Modelling carbon sink of urban street trees and soil in Helsinki, Finland

A high proportion of anthropogenic carbon dioxide emissions originate from urban areas, which has led cities to become interested in reducing their own emissions and determining how much carbon could be sequestered by their own vegetation and soil. The challenge with the latter is that our current knowledge on carbon storage is based on data and models from natural and forest ecosystems, whereas the response of vegetation and soil to environmental factors most probably is altered in urban green space where the soil conditions, water availability and temperature are highly variable. Therefore, ecosystem models are required to correctly account for urban vegetation and soil to understand and quantify the biogenic carbon cycle in urban areas. In this study, urban land surface model SUEWS (the Surface Urban Energy and Water Balance Scheme) and the soil carbon decomposition model Yasso15 are used to simulate urban carbon cycle on two streets in Helsinki, Finland for years 2003-2016. Curbside trees (Alnus glutinosa and Tilia x Vulgaris) were planted while the two test streets were constructed in 2002. Thereafter, carbon and water fluxes and pools with detailed street tree soil compositions were monitored in 2002-2014. SUEWS creates a local spatially variable temperature and specific humidity environment which is used in the model runs. The modelled evaporation is evaluated against sap flow measurements and modelled soil moisture against soil moisture observations. The Yasso15 model is evaluated against loss-on-ignition based soil carbon measurements as it has not been previously evaluated in urban soils. The modelled carbon dioxide flux combined with the changes in the soil carbon stock is used to estimate the carbon cycle of urban street trees and soils.

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Temporal Variability of Soil Carbon Dioxide Flux

Soil Science Society of America Journal ◽

10.2136/sssaj2004.1234 ◽

2004 ◽

Vol 68 (4) ◽

pp. 1234-1241 ◽

Cited By ~ 53

Author(s):

Timothy B. Parkin ◽

Thomas C. Kaspar

Keyword(s):

Carbon Dioxide ◽

Soil Carbon ◽

Temporal Variability ◽

Carbon Dioxide Flux ◽

Soil Carbon Dioxide

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Soil Carbon Dioxide Flux in Response to Wheel Traffic in a No-Till System

Soil Science Society of America Journal ◽

10.2136/sssaj2011.0129 ◽

2011 ◽

Vol 75 (6) ◽

pp. 2296-2304 ◽

Cited By ~ 5

Author(s):

T. C. Kaspar ◽

T. B. Parkin

Keyword(s):

Carbon Dioxide ◽

Soil Carbon ◽

Carbon Dioxide Flux ◽

Soil Carbon Dioxide ◽

No Till

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Soil carbon dioxide flux in a no-tillage winter system

African Journal of Agricultural Research ◽

10.5897/ajar2014.9399 ◽

2015 ◽

Vol 10 (6) ◽

pp. 450-457 ◽

Cited By ~ 4

Author(s):

Pivotto Bortolotto Rafael ◽

Jorge Carneiro Amado Telmo ◽

Dalla Nora Douglas ◽

Keller Cristiano ◽

Roberti Debora ◽

...

Keyword(s):

Carbon Dioxide ◽

Soil Carbon ◽

Carbon Dioxide Flux ◽

No Tillage ◽

Soil Carbon Dioxide

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Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

Monthly Weather Review ◽

10.1175/mwr-d-17-0125.1 ◽

2017 ◽

Vol 145 (12) ◽

pp. 4997-5014 ◽

Cited By ~ 11

Author(s):

Liao-Fan Lin ◽

Ardeshir M. Ebtehaj ◽

Alejandro N. Flores ◽

Satish Bastola ◽

Rafael L. Bras

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Specific Humidity ◽

Surface Model ◽

Precipitation Data ◽

Near Surface ◽

Satellite Precipitation ◽

Soil Moisture Data ◽

Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

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Soil Carbon Dioxide Flux in Antarctic Dry Valley Ecosystems

Ecosystems ◽

10.1007/s10021-003-0132-1 ◽

2004 ◽

Vol 7 (3) ◽

Cited By ~ 88

Author(s):

Andrew N. Parsons ◽

J. E. Barrett ◽

Diana H. Wall ◽

Ross A. Virginia

Keyword(s):

Carbon Dioxide ◽

Soil Carbon ◽

Carbon Dioxide Flux ◽

Soil Carbon Dioxide ◽

Dry Valley

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Carbon sequestration potential of street tree plantings in Helsinki

10.5194/bg-2021-242 ◽

2021 ◽

Author(s):

Minttu Havu ◽

Liisa Kulmala ◽

Pasi Kolari ◽

Timo Vesala ◽

Anu Riikonen ◽

...

Keyword(s):

Soil Moisture ◽

Carbon Sequestration ◽

Soil Respiration ◽

Carbon Cycle ◽

Soil Carbon ◽

Carbon Sequestration Potential ◽

Initial Carbon ◽

Street Tree ◽

Sequestration Potential ◽

Forested Ecosystems

Abstract. Cities have become increasingly interested in reducing their greenhouse gas emissions, and increasing carbon sequestration and storage in urban vegetation and soil as part of their climate mitigation actions. However, most of our knowledge on biogenic carbon cycle is based on data and models from forested ecosystems even though urban nature and microclimate are very different to those in natural or forested ecosystems. There is a need for modelling tools that can correctly consider temporal variations of urban carbon cycle and take the urban specific conditions into account. The main aims of this study are to examine the carbon sequestration potential of two commonly used street tree species (Tilia x vulgaris and Alnus glutinosa) and their soils by taking into account the complexity of urban conditions, and evaluate urban land surface model SUEWS and soil carbon model Yasso15 in simulating carbon sequestration of these street tree plantings at different temporal scales (diurnal, monthly and annual). SUEWS provides the urban microclimate, and photosynthesis and respiration of street trees whereas the soil carbon storage is estimated with Yasso. Both models were run for 2002–2016 and within this period the model performances were evaluated against transpiration estimated from sap flow, soil carbon content and soil moisture measurements from two street tree sites located in Helsinki, Finland. The models were able to capture the variability in urban carbon cycle due to changes in environmental conditions and tree species. SUEWS simulated the stomatal control and transpiration well (RMSE < 0.31 mm h−1) and was able to produce correct soil moisture in the street soil (nRMSE < 0.23). Yasso was able to simulate the strong decline in initial carbon content but later overestimated respiration and thus underestimated carbon stock slightly (MBE > −5.42 kg C m−2). Over the study period, soil respiration dominated the carbon exchange over carbon sequestration, due to the high initial carbon loss from the soil after the street construction. However, the street tree plantings turned into a modest sink of carbon from the atmosphere on annual scale as the tree and soil respiration approximately balanced photosynthesis. The compensation point when street trees plantings turned from annual source to sink was reached faster by Alnus trees after 12 years, while by Tilia trees after 14 years. Overall, the results indicate the importance of soil in urban carbon sequestration estimations.

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Impact of urban canopy meteorological forcing on aerosol concentrations

10.5194/acp-2018-415 ◽

2018 ◽

Author(s):

Peter Huszar ◽

Michal Belda ◽

Jan Karlický ◽

Tatsiana Bardachova ◽

Tomas Halenka ◽

...

Keyword(s):

Land Surface ◽

Urban Areas ◽

Urban Canopy ◽

Specific Humidity ◽

Surface Model ◽

Meteorological Forcing ◽

Turbulent Diffusion Coefficient ◽

Temperature Impact ◽

The Impact ◽

Meteorological Effects

Abstract. The regional climate model RegCM4 extended with the land-surface model CLM4.5 was coupled to the chemistry transport model CAMx to analyze the impact of urban meteorological forcing on the surface fine aerosol (PM2.5) concentrations for summer conditions over the 2001–2005 period focusing on the area of Europe. Starting with the analysis of the meteorological modifications caused by urban canopy forcing we found significant increases of urban surface temperatures (up to 2–3 K), decrease of specific humidity (by up to 0.4–0.6 g/kg) reduction of wind speed (up to −1 m/s) and enhancement of vertical turbulent diffusion coefficient (up to 60–70 m2/s). These modifications translated into significant changes in surface aerosol concentrations that were calculated by cascading experimental approach. First, none of the urban meteorological effects were considered. Than, the temperature effect was added, than the humidity, the wind and finally, the enhanced turbulence was considered in the chemical runs. This facilitated the understanding of the underlying processes acting to modify urban aerosol concentrations. Moreover, we looked at the impact of the individual aerosol components as well. The urban induced temperature changes resulted in decreases of PM2.5 by −1.5 to −2 μg/m3, while decreased urban winds resulted in increases by 1–2 μg/m3. The enhanced turbulence over urban areas results in decreases of PM2.5 by −2 μg/m3. The combined effect of all individual impact depends on the competition between the partial impacts and can reach up to −3 μg/m3 for some cities, especially were the temperature impact was stronger in magnitude than the wind impact. The effect of changed humidity was found to be minor. The main contributor to the temperature impact is the modification of secondary inorganic aerosols, mainly nitrates, while the wind and turbulence impact is most pronounced in case of primary aerosol (primary black and organic carbon and other fine particle matter). The overall as well as individual impacts on secondary organic aerosol is very small with the increased turbulence acting as the main driver. The analysis of the vertical extend of the aerosol changes showed that the perturbations caused by urban canopy forcing, besides being large near the surface, have a secondary maximum for turbulence and wind impact over higher model levels, which is attributed to the vertical extend of the changes in turbulence over urban areas. The validation of model data with measurements showed good agreement and we could detect a clear model improvement at some areas when including the urban canopy meteorological effects in our chemistry simulations.

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SMAP retrieval assimilation improves soil moisture estimation across irrigated areas in South Asia

10.5194/hess-2021-460 ◽

2021 ◽

Author(s):

Jawairia A. Ahmad ◽

Barton A. Forman ◽

Sujay V. Kumar

Keyword(s):

Soil Moisture ◽

South Asia ◽

Land Surface ◽

Land Surface Model ◽

Cumulative Distribution ◽

Spatiotemporal Variability ◽

Soil Conditions ◽

Surface Model ◽

Near Surface ◽

Moisture Estimation

Abstract. A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and GPM Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF) corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF-matching is implemented to map the statistical moments of the SMAP soil moisture retrievals to the land surface model climatology. Comparison of assimilated and model-only soil moisture estimates with publicly available in-situ measurements highlight the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 % even though assimilation only occurred during less than 10 % of the study period due to frozen soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. SMAP retrieval assimilation corrected biases associated with unmodeled hydrologic phenomenon (e.g., anthropogenic influences due to irrigation). The highest influence of assimilation was observed across croplands. Improvements in soil moisture translated into improved spatiotemporal patterns of modeled evapotranspiration, yet limited influence of assimilation was observed on states included within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data scarce regions.

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