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Understanding sediment (dis)connectivity is essential for predicting sediment transport, managing sediment-related hazards, and designing effective river restoration and flood management strategies. While significant progress has been made in understanding landscape sensitivity and (dis)connectivity, the influence of different types of climate extremes—such as event magnitude, duration, and intensity—remains insufficiently understood. To address this knowledge gap, we analyze global river discharge and sediment transport data across catchments spanning a wide range of sizes (20–4,680,000 km²), landscape types, and drainage network structures. Our results reveal a critical divergence in streamflow behavior and flood magnitude–frequency relationships. This divergence is linked to marked variations in hydrological and sediment (dis)connectivity and the nature of river feedback loops. We present a novel statistical framework for clustering rivers globally based on these patterns, enabling a synthesis of how discharge regimes shape (dis)connectivity and feedback dynamics. Notably, we show that negative feedback loops and river capacity restoration—driven by frequent low-magnitude floods and rare high-magnitude events—occur only in specific rivers, not universally. Such rivers have a poor connectivity between sediment transport and discharge magnitude. In contrast, some rivers have highly variable discharge regimes with rare or seasonal high-magnitude floods and prolonged low-flow periods, during which discharge falls below sediment transport thresholds. These latter conditions foster strong sediment connectivity and cumulative (cascading), positive feedbacks, leading to lack of river restoration. Our findings further show that distinct river discharge regimes and (dis)connectivity are linked to hydroclimatic conditions, and source and pathways of moisture delivery. 

Understanding river floods will most likely become increasingly important as the climate continues to change. The generation of river floods depends on river hydrologic regimes that are in turn a function of landscape features, climatic conditions and human interventions. A parallel study developed a mathematical grouping method to distinguish river hydrological regimes according to flood magnitude–frequency relationships. In this study, we explore how these flood magnitude–frequency relationships relate to landscape, climate and human drivers. We analyze whether the groups with similar flood magnitude–frequency relationships occur in same hydroclimates, or are significantly influenced by drainage size and gradient, or human activities, such as irrigation and damming. We do so by identifying hydroclimate types along the river lengths, their drainage sizes and gradients, and the presence or absence of major human interventions for rivers in each group. This research will advance our understanding of how climate variability, landscape characteristics, and human interventions shape river hydrologic regimes and drive geomorphic change. For example, rivers that only experience intermittent or seasonal large magnitude flood events and have very low to no flow a majority of the time, do most of the geomorphological work during these high magnitude flooding events. In contrast, rivers that experience many moderate floods and rare high-magnitude floods do most of their geomorphological work during the moderate events and likely restore the high-magnitude event effects. 

Understanding river floods will most likely become increasingly important as the climate continues to change. The generation of river floods depends on river hydrologic regimes that are in turn a function of landscape features, climatic conditions and human interventions. A parallel study developed a mathematical grouping method to distinguish river hydrological regimes according to flood magnitude–frequency relationships. In this study, we explore how these flood magnitude–frequency relationships relate to landscape, climate and human drivers. We analyze whether the groups with similar flood magnitude–frequency relationships occur in same hydroclimates, or are significantly influenced by drainage size and gradient, or human activities, such as irrigation and damming. We do so by identifying hydroclimate types along the river lengths, their drainage sizes and gradients, and the presence or absence of major human interventions for rivers in each group. This research will advance our understanding of how climate variability, landscape characteristics, and human interventions shape river hydrologic regimes and drive geomorphic change. For example, rivers that only experience intermittent or seasonal large magnitude flood events and have very low to no flow a majority of the time, do most of the geomorphological work during these high magnitude flooding events. In contrast, rivers that experience many moderate floods and rare high-magnitude floods do most of their geomorphological work during the moderate events and likely restore the high-magnitude event effects. 

River floods are among Earth’s most common and destructive natural hazards, and their impacts are expected to intensify. Despite many advances in data collection, statistical methods and modeling, many flood-mitigation strategies remain ineffective—and in some cases, may even exacerbate flood damage in rivers with distinct hydrological regimes. To address this knowledge gap, we analyze global river discharge and sediment data across catchments spanning a wide range of sizes (20–4,680,000 km²), landscape types, and drainage network structures. Our findings reveal a critical divergence in hydrologic regimes and flood magnitude–frequency relationships. This divergence is also linked to marked variations in specific flood hazards. We introduce a new statistical framework for clustering rivers globally according to their hydrological regimes, and synthesize how these regimes shape flood behavior and hazards. For instance, rivers with certain hydrological regimes experience inundation as the key flood hazard. In contrast, rivers with other hydrological regimes generate high sediment loads that reduce channel capacity, increasing the likelihood of unexpected channel shifts and damaging sedimentation on agricultural land and infrastructure. Notably, we show that negative feedback loops—driven by frequent low-magnitude floods and rare high-magnitude events—occur only in specific hydrologic regimes, not universally. Some regimes foster positive feedbacks, leading to cascading flood effects, where prior events amplify the impacts of subsequent floods. These can be further enhanced by human intervention, such as flood mitigation infrastructure or urbanization, aggravating flood damage and even causing catastrophic consequences from moderate meteorological events or discharges. Our findings further show that river hydrological regimes are linked to hydroclimatic conditions, and source and pathways of moisture delivery. 

Devastating, lethal floods underscore the need for a more comprehensive understanding of flood risks, especially as these floods are expected to become more extreme. The generation of river floods partially depends on river hydrologic regimes reflected in hydrograph shapes. To develop an understanding of these hydrologic regimes, we created metrics that distinguish hydrograph shapes according to how the river discharge data informs different patterns of geomorphological work. After an initial analysis of hydrographs from rivers around the world, we determined that grouping the rivers by drainage size or hydroclimate type did not effectively distinguish rivers by magnitude and frequency of flood events. We then developed a mathematical grouping method which independently focuses on flood magnitude–frequency relationships. We initially identified variance as a compelling metric for discharge variability; however, variance does not preserve information about variability at different time scales. Instead of using variance itself, we decided to dissect this measure by using a Power Spectral Density (PSD) analysis. PSD quantifies how variance at different time scales affects the overall variance and thus allows us to identify and use K-means clustering to group rivers by river discharge patterns that repeat over different lengths of time. By developing quantitative metrics to classify rivers based on magnitude and frequency of flood events, we will ultimately develop a more nuanced understanding of flood extremes—including event magnitude, duration and intensity—and their role in driving geomorphic change. This research will further advance our understanding of how climate variability, landscape characteristics, and human interventions shape river hydrologic regimes. 

River floods are among Earth’s most common and destructive natural hazards, and their impacts are expected to intensify. Despite many advances in data collection, statistical methods and modeling, many flood-mitigation strategies remain ineffective—and in some cases, may even exacerbate flood damage in rivers with distinct hydrological regimes. To address this knowledge gap, we analyze global river discharge and sediment data across catchments spanning a wide range of sizes (20–4,680,000 km²), landscape types, and drainage network structures. Our findings reveal a critical divergence in hydrologic regimes and flood magnitude–frequency relationships. This divergence is also linked to marked variations in specific flood hazards. We introduce a new statistical framework for clustering rivers globally according to their hydrological regimes, and synthesize how these regimes shape flood behavior and hazards. For instance, rivers with certain hydrological regimes experience inundation as the key flood hazard. In contrast, rivers with other hydrological regimes generate high sediment loads that reduce channel capacity, increasing the likelihood of unexpected channel shifts and damaging sedimentation on agricultural land and infrastructure. Notably, we show that negative feedback loops—driven by frequent low-magnitude floods and rare high-magnitude events—occur only in specific hydrologic regimes, not universally. Some regimes foster positive feedbacks, leading to cascading flood effects, where prior events amplify the impacts of subsequent floods. These can be further enhanced by human intervention, such as flood mitigation infrastructure or urbanization, aggravating flood damage and even causing catastrophic consequences from moderate meteorological events or discharges. Our findings further show that river hydrological regimes are linked to hydroclimatic conditions, and source and pathways of moisture delivery. 

Devastating, lethal floods underscore the need for a more comprehensive understanding of flood risks, especially as these floods are expected to become more extreme. The generation of river floods partially depends on river hydrologic regimes reflected in hydrograph shapes. To develop an understanding of these hydrologic regimes, we created metrics that distinguish hydrograph shapes according to how the river discharge data informs different patterns of geomorphological work. After an initial analysis of hydrographs from rivers around the world, we determined that grouping the rivers by drainage size or hydroclimate type did not effectively distinguish rivers by magnitude and frequency of flood events. We then developed a mathematical grouping method which independently focuses on flood magnitude–frequency relationships. We initially identified variance as a compelling metric for discharge variability; however, variance does not preserve information about variability at different time scales. Instead of using variance itself, we decided to dissect this measure by using a Power Spectral Density (PSD) analysis. PSD quantifies how variance at different time scales affects the overall variance and thus allows us to identify and use K-means clustering to group rivers by river discharge patterns that repeat over different lengths of time. By developing quantitative metrics to classify rivers based on magnitude and frequency of flood events, we will ultimately develop a more nuanced understanding of flood extremes—including event magnitude, duration and intensity—and their role in driving geomorphic change. This research will further advance our understanding of how climate variability, landscape characteristics, and human interventions shape river hydrologic regimes. 

Abstract Mercury (Hg) is a global pollutant whose atmospheric deposition is a major input to the terrestrial and oceanic ecosystems. Gas‐particle partitioning (GPP) of gaseous oxidized mercury (GOM) redistributes speciated Hg between gas and particulate phase and can subsequently alter Hg deposition flux. Most 3‐dimensional chemical transport models either neglected the Hg GPP process or parameterized it with measurement data limited in time and space. In this study, CMAQ‐newHg‐Br (Ye et al., 2018,https://doi.org/10.1002/2017ms001161) was updated to CMAQ‐newHg‐Br v2 by implementing a new GPP scheme and the most up‐to‐date Hg redox chemistry and was run for the northeastern United States over January‐November 2010. CMAQ‐newHg‐Br v2 reproduced the measured spatiotemporal distributions of gaseous elemental mercury (GEM) and particulate bound mercury (PBM) concentrations and Hg wet deposition flux within reasonable ranges and simulated dry deposition flux in agreement with previous studies. The GPP scheme improved the simulation of PBM via increasing winter‐, spring‐ and fall‐time PBM concentrations by threefold. It also improved simulated Hg wet deposition flux with an increase of 2.1 ± 0.7 μgm²in the 11‐month accumulated amount, offsetting half of the decreasing effect of the updated chemistry (−4.2 ± 1.8 μgm²). Further, the GPP scheme captured the observedK_p‐T relationship as reported in previous studies without using measurement data and showed advantages at night and in rural/remote areas where existing empirical parameterizations failed. Our study demonstrated CMAQ‐newHg‐Br v2 a promising assessment tool to quantify impacts of climate change and emission reduction policy on Hg cycling.