Progress in Physical Geography: Earth and Environment

Drought is mainly triggered by the lack of precipitation, which can lead to insufficient water supply for crops thus affecting their growth and development. Reliable drought monitoring is crucial to understanding drought risk and avoiding drought-induced crop yield losses. Based on the Stacking regression method and multiple remotely-sensed drought factors from 2001 to 2017, this study developed an ensemble learning framework for monitoring agricultural drought in major winter wheat-producing areas in China. Stacking used five machine learning algorithms, namely, extreme gradient boosting, support vector regression, extra trees, and multi-layer perceptron, as the base learners to model the relationship between remote sensing drought factors and 1-, 3-, and 6-month standardized precipitation evapotranspiration index (SPEI). In this study, county-level winter wheat yield records and drought maps provided by the Global SPEI database (SPEIbase) were adopted to assess the suitability of Stacking-predicted SPEI drought maps in agricultural drought monitoring. The results show that Stacking outperformed other machine learning algorithms in terms of estimation accuracy, with the highest R² value of 0.77 and the lowest root mean square error (RMSE) of 0.47. The longer the time scale of model-predicted SPEI, the higher its correlation with detrended winter wheat yields. The comparison with the drought maps of SPEIbase shows that the Stacking-predicted drought maps successfully captured the spatial pattern and intensity change of drought events. The approach presented in the study has good applicability for agricultural drought monitoring and could be extended to the rest of the areas.

Since the 1990s, an international consensus has been emerging with respect to the methods used to identify and assess geosites. These conventional methodologies generally differentiate between the intrinsic or scientific-natural dimension and the social dimension in the potential value of the geological elements. The aim of this article is to analyse the possible existence of other concepts that may underlie these criteria and that could be operating as evaluation parameters even though they are not explicitly and formally described as such in the published studies. An exhaustive analysis of articles on geosite inventories published in Science Citation Index journals in the last 15 years revealed a systematic reference to conceptions of magnitude (in the sense of degree of development and physical prominence) in geosite descriptions around the world, and in particular with respect to sites of geomorphological interest. To contrast this indication, we subjected the geosites inventory of Lanzarote (Canary Islands, Spain) to a Magnitude Rule hypothesis. This inventory is part of the Spanish Geosites Inventory (IELIG, for its initials in Spanish), which was carried out using conventional assessment procedures. Applying this rule, according to which each geosite is the most prominent example of its typology within the reference geological domain, we obtained an almost complete match. That is, geomorphological sites that were selected, a priori, according to intrinsic and social dimension were also (or one could even say ‘actually’) selected, implicitly, on the predominant basis of magnitude. This paper discusses why and how the magnitude parameter may be playing a key role as a subliminal non-explicit driver in the evaluation of geosite relevance, and how this finding may open new perspectives and bring methodological advantages in the inventory of geological heritage.

Infilled lakes are a prevalent geomorphic feature in the intricate high mountain landscape of the Cordillera Blanca, Peru. Despite their apparent geomorphic, hydrological, and ecological importance, a systematic inventory of these areas has been lacking. This study presents an inventory of infilled lakes in the Cordillera Blanca. A total of 962 infilled lake polygons have been manually mapped, covering an area of nearly 90 km² (the area of individual mapped polygons ranges from 0.001 km² to 1.760 km²), more than double the area of existing lakes (40 km²) and the majority of flat areas (62% of areas with slope ≤5°). The study reveals that infilled bedrock-dammed lakes are the most common type (42%), while moraine-dammed lakes account for the majority of the infilled lake area (52%) and sediment volume (52% to 57%). Considering high uncertainty of infilled basins’ morphology, the estimated sediment volume of infilled lakes ranges between 0.9 km³ and 2.3 km³ (compared to 0.79 km³ to 1.15 km³ of water stored in existing lakes). The case study of Lake Aguascocha catchment reveals a mean sediment yield of 0.64 to 1.63∙10⁶ m³∙km⁻² during the past 10.7 ± 0.3 ka, that is, a mean annual sediment yield of 58.5 to 156.4 m³∙km⁻²∙yr⁻¹. Furthermore, 65 locations where preserved geomorphic evidence indicates possible outburst floods in the past are identified. These areas are particularly important for understanding patterns of lake outburst occurrence on longer timescales than traditionally considered in lake outburst flood hazard studies. The dataset presented in this study is intended to serve as a basis for identifying sites suitable for further site-specific paleo-geographical, sedimentological, geochronological as well as broader mountain landscape evolution studies.

The primary factors controlling regional gully distribution in mountainous areas are poorly understood. To investigate the spatial characteristics and controlling factors of mountainous gullies at the regional scale, kernel density (KD) estimation, semivariogram, and Geodetector methods were used based on 11 environmental factors of gullies in the Yuanmou dry-hot valley. The results show that (a) gullies are widely but unevenly distributed in the valley, with an average KD of 1.155 km/km², and gully distribution displays spatial autocorrelation with environmental factors over diverse scales; (b) relief amplitude (Ra), landform type, and slope are the primary factors controlling gully distribution, and land use type, precipitation, and elevation are also important factors; and (c) a high risk of gully erosion occurs in areas with an elevation of more than 1437 m, slope greater than 15°, and Ra greater than 173 m, with grassland vegetation or luvisols and cambisols soil types. These results will not only help to understand the spatial pattern and formation mechanism of gullies at the macroscopic scale but also provide a scientific reference for regional gully management.

Formulating ecological restoration strategies requires accurately quantifying how climate and anthropogenic factors influence net primary production (NPP). A Carnegie-Ames-Stanford approach (CASA) model was applied to estimate China’s terrestrial NPP from 2001 to 2020. We adopted a random forest (RF) method to identify the main driving forces for NPP change in China. Total NPP in China increased noticeably with a 24.91 Tg C/yr rate, as shown in our results. The significantly increased NPP was mainly attributed to human activities (64.29 ± 0.17%), chiefly due to human management and ecological projects (afforestation or other) fostered vegetation growth. The primary drivers of NPP variation varied in different geographic regions. Climate dominated the NPP dynamic in north China (52.38 ± 0.91%), where the main factor that restricted the increase of NPP was precipitation. Human activities strongly impacted the NPP variation in the remaining regions. Human management measures increased NPP in northwest and southwest China. In the northeast, east, and south-central China, the NPP change resulted from land use change, primarily grassland, cropland, and forest change. Collectively, our study expands the understanding of the driving forces of NPP change, informing different strategies for achieving ecological restoration and carbon neutrality.

Circular depressions are concave, shallow depressions found on planar landscape surfaces in the southern Namib Desert. They occur on gravelly substrates with nearly level to very slightly inclined surfaces. The depressions range from 6 to 10 m in diameter with centers typically depressed 10–20 cm below the level of the surrounding terrain. Locations of individual circular depressions were mapped at one site using ground-based measurements and at three additional sites using Google Earth imagery. At all sites, circular depressions are highly overdispersed with densities ranging from approximately 10–20/ha and corresponding nearest neighbor distances of 17–24 m. Large fragments of weathered calcrete and stones occur on soil surfaces surrounding circular depressions, but not within the depressions. Circular depressions at one site contained active burrow systems of Brants’ whistling rat (Paratomys brantsii). Bioturbation by these rodents contributes to the non-cohesive nature of the sandy substrate, which promotes aeolian deflation and formation of the depressions. Excavations of the burrow systems by the honey badger (Mellivora capensis) in search of rodent prey can transfer large stones and calcrete fragments from the subsurface to the surface and subsequently move those materials about the surface. Even if such sequential, horizontal displacements are in random directions, such movements can eventually yield a central, clast-free area surrounded by a peripheral zone where the clasts accumulate once they have been displaced beyond the margin of the area to which the predator is drawn in search of rodent prey. A conceptual model consisting of a two-dimensional random walk of large clasts about the surface until they are displaced from the focal “arena” of rodent occupation provides a novel explanation for origin of a spatially organized pattern that is initiated through the random displacement of those materials. Comparable microtopographic patterning associated with bioturbation in other arid and semi-arid environments may have similar origins.