Soil moisture prediction with the ensemble Kalman filter: Handling uncertainty of soil hydraulic parameters
Authors:
- N. BrandhorstNBN. Brandhorst
- D. ErdalDED. Erdal
- I. NeuweilerINI. Neuweiler
- 1 December 2017
- journal article
- Published by Elsevier in Advances in Water Resources
- Vol. 110, 360-370
Abstract
No abstract availableFunding Information
- DFG (NE 824/12-1)
This publication has 32 references indexed in Scilit:
- EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrologyHydrology and Earth System Sciences, 2016
- The numerical solution of Richards’ equation by means of method of lines and ensemble Kalman filterMathematics and Computers in Simulation, 2016
- The importance of state transformations when using the ensemble Kalman filter for unsaturated flow modeling: Dealing with strong nonlinearitiesAdvances in Water Resources, 2015
- Using a bias aware EnKF to account for unresolved structure in an unsaturated zone modelWater Resources Research, 2013
- Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture dataAdvances in Water Resources, 2013
- Radiance data assimilation for operational snow and streamflow forecastingAdvances in Water Resources, 2011
- State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequencyWater Resources Research, 2007
- A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and ForecastsMonthly Weather Review, 1999
- Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statisticsJournal of Geophysical Research: Oceans, 1994
- Developing joint probability distributions of soil water retention characteristicsWater Resources Research, 1988