Next Article in Journal
An Evaluation of Risk-Based Agricultural Land-Use Adjustments under a Flood Management Strategy in a Floodplain
Next Article in Special Issue
A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services
Previous Article in Journal
Common Pool Resource Management: Assessing Water Resources Planning for Hydrologically Connected Surface and Groundwater Systems
Previous Article in Special Issue
On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling
Article

Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

Iowa Flood Center, The University of Iowa, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Hydrology 2021, 8(1), 52; https://doi.org/10.3390/hydrology8010052
Received: 28 February 2021 / Revised: 18 March 2021 / Accepted: 19 March 2021 / Published: 20 March 2021
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting. View Full-Text
Keywords: satellite soil moisture; data assimilation; Ensemble Kalman Filter; flood prediction satellite soil moisture; data assimilation; Ensemble Kalman Filter; flood prediction
Show Figures

Figure 1

MDPI and ACS Style

Jadidoleslam, N.; Mantilla, R.; Krajewski, W.F. Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions. Hydrology 2021, 8, 52. https://doi.org/10.3390/hydrology8010052

AMA Style

Jadidoleslam N, Mantilla R, Krajewski WF. Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions. Hydrology. 2021; 8(1):52. https://doi.org/10.3390/hydrology8010052

Chicago/Turabian Style

Jadidoleslam, Navid, Ricardo Mantilla, and Witold F. Krajewski 2021. "Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions" Hydrology 8, no. 1: 52. https://doi.org/10.3390/hydrology8010052

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop