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The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order "n" is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2). Plain Language Summary: In hydrology, as in other endeavors, location matters. This study presents a new type of data that describes the location of a point on the landscape in the context of the network of streams that are present across the continental United States. The new data are presented as maps, and different patterns can be recognized in different areas of the United States. The patterns that can be seen also vary as one looks more or less closely at an area. The patterns that are present in these maps are shown to be useful for the purposes of mapping water resources. Key Points: Multiorder hydrologic position (lateral position and distance from stream to divide) quantifies the location of a point on the landscapeMultiorder hydrologic position is mapped for nine hydrologic orders at a 30‐m resolution for the conterminous United StatesMultiorder hydrologic position is used in machine learning models for the purposes of physiographic and hydrologic mapping [ABSTRACT FROM AUTHOR]

Temporal shifts in precipitation and runoff regime curves appear throughout the continental United States, but differ from region to region. This paper explores these regime shifts by building upon a hydroclimatic classification system that partitions the United States into clusters of similarly behaved catchments using four simple hydroclimatic indicators. Hydroclimate data from over four hundred catchments over a 55 year period (belonging to the MOPEX data set) are analyzed to reveal how the indicators have shifted before and after 1970, before and after 1975, and before and after 1980. Statistically significant hydroclimatic changes in these indicators are explored qualitatively, suggesting which catchments today might resemble other catchments tomorrow. Thus, a preview of current locations in one class under future conditions is provided by observing existing locations of another class. The classification system structure enables organization of these data, allowing patterns of regime change to emerge without highly specified models at each individual site. Regional analyses explore changes in mean seasonal precipitation/runoff regimes, including shifts in the daily variability of precipitation and runoff. Additionally, changes in regime curves of minimum and maximum precipitation/runoff observations are analyzed and discussed. Results indicate that after 1980, classifications typically found in the southeastern quarter of the United States have expanded northward and westward. Regionally, the Midwest and Rocky Mountains seem to demonstrate more frequent, but less intense storms after 1980, while southeastern catchments receive much less water in the form of precipitation and runoff than in previous years. [ABSTRACT FROM AUTHOR]

Artificial levees are anthropogenic structures designed to hydrologically disconnect rivers from floodplains. The extent of artificial levees in the contiguous United States (CONUS) is unknown. To better estimate the distribution of artificial levees, we tested several different geomorphic, land cover, and spatial variables developed from the National Elevation Dataset, the National Land Cover database, and the National Hydrology Dataset HR Plus. We used known levee locations from the National Levee Database as training data. We tested machine learning and general logistic models' ability to detect artificial levees in a 100‐year hydrogeomorphic floodplain of seven geographically diverse 8‐digit HUC basins. Random forest models outperformed other models in predicting the location of levees using variables representing geomorphic attributes, land cover, and distance from streams ranging in size between stream order one through six. To demonstrate the ability of our approach to detect unknown levees, we conducted a leave‐one‐out cross‐validation in the lower Mississippi Basin using approximately 1,100 artificial levees. This approach detected known levees constituting 94% of the total levee length in the basin. Scaling up to the CONUS, we applied a high performing (overall accuracy of 97%) random forest model using land cover and stream order variables. We detected 182,213 km of potential levees, mostly along streams of order 2–6 in the Mississippi and Missouri River Basins, indicating that the national levee database contains 20.4% of levee length. Potential levees and those documented in the national levee database modify 2% of the total length of streams in the contiguous United States. Plain Language Summary: There has been a lot of research exploring how humans have impacted river systems to include impacts from dams and roads at national and global scales. However, the study of artificial levees has been limited to local studies due to incomplete databases of levee location. Artificial levees are linear dirt mounds, built next to rivers to stop flooding of property built on floodplains. Because the location of every artificial levee is not known in the US, we do not know to what extent floodplains have been separated from their rivers. To ameliorate this, our study explores different methods to detect unknown levees and applies an algorithm that is 97% accurate to the contiguous U.S. Most levees are built within floodplains, so we limited our study to the 100‐year floodplain. We tested different types of algorithms in a limited study with variables explaining levee shape, human land cover, and distance from streams of different sizes. Then, we applied the most effective models to the contiguous U.S. We located over 182,000 km of potential levees, potential areas with artificial levees not identified in the NLD, most of which were built on smaller streams and concentrated in the Mississippi and Missouri River basins. Key Points: An incomplete database of artificial levees inhibits the study of artificial levee impacts to floodplain extent in the contiguous USADifferent methods of detecting artificial levees are tested in case study basins and in the contiguous USAThis study detects 182,000 km of potential levees, along smaller streams in the Mississippi and Missouri Basins [ABSTRACT FROM AUTHOR]

Both climate change and human activities are known to have induced changes to hydrology. Consequently, quantifying the net impact of human contribution to the streamfiow change is a challenge. In this paper, a decomposition method based on the Budyko hypothesis is used to quantify the climate (i.e., precipitation and potential evaporation change) and direct human impact on mean annual streamflow (MAS) for 413 watersheds in the contiguous United States. The data for annual precipitation, runoff, and potential evaporation are obtained from the international Model Parameter Estimation Experiment (MOPEX), which is often assumed to only include gauges unaffected by human interferences. The data are split into two periods (1948-1970 and 1971-2003) to quantify the change over time. Although climate is found to affect MAS more than direct human impact, the results show that assuming the MOPEX data set to be unaffected by human activities is far from realistic. Climate change causes increasing MAS in most watersheds, while the direct human-induced change is spatially heterogeneous in the contiguous United States, with strong regional patterns, e.g., human activities causing increased MAS in the Midwest and significantly decreased MAS in the High Plains. The climate- and human-induced changes are found to be more severe in arid regions, where water is limited. Comparing the results to a collection of independent data sets indicates that the estimated direct human impacts on MAS in this largely nonurban set of watersheds might be attributed to several human activities, such as cropland expansion, irrigation, and the construction of reservoirs. [ABSTRACT FROM AUTHOR]

A catchment's hydrological response is controlled by climatic forcing and by the landscape through which water moves. Yet when we compare large samples of catchments, we often find climate to be the only good predictor of the hydrological response and a lot of variability is left unexplained. This contradicts extensive evidence from field and regional studies which shows the importance of catchment form (e.g., geology) on catchment hydrological processes, particularly on baseflow processes. We hypothesize that this is due to limitations in (a) the catchment attributes we use to inform our analyses and (b) the hydrological signatures we use to describe the hydrological response. To test these hypotheses, we use a large sample of catchment data across the contiguous United States. By reviewing literature from several U.S. regions, we show that region‐specific knowledge is underutilized in large sample studies. To organize the findings from these regions, we propose and apply a framework based on standardized perceptual models. Informed by these perceptual models, we use both available and newly calculated catchment attributes to show that baseflow signature predictions can be improved regionally. Multiple baseflow signatures are needed to better distinguish between different baseflow sources, such as the subsurface, surface water bodies, and snow. We conclude with pointing at potential future directions and argue that we should aim at a more systematic and hydrologically motivated selection of catchment attributes and hydrological signatures. Plain Language Summary: River flow dynamics are influenced by climate and by the landscape through which a river flows. However, when we investigate many river catchments using large‐scale data sets such as global maps, we often cannot find a link between river flow dynamics and landscape characteristics (e.g., geology). We show (a) that such maps are often too general and do not describe aspects relevant for river flow dynamics and (b) that we need to pay more attention to the metrics we use to quantify river flow dynamics. There is a wealth of information contained in articles and data sets focusing on the regional scale which we can and should make use of. Since such information is often very specific to a certain region, we propose a conceptual framework that facilitates the use of regional knowledge for comparison between different river catchments. Key Points: Region‐specific hydro(geo)logical knowledge is underutilized in large sample hydrologyMultiple baseflow signatures are needed to better distinguish between different baseflow sourcesWe propose and apply a framework based on standardized perceptual models to organize findings from hydrologically diverse regions [ABSTRACT FROM AUTHOR]

Forty-five water years (1962-2006) of carefully measured temperature, precipitation, snow, and streamflow data for valley bottom, midelevation, and high-elevation sites within the Reynolds Creek Experimental Watershed, located in the state of Idaho, United States, were analyzed to evaluate the extent and magnitude of the impact of climate warming on the hydrology and related resources in the interior northwestern United States. This analysis shows significant trends of increasing temperature at all elevations, with larger increases in daily minimum than daily maximum. The proportion of snow to rain has decreased at all elevations, with the largest and most significant decreases at midelevations and low elevations. Maximum seasonal snow water equivalent has decreased at all elevations, again with the most significant decreases at lower elevations, where the length of the snow season has decreased by nearly a month. All trends show a significant elevation gradient in either timing or magnitude. Though interannual variability is large, there has been no significant change in water year total precipitation or streamflow. Streamflow shows a seasonal shift, stronger at high elevations and delayed at lower elevations, to larger winter and early spring flows and reduced late spring and summer flows. [ABSTRACT FROM AUTHOR]