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Field measurements of rainfall and soil moisture data used to support understanding of infiltration and runoff following the 2007 Canyon Fire, Malibu, CA, USA


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Schmidt, K.M., Hanshaw, M.N., and Stock, J.D., 2020, Field measurements of rainfall and soil moisture data used to support understanding of infiltration and runoff following the 2007 Canyon Fire, Malibu, CA, USA: U.S. Geological Survey data release,


This data release includes information used to support interpretations of relations between precipitation and soil moisture for a U.S. Geological Survey post-fire monitoring array installed near Malibu, CA following the 2007 Canyon fire. The 3 datasets are: 1) a time series of precipitation from three tipping bucket rain gages in individual files (Schmidt_2020_CANVQRG1.csv, Schmidt_2020_CANVQRG2.csv, and Schmidt_2020_CANTPRG3.csv; where RG in file name is abbreviation for rain gage), 2) a time series of a total of 9 soil moisture probes distributed with three soil moisture probes installed at varying depths from 3 individual soil pits in 3 individual files grouped by pit (Schmidt_2020_CANVQSM1.csv, Schmidt_2020_CANVQSM2.csv, and Schmidt_2020_CANVQSM3.csv; [...]


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The 2007 Canyon Fire near Malibu, California burned an area of roughly 18.5 square kilometers including 22 structures and steep slopes of Winter Canyon in the Santa Monica Mountains near Pepperdine University Malibu Campus along Malibu Canyon Rd ( To better understand relations between precipitation, soil moisture, and surface-water runoff, the U.S. Geological Survey installed instrumental monitoring arrays following the Canyon fire to gage hydrologic response during the following 2007-2008 winter. The enclosed data include precipitation records from three rain gages and a total of nine soil moisture probes distributed over three soil pits with three probes inserted at different depths at each pit. The tipping-bucket style rain gages (HOBO RG3) ( recorded 0.2 mm per tip. Each bucket tip is detected when a magnet attached to the tipping-bucket actuates a magnetic switch as the bucket tips, thus effecting a momentary switch closure for each tip. The spent rainwater drains out of the bottom of the housing. The switch was connected to a data logger which recorded the time of each tip. The dielectric style soil moisture probes (Decagon Devices EC-5/ONSET S-SMC-M005) ( measured moisture content throughout roughly a 0.3-liter volume of soil with reported +/-3% accuracy in typical soil conditions which can be conservatively estimated to be ~0.001 cm^3/cm^3 resolution. By creating a voltage proportional to the dielectric permittivity of surrounding soil water content, the probes document instantaneous volumetric water content and a time series recorded on an attached data logger (ONSET U30-NRC) ( Prior to the fire, the site was covered by chaparral vegetation, but fire consumed most of the vegetation and altered the soil infiltration properties. After wildfires in chaparral ecosystems, it has been observed that increased erosion can be triggered by floods and debris flows from steep, burned watersheds. Forecasting runoff conditions, as a result of rainfall, from such burned landscapes is challenging because the hydrologic response of the ground is dramatically altered by the high-temperature fire. Typically, the fire consumes the vegetation, fuses soil particles together, deposits layers of ash, and locally forms hydrophobic layers. These data assist with the interpretation of post-fire hydrologic response within soil on steep landscapes and how those properties may change over time as rainfall and bioturbation transform ground surface conditions. The soil moisture data likely do not represent typical response times or magnitudes of response indicative of undisturbed nearby landscapes. The following citations relate to this data release: 1) Basak, A., Kulkarni, C., Schmidt, K., and Mengshoel, O., 2015, Forecasting wetting and drying of post-wildfire soils in response to precipitation: A time series optimization approach, Abstract H33A-1567 presented at 2015 Fall Meeting, AGU, San Francisco, Calif, 14-18 Dec. 2) Basak, A., Mengshoel, O. J., Kulkarni, C., Schmidt, K., Shastry, P., and Rapeta, R., 2017, Optimizing the decomposition of time series using evolutionary algorithms: soil moisture analytics, In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1073-1080, (see files: Schmidt_2020_CANVQSM1.csv and Schmidt_2020_CANVQRG1.csv) 3) Basak, A., Mengshoel, O. J., Schmidt, K., and Kulkarni, C., 2018, Wetting and Drying of Soil: From Data to Understandable Models for Prediction, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, pp. 303-312, doi: 4) Basak, A., Mengshoel, O. J., Schmidt, K., IP-07324, manuscript to be submitted to Journal of Data Analytics, (see files: Schmidt_2020_CANVQSM1.csv and Schmidt_2020_CANVQRG1.csv for analyses in journal article). Citation to be updated in Science Base upon article publication. 5) Hanshaw, M. N., Schmidt, K. M., Jorgensen, D. P., Stock, J. D., 2008, By Air and land: Estimating post-fire debris-flow susceptibility through high-resolution radar reflectivity and tipping-bucket gage rainfall, EOS Trans. AGU, Fall Meet. Suppl., Abstract H51D-0850. 6) Hanshaw, M.N., Schmidt, K.M., Jorgensen, D., Stock, J.D., 2009, Comparing high-resolution radar reflectivity and tipping-bucket gage rainfall to estimate post-fire debris-flow susceptibility, International Association of Geomorphology, Melbourne, Australia July 2009, Paper 281. 7) Kulkarni, C., Mengshoel, O., Basak, A., and Schmidt, K., 2015, Optimizing the decomposition of soil moisture time-series data using genetic algorithms, Abstract IN23C-1741 presented at 2015 Fall Meeting, AGU, San Francisco, Calif, 14-18 Dec.

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DOI doi:10.5066/P9CZB0Z7

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