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

Lu Yi,Xiangyang Li,Wanchang Zhang

doi:10.3390/rs10121872

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.

Highlights

Numerical simulation of the rainfall-runoff process is an important way to research water cycle, flood monitoring, water resource management and environmental conservation [1,2,3,4,5]
The 1-km grid data of the China Meteorological Administration (CMA), Global Precipitation Measurement (GPM) IMERG, merged Center Morphing Technique (CMORPH), Weather Research and Forecasting (WRF) model and WRF 4D-Var were accumulated in those two days as well and compared to the accumulated CMWR data
In order to investigate the effectivenesses of different precipitation datasets on hydrological modelling, five different precipitation datasets were used to simulate a two-week runoff process after a heavy rainfall event in the WJB watershed (30,000 km2)

Summary

Introduction

Numerical simulation of the rainfall-runoff process is an important way to research water cycle, flood monitoring, water resource management and environmental conservation [1,2,3,4,5]. Used satellite precipitation products contain the Global Precipitation Climatology Project (GPCP) [23], the Climate Prediction Center Morphing Technique (CMORPH) [24], the Tropical Rainfall Measuring Mission (TRMM) [25] and its successor, the Global Precipitation Measurement (GPM) [26]. The third method is obtaining precipitation from a numerical weather prediction (NWP) model Because this atmospheric model is built on precise physical governing equations, an NWP model can describe the inherent dynamics of precipitation, present nearly the entire precipitation process with specific atmospheric reanalysis data [29,30,31]. The generally used NWP models include the National Meteorological Center (NMC) forecast model [42], the next-generation Weather Research and Forecasting (WRF) model [43], the operational Japan Meteorological Agency (JMA) mesoscale model [44] and the European Centre for Medium-Range Weather Forecasts (ECMWF) [45]

Methods

Results

Conclusion