Global 1-km Soil Respiration Estimates from SRDB-V5

ORNL_CLOUD provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) at 1 km resolution. The dataset was generated using a quantile regression forest model trained on 4,115 records from 1,036 studies in the Soil Respiration Database Version 5 (SRDB-V5), combined with global meteorological, land cover, and topographic covariates. These model inputs cover the period from 1961 to 2016.

Use Cases

• Validating and benchmarking terrestrial ecosystem and Earth system models that simulate soil carbon fluxes.
• Analyzing spatial patterns of soil respiration and its heterotrophic component across different biomes and climate zones.
• Assessing uncertainty in global soil carbon flux estimates using the provided standard deviation and coefficient of variation layers.
• Studying the relationship between soil respiration and environmental drivers like mean annual temperature and seasonal precipitation.

Strengths

• The model is based on a substantial empirical foundation, utilizing 4,115 soil respiration records from 1,036 global field studies.
• It offers improved global representation by incorporating more data from Russian and Chinese scientific literature.
• Provides high-resolution (1 km) global gridded estimates for both total and heterotrophic soil respiration, along with associated uncertainty metrics.

Limitations

• Specific column names and the exact data structure are not detailed in the provided platform descriptions.
• There is a conflict in the 'last updated' metadata, with one source listing 2026-03-13 and another listing 2016-06-01, casting doubt on the provenance timeliness.

Provenance

Source

ORNL_CLOUD, utilizing the global Soil Respiration Database Version 5 (SRDB-V5).

Collection Method

Quantile regression forest modeling applied to field study records combined with global covariate datasets.

Time Range

1961-2016

Freshness

2026-03-13 01:53:44.262218

Geography

Global