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This study explores multi‐scale variability in the relationship between turbidity (Tn) and flow (Q) in 162 watersheds across the contiguous United States. Sites are selected where Tn acts as a good surrogate for suspended sediment concentration. We use dynamic linear models (DLMs) to infer time‐varying parameters of Tn‐Q rating curves at each site, and calibrate a hyper‐parameter of the DLM model (δ $\delta $) to quantify the degree of dynamicity in the rating curve relationship. The DLM can capture dynamics in the Tn‐Q relationship at the resolution of the data (daily in this study), enabling an analysis of the dynamics across time scales. Regional multivariate regressions are used to identify physiographic features that relate to the magnitude of δ $\delta $ and spectral signatures in the DLM parameters across sites. Results show that watersheds in the Midwest and Pacific Northwest tend to exhibit more variable Tn‐Q relationships, while these relationships are more stable in watersheds in the humid east and Lower Mississippi River basin. Stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network are associated with the dynamicity in the Tn‐Q relationship. DLM parameters exhibit cyclic behavior at sub‐monthly, sub‐annual, and annual time scales at sites across the country, with annual cycling associated with basin features that reflect watershed sediment availability and the erosive power of rivers. Overall, our analysis highlights significant multi‐scale variability in Tn‐Q relationships across the nation, with important implications for how sediment dynamics should be measured and managed at the watershed‐scale. Plain Language Summary: Effective sediment management often requires accurate sediment yield predictions and forecasts. However, the predictive skill of various models often declines in regions where the sediment transport processes are variable across timescales. This study aims to improve our understanding of variability in the relationship between turbidity (Tn) and flow (Q) through time, focusing on sites where turbidity is a good proxy for suspended sediment concentration. We use regression models with parameters that can vary over time to quantify the variability in the Tn‐Q relationship and to highlight the presence of seasonal patterns in transport processes. We then explore how watershed characteristics influence the variability and seasonality in the Tn‐Q relationship. Our results suggest that stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network influence the overall variability in the Tn‐Q relationship, while the seasonality is mainly associated with basin features that reflect watershed sediment availability and the erosive power of rivers. The results of the study have important implications for how sediment dynamics should be addressed in watershed management studies. Key Points: The dynamicity of sediment transport processes is quantified using dynamic linear rating curve models of turbidity and flowThe degree of rating curve dynamicity varies spatially and is partially influenced by natural and anthropogenic physiographic featuresDynamic rating curves exhibit cyclic behavior at various timescales across the US, which is also partially explained by basin features [ABSTRACT FROM AUTHOR]

Understanding the dominant drivers of hydrological change is essential for water resources management. Watersheds in the United States are experiencing different types of changes (e.g., wet gets wetter and dry gets drier); however, few studies have analyzed what drivers are responsible for these changes, and how the dominant drivers vary over time and as a function of the climate/water regime and land cover. This study uses a time‐varying Budyko framework to quantify the relative importance of precipitation, potential evapotranspiration, and other factors (e.g., climate seasonality, agricultural drainage, and urbanization) in 889 watersheds in the contiguous United States from 1950 to 2009. Results show that watersheds that are getting wetter are primarily due to increases in precipitation. However, watersheds in dry climates that are getting drier are primarily due to other factors, while watersheds in wet climates that are getting drier are primarily due to precipitation. The drivers causing statistically significant streamflow trends vary depending on dominant land‐use types. Temporally, the increasing effects of other factors are more pronounced after the 1980s in the Midwest. The dominant drivers of streamflow in the United States are time‐varying instead of constant. This is consistent with non‐stationary patterns of streamflow. The time‐varying drivers provide information on the processes that are increasingly important and require the most attention in water resources management. Key Points: Dominant drivers of streamflow in the United States are shown to be time‐varying between 1950 and 2009Precipitation is the dominant driver in wet gets drier watersheds, while other factors are the dominant driver in dry gets drier watershedsPrecipitation is the dominant driver in reference watersheds, and other factors are the dominant driver in agricultural and urban watersheds [ABSTRACT FROM AUTHOR]

The challenge of estimating design flood magnitude under climate change has led to the development of multiple approaches to long‐term flood projection: stationarity, informed‐parameter (composed of both trend informed and climate informed), and hydrologic simulation. This is the first study to compare these approaches across a large set of hydro‐climatologically diverse basins located throughout the contiguous United States, grouped into distinct clusters based on catchment and flood characteristics. The comparison is achieved using a split‐sample test conducted over the historic period; the climate (specifically, precipitation) informed and hydrologic simulation (specifically, Sacramento Soil Moisture Accounting model) approaches are forced with observations and downscaled general circulation model (GCM) simulations. The results provide a quantitative perspective on key long‐term flood projection issues. The precipitation informed approach can be informative for projections of regional change; the hydrologic simulation approach can be informative for direction of change for a single basin. When forced with GCM outputs, precipitation informed is both more accurate and more precise than hydrologic simulation, after accounting for model error. For hydrologic simulation, forcing with GCMs will add positive bias for basins in the Rocky Mountains and Southwest and will add negative bias with large uncertainty across GCMs for basins in the lower Midwest, the South, and the mid‐Atlantic seaboard. The results illustrate the need for continued improvement in long‐term flood projection approaches and for design paradigms incorporating uncertainty. Key Points: Long‐term flood projection approaches are compared across 233 U.S. river basins using split‐sample tests over the historic periodPrecipitation informed is useful for regional change projections; hydrologic simulation is useful for direction of change in a single basinGeneral circulation model outputs as forcing introduces less additional bias for precipitation informed compared to hydrologic simulation [ABSTRACT FROM AUTHOR]