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Abstract Meeting rising food demand under intensifying climate variability, soil degradation, and groundwater decline requires agriculture to produce more with less freshwater. We advance critical zone agrohydrology (CZA) as a unifying framework that treats agricultural landscapes as human-managed critical zones—coupled systems extending from canopy to bedrock and operating from seasons to decades. CZA is organized around the four deeps (deep time, deep depth, deep coupling, and deep practice) and operationalized through a 5M cycle of measuring, mapping, monitoring, modeling, and managing. This perspective expands conventional agrohydrology by accounting for long-term soil change, subsurface storage and flow, biogeochemical feedbacks, and human decision-making, thereby linking field efficiency with basin sufficiency. We illustrate implications for multifunctional soil management, nutrient-loss control, salinity rehabilitation, drought resilience, managed aquifer recharge, and cross-scale governance. By reframing agriculture as a potential contributor to aquifer stability, water quality, carbon storage, biodiversity, and durable productivity, CZA offers a practical pathway toward more resilient and basin-aware agricultural water management. 

Although the tropical intraseaonal variability (TISV), as the most important predictability sources for subseasonal-to-seasonal (S2S) prediction, is dominated by Madden-Julian oscillation (MJO), its significant fraction does not always share the canonical MJO features, especially when the convective activity arrives at Maritime Continent. In this study, using principal oscillation pattern (POP) analysis on the combined fields of daily equatorial convection and zonal wind, two distinct leading TISV modes with relatively slower e-folding decay rates are identified. One is an oscillatory mode with the period of 51 days and e-folding time of 19 days, capturing the eastward propagating (EP) feature of the canonical MJO. The other is a non-oscillatory damping mode with e-folding time of 13.6 days, capturing a standing dipole (SD) with convection anomalies centered over the Maritime Continent and tropical central Pacific, respectively. Compared to the EP mode, the leading moisture anomalies at low level to the east of convection center are diminish for the SD mode, and instead, the strong negative anomalies of moisture and subsidence motion emerge in the tropical central Pacific area, which may be responsible for the distinct propagation features. Without filtering methods used, timeseries of the two POPs could be applied to the real-time monitoring of EP and SD events in the phase-space diagram. The two modes can serve as the simple and objective approach for a better characterization for diverse natures of TISV beyond the canonical MJO description, which may further shed light on dynamics of the TISV and its predictability. 

Soil moisture data assimilation (SM-DA) is a valuable approach for enhancing streamflow prediction in rainfall-runoff models. However, most studies have focused on incorporating remotely sensed SM, and their results strongly depend on the quality of satellite products. Compared with remote sensing products, in situ observed SM data provide greater accuracy and more effectively capture temporal fluctuations in soil moisture levels. Therefore, the effectiveness of SM-DA in improving streamflow prediction remains site-specific and requires further validation. Here, we employed the Ensemble Kalman filter (EnKF) to integrate daily SM into lumped and distributed approaches of the Xinanjiang (XAJ) hydrological model to assess the importance of SM-DA in streamflow prediction. We observed a general improvement in streamflow prediction after conducting SM-DA. Specifically, the Nash-Sutcliffe efficiency increased from 0.61 to 0.65 for the lumped and from 0.62 to 0.70 for the distributed approaches. Moreover, the efficiency of SM-DA exhibits seasonal variation, with in situ SM proving particularly valuable for streamflow prediction during the wet-cold season compared to the dry-warm season. Notably, daily SM data from deep layers exhibit a stronger capability to improve streamflow prediction compared to surface SM. This indicates the significance of deep SM information for streamflow prediction in mountain areas. Overall, this study effectively demonstrates the efficacy of assimilating SM data to improve hydrological models in streamflow prediction. These findings contribute to our understanding of the connection between SM, streamflow, and hydrological connectivity in headwater catchments. 

A Cu or Fe-catalyzed radical ring expansion/coupling cascade was developed. It provides a new access to medium-sized and macrolactones with CN, N₃, SCN, and X groups. DFT calculations suggest a Cu(i)/Cu(ii)/Cu(iii) catalytic cycle for the cyanation.