Soil hydrological processes (SHP) support ecosystems, modulate the impact of climate change on terrestrial systems and control feedback mechanisms between water, energy and biogeochemical cycles. However, land-use changes and extreme events are increasingly impacting these processes. In this Review, we describe SHP across scales and examine their links with soil properties, ecosystem processes and climate. Soil structure influences SHP such as infiltration, soil water redistribution and root water uptake on small scales. On local scales, SHP are driven by root water uptake, vegetation and groundwater dynamics. Regionally, SHP are impacted by extreme events such as droughts, floods, heatwaves and land-use change; however, antecedent and current SHP partially determine the broader effects of extreme events. Emerging technologies such as wireless and automated sensing, soil moisture observation through novel synthetic aperture radars satellites, big data analysis and machine learning approaches offer unique opportunities to advance soil hydrology. These advances, in tandem with the inclusion of more key soil types and properties in models, will be pivotal in predicting the role of SHP during global change.

Soil water status, which refers to the wetness or dryness of soils, is crucial for the productivity of agroecosystems, as it determines nutrient cycling and uptake physically via transport, biologically via the moisture‐dependent activity of soil flora, fauna, and plants, and chemically via specific hydrolyses and redox reactions. Here, we focus on the dynamics of nitrogen (N), phosphorus (P), and sulfur (S) and review how soil water is coupled to the cycling of these elements and related stoichiometric controls across different scales within agroecosystems. These scales span processes at the molecular level, where nutrients and water are consumed, to processes in the soil pore system, within a soil profile and across the landscape. We highlight that with increasing mobility of the nutrients in water, water‐based nutrient flux may alleviate or even exacerbate imbalances in nutrient supply within soils, for example, by transport of mobile nutrients towards previously depleted microsites (alleviating imbalances), or by selective loss of mobile nutrients from microsites (increasing imbalances). These imbalances can be modulated by biological activity, especially by fungal hyphae and roots, which contribute to nutrient redistribution within soils, and which are themselves dependent on specific, optimal water availability. At larger scales, such small‐scale effects converge with nutrient inputs from atmospheric (wet deposition) or nonlocal sources and with nutrient losses from the soil system towards aquifers. Hence, water acts as a major control in nutrient cycling across scales in agroecosystems and may either exacerbate or remove spatial disparities in the availability of the individual nutrients (N, P, S) required for biological activity.

Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns.