ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

Abstract

e assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. is study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. e results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. e less-effective performance on largeflood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. is study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods.

Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

“…The ESA CCI program provides daily soil moisture (CCI-SM) at 0.25 • spatial resolution for approximately the top few millimeters to centimeters of soil from 1978 to 2016. The daily CCI-SM product (v03.2) is produced at 0.25 • spatial resolution from the microwave retrieved surface soil moisture data and is merged from multiple sensors Liu et al, 2011Liu et al, , 2012Wagner et al, 2012; Wagner et al, 2013) satellites. In this product, the absolute soil moisture was rescaled against the 0.25 • land surface modeling soil moisture (GLDAS-NOAH, Rodell et al, 2004) using cumulative density function matching.…”

“…In this study, we used the merged product of active and passive soil moisture data which showed better accuracy than either the passive or active data alone (Liu et al, 2011). To match the spatial resolution of our CLM3.5 setup, the original SM values were re-sampled and re-gridded to 0.0275 • using the first-order conservative interpolation method (Jones, 1999), which is based on the ratio of source cell area overlapped with the corresponding destination cell area.…”

Spatiotemporal variation of soil moisture in Northern China based on climate change initiative data

“…This project provides the possibility for long‐term dynamic analysis of soil moisture (Hollmann et al., 2013). The ESA CCI soil moisture data products have been applied widely in China, with most studies focused on small‐scale quality assessment and drought assessment (An et al., 2016; Liu, Wang, & Liu, 2018; Yao et al., 2018; Zhang et al., 2019).…”

Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

“…New approaches have been developed to facilitate the incorporation of remotely sensed data to support watershed scale studies [14]. Of particular promise are data assimilation (DA) techniques adopted from the atmospheric science community, which have been increasingly applied to watershed hydrology studies [15][16][17]. However, the improvements that can be potentially conferred by DA have limitations.…”