Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

Montero R. A., Schwanenberg D., Krahe P., Lisniak D., ŞENSOY ŞORMAN A., Sorman A. A., ...More

ADVANCES IN WATER RESOURCES, vol.92, pp.248-257, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 92
  • Publication Date: 2016
  • Doi Number: 10.1016/j.advwatres.2016.04.011
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.248-257
  • Keywords: Hydrological modelling, Remote sensing, Data assimilation, Moving Horizon Estimation, Variational methods, ENSEMBLE KALMAN FILTER, SENSED SOIL-MOISTURE, VARIATIONAL ASSIMILATION, STREAMFLOW OBSERVATIONS, SCALE, STATE, INFORMATION
  • Anadolu University Affiliated: Yes


Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables.