Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Abstract

To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

“…According to [77], the underestimation in discharge simulation due to the underestimated rainfall estimates. Similar findings are also reported in [17,73,78,79]. Therefore, we could conclude that GSMaP-NRT has a positive bias in this extreme event.…”

Derivation of Vegetation Optical Depth and Water Content in the Source Region of the Yellow River using the FY-3B Microwave Data

“…where τ p veg is the microwave optical thickness of vegetation and µ is the observation angle of the sensor. Equations (1) and (2) are usually called the τ-ω model. Given that the other parameters in Equations (1) and (2) are known, one can derive VOD.…”

“…where the parameter α is not a function of f w . By combining the τ-ω model and the dual-polarized slope parameter (Equations (1), (2), and (6)), the effective optical depth of land fraction is determined by considering the influence of waterbodies as follows [14]:…”

“…Vegetation water content, vegetation canopy biomass, and soil moisture strongly affect fluxes of the surface water vapor and energy, control the proportion of surface sensible heat, and latent heat allocated to the usable surface energy, as well as the proportion of rainfall among the surface runoff, infiltration, and evapotranspiration, and thus can have significant impacts on numerical weather, climate and hydrologic predictions [1,2]. Optical-infrared satellite remote sensing, such as the Normalized Difference Vegetation Index (NDVI) and Leaf-Area Index (LAI), is commonly used to determine vegetation water content.…”

Editorial for the Special Issue “Assimilation of Remote Sensing Data into Earth System Models”

“…This Special Issue also addresses the relationship between soil moisture and different land surface-atmosphere fields (precipitation, surface air temperature, total cloud cover, and total water storage) and Pangaluru et al [14] show that assimilation of Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) over India improves their consistency. Furthermore, Yi et al [15] compare the use of different precipitation products in the hydrological modeling of the Wangjiaba (WJB) watershed in China with the Soil Moisture Active and Passive (SMAP) data used to validate soil moisture. They show that although in situ precipitation reports provide the most reliable local information, precipitation from numerical weather prediction models provides the gridded and future information necessary for flood forecasting.…”