ArticleScientific reports2026
High-resolution forecasting of soil thermal regimes using different deep learning frameworks under climate change.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.