Abstract
Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.
Highlights
Soil moisture is a key element in the hydrological cycle, regulating evapotranspiration, precipitation infiltration, and overland flow (Wanders et al, 2014)
Five statistical indicators are used for the soil moisture estimation analysis: Pearson product moment correlation coefficient (r), mean-squared error (MSE), which is the same value as the gamma statistic, standard error (SE), Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), and root mean square error (RMSE)
A hydrological soil moisture product is produced for the Pontiac catchment using the gamma test (GT) and the local linear regression (LLR) modelling techniques based on four data-input schemes
Summary
Soil moisture is a key element in the hydrological cycle, regulating evapotranspiration, precipitation infiltration, and overland flow (Wanders et al, 2014). The antecedent wetness condition of a catchment is among the most significant factors for accurate flow generation processes (Berthet et al, 2009; Matgen et al, 2012a). There have been many soil moisture measuring projects (e.g., satellite missions such as advanced scatterometer (ASCAT), soil moisture and ocean salinity (SMOS), and Soil Moisture Active Passive (SMAP); ground-based networks such as Soil Climate Analysis Network (SCAN), U.S Surface Climate Observing Reference Networks, and COsmic-ray Soil Moisture Observing System), they are not sufficiently used in hydrology due to the following reasons: (1) misconception between the hydrological soil moisture state variable and the real-field soil moisture content (Zhuo and Han, 2016a); (2) unawareness of data availability and strength/weakness of different data sources; (3) the existing soil moisture products are mainly evaluated against point-based ground soil moisture observations or airborne retrievals, which have significant spatial mismatch (both horizontally and vertically) to catchment-scales, and are there-
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