Abstract
Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area.
Highlights
Precipitation is one of the most critical factors of hydrometeorological applications [1,2]
The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the Soil and Water Assessment Tool (SWAT) model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties
To qualitatively evaluate precipitation data derived from three satellite products with rain gauge observations on different spatial and temporal scales, a set of widely used metrics were adopted including correlation coefficient (CC), root mean squared error (RMSE), mean absolute error (MAE)
Summary
Precipitation is one of the most critical factors of hydrometeorological applications [1,2]. Accurate precipitation estimates play an increasingly important role in the management of water resources. Precipitation data can be obtained in two ways: surface-based observations and satellite and remote sensing datasets [3]. Surface-based observations are relatively straightforward and accurate;. E.g, rain gauges and weather radars [4]. Surface-based observations at one station are usually utilized to represent the precipitation of an area with a size of 10–100 km , especially in remote regions [5]. The rare and uneven networks of gauges make these observations inaccurate and unrepresentative [6]. In some areas, such as mountainous regions, the quality of radar datasets is not high due to beam blockage, propagation errors, and vertical variability of reflectivity
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