Sensitivity Analysis of the RothC Model Using Two Climatic Datasets: A Case Study of Arable Soils in Rostov Oblast

The Rothamsted Carbon (RothC) model was developed to simulate soil organic carbon dynamics under different types of land use, different climate conditions, and different agricultural technologies. The model has been effectively applied across different scales and in different environmental zones; however, it is sensitive to key input variables. This study estimates the sensitivity of soil carbon storage projections in response to variations in climate inputs using the RothC model. Two climate data sets were used for the sensitivity analysis: the Climatic Research Unit (CRU) dataset (CRU TS v4.05, 1901–2020), which has a spatial resolution of about 50 × 50 km2 and is derived from interpolated data from ground-based weather stations, and TerraClimate database, which is built based on CRU data and enhances the resolution to around 4 × 4 km2 through additional modeling and data inputs. Sequestration estimates for Rostov oblast have been derived using the CRU database. The accuracy of estimates for Rostov oblast is of particular importance, since the proportion of Chernozems in the total land structure is almost 65%. The sensitivity analysis has revealed that the replacement of the CRU data with TerraClimate meteorological data significantly underestimates the sequestration rate. On average, this underestimation was about 100 kg C ha–1 year–1 across all scenarios. For application in Rostov oblast, the TerraClimate database should be adjusted, particularly in the range of minimum precipitation values, using data from local meteorological stations or other models.

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Journal
Moscow University Soil Science Bulletin
Published
2024-12-01
DOI
https://doi.org/10.3103/s0147687424700649
Primary Topic
Soil Geostatistics and Mapping
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article
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article

Sensitivity Analysis of the RothC Model Using Two Climatic Datasets: A Case Study of Arable Soils in Rostov Oblast

J. L. Meshalkina, В. А. Романенков, A. Yu. Gorbacheva, S. A. Antonova et al.
Moscow University Soil Science Bulletin
Soil Geostatistics and Mapping
article

Sensitivity Analysis of the RothC Model Using Two Climatic Datasets: A Case Study of Arable Soils in Rostov Oblast

J. L. Meshalkina, В. А. Романенков, A. Yu. Gorbacheva, S. A. Antonova, V. A. Dobrovolskaya, Darya Shabalina
article en

Abstract

The Rothamsted Carbon (RothC) model was developed to simulate soil organic carbon dynamics under different types of land use, different climate conditions, and different agricultural technologies. The model has been effectively applied across different scales and in different environmental zones; however, it is sensitive to key input variables. This study estimates the sensitivity of soil carbon storage projections in response to variations in climate inputs using the RothC model. Two climate data sets were used for the sensitivity analysis: the Climatic Research Unit (CRU) dataset (CRU TS v4.05, 1901–2020), which has a spatial resolution of about 50 × 50 km2 and is derived from interpolated data from ground-based weather stations, and TerraClimate database, which is built based on CRU data and enhances the resolution to around 4 × 4 km2 through additional modeling and data inputs. Sequestration estimates for Rostov oblast have been derived using the CRU database. The accuracy of estimates for Rostov oblast is of particular importance, since the proportion of Chernozems in the total land structure is almost 65%. The sensitivity analysis has revealed that the replacement of the CRU data with TerraClimate meteorological data significantly underestimates the sequestration rate. On average, this underestimation was about 100 kg C ha–1 year–1 across all scenarios. For application in Rostov oblast, the TerraClimate database should be adjusted, particularly in the range of minimum precipitation values, using data from local meteorological stations or other models.

Moscow University Soil Science BulletinVol. 79(5)
Lomonosov Moscow State University (RU), All-Russian Scientific Research Institute of Agrochemistry named after D.N. Pryanishnikova (RU)
Climate action
Openalex Percentile: Top 78%
Soil Geostatistics and Mapping
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