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

Abstract

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.

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

“…A majority of studies have examined the DA schemes in a focused area, and typically over relatively few catchments (e.g., <4), making it difficult to make conclusive statements on the utility of such DA approaches (see Table 1 and Supplementary Figure S2). Several studies that have included large samples of catchments concluded that a hydrological model with a SM-based DA framework may not significantly improve streamflow simulations, compared to the hydrological model without the DA [37,38].…”

Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a Rainfall–Runoff Model in the U.S. Corn Belt

“…Furthermore, SMAP provided a higher degree of improvement for streamflow prediction than SMOS. We have determined in the present study that HLMr provides better soil moisture predictions compared to in situ measurements than original HLM used in [5]. Moreover, HLMr has a top-layer depth that is more representative of the L-band microwave sampling depth.…”

“…We have explored some aspects of this uncertainty/variability in several previous studies over Iowa. For example, we demonstrated that there is information in the satellite soil moisture about runoff generation [3], spatial variability [4], and the potential for useful correction of streamflow predictions [5]. We have also demonstrated that there is seasonal bias in satellite-based soil moisture retrieval [6].…”

“…Jadidoleslam et al [5] found that assimilation of SMAP and SMOS satellite estimations in the original HLM model improve streamflow predictions. Furthermore, SMAP provided a higher degree of improvement for streamflow prediction than SMOS.…”

“…We conduct numerical simulations with HLM and HLMr for 2015 to 2019 following a calibration-free approach (e.g., [5], [58]). For comparisons with satellite estimations and in situ sensor averages, we up-scaled the model soil moisture to the SMAP grid by calculating areal average soil moisture for the hillslopes corresponding to SMAP pixels.…”