Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

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.

Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

“…Precipitation is one of the most important water balance components of the global water cycle, and has great variability across different spatial and temporal scales [1,2]. The accurate observation or estimation of precipitation has important theoretical and practical significance for flood warnings, drought monitoring, and water resource management [3,4]. Gauge observations provide relatively accurate point-based measurements of precipitation [5]; however, owing to significant precipitation heterogeneity across a variety of spatiotemporal scales, rain gauge observations only represent local conditions, and can result in potential errors when interpolated to larger scales, especially in mountainous areas with complex terrain [6].…”

“…Furthermore, the error characteristics change depending on the climate region, season, altitude, and other factors [10,26]. In general, quantitative statistical and hydrological modelling evaluations are effective tools that are used to evaluate the precision of SPPs [4,17]. The former focuses on the comparison and evaluation of SPPs against gauge data or ground-based radar estimates.…”

“…A range of studies comparing the TMPA and GPM products for the United States [37], Brazil [38], Africa [39], Iran [40], India [41], Pakistan [42], Malaysia [43], Singapore [44], and China [45] indicated that GPM is superior to TMPA products. In China, evaluation and validation using hydrologic simulations have been explored over many basins, including the Ganzi River basin [46], the upper Yangtze River and upper Yellow River basins over the Tibetan Plateau [47], the Huaihe River basin of eastern China [18], the Beijiang and Dongjiang River basins of southern China [5], the Luanhe River basin of northern China [4], the Tiaoxi watershed, which is part of the southern catchment of Taihu Lake in southeastern China [48], the Lancang River basin of southwest China [49], and the Huifa River basin of northeast China [50]. These studies all highlighted the great potential of different SPPs in hydrologic simulations, although SPPs have variable accuracy and distinct hydrological performance in different basins.…”

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

“…The results showed a satisfactory performance, given that TRMM and CMADS rainfall data simulated the Han River streamflow with acceptable accuracy. A similar study over the Luanhe River Basin in China by Ren et al [57] concluded that TMPA-3B42 rainfall had the highest performance for estimating monthly runoff, while PERSIANN showed unsatisfactory results. Thus, the study provided information on the performance of different satellite rainfall products in hydrologic modeling for the Han River Basin.…”

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

“…The postprocessed estimates are unavailable in NRT, due to the lack of ground-based rainfall observations, which is an essential requirement to improve the NRT SREs during postprocessing stage [19]. At the same time, the direct use of NRT SREs for disaster monitoring is problematic, as they are associated with large errors especially at local and catchment scales [28][29][30]. Hence, it is essential to employ error reduction methods to improve the NRT SREs before their application [31].…”