ABSTRACT:

This directory includes channel length survey data (outlet discharge and surveyed wetted channel extent for each survey). These data were used, in conjunction with discharge data, to find the scaling factor (α) and scaling exponent (β) for the power function that relates wetted channel extent and discharge (L = αQ^β) reported in the metadata table. Resources associated with channel length include survey data, data ‘thieved’ plots, and studies that reported channel length survey data.

ABSTRACT:

Analysis of the USGS’s blue line network from 7.5’ topographic maps and how these persistent and intermittent stream network length extents compared to the length-duration curves that appear in Lapides et al. (Figure 2). Resources associated with blue line analysis include calculated values for average channel length, shapefiles derived from USGS TopoView maps, a composite image of all watersheds where blue line analysis has been applied, as well as network validation images. Only watersheds located in the U.S. are included in this analysis.

Blueline networks were extracted from the TopoView maps with Safe Software's FME program.

ABSTRACT:

Wetted channel networks expand and contract throughout the year. Direct observation of this process can be made by multiple intensive surveys of a catchment throughout the year. Godsey et al. (2014) suggest that the extent of the wetted channel network scales with discharge at the outlet by a power law (L = αQ^β). Using this relationship, we developed a framework to assess variability in the extent of wetted channels as a function of β and the variability in streamflow Q (Lapides et al., In Review, https://eartharxiv.org/mc6np/). This resource constitutes the empirical basis for that study, a comprehensive dataset compiled from literature including:

1 - Channel length survey data (csv files)
2 - Discharge time series data (csv files)
3 - Watershed metadata (csv files)
4 - Blueline network files (pdf, png, and shp files)

This collection is comprehensive in that it includes all watersheds where at least three channel length surveys have been conducted and where a corresponding discharge time series dataset is available. The requirement of a minimum of three channel length surveys stems from the data requirements to find α and β for the power law relationship between discharge and stream network length for headwater catchments (Godsey et al., 2014). At present, data for 14 watersheds worldwide are included in the collection along with reference maps, watershed metadata, shapefiles and a composite of USGS blueline stream network imagery with terrain for watersheds of interest in the United States. Notably, this collection brings data from a variety of earth science agencies worldwide into a common, clearly labelled format.

Methods used to process the datasets or create other assets in this collection are included in the abstracts or additional metadata for each of the four resources listed above. Python code used to process data, compute variables, and create graphics is available at: https://zenodo.org/record/4057320

ABSTRACT:

Watershed metadata was collected for 14 watersheds from studies where channel length survey data was presented. For variables not found in the publications associated with the channel length surveys, additional sources are referenced. These sources are included in the notes column. Variables without sources were calculated, as described in the Additional Metadata section below. Examples of calculated values include, q_avg_mm_per_day, beta, and l_avg_km.

For Python packages, modules, and functions used to find calculated values, please see the associated GitHub repository: https://zenodo.org/record/4057320

ABSTRACT:

Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2021).

ABSTRACT:

Streamflows derived from hydrological models are widely used in decision-making processes in a broad array of natural resources applications. With an increase in computational power and data availability, data-driven modeling methods are becoming more powerful and popular. While it is well-recognized that reasonable model uncertainty is important to support good decision-making, there remain substantial challenges in quantifying uncertainty in hydrological models. One challenge is an inequality in data availability. While large amounts of data are available for well-monitored streams, the vast majority of streams globally are ungauged, with very limited or no streamflow monitoring. In this study, I evaluated the accuracy of a mixed-effects model for streamflow (flow-duration curves) across the state of Wisconsin, the Natural Community Model (NCM), trained on continuously monitored streamflow stations. The NCM is used as the basis for scientific studies and management decisions in Wisconsin, but uncertainty in the NCM has not been quantified yet, and performance has not been assessed formally except at continuously monitored streamflow stations. There are about 4,000 streamflow monitoring stations in Wisconsin, but about 3,500 have fewer than 5 sporadic streamflow measurements. I used an index gauge approach to estimate long-term streamflow percentiles (with uncertainty) from short-term or sporadic streamflow monitoring. I then used these estimates to estimate a flow-duration curve for each short-term or sporadic streamflow station (with uncertainty). These flow-duration targets formed the basis for an assessment of NCM accuracy in ungauged streams. I developed a random forest model for NCM error that provides a qualitative understanding of sources of error in the NCM as well as a quantitative way to correct the NCM using information from the sporadic/short-term streamflow stations that could not be included in the original NCM training set. The updated NCM has significantly reduced error, and I defined a reasonable level of uncertainty to be used with the updated NCM in decision-making and research applications.

ABSTRACT:

April 1 SWE is used across the western USA as a predictor of spring streamflow. Here, we use SNODAS data (https://nsidc.org/data/g02158) to map 10, 25, 50, 75, and 90th percentiles of April 1 SWE across the contiguous USA. This data is part of the data supplement for Lapides et al., 20XX.

ABSTRACT:

Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2021).

ABSTRACT:

Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2022).