The temperature sensitivity (Q10) of soil respiration is a critical parameter in modeling soil carbon dynamics; yet the regulating factors and the underlying mechanisms of Q10 in peat soils remain unclear. To address this gap, we conducted a comprehensive synthesis data analysis from 87 peatland sites (350 observations) spanning boreal, temperate, and tropical zones, and investigated the spatial distribution pattern of Q10 and its correlation with climate conditions, soil properties, and hydrology. Findings revealed distinct Q10 values across climate zones: boreal peatlands exhibited the highest Q10, trailed by temperate and then tropical peatlands. Latitude presented a positive correlation with Q10, while mean annual air temperature and precipitation revealed a negative correlation. The results from the structural equation model suggest that soil properties, such as carbon-to-nitrogen ratio (C/N) and peat type, were the primary drivers of the variance in Q10 of peat respiration. Peat C/N ratios negatively correlated with Q10 of peat respiration and the relationship between C/N and Q10 varied significantly between peat types. Our data analyses also revealed that Q10 was influenced by soil moisture levels, with significantly lower values observed for peat soils under wet than dry conditions. Essentially, boreal and temperate peatlands seem more vulnerable to global warming-induced soil organic carbon decomposition than tropical counterparts, with wet peatlands showing higher climate resilience.

Abstract Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: ( go-fair.org )). Easy availability of FAIR data has become possible because the hydrology‐oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.