This literature review synthesizes the role of soil moisture in regulating carbon sequestration and greenhouse gas emissions (CS-GHG). Soil moisture directly affects photosynthesis, respiration, microbial activity, and soil organic matter dynamics, with optimal levels enhancing carbon storage while extremes, such as drought and flooding, disrupt these processes. A quantitative analysis is provided on the effects of soil moisture on CS-GHG across various ecosystems and climatic conditions, highlighting a “Peak and Decline” pattern for CO₂ emissions at 40% water-filled pore space (WFPS), while CH₄ and N₂O emissions peak at higher levels (60–80% and around 80% WFPS, respectively). The review also examines ecosystem models, discussing how soil moisture dynamics are incorporated to simulate photosynthesis, microbial activity, and nutrient cycling. Sustainable soil moisture management practices, including conservation agriculture, agroforestry, and optimized water management, prove effective in enhancing carbon sequestration and mitigating GHG emissions by maintaining ideal soil moisture levels. The review further emphasizes the importance of advancing multiscale observations and feedback modeling through high-resolution remote sensing and ground-based data integration, as well as hybrid modeling frameworks. The interactive model-experiment framework emerges as a promising approach for linking experimental data with model refinement, enabling continuous improvement of CS-GHG predictions. From a policy perspective, shifting focus from short-term agricultural productivity to long-term carbon sequestration is crucial. Achieving this shift will require financial incentives, robust monitoring systems, and collaboration among stakeholders to ensure sustainable practices effectively contribute to climate mitigation goals.

Soil biota generates carbon that exports vertically to the atmosphere (CO₂) and transports laterally to streams and rivers (dissolved organic and inorganic carbon, DOC and DIC). These processes, together with chemical weathering, vary with flow paths across hydrological regimes; yet an integrated understanding of these interactive processes is still lacking. Here we ask: How and to what extent do subsurface carbon transformation, chemical weathering, and solute export differ across hydrological and subsurface structure regimes? We address this question using a hillslope reactive transport model calibrated using soil CO₂ and water chemistry data from Fitch, a temperate forest at the ecotone boundary of the Eastern temperate forest and mid‐continent grasslands in Kansas, USA. Model results show that droughts (discharge at 0.08 mm/day) promoted deeper flow paths, longer water transit time, carbonate precipitation, and mineralization of organic carbon (OC) into inorganic carbon (IC) (∼98% of OC). Of the IC produced, ∼86% was emitted upward as CO₂ gas and ∼14% was exported laterally as DIC into the stream. Storms (8.0 mm/day) led to carbonate dissolution but reduced OC mineralization (∼88% of OC) and promoted DOC production (∼12% of OC) and lateral fluxes of IC (∼53% of produced IC). Differences in shallow‐versus‐deep permeability contrasts led to smaller difference (<10%) than discharge‐induced differences and were most pronounced under wet conditions. High permeability contrasts (low vertical connectivity) enhanced lateral fluxes. Model results generally delineate hillslopes as active CO₂ producers and vertical carbon transporters under dry conditions, and as active DOC producers and lateral carbon transporter under wet conditions.

Core Ideas: Estimation of K _s using established PTFs for one specific field was evaluated. Selected PTFs exhibited unsatisfactory prediction of K _s at the field scale. Taking macropore volume effect into account improved the prediction of K _s with PTFs. Saturated hydraulic conductivity ( K _s ) is one of the crucial hydraulic properties for assessing water and solute transport in soils. However, direct measurement of K _s is time consuming and arduous. Alternatively, pedotransfer functions (PTFs) have been developed to estimate K _s indirectly through more easily measurable soil properties that are part of regional, national, or international databases. These PTFs are usually based on datasets collected from large regions. However, their validity for a specific site remains unclear. The objectives of this study were to evaluate the performance of established PTFs in estimating K _s in a specific field and improve PTFs to arrive at a locally adapted estimation result for K _s . Forty‐one soil samples were collected from 10 locations at five depths at a farmland in western Kentucky for hydraulic conductivity and physical property measurements. The performance of seven PTFs in estimating K _s was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination ( R ² ). At this scale, all the selected PTFs exhibited unsatisfactory prediction of K _s (high RMSE, low NSE and R ² ). In the field studied, approximately 60% of variance in K _s could be explained by soil texture and macropore components based on factor analysis. Clay content and macroporosity were identified as the most representative variables for each component. The performance of a PTF in estimating K _s for the field site investigated was significantly improved by including macroporosity (pores with diameter >75 μm) as a predictor. The results confirmed that soil structure was crucial in characterizing soil hydraulic conductivity.