Elsevier

Geoderma

Volume 417, 1 July 2022, 115840
Geoderma

Urban soil management in the strategies for adaptation to climate change of cities in the Tropical Andes

https://doi.org/10.1016/j.geoderma.2022.115840Get rights and content

Highlights

  • Variations in the OC storage capacity of urban land would have regional and global effects.

  • Urban land management is a determining factor in enhancing the eco-systemic service of regulation and adaptation to change.

  • Urban soil genesis and anthropogenic factors influence the capacity of soils to store OC.

Abstract

The unique characteristics of a city amplify the impacts of climate change; therefore, urban planning in the 21st century is challenged to apply mitigation and adaptation strategies that ensure the collective well-being. Despite advances in monitoring urban environmental change, research on the application of adaptation-oriented criteria remains a challenge in urban planning in the Global South. This study proposes to include urban land management as a criterion and timely strategy for climate change adaptation in the cities of the Tropical Andes. Here, we estimate the distribution of the soil organic carbon stock (OCS) of the city of Quito (2,815 m.a.s.l.; population 2,011,388; 197.09 km2) in the following three methodological moments: i) field/laboratory: city-wide sampling design established to collect 300 soil samples (0–15 cm) and obtain data on organic carbon (OC) concentrations in addition to 30 samples for bulk density (BD); ii) predictors: geographic, spectral and anthropogenic dimensions established from 17 co-variables; and iii) spatial modeling: simple multiple regression (SMRM) and random forest (RFM) models of organic carbon concentrations and density as well as OCS stock estimation. We found that the spatial modeling techniques were complementary; however, SMRM showed a relatively higher fit both (OC: r2 = 20%, BD: r2 = 16%) when compared to RFM (OC: r2 = 8% and BD: r2 = 5%). Thus, soil carbon stock (0–0.15 m) was estimated with a spatial variation that fluctuated between 9.89 and 21.48 kg/m2; whereas, RFM showed fluctuations between 10.38 and 17.67 kg/m2. We found that spatial predictors (topography, relative humidity, precipitation, temperature) and anthropogenic predictors (population density, roads, vehicle traffic, land cover) positively influence the model, while spatial predictors have little influence and show multicollinearity with relative humidity. Our research suggests that urban land management in the 21st century provides key information for adaptation and mitigation strategies aimed at coping with global and local climate variations in the cities of the Tropical Andes.

Introduction

Anthropogenic activity is the most important driver of landscape transformation (Butzer, 1964, Ellis, 2015; Ellis et al., 2013; Goldewijk et al., 2017, Kaplan et al., 2011). Human disturbance affects surface reservoirs and exacerbates potential changes in the global climate system (Eglinton et al., 2021, Friedlingstein et al., 2018). In this sense, landscapes with a predominantly urban matrix show alterations in their biogeochemical cycles that potentially affect regional and global atmospheric climates (Lorenz & Lal, 2009).

On the other hand, the carbon residing in vegetation and soils is three times that of atmospheric carbon (Eglinton et al., 2021) and is a key player in mitigating or increasing the accumulation of greenhouse gases. However, the terrestrial carbon cycle constitutes one of the major uncertainties affecting global change modeling (Carvalhais et al., 2014).

By 2018, the global urban land area increased at a rate of change of 1.5 times compared to 1990, reaching a total coverage of approximately 797,076 km2 (Gong et al., 2020). This increase happens mainly in the US and China, but countries such as India, Russia, Brazil, France, among others, show sustained increases in urbanization (Seto et al., 2012, Liu et al., 2018; Gong et al., 2020).

These regions engendered a carbon footprint five times larger when compared to developing regions (Müeller et al., 2013), such as tropical areas. Urban expansion in the tropics would contribute about 5% of atmospheric emissions due to deforestation and land use change (Baccini et al., 2012; Bonilla-Bedoya et al., 2020). This scenario intensifies the conversion of the global biogeochemical cycle which latently affects climate and surface carbon pools (Butzer, 1964, Lorenz and Lal, 2009, Kaplan et al., 2011, Ellis, 2015; Ellis et al., 2013; Goldewijk et al., 2017, Friedlingstein et al., 2018, Eglinton et al., 2021).

Therefore, the skyrocketing global increase of impervious surface (Chen et al., 2021, Liu et al., 2018) requires an accurate spatial analysis of the world's built-up areas; thus, having recent spatio-temporal databases for studying the world's built-up areas expansion (Klein Goldewijk et al., 2010, Liu et al., 2018, Potere et al., 2009, Schneider et al., 2010; Seto et al., 2018; Gong et al., 2020) will contribute to the improvement of spatio-temporal modeling of local and global carbon dynamics in an urban world.

In regions such as Latin America and specifically in the cities of Tropical Andes countries (TACs), urbanization and its associated impacts potentially began in the pre-Columbian period with the transition from country to city driven by an agricultural technological revolution. The European conquest of the region put an end to the evolution of Amerindian urbanism, and urban centers were used by the state as a means of political consolidation and social and economic control. Thus, in the 19th century, independence, characterized by a warlike environment, left the urban territories divided into smaller nation-states. In the second half of the 19th century and the beginning of the 20th century, the reconstruction of the state was consolidated, associated with the emergence of European industrial economies and the primary city, where goods for export were collected and processed (Reid, 1986; Carrión, 2010).

The presence of regional urban centers supported by rural production and the industrialization of the 20th century continued to drive rural–urban migration and the trend toward the intensification of the city (Reid, 1986) and its peripheries and metropolization. During the second half of the 20th century, introspection became more prominent along with a shift to consider the built city in a framework of globalization (Carrión, 2010). Now, in the first two decades of the 21st century, urban sprawl, hyper-urbanization, and the development of intermediate cities are some of the elements that characterize the urbanization dynamics in the region (Bonilla-Bedoya et al., 2020; Inostrosa et al., 2013).

Research has shown that the cities of TACs and their mountain ecosystems are at greater risk of global warming impacts than cities in other regions and ecosystems (FAO, 2015, Herzog et al., 2011). The neo-tropical highlands (1000–3500 m.a.s.l.) maintain a historical population concentration that is explained by climate and soil fertility. Cities such as Bogota (population 11.2 M, altitude 2640 m.a.s.l.), Quito (population 2011 M, altitude 2815 m.a.s.l.), and La Paz (population 0.766 M, altitude 3640 m.a.s.l.) are shaped by the landscapes of the inter-Andean valleys of volcanic origin. In this sense, the geographic scope of urban processes and the demographic trend in the region indicate that 80% of the population is or will be urban by 2050. This phenomenon, complemented with a scenario of change, is an urgent challenge for urban planning focused on sustainable development (Bai et al., 2016) and oriented to the welfare and environmental justice of the cities in the region (Bonilla-Bedoya et al., 2020). For example, for Quito, in the last 100 years, it was estimated that the average temperature increase fluctuated between 1.2 and 1.4 °C (Zambrano-Barragán et al., 2011).

Quantifying the effects of urban environments on the physical, chemical, and biological properties of soil from a spatio-temporal perspective is complex because urban soils have high spatial and temporal variability (Bonilla-Bedoya et al., 2013), which is dependent on the parent material, the urban land use, and other factors. Thus, it is difficult to define a soil or a community of soils in space–time (Canedoli et al., 2019, Morel et al., 2015, Pouyat et al., 2002, Pouyat et al., 2015). However, contemporary urban planning will need to understand the relationship between the soil organic carbon stock and the different biophysical and anthropic dimensions of an urban socio-ecological system in order to maintain or optimize its functions, services, and benefits and avoid its loss or degradation through informed management decisions, citizen participation, and the application of technology (Rawlins et al., 2013).

Therefore, the aim of this research was to determine the spatial distribution of soil OCS in a city in the inter-Andean valley and to analyze the potential relationships between this distribution and the different dimensions of an urban socio-ecological system in a dynamic of change. Such information will be critical for the development of nature-based strategies (Hobbies & Grimm, 2020) and policies that aim to improve the climate adaptation and resilience capabilities of urban environments experiencing environmental variations.

For this purpose, the following objectives were set. First was the collection of urban soil samples considering a spatial design that allows the reduction of spatial variability and the establishment of a baseline for temporal analysis. Second was the estimation of the organic carbon pool in the soil through the application of geostatistical and geoinformatics models based on linear and machine learning methods that estimate the spatial distribution of organic carbon contents and estimate soil bulk density via variables relevant to the socio-ecological system. From a spatial perspective, the three types of predictors were (i) the geographic proximity and spatial relationship between samples, (ii) spectral predictors from the SPOT 7 sensor, and (iii) spatial predictors of urban planning contained in a series of cartographies developed for this research. The third objective was to discuss the effects of biophysical and anthropic predictors on model formulation.

Section snippets

Quito

Urban Quito expands along an inter-mountain valley (197.09 km2) over a deposit of volcanic material elongated in the north–south direction and narrow in the east–west direction (Fig. 1). This city, located in the Tropical Andes (2,815 m.a.s.l.), is inhabited by ∼ 1.874 million people. However, its population is projected to increase to ∼ 2.353 million people by 2035 (Bonilla-Bedoya et al., 2019; UN, 2015, 2019).

The predominant soil orders in the Quito valley are Andisols, Molisols, and Entisols

Results

The spatial distributions of the soil OCS concentrations and bulk density were obtained from geo-statistical and geo-computational models (Fig. 2, Fig. 4, Fig. 5). The application of SMRM (Table 2) was effective in understanding the relationships between the socio-ecosystem and urban soil carbon stock. The estimated goodness-of-fit magnitude of the model explained r2 = 20% (standard error: 0.09) and r2 = 16% (standard error: 0.09) of the variation of OC and BD, respectively. This model yielded

Discussion

Our results contribute to develop some methodological design strategies that consider soil (Hobbie & Grimm, 2020) as a key component of adaptation measures towards global climate variation in tropical Andean cities. Thus, we provide spatial evidence of the potential eco-systemic functions of urban soil (Bonilla-Bedoya, 2016). However, the estimates presented here should be monitored from a spatiotemporal approach, improving the sampling designs, the number of samples collected and the search

Conclusions

The monitoring and management of urban soils is a criterion that is perfectly integrated in the development of sustainable cities and in the design of adaptation and mitigation strategies aimed at assessing/estimating climate variation that affects the common welfare of citizens in the urban century.

Tropical high Andean cities are established in soils with Andean characteristics and native ecosystems disturbed by anthropic action; the traces left by these formerly natural landscapes are still

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to thank to Universidad Tecnológica Indoamérica and Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia - CEDIA for the financial support given to the present research, development, and innovation work through its CEPRA program, especially for the “Resiliencia ambiental en sistemas socioecológicos urbanos de ciudades neo-tropicales, casos Quito y Guayaquil” fund. Mgt. Juan Yépez, Eng. Kevin Velancia, Eng. Sebastián Segura for their support in field

References (78)

  • C. Luederitz et al.

    A review of urban ecosystem services: Six key challenges for future research

    Ecosyst. Serv.

    (2015)
  • D. Manuel-Navarrete et al.

    Intentional disruption of path-dependencies in the Anthropocene: Gray versus green water infrastructure regimes in Mexico City, Mexico

    Anthropocene

    (2019)
  • L.S. Merriman et al.

    Evaluation of factors affecting soil carbon sequestration services of stormwater wet retention ponds in varying climate zones

    Sci. Total Environ.

    (2017)
  • C.M. Njeru et al.

    Assessing stock and thresholds detection of soil organic carbon and nitrogen along an altitude gradient in an east Africa mountain ecosystem

    Geoderma Regional

    (2017)
  • A. Patri et al.

    Random forest and stochastic gradient tree boosting based approach for the prediction of airfoil self-noise

    Procedia Comput. Sci.

    (2015)
  • N. Pettorelli et al.

    Using the satellite-derived NDVI to assess ecological responses to environmental change

    Trends Ecol. Evol.

    (2005)
  • A. Schneider et al.

    Mapping global urban areas using MODIS 500-m data: new methods and datasets based on “urban ecoregions”

    Remote Sens. Environ.

    (2010)
  • K. Vaysse et al.

    Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France)

    Geoderma Regional

    (2015)
  • A.J. Arnfield

    Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island

    Int. J. Climatol.

    (2003)
  • A. Baccini et al.

    Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps

    Nat. Clim. Change

    (2012)
  • X. Bai et al.

    Six research priorities for cities and climate change

    Nature

    (2018)
  • A. Barkhordarian et al.

    Observed warming over northern South America has an anthropogenic origin

    Clim. Dyn.

    (2018)
  • S. Bonilla-Bedoya

    Gestión de Paisajes Forestales Tropicales en la Amazonía Andina

    (2016)
  • S. Bonilla-Bedoya et al.

    Piaroa shifting cultivation: temporal variability of soil characteristics and spatial distribution of crops in the Venezuelan Orinoco

    Agrofor. Syst.

    (2013)
  • L. Breiman

    Random forests

    Machine Learning

    (2001)
  • Butzer, K., 1964. Environment and Archeology: An Introduction to Pleistocene (Issue...
  • P. Calaza et al.

    Building green infrastructure and urban landscapes

    Unasylva

    (2018)
  • C. Canedoli et al.

    Soil organic carbon stock in different urban land uses: high stock evidence in urban parks

    Urban Ecosystems

    (2019)
  • J.A. Carmin et al.

    Urban climate adaptation in the global south: planning in an emerging policy domain

    J. Plann. Educ. Res.

    (2012)
  • A.M. Carmona et al.

    Detection of long-term trends in monthly hydro-climatic series of Colombia through Empirical Mode Decomposition

    Clim. Change

    (2014)
  • Carrión, F., 2010. Ciudad, memoria y...
  • N. Carvalhais et al.

    Global covariation of carbon turnover times with climate in terrestrial ecosystems

    Nature

    (2014)
  • L. Chen et al.

    Soil carbon persistence governed by plant input and mineral protection at regional and global scales

    Ecol. Lett.

    (2021)
  • Cutler, A., & Cutler, D. R., 2012. Ensemble Machine Learning. Ensemble Machine Learning, February 2014....
  • G. Darrel Jenerette et al.

    Ecosystem services and urban heat riskscape moderation: water, green spaces, and social inequality in Phoenix, USA

    Ecol. Appl.

    (2011)
  • E.A. Davidson et al.

    Soil warming and organic carbon content

    Biogeochemistry

    (2000)
  • J.L. Edmondson et al.

    Organic carbon hidden in urban ecosystems

    Sci. Rep.

    (2012)
  • J.L. Edmondson et al.

    Soil surface temperatures reveal moderation of the urban heat island effect by trees and shrubs

    Sci. Rep.

    (2016)
  • T.I. Eglinton et al.

    Climate control on terrestrial biospheric carbon turnover

    Proc. Natl. Acad. Sci. U.S.A.

    (2021)
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