to the underestimation of the anthropogenic heat flux (AHF). These results confirm the potential of LCZs in providing internationally consistent urban data for weather and climate modelling applications, as well as supplementing more comprehensive approaches. Yet our results also underline the continued need to improve the description of built-up and impervious areas and the AHF in urban parameterizations.

Line generalization is an essential data processing operation in geographic information systems and cartography. Many point reduction and simplification algorithms have been developed for this purpose. During the last three decades, several attempts have been made to develop approaches of generalization that adapt to the geometric properties of the processed lines. They typically incorporate line segmentation based on quantitative descriptions of the line shape. Little attention has been paid to the cases in which heterogeneous lines of different geometric character are mixed in one dataset. One common example is administrative borders, which often contain natural and artificial, smooth and sharp, schematic and non-schematic, and regular and irregular shapes. The tuning of the simplification algorithm based on a quantitative description of the shape would be ineffective here, since different algorithms must be applied to lines of different characters. In this article, we present a general method and generalization model for the simplification of such datasets. The properties of schematism, smoothness and regularity are used to differentiate various line characters. Three line characters, including irregular non-schematic, irregular schematic and regular orthogonal schematic, were selected for the current study. The developed generalization model consists of preprocessing, processing and postprocessing stages. Line segmentation based on the detection of different characters is performed during the preprocessing stage. Then, Li–Openshaw, Douglas–Peucker and orthogonal simplification are applied to the appropriate segments in the processing stage. Postprocessing enables the addition of extra regularity to the simplified shape. A visual and quantitative assessment of the results is provided and demonstrates the effectiveness of the developed approach in comparison with the global application of a single simplification algorithm.

The river runoff regime significantly changed worldwide in the end of XX – the beginning of XXI century. In the European part of Russia the current changes are manifested in different ways in various regions. The main trend that is observed for most river basins is the ‘leveling’ of annual hydrographs. The interception of melt water during occasional thaw floods has resulted in a decrease in river runoff during the spring months. Simultaneously an increase in the low water period during winter and summer occurred. Reducing in the depth of soil freezing, caused by warm winters, lead to higher groundwater replenishment. At the same time, the loss of melt water to surface retention as well as evaporation has increased. Almost in all seasons of the year the number of occasional flood peaks increases. Occasional floods are superimposed both on the rise and the decline of seasonal snowmelt flood wave, making it difficult to separate it as an independent phase of the water regime. Changes in seasonal characteristics of river runoff are the most evident in the Don and Oka basins. A significant transformation of the water regime is observed in the north-west of European Russia as well. To a lesser extent, the described patterns are typical for the northern rivers and the eastern part of the European Russia – the Kama and the Ural Basin.

One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. Recent advancements in the coverage, quality, and resolution of global DEMs facilitate the overall improvement of the detail and reliability of terrain-related research. At the same time, geographic problem solving is conducted in a wide variety of scales, and the data used for mapping should have the corresponding level of detail. Specifically, at small scales, intensive generalization is needed, which is also true for elevation data. With the widespread accessibility of detailed DEMs, this principle is often violated, and the data are used for mapping at scales far smaller than what is appropriate. Small-scale relief shading obtained from fine-resolution DEMs is excessively detailed and brings an unclear representation of the Earth’s surface instead of emphasizing what is important at the scale of visualization. Existing coarse-resolution global DEMs do not resolve the issue, since they accumulate the maximum possible information in every pixel, and therefore also require reduction in detail to obtain a high-quality cartographic image. It is clear that guidelines and effective principles for DEM generalization at small scales are needed. Numerous algorithms have been developed for the generalization of elevation data represented either in gridded, contoured, or pointwise form. However, the answer to the most important question—When should we stop surface simplification?—remains unclear. Primitive error-based measures such as vertical distance are not effective for cartography, since they do not account for the landform structure of the surface perceived by the map reader. The current paper approached the problem by elaborating the granularity—a newly developed property of DEMs, which characterizes the typical size of a landform represented on the DEM surface. A methodology of estimating the granularity through a landform width measure was conceptualized and implemented as software. Using the developed program tools, the optimal granularity was statistically learned from DEMs reconstructed for multiple fragments of manually drawn 1:200,000, 1:500,000, and 1:1,000,000 topographic maps covering different relief types. It was shown that the relative granularity should be 5–6 mm at the mapping scale to achieve the clearness of relief presentation typical for manually drawn maps. We then demonstrate how the granularity measure can be used effectively as a constraint during DEM generalization. Experimental results on a combination of contours, hypsometric tints, and hill shading indicated clearly that the optimal level of detail in small-scale cartographic relief presentation can be achieved by DEM generalization constrained by granularity in combination with fine DEM resolution, which facilitates high-quality rendering.