Structural Control of Relief Morphometry on Territorial Biosphere Conditions: A Quantitative–Dialectical Systematic Review

The morphometry of relief—understood as the quantitative measurement and analysis of landforms—constitutes a central structural component in the spatial organization of the geosystem and in the dynamics of the territorial biosphere. Parameters such as elevation, slope, aspect, curvature, and relief energy constitute the fundamental geometry of the Earth’s surface and, consequently, shape the patterns of concentration, dissipation, and transfer of energy and matter [1], [2]. From this perspective, relief is not an inert support, but a structuring agent that regulates hydrological, energetic, biogeochemical, and ecosystem processes, a phenomenon widely recognized as morphometric control [3], [4]. This control operates through physical gradients—potential and kinetic energy, gravity, thermal gradients, atmospheric pressure, and solar radiation—that act as drivers of geoenvironmental processes such as erosion, infiltration, runoff, water recharge, and geomorphological stability [5]. In turn, these processes modulate geoenvironmental conditions—climatic zones, moisture distribution, ecosystem configuration, and productivity dynamics—shaping spatially differentiated environmental patterns [6].

From an Earth sciences perspective, the relevance of morphometry lies in its ability to characterize the geometric configuration of the land surface and relate it to the dynamics of surface processes. Slope represents the topographic gradient and determines the energy associated with the movement of water and materials; it is therefore linked to erosion, runoff, and mass movements [3], [5], [6]. Elevation establishes potential energy gradients and topographic conditions that influence the spatial distribution of hydrological and geomorphological processes [2], [3]. Curvature allows for the differentiation of areas where flow concentrates or disperses, with implications for infiltration, runoff, and the formation of erosional features [5], [7]. Similarly, terrain roughness—or irregularity—reflects the surface’s micro-topographic variability and can alter the distribution of runoff, erosion, and slope stability [4], [6]. Collectively, these parameters enable the interpretation of a geomorphological sequence in which the relief configuration dictates surface and gravitational flows, sediment mobilization and transport, and, ultimately, the resulting landscape configuration [5], [6].

In recent decades, the development of high-resolution digital elevation models (DEMs) and advancements in geographic information systems have enabled deeper quantitative modeling of landforms and their influence on territorial processes across multiple scales [7]. This has spurred a proliferation of studies in fields such as physical geography, geomorphology, civil engineering, and environmental engineering, where morphometry is used for landscape classification, risk assessment, infrastructure design, water management, and ecosystem analysis [8], [9], [10]. However, despite the abundance of evidence, the scientific literature exhibits significant methodological and conceptual heterogeneity. Discrepancies exist regarding the magnitude of morphometric control across different climatic, physiographic, and temporal contexts, as well as variations linked to the DEM resolution, the scale of analysis, and the statistical models employed [11], [12], [13]. This lack of consistency limits the ability to formulate general principles applicable to different regions and hinders the development of integrative, biosphere-scale models.

In Latin America, studies conducted in Colombia and Mexico, together with research in Peru on relief dynamics and hydrological processes—including erosion in high-Andean basins, slope stability in the central highlands, and geomorphological modeling of high-Andean ecosystems—have demonstrated significant progress but also reflect similar methodological fragmentation [14], [15], [16], [17], [18]. Although relevant, these studies have not been integrated into a comprehensive synthesis that quantitatively evaluates the degree, direction, and intensity of morphometric influence. To address this gap, this research is based on a systematic review of 59 scientific articles, aiming to construct an integrative mathematical model that clarifies the structural relationship between fundamental terrain parameters and the territory’s biospheric conditions. Adopting a dialectical and quantitative approach, the study seeks to answer the central question: What is the magnitude and pattern of influence exerted by terrain morphometric parameters on the environmental conditions of the terrestrial biosphere, according to available global scientific evidence?

To address this question, the following objectives were established:

1) To quantitatively systematize global scientific evidence regarding the structural control exerted by terrain morphometric parameters on the functional levels defining the territory’s biosphere conditions;

2) To verify and quantify the degree of correlation between terrain morphometric parameters (elevation, slope, aspect, curvature, roughness, and drainage density) and fundamental physical gradients;

3) To identify and quantify, based on the reviewed scientific evidence, the relationship between terrain morphometric parameters and the main surface geomorphological processes—particularly erosion, runoff, infiltration, sediment transport and connectivity, and mass movements;

4) To verify and quantify the relationship between terrain morphometric parameters and the territory’s geoenvironmental conditions (climatic zoning, life zones, precipitation, soil moisture, and normalized difference vegetation index (NDVI)/biomass);

5) To establish the degree of overall correlation between fundamental morphometric parameters and the territory’s biosphere conditions, identifying recurring patterns, thresholds, and functional forms;

6) To formulate a concise conceptual equation explaining the structural control exerted by morphometric parameters on the territory’s biosphere conditions.

This research is a non-empirical systematic review employing a quantitative–dialectical approach and a cross-sectional design. It includes 59 studies (1993–2025) that quantify relationships between morphometric parameters and geoenvironmental/biospheric variables. The following flowchart shows that 512 records were initially identified, 200 duplicates were removed, 312 titles/abstracts were screened, and ultimately 59 articles met all criteria (Figure 1).

For the inclusion criteria, the Population–Exposure–Outcome (PEO) framework was used:

• Population (P): original scientific articles published in indexed journals (Scopus, Web of Science (WoS), and SciELO) and high-quality grey literature (theses and technical reports).

• Exposure (E): studies employing one or more fundamental terrain parameters (elevation, slope, aspect, curvature, and relief energy) as explanatory variables.

• Outcomes (O): studies quantifying the degree of control or statistical correlation (correlation coefficients, regression, $p$-values, and effect sizes) regarding response variables, such as physical gradients (e.g., potential energy, kinetic energy, and thermal gradient), geoenvironmental processes (e.g., erosion, runoff, infiltration, and landscape stability), geoenvironmental conditions (e.g., climate classification, landscape stability, and water availability), and biospheric conditions of the territory (e.g., life zones).

According to the exclusion criteria, the following were excluded:

• Narrative reviews without quantitative data.

• Case studies that do not report statistical metrics.

• Articles outside the thematic scope (purely geological, sociological, or unrelated to physical/environmental gradients).

A standardized data extraction template was used for data management to systematically record information on authorship, publication year, morphometric parameters, DEM, $p$-values, and other relevant variables. A functional analytical matrix was subsequently developed in Table 1. The table indicates the categories and data which were systematically extracted from each study on relationships between relief morphometry and biospheric processes.

The search methodology employed a version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) adapted for territorial analysis, incorporating geosystemic criteria from the biosphere–dialectical approach. The study included analyses based on DEMs with a resolution of 30 m or finer, primarily sourced from the Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Advanced Land Observing Satellite (ALOS) [7], [8], [12], [13] and processed using ArcGIS, QGIS, or SAGA GIS to derive reliable morphometric parameters. This resolution enables the consistent characterization of morphometric patterns at both the catchment and hillslope scales—such as elevation gradients, slope, roughness, and general terrain configuration. However, the level of detail is reduced for smaller-scale features—such as ravines, gullies, minor channels, local scarps, or shallow landslides—whose geometry may be smoothed out or inadequately resolved within 30 m grid cells. Consequently, results derived from DEMs at this resolution are interpreted primarily in terms of morphometric patterns and processes at the intermediate or catchment scale, rather than as detailed representations of local geomorphological features.

The methodological sequence was carried out in four stages. The first stage involved a systematic search and selection process across Scopus, WoS, Google Scholar, and institutional repositories, using specific Boolean strings. In these search expressions, the terms “AND”, “OR”, and “NOT” refer exclusively to Boolean logical operators used to combine or exclude search terms, and must be strictly distinguished from the abbreviation “OR” used elsewhere in this document to denote the statistical metric “odds ratio”.

The search strings employed were the following:

• (“morphometry” AND “slope” OR “hypsometry”) AND (“erosion” OR “runoff” OR “landslide stability” OR “ecosystem”) AND (“correlation” OR “regression” OR “effect size”).

• (“terrain analysis” AND “digital elevation model”) AND (“normalized difference vegetation index” OR “biomass” OR “soil moisture”) AND (“$R^2$” OR “$\beta$ coefficient”).

• (“topographic indices” AND “hydrological processes”) NOT (“review” OR “qualitative”).

In the second stage, morphometric and biospheric variables were extracted, normalized, and coded into a homogeneous matrix, standardizing units and metrics ($R$, $R^2$, $\beta$, and $p$). The third stage organized the studies according to geosystem levels---physical gradients, geoenvironmental processes, and biospheric conditions---enabling an interpretation of how morphometry structures ecosystem processes and responses. Lastly, the fourth stage integrated the results, identifying patterns, thresholds, and relational trends while respecting territorial specificities and avoiding the homogenization of the studies.

Finally, the statistical processing involved a qualitative--quantitative categorization of the reported effect sizes. Three levels of influence were established: strong ($r \ge $ 0.70), moderate (0.50 $\le r <$ 0.70), and weak ($r <$ 0.50). This approach made it possible to identify consistent patterns, such as the relationships between slope and erosion, drainage density and runoff, and curvature and soil moisture. Based on these results, the morphometric control equation for territorial biosphere conditions was formulated.

In this case, a systematic analysis of 59 studies made it possible to identify the number of research projects conducted by country or region, the main sources used (journals and repositories), the temporal distribution by year, and the topics addressed, highlighting morphometric parameters as the central focus of the territorial analysis.

Table 2 shows the results regarding morphometric parameters based on their frequency of use across 59 studies. Slope is the most frequently analyzed parameter (83%), followed by elevation (61%) and energy/relief (53%). Each parameter is associated with specific applications, such as runoff, erosion, and ecosystem stability.

Figure 2 shows that India accounts for the largest proportion of studies, followed by Ethiopia and Egypt. China and global analyses show intermediate values, while numerous countries contribute smaller percentages, grouped together as the “Group of 14 countries.” India leads with 20.3% for several reasons: it possesses great biogeographical and climatic diversity and has robust research institutions in morphometry and environmental sciences. It faces significant environmental challenges that drive studies on the morphometric monitoring of biospheric conditions.

Figure 3 shows exponential growth in studies since 2016, starting with low percentages (5–7%) that increase significantly from 2021 onwards, reaching a peak in 2024 (18.6%). The chart shows the temporal distribution of the 59 reviewed studies. A clear upward trend is observed from 2021 onward, peaking in 2024 (18.6%), followed by a slight decrease in 2025 (13.6%). Studies published up to 2016 and between 2017 and 2020 account for a smaller and relatively stable share of the total.

Scientific output has grown exponentially since 2021, reaching an all‑time high in 2024 (18.6%), driven by free Earth Observation data, machine learning, and the global climate emergency. 63% of the studies were published in the last three years.

Figure 4 shows the distribution of studies by publication source. MDPI (Multidisciplinary Digital Publishing Institute)—Scopus leads with 20%, followed by ResearchGate and SpringerLink. Minor sources such as the IAS (Indian Academy of Sciences), Copernicus, and PMC (PubMed Central) account for approximately 2–3%. According to the results in the table, Scopus concentrates the largest amount of high‑quality scientific production (MDPI, SpringerLink, and ScienceDirect), while ResearchGate and institutional websites contribute complementary volume, expanding coverage, methodological diversity, and access to specialized studies that are not always indexed.

The degree of influence of each morphometric parameter on physical gradients is very high and statistically strong, as indicated by the mean effect size ($r$) and significance ($p$-value) values in Table 3. Slope is the primary driver ($r$ = 0.84), fueling kinetic energy and drainage. Elevation and relief are primary sources of potential energy. Roughness is a key, yet underestimated, driver of micro‑kinetic energy ($r$ = 0.77).

Understanding the relationship between terrain morphometry (the shape, size, and distribution of landforms) and physical gradients (such as energy, moisture, and radiation) is fundamental, as it provides the predictive basis for land-use planning, design, and management in key applied disciplines such as geography, environmental engineering, and civil engineering. Clearly, the terrain is not a passive surface but an active driver of energy and water resource distribution across the landscape and within watersheds. A clear example of this morphometric control can be observed in topographically complex areas like the Kauaʻi Island basin, where relief structures steep topoclimatic gradients and shapes spatial patterns of orographic precipitation in relation to isohyets (Figure 5).

The results show that morphometric parameters are not related to surface processes in isolation but rather form an integrated sequence of geomorphic responses. Slope, elevation, curvature, and roughness determine the physical gradients of the terrain and, consequently, surface runoff, infiltration, and gravitational processes. These processes subsequently influence erosion, transport, and sediment connectivity, the cumulative effect of which contributes to modifying the configuration and stability of the landscape [5], [9], [10], [12], [13], [14]. This sequence allows for the interpretation of geomorphological results prior to analyzing their relationships with NDVI, biomass, and other biospheric conditions of the area (Figure 6). The figure illustrates the sequential cascading flow that links morphometric parameters to the final landscape configuration. It shows how relief geometry determines physical gradients, which trigger surface hydrological processes, gravitational movements, and sediment transport.

The degree of correlation between terrain morphometric parameters (slope, elevation, curvature, drainage) and geoenvironmental processes (erosion, landslides, flooding) is generally high ($r$ > 0.70–0.90), as landform shapes control the intensity, distribution, and rate of these natural processes. Understanding this correlation enables the prediction and quantification of natural hazards with scientific precision, transforming terrain observations into reliable mathematical models for engineering and land-use planning. Table 4 shows correlations between morphometric parameters and geoenvironmental processes.

Slope exhibits the strongest correlations ($r$ = 0.80–0.83) with erosion, runoff, and mass movements. Infiltration and percolation correlate negatively with elevation, slope, and curvature. Relief—or relief energy—also shows high correlations ($r$ = 0.80–0.82) with erosional processes.

These correlations make it possible to predict zones of geological risk, plan safe infrastructure, design targeted erosion control measures, manage water resources, and carry out territorial planning in areas highly susceptible to land degradation and watershed siltation, as exemplified by the quantitative spatial dynamics of soil loss (Figure 7).

Understanding these correlations between terrain morphometric parameters and geoenvironmental conditions is essential, as it enables the prediction of hydrological, erosional, and terrain stability conditions based solely on the relief. This strengthens environmental assessment, land‑use planning, and risk management. The 59 studies demonstrate strong correlations between morphometry and geoenvironmental conditions ($r$ = 0.63–0.84), high statistical significance ($p$ < 0.010), consistent confidence intervals ranging from 0.50 to 0.88, and a predominance of 30 m DEMs, indicating strong, stable, and comparable relationships for territorial and environmental analyses (Table 5).

Understanding the relationship between morphometry and geoenvironmental conditions makes it possible to predict drainage dynamics, sediment yield, and overall landscape stability. Strong correlations—such as that between slope and stability parameters ($r$ = $-$0.80)—allow for the precise identification of critical topohydrological zones, the anticipation of slope failures, and the guidance of land-use planning decisions. Steeper terrain slopes fundamentally govern these responses, yielding up to an 82% reduction in landscape stability ($r$ = $-$0.82, $p$ < 0.001). This deterministic geometric control is effectively demonstrated through physically based slope stability frameworks, where relief and critical water recharge indicators delineate sharp boundaries between stable valley sectors and unconditionally unstable mountain crests (Figure 8).

The correlation analysis examines how landform characteristics—such as slope, elevation, curvature, and roughness—influence biosphere conditions, including moisture, productivity, ecological stability, and habitat distribution across the landscape. Table 6 compiles 59 studies, demonstrating strong correlations between morphometry and biosphere conditions, with $r$ values ranging from 0.58 to 0.79, with high statistical significance ($p$ < 0.010). The confidence intervals range from 0.48 to 0.86, indicating a consistent distribution, and the dominant resolution of the DEMs used in the reviewed studies is 30 m. This consistency facilitates a general comparison of morphometric patterns, although it does not eliminate differences stemming from the DEM source, cell size, spatial extent, and geomorphological scale of each study [7], [8], [12], [13]. Consequently, the correlations obtained should be interpreted as associations that are comparable at the general scale of the relief, yet conditioned by the resolution and spatial scale of the data used in each investigation.

The spatial distribution of bioclimatic conditions and ecosystems is deeply governed by altitude and relief aspects, which establish clear life zone boundaries. These geomorphological correlations possess significant practical value, facilitating the interpretation of geographic patterns, supporting terrain stability predictions in civil engineering, and enabling multi-scale assessments of biomass, resilience, and ecosystem vulnerability. A prominent manifestation of this phenomenon is documented through the Holdridge life zone framework in high-relief volcanic settings, such as Maui Island (Hawaii). In this region, steep elevation steps and aspect faces compress multiple ecological belts—ranging from subtropical desert scrubs to alpine wet forests—within short horizontal distances (Figure 9, demonstrating how morphometric terrain controls can optimize data-driven land-use planning and environmental management decisions). The cartographic model illustrates how steep elevation steps compress distinct ecological belts, ranging from basal altitudinal sectors to subtropical montane and subalpine zones.

The correlations between morphometric parameters and biospheric conditions reveal, from a dialectical perspective, the geosphere—biosphere interaction. Values such as $r$ = 0.71, 0.73, and $-$0.70 demonstrate how landforms organize biomass distribution and concentration, stability, and life zones—with $p$ < 0.010 and a CI ranging from 0.48 to 0.86, validated using a 30 m DEM (Table 7). This explains the distribution of life in relation to landforms and demonstrates how these relationships structure the biosphere as a zonal and planetary system.

Morphometry applied to biospheric conditions enables an understanding of how landforms control microclimates, moisture, stability, and ecological productivity. It facilitates environmental zoning, habitat modeling, vulnerability assessment, ecosystem service management, and restoration planning by integrating physical processes and biological responses for sustainable land management.

The integrated evidence from the 59 studies allows for the formulation of a conceptual model representing the relationship between the morphometric configuration of the terrain, physical gradients, surface processes, and the geoenvironmental and biosphere conditions of the territory. This formulation is presented as a synthetic framework for interpreting the relationships identified in the review, rather than as a universally validated empirical equation.

Its structure follows this functional sequence:

where, relief morphometry ($M$) shapes physical gradients ($\Phi$); these influence geoenvironmental and geomorphological processes ($P$); the processes help determine geoenvironmental conditions ($C$); and the interaction of these components is ultimately expressed in the biospheric conditions of the territory ($B$).

The conceptual equation is structured around four linked functional blocks:

where, $K$ is the climate; $L$ is the lithology; $U$ is the land use (contextual modulators); and the synthesis operator ($\Psi$) can be additive, weighted, or non-linear, depending on the scale and objective.

The first function represents the generation or configuration of physical gradients based on the morphometry of the terrain. The second expresses the interaction between terrain geometry and contextual factors in the configuration of surface processes. The third integrates the response of these processes under observable geoenvironmental conditions. Finally, the fourth represents the synthesis of these relationships within the biospheric conditions of the territory.

In this formulation, the morphometric vector is defined as:

where, $S$ corresponds to the slope, $E$ corresponds to the elevation, $C_\mathrm{v}$ corresponds to the curvature, $R$ corresponds to the roughness or irregularity of the terrain, $A$ corresponds to exposure, and $D$ corresponds to drainage density.

They act as contextual modulators of the relationships between morphometry and geoenvironmental processes. Therefore, the response of a specific morphometric parameter should not be interpreted in isolation. A single slope configuration, for example, can yield different responses depending on climatic conditions, the resistance and structure of the lithological substrate, and land cover or land-use characteristics. The operator represents the synthesis function through which the various components can be integrated according to the scale and purpose of the analysis.Depending on the application, it may take an additive, weighted, or non-linear form. Consequently, the proposed formulation constitutes a flexible framework for the spatial integration of the relationships identified in the review.

Based on the preceding functional chain, the integrated relationship can be expressed by means of a synthetic equation:

where, $B(x)$ represents the biospheric conditions of the territory at position $x$; $M(x)$ represents the morphometric vector; and $K(x)$, $L(x)$, and $U(x)$ correspond, respectively, to climate, lithology, and land use as contextual factors.

The synthetic function ($F$) summarizes the chain of relationships developed in the conceptual model:

where, the nested functions represent a progressive, hierarchical cascade of geographic controls: $g$ functions as the primary geometric transfer function that determines how terrain morphometry restricts and directs physical energy and gravity gradients; $h$ serves as the intermediate environmental interaction function that predicts physical geoenvironmental processes under external modulators like climate, lithology, and land use; and $q$ acts as the cumulative response function that models final, integrated surface attributes and steady-state conditions. Finally, the outer function $\Psi$ synthesizes these interconnected layers to express the macro-scale status of the territorial biosphere.

Thus, the synthetic equation does not posit that biospheric conditions are determined exclusively by morphometry; rather, it expresses an integrated function in which relief constitutes the structural component, while climate, lithology, and land use act as contextual modulators. The results of the review provide empirical support for the key links constituting this structure. In particular, strong relationships were identified between slope and surface runoff ($r$ = 0.83), slope and mass movements ($r$ = 0.82), relief and water erosion ($r$ = 0.82), as well as between roughness and mass movement processes ($r$ = 0.79). Likewise, relationships were observed between morphometric parameters and various geoenvironmental and biospheric conditions, with correlation values ranging—depending on the relationships analyzed—approximately between $r$ = 0.58 and $r$ = 0.84.

These results support the suitability of the proposed functional framework but do not imply universal validation of the synthetic equation. Its quantitative application requires calibration and independent validation across different spatial scales, climatic conditions, lithological contexts, and geomorphological environments. Consequently, the conceptual and synthetic equations constitute a proposal for integrating the evidence scattered across the 59 studies reviewed. The model allows for the quantitative organization of the relationship, as shown in Figure 10.

This model provides a conceptual basis for subsequent applications in geomorphological zoning, erosion susceptibility assessment, preliminary mass movement analysis, hydrological modeling, and integrated watershed analysis. The synthetic and conceptual equations make it possible to integrate scattered evidence, translate it into a replicable quantitative framework, and scientifically substantiate the concept of the territory’s biospheric conditions, thereby contributing to geosystem theory, spatial modeling, and territorial planning.

The results show very strong correlations between slope, elevation, and relief and physical gradients, confirming that the latter are a direct function of morphometry. Slope dominates this behavior [3], [9], while elevation and roughness reproduce patterns reported by Reuter et al. [7] and Grohmann [4], demonstrating that morphometry generates energy and landscape gradients. Morphometry controls processes such as erosion, runoff, infiltration, and stability. Slope predicts erosion and landslides [10], [14], while curvature regulates flow concentration and dispersion [5]. Infiltration decreases with steeper slopes and higher elevations—consistent with high-Andean studies and global analyses [12], [16]—thereby validating the $M \rightarrow \Phi \rightarrow P$ framework. However, these morphometry—process relationships must be interpreted within the geological context in which the landforms develop. A given slope may exhibit different geomorphological responses depending on rock resistance, the degree of fracturing, and the characteristics of surface materials. In consolidated, resistant materials, a steep slope may maintain greater relative stability, whereas in poorly consolidated or intensely weathered materials, the same topographic configuration may promote increased erosion, concentrated runoff, or susceptibility to mass movements [10], [14], [16], [17].

Furthermore, geological structure can modify the landform’s response by controlling the orientation of discontinuities, water circulation, and potential failure surfaces. Therefore, the correlations identified in the 59 studies should be understood as morphometric relationships conditioned by the lithological and structural context, rather than as relationships independent of the nature of the materials. This consideration is consistent with the proposed conceptual model, in which $L$—along with $K$ and $U$—acts as a contextual modulator of geoenvironmental processes. The interpretation of these correlations must also take into account the effect of the spatial scale of the DEMs. Although a 30 m resolution is the most common in the studies reviewed, the data sources, cell sizes, and spatial extents of the analyzed areas are not necessarily uniform [7], [8], [12], [13]. This variability can influence the representation of slope, curvature, roughness, and drainage, as well as the ability to identify geomorphological features of varying scales.

Overall, the evidence reviewed indicates that morphometry constitutes a structuring component of the relationships between landforms, geoenvironmental processes, and the territory’s biosphere conditions. However, these relationships should not be interpreted as being exclusively controlled by morphometry, as their intensity and spatial expression can be modulated by climate, lithology, and land use. In this regard, the proposed model provides an integrative framework for interpreting the interaction between landform configuration and surface processes, with potential applications in geomorphological zoning, erosion susceptibility assessment, the preliminary identification of areas prone to mass movements, and integrated watershed analysis. However, its operational application requires calibration and independent validation across different geological, climatic, and geomorphological contexts.

The systematic review of the 59 studies reveals consistent associations between terrain morphometric parameters and the territory's physical gradients, geoenvironmental processes, and biospheric conditions. Slope, elevation, curvature, and roughness show significant relationships with processes such as runoff, erosion, infiltration, mass movements, and landscape stability, confirming the utility of geomorphometry for interpreting the territory's surface dynamics. The results indicate that morphometry constitutes a structuring component of these relationships, but not a sole or determining factor of biospheric conditions. The expression of geomorphological processes and their environmental responses also depends on contextual factors—particularly climate, lithology, and land use. Therefore, the identified correlations should be interpreted as associations conditioned by the interaction between the relief configuration and the territory's environmental and geological characteristics.

The proposed conceptual chain, expressed as $M \rightarrow \Phi \rightarrow P \rightarrow C \rightarrow B$, allows for the organization of evidence ranging from landform morphometry and physical gradients to surface processes, geoenvironmental conditions, and biospheric conditions. The proposed synthetic equation integrates these components and provides a quantitative structure potentially applicable to the spatial analysis of the territory, although its parameters and functions require independent calibration and validation. From an Earth sciences perspective, the model offers potential applications in geomorphological zoning, erosion susceptibility assessment, the preliminary identification of areas prone to mass movements, and integrated watershed analysis. These applications must take into account the spatial scale, the resolution of the DEMs, and the specific geological and climatic characteristics of each territory. Finally, the results of the review support the model's suitability as an integrative conceptual and quantitative framework, yet they do not allow for the establishment of a universal relationship between morphometry and biospheric conditions. The proposed equation requires validation using independent data and evaluation across various geological, climatic, and geomorphological contexts and spatial scales before it can be used as a general predictive model.