Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin‐wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach‐scale geomorphic channel types using publicly available geospatial data. A bottom‐up machine learning approach selects the most accurate and stable model among ∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three‐tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred‐meter reaches. Performance in statistical learning is reasonable with a 61% median cross‐validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large‐scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy‐based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors.

The dynamic interaction between a river and its floodplain is important for a variety of hydrologic, ecological, and geomorphic processes. However, water management activities have widely disrupted the natural flow regime and in many cases reduced floodplain connectivity. Recent environmental flow research has called for techniques that incorporate hydrogeomorphic processes, which are important for ecological and riverscape health. The objective of this study was to evaluate the impacts of hydrologic alterations on floodplain dynamics and connectivity.Changes in floodplain inundation dynamics and interface dynamics were investigated for 2 hydrologic scenarios on 2 distinct rivers-the Gila River and the Rio Grande, both in New Mexico, USA.The objective was achieved using a combination of 2-D hydrodynamic models and analysis techniques to evaluate large spatial and temporal datasets. The results improved understanding of inundation patterns and water flux between the channel and floodplain under baseline and altered hydrologic scenarios. Due to the distinct qualities of the study sites, unique insights were gleaned. In the Gila River, discernible changes in floodplain dynamics were observed in spite of the relatively minor alterations from the baseline hydrologic conditions. In contrast, the Rio Grande results revealed the importance of not only hydrologic alterations but also channel incision on reduced floodplain connectivity. The proposed techniques can be adapted to a wide range of river systems depending on the nature of hydrologic or geomorphic alterations under consideration. As a result, the degree of alteration of floodplain connectivity can be better understood, leading to improved river management.

Reach-scale morphological channel classifications are underpinned by the theory that each channel type is related to an assemblage of reach-and catchment-scale hydrologic, topographic, and sediment supply drivers. However, the relative importance of each driver on reach morphology is unclear, as is the possibility that different driver assemblages yield the same reach morphology. Reach-scale classifications have never needed to be predicated on hydrology, yet hydrology controls discharge and thus sediment transport capacity. The scientific question is: do two or more regions with quantifiable differences in hydrologic setting end up with different reach-scale channel types, or do channel types transcend hydrologic setting because hydrologic setting is not a dominant control at the reach scale? This study answered this question by isolating hydrologic metrics as potential dominant controls of channel type. Three steps were applied in a large test basin with diverse hydrologic settings (Sacramento River, California) to: (1) create a reach-scale channel classification based on local site surveys, (2) categorize sites by flood magnitude, dimensionless flood magnitude, and annual hydrologic regime type, and (3) statistically analyze two hydrogeomorphic linkages. Statistical tests assessed the spatial distribution of channel types and the dependence of channel type morphological attributes by hydrologic setting. Results yielded ten channel types. Nearly all types existed across all hydrologic settings, which is perhaps a surprising development for hydrogeomorphology. Downstream hydraulic geometry relationships were statistically significant. In addition, cobble-dominated uniform streams showed a consistent inverse relationship between slope and dimensionless flood magnitude, an indication of dynamic equilibrium between transport capacity and sediment supply. However, most morphological attributes showed no sorting by hydrologic setting. This study suggests that median hydraulic geometry relations persist across basins and within channel types, but hydrologic influence on geomorphic variability is likely due to local influences rather than catchment-scale drivers.

Reach-scale morphological channel classifications are underpinned by the theory that each channel type is related to an assemblage of reach-and catchment-scale hydrologic, topographic, and sediment supply drivers. However, the relative importance of each driver on reach morphology is unclear, as is the possibility that different driver assemblages yield the same reach morphology. Reach-scale classifications have never needed to be predicated on hydrology, yet hydrology controls discharge and thus sediment transport capacity. The scientific question is: do two or more regions with quantifiable differences in hydrologic setting end up with different reach-scale channel types, or do channel types transcend hydrologic setting because hydrologic setting is not a dominant control at the reach scale? This study answered this question by isolating hydrologic metrics as potential dominant controls of channel type. Three steps were applied in a large test basin with diverse hydrologic settings (Sacramento River, California) to: (1) create a reach-scale channel classification based on local site surveys, (2) categorize sites by flood magnitude, dimensionless flood magnitude, and annual hydrologic regime type, and (3) statistically analyze two hydrogeomorphic linkages. Statistical tests assessed the spatial distribution of channel types and the dependence of channel type morphological attributes by hydrologic setting. Results yielded ten channel types. Nearly all types existed across all hydrologic settings, which is perhaps a surprising development for hydrogeomorphology. Downstream hydraulic geometry relationships were statistically significant. In addition, cobble-dominated uniform streams showed a consistent inverse relationship between slope and dimensionless flood magnitude, an indication of dynamic equilibrium between transport capacity and sediment supply. However, most morphological attributes showed no sorting by hydrologic setting. This study suggests that median hydraulic geometry relations persist across basins and within channel types, but hydrologic influence on geomorphic variability is likely due to local influences rather than catchment-scale drivers.

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