Search

Menu
Search
in Ecology

High-resolution forecasting of soil thermal regimes using different deep learning frameworks under climate change

26 Views


Abstract

Soil temperature is a critical parameter influencing ecological and hydrological processes, yet its accurate projection under climate change remains challenging due to coarse-resolution climate models and complex soil-atmosphere interactions. This study develops a deep learning framework to downscale soil temperature (5 cm depth) in western Iran, under climate change scenarios. Using an ensemble of three complementary techniques—Random Forest (Gini) importance, Permutation Importance, and SHAP (SHapley Additive exPlanations) analysis—we identified optimal predictors from the 26 available CanESM5 (CMIP6) variables. Four deep learning models—CNN, LSTM, GRU, and a hybrid CNN-LSTM—were evaluated for downscaling performance using historical data (1980–2014). The hybrid CNN-LSTM model outperformed others, achieving the highest accuracy (NSE > 86%, RMSE < 4.3°C) by capturing spatial and temporal dependencies in soil thermal dynamics. Assessing the plausibility of each scenario’s trend (2015–2020) revealed regional climate patterns: western stations, more arid and warming-sensitive, aligned with SSP245/SSP370, while eastern stations, influenced by the Zagros Mountains, showed cooling and precipitation feedbacks favoring SSP119/SSP126. Future projections under SSP126, SSP245, and SSP585 scenarios indicated nonlinear soil temperature responses, with high-emission pathways (SSP585) causing initial cooling (-4.11°C by 2040) followed by accelerated warming (+ 2.09°C by 2100). In stark contrast, low- and mid-emission pathways (SSP126, SSP245) lead to stable, moderate warming. This creates a dramatic reversal in decadal trends for SSP585, shifting from strong cooling to rapid heating, and alters the climate’s statistical profile. The findings emphasize that high emissions defer but ultimately cause the most intense warming, highlighting the critical influence of emission pathways on the future pace and pattern of soil temperature change.

Data availability

The Raw data will be made available on https://github.com/mnazeri/Soil-Thermal-Regimes-.git. Also, the data from the CanESM5 climate model was taken from the website https://climate-scenarios.canada.ca.

References

  1. Bradford, J. B. et al. Climate-driven shifts in soil temperature and moisture regimes suggest opportunities to enhance assessments of dryland resilience and resistance. Front. Ecol. Evol. 7, 358 (2019).

    Google Scholar 

  2. Fischer, G., Cleves-Leguizamo, J. A. & Balaguera-López, H. E. Impact of soil temperature on fruit species within climate change scenarios. Rev. Colomb. Cienc. Hortíc. 16(1), e12769 (2022).

    Google Scholar 

  3. Suseela, V., Conant, R. T., Wallenstein, M. D. & Dukes, J. S. Effects of soil moisture on the temperature sensitivity of heterotrophic respiration vary seasonally in an old-field climate change experiment. Glob. Change Biol. 18(1), 336–348 (2012).

    Google Scholar 

  4. Davidson, E. A., Samanta, S., Caramori, S. S. & Savage, K. The D ual A rrhenius and M ichaelis–M enten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob. Change Biol. 18(1), 371–384 (2012).

    Google Scholar 

  5. Ameloot, N., Graber, E. R., Verheijen, F. G. & De Neve, S. Interactions between biochar stability and soil organisms: Review and research needs. Eur. J. Soil Sci. 64(4), 379–390 (2013).

    Google Scholar 

  6. Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochem. Cycles 30(1), 40–56 (2016).

    Google Scholar 

  7. Pugnaire, F. I. et al. Climate change effects on plant-soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems. Sci. Adv. 5(11), eaaz1834 (2019).

    Google Scholar 

  8. Malhi, G. S., Kaur, M. & Kaushik, P. Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability 13(3), 1318 (2021).

    Google Scholar 

  9. Elmi, A., Anwar, S. A. & Al-Dashti, H. Measurements and regional climate modeling of soil temperature with and without bias correction method under arid environment: Can soil temperature outperform air temperature as a climate change indicator?. Environ. Model. Assess. 29(2), 279–289 (2024).

    Google Scholar 

  10. Hu, G. et al. New Fourier-series-based analytical solution to the conduction–convection equation to calculate soil temperature, determine soil thermal properties, or estimate water flux. Int. J. Heat Mass Transfer. 95, 815–823 (2016).

    Google Scholar 

  11. Wang, C., Fu, B., Zhang, L. & Xu, Z. Soil moisture–plant interactions: An ecohydrological review. J. Soils Sediments 19, 1–9 (2019).

    Google Scholar 

  12. Alizamir, M. et al. Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One 15(4), e0231055 (2020).

    Google Scholar 

  13. Elsayed, S. et al. Interpretation the influence of hydrometeorological variables on soil temperature prediction using the potential of deep learning model. Knowledge-Based Eng. Sci. 4(1), 55–77 (2023).

    Google Scholar 

  14. Imanian, H. et al. A comparative analysis of deep learning models for soil temperature prediction in cold climates. Theoret. Appl. Climatol. 155(4), 2571–2587 (2024).

    Google Scholar 

  15. Jin, Y., Zhang, H., Yan, Y. & Cong, P. A semi-parametric geographically weighted regression approach to exploring driving factors of fractional vegetation cover: A case study of Guangdong. Sustainability 12(18), 7512 (2020).

    Google Scholar 

  16. Balmaceda-Huarte, R., Olmo, M. E. & Bettolli, M. L. Regional climate projections of daily extreme temperatures in Argentina applying statistical downscaling to CMIP5 and CMIP6 models. Clim. Dyn. 62(6), 4997–5018 (2024).

    Google Scholar 

  17. Sun, Y. et al. Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review. ISPRS J. Photogramm. Remote. Sens. https://doi.org/10.1016/j.isprsjprs.2023.12.011 (2024).

    Google Scholar 

  18. Ranjbar, A., Heydarnejad, S., Mousavi, S. H. & Mirzaei, R. Mapping desertification potential using life cycle assessment method: A case study in Lorestan Province, Iran. J. Arid Land 11, 652–663 (2019).

    Google Scholar 

  19. Swart, N. C. et al. The Canadian earth system model version 5 (CanESM5. 0.3). Geosci. Model Dev. 12(11), 4823–4873 (2019).

    Google Scholar 

  20. Tahroudi, M., Mirabbasi, R. & Nasrolahi, A. Investigating the possibilities of temperature concentration distribution in Zayanderood based on climate change. Dyn. Atmos. Oceans https://doi.org/10.1016/j.dynatmoce.2024.101454 (2024).

    Google Scholar 

  21. Amirabadizadeh, M., Ramezani, Y., Nazeri Tahroudi, M. & Zeynali, M. J. Assessment of data-driven models in downscaling of the daily temperature in Birjand synoptic station. AUT J. Civ. Eng. 3(2), 193–200 (2019).

    Google Scholar 

  22. Pizzigalli, C. et al. Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area. Ital. J. Agron. 7(1), e2 (2012).

    Google Scholar 

  23. Gers, F. A., Schmidhuber, J. & Cummins, F. Learning to forget: Continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000).

    Google Scholar 

  24. Saeidinia, M., Haghiabi, A. H., Nazeri Tahroudi, M., Nasrolahi, A. & De Michele, C. Deep learning and vine copula-based sequencing: Approaches under investigation for forecasting soil temperature dynamics. Stoch. Environ. Res. Risk Assess. 39, 1–35 (2025).

    Google Scholar 

  25. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).

  26. Xie, H., Zhang, L. & Lim, C. P. Evolving CNN-LSTM models for time series prediction using enhanced grey wolf optimizer. IEEE Access 8, 161519–161541 (2020).

    Google Scholar 

  27. Cao, L. et al. A novel transformer-CNN approach for predicting soil properties from LUCAS Vis-NIR spectral data. Agronomy 14(9), 1998 (2024).

    Google Scholar 

  28. Dong, Z. et al. Prediction of soil organic carbon content in complex vegetation areas based on CNN-LSTM model. Land 13(7), 915 (2024).

    Google Scholar 

  29. Kirkland, E. J. Bilinear interpolation. In Advanced computing in electron microscopy 261–263 (Springer, US, 2010).

    Google Scholar 

  30. Byun, K. & Hamlet, A. F. An improved empirical quantile mapping approach for bias correction of extreme values in climate model simulations. Environ. Res. Lett. 20(1), 014041 (2024).

    Google Scholar 

  31. Farhangmehr, V. et al. A spatiotemporal CNN-LSTM deep learning model for predicting soil temperature in diverse large-scale regional climates. Sci. Total Environ. 968, 178901. https://doi.org/10.1016/j.scitotenv.2025.178901 (2025).

    Google Scholar 

  32. Imanian, H. et al. A comparative analysis of deep learning models for soil temperature prediction in cold climates. Theoret. Appl. Climatol. https://doi.org/10.1007/s00704-023-04781-x (2023).

    Google Scholar 

  33. Malik, A. et al. Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India. Environ. Sci. Pollut. Res. 29, 71270–71289. https://doi.org/10.1007/s11356-022-20837-3 (2022).

    Google Scholar 

  34. Kheir, A., Elnashar, A., Mosad, A. & Govind, A. An improved deep learning procedure for statistical downscaling of climate data. Heliyon https://doi.org/10.1016/j.heliyon.2023.e18200 (2023).

    Google Scholar 

  35. Wetterhall, F., Bárdossy, A., Chen, D., Halldin, S. & Xu, C. Y. Daily precipitation-downscaling techniques in three Chinese regions. Water Resour. Res. https://doi.org/10.1029/2005WR004573 (2006).

    Google Scholar 

  36. Turco, M., Llasat, M. C., Herrera, S. & Gutiérrez, J. M. Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique. J. Geophys. Res.: Atmos. 122(5), 2631–2648 (2017).

    Google Scholar 

  37. Schepen, A., Everingham, Y. & Wang, Q. J. Coupling forecast calibration and data-driven downscaling for generating reliable, high-resolution, multivariate seasonal climate forecast ensembles at multiple sites. Int. J. Climatol. 40, 2479–2496 (2020).

    Google Scholar 

  38. Wang, F., Tian, D., Lowe, L., Kalin, L. & Lehrter, J. Deep learning for daily precipitation and temperature downscaling. Water Resour. Res. 57(4), e2020WR029308 (2021).

    Google Scholar 

  39. Nazeri Tahroudi, M., Ramezani, Y., De Michele, C. & Mirabbasi, R. Application of copula-based approach as a new data-driven model for downscaling the mean daily temperature. Int. J. Climatol. 43(1), 240–254 (2023).

    Google Scholar 

  40. Huang, R. et al. Soil temperature estimation at different depths, using remotely-sensed data. J. Integr. Agric. https://doi.org/10.1016/s2095-3119(19)62657-2 (2020).

    Google Scholar 

  41. García-García, A. et al. Soil heat extremes can outpace air temperature extremes. Nat. Clim. Chang. 13, 1237–1241. https://doi.org/10.1038/s41558-023-01812-3 (2023).

    Google Scholar 

  42. Yang, H. et al. Stronger impact of extreme heat event on vegetation temperature sensitivity under future scenarios with high-emission intensity. Remote Sens. 16, 3708. https://doi.org/10.3390/rs16193708 (2024).

    Google Scholar 

  43. Jia, M. et al. Characteristics of Soil temperature change in lhasa in the face of climate change. Atmosphere https://doi.org/10.3390/atmos15040450 (2024).

    Google Scholar 

  44. Zhong, H. & Guo, Y. Long-term persistence in observed temperature and precipitation series. Fractal Fract. https://doi.org/10.3390/fractalfract9060385 (2025).

    Google Scholar 

  45. Chiang, F. et al. Responses of compound daytime and nighttime warm-dry and warm-humid events to individual anthropogenic forcings. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ac80ce (2022).

    Google Scholar 

  46. Jiang, K. et al. Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background. iScience https://doi.org/10.1016/j.isci.2023.106837 (2023).

    Google Scholar 

  47. Mohammadi, M., Shafaie, V., Samani, A., Garizi, A. & Rad, M. Assessing future hydrological variability in a semi-arid mediterranean basin: Soil and water assessment tool model projections under shared socioeconomic pathways climate scenarios. Water https://doi.org/10.3390/w16060805 (2024).

    Google Scholar 

  48. You, Q. et al. Temperature dataset of CMIP6 models over China: Evaluation, trend and uncertainty. Clim. Dyn. 57, 17–35. https://doi.org/10.1007/s00382-021-05691-2 (2021).

    Google Scholar 

  49. Alaminie, A. et al. Evaluation of past and future climate trends under CMIP6 scenarios for the UBNB (Abay), Ethiopia. Water https://doi.org/10.3390/w13152110 (2021).

    Google Scholar 

  50. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev. 99(3–4), 125–161 (2010).

    Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Water Engineering, Lorestan University, Khorramabad, Iran

    Mehri Saeidinia, Amir Hamzeh Haghiabi, Mohammad Nazeri Tahroudi & Aliheidar Nasrolahi

  2. Department of Civil and Environmental Engineering, Politecnico Di Milano, Milan, Italy

    Carlo De Michele

Authors

  1. Mehri Saeidinia
    View author publications

    Search author on:PubMed Google Scholar

  2. Amir Hamzeh Haghiabi
    View author publications

    Search author on:PubMed Google Scholar

  3. Mohammad Nazeri Tahroudi
    View author publications

    Search author on:PubMed Google Scholar

  4. Aliheidar Nasrolahi
    View author publications

    Search author on:PubMed Google Scholar

  5. Carlo De Michele
    View author publications

    Search author on:PubMed Google Scholar

Contributions

MS and MNT: Methodology, data curation, methodology, software, formal analysis, writing—original draft, preparing the revised version. AHH, AHN, and CDM: conceptualization, validation, writing—review & editing. All authors reviewed the revised manuscript.

Corresponding author

Correspondence to
Mehri Saeidinia.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Saeidinia, M., Haghiabi, A.H., Nazeri Tahroudi, M. et al. High-resolution forecasting of soil thermal regimes using different deep learning frameworks under climate change.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-38496-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-38496-6

Keywords

  • Climate change scenarios
  • CMIP6
  • CNN-LSTM
  • Deep learning
  • Soil temperature downscaling
  • SSPs

Source: Ecology - nature.com

Nutrient availability drives local seasonal movements of an endangered marine megafauna species

Long-term biodiversity monitoring data from two hydroelectric dam projects in northeast Portugal, 2006–2023

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close