Algorithms for model parameter estimation and state estimation applied to a state-space model for one-dimensional vertical infiltration incorporating snowmelt rate as a system input

Release Date

2022-01-01

Start Date

2013-01-01

End Date

2018-12-31

Publication Date

2023-09-15

Shapiro, A.M., 2022, Algorithms for model parameter estimation and state estimation applied to a state-space model for one-dimensional vertical infiltration incorporating snowmelt rate as a system input: U.S. Geological Survey data release, https://doi.org/10.5066/P9MRGR88.

Type: Related Primary Publication Type: browseImage

The algorithms and data presented in this data release were developed to demonstrate the application of a State-Space Model (SSM) of one-dimensional vertical infiltration that interprets time-series data for water-table altitudes, liquid precipitation, snowmelt, and potential evapotranspiration to estimate time-varying groundwater recharge. The SSM is coupled with the Kalman Filter (KF) to perform the estimation of the system states. The SSM of infiltration incorporates preferential and diffuse flow to the groundwater table through the unsaturated zone and estimates model parameters for an observation period. Observation periods are defined as seasons (winter, spring, summer, and fall), where the model parameters are assumed to be constant of the observation period. Model parameters can vary from one observation period to the next. The SSM and KF are demonstrated on groundwater monitoring wells in northwestern New York, USA, where both liquid precipitation and snowmelt in the fall, winter, and spring seasons contribute to groundwater recharge. The development of the model input and output files included in this data release are documented in the journal article available at https://doi.org/10.1111/gwat.13206.

• Model Data Management Function (MDMF)