Summary of results

• summary_data_streamflow_info.csv: Table of summary data for streamflow at Dry Creek, CA. These data include: streamflow, overland flow, areal fraction of landscape saturated, direct precipitation on saturated area (DPSA, rainfall intensity multipled by areal fraction of landscape saturated), fraction of streamflow from overland flow, and pressure head at piezometer MNP3.

• summary_data_ages.csv: Table of summary results from StorAge Selection (SAS) modeling of streamflow ages at Dry Creek, CA. These data include: median age of water in storage, median age of water in streamflow, mean fraction of streamflow younger than 1 day, and mean fraction of streamflow from the youngest 10th percentile of storage. For each data type, the reported mean/25th percentile/75th percentile is calculated amongth the top 95th percentile of paramter sets for the SAS model.

Isotopic analysis at Dry Creek, CA

Lapides et al. (2021) collected precipitation and streamflow isotopes at the Dry Creek catchment in Northern California. Here, we provide raw data for measured isotope values and hydrologic monitoring information including streamflow, evapotranspiration, precipitation, and piezometer measurements. These data can be found at:

• Raw isotope data

• Clean precipitation isotope data

• Standard format streamflow isotope data

• Precipitation and streamflow data

• Evapotranspiration data

• Piezometer data

• Google Colab notebook for rainfall isotope analysis and data visualization

• Google Colab notebook for modeling saturation extent with a logistic regression

Hydrograph and runoff analysis

We calculated runoff coefficients and performed lag to peak analysis for a set of well-defined runoff events at Dry Creek to support understanding of runoff generation. The code used for this analysis can be found at:

• Runoff ratio analysis

• Lag to peak analysis

SAS Modeling in Python

We translated the StorAge Selection (SAS) function Matlab code written by Benettin and Bertuzzo (2018) into Python. All simulations begin on 10/1/2016 and are conducted in 4 hr increments. Here, we provide SAS code in Python along with modeled output and visualized results, including:

• Google Colab notebook with Python SAS modeling and visualized output

• Calibration data with low flows excluded

• Calibration data for full dataset during study period only

• Monte Carlo calibration output for top 95th percentile parameter sets (evaluation on 2019-2020 water year)

• Areal extent of saturation over time

• Cumulative age distributions for top 95th percentile of parameter sets (filename in this repository: modeled_cdf_weighted_final.csv)

• Streamflow age data over time for top 95th percentile of parameter sets (files found in this repository in subdirectory: ensemble_ages). Code to work with this data is included in the Google Colab notebook.

  • meanAges_ensemble_1day_final.csv: mean streamflow age at each timestep (row in 4hr increments) for each simulation (column)

  • meanStorAges_ensemble_1day_final.csv: mean storage age at each timestep (row in 4hr increments) for each simulation (column)

  • medianAges_ensemble_1day_final.csv: median streamflow age at each timestep (row in 4hr increments) for each simulation (column)

  • medianStorAges_ensemble_1day_final.csv: median storage age at each timestep (row in 4hr increments) for each simulation (column)

  • modeled_ensemble_1day_final.csv: modeled dD at each timestep (row in 4hr increments) for each simulation (column)

  • youngFraction_ensemble_1day_final.csv: fraction of streamflow younger than 1 day at each timestep (row in 4hr increments) for each simulation (column)

  • youngFractionPercent_ensemble_1day_final.csv: fraction of streamflow from youngest 10th percentile of storage at each timestep (row in 4hr increments) for each simulation (column)

• Ensemble mean parameters and interquartile range for all simulation outputs in ensemble_ages (files found in this repository in subdirectory: ensemble_means). Code to visualize this data is included in the Colab notebook.

  • meanAges_ensemble.pkl: mean streamflow age and interquartile range over time (row in 4hr increments)

  • meanStorAges_ensemble.pkl: mean storage age and interquartile range over time (row in 4hr increments)

  • medianAges_ensemble.pkl: median streamflow age and interquartile range over time (row in 4hr increments)

  • medianStorAges_ensemble.pkl: median storage age and interquartile range over time (row in 4hr increments)

  • youngFraction_ensemble.pkl: fraction of streamflow younger than 1 day over time with interquartile range (row in 4hr increments)

  • youngFractionPercent_ensemble.pkl: fraction of streamflow from youngest 10th percentile of storage at each timestep with interquartile range (row in 4hr increments)

References:

Benettin, Paolo, and Enrico Bertuzzo. "tran-SAS v1. 0: a numerical model to compute catchment-scale hydrologic transport using StorAge Selection functions." Geoscientific Model Development 11.4 (2018): 1627-1639.

Lapides, Dana A, Dralle, David N, Rempe, Daniella N, Dietrich, William E., and Hahm, W. Jesse. "Controls on streamwater age in a saturation overland flow-dominated catchment." In Preparation.