Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility

Wang, Zhaoli; Zhong, Rong; Chen, Jiachao

doi:10.1016/j.atmosres.2017.06.020

Cited by 240 publications

(160 citation statements)

References 36 publications

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“…These results agree with the findings reported in the Beijing River Basin [12] and Malaysia [7]. In the current study, the IMERG_F only shows improvement in term of the bias systematic error for the entire period, while its reliability during the 2014-2015 flood period is similar to those near real-time products.…”

Section: Discussionsupporting

confidence: 82%

“…Similarly, the IMERG_F presents a slightly better performance than the IMERG_E and IMERG_L in term of systematic bias, with a lower RB ( Figure 3 and Table 1). These results are consistent with Wang et al [12], who also found that the IMERG_E and IMERG_L underestimated daily precipitation, but the IMERG_F showed an overestimation over the Beijing River Basin, China. The GPM IMERG products accurately detected the precipitation days and non-precipitation days as the ACC values varying from 0.7 to 0.71, indicating about 70% of correct detections.…”

Section: Meteorological Assessmentsupporting

confidence: 82%

“…For instance, through the application of the same hydrological model and statistical metrics in the assessment. To date, numerous hydrological models were applied in the GPM hydrological assessment, including Xinanjiang model [10], Variable Infiltration Capacity (VIC) model [12], Coupled Routing and Excess Storage (CREST) model [13,14], and SWAT model (this study). Each hydrological model has its own characteristics and computational mechanisms, causing a hydrological model uncertainty.…”

Section: Discussionmentioning

confidence: 99%

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Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

et al. 2018

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Abstract:The rapid development of Satellite Precipitation Products (SPPs) has heightened the need for a hydro-meteorological assessment of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products in different climate and geographical regions. Reliability of the IMERG early (IMERG_E), late (IMERG_L) and final (IMERG_F) run products in precipitation estimations was evaluated over the Kelantan River Basin, Malaysia from 12 March 2014 to 31 December 2016. The three IMERG products were then incorporated into a calibrated Soil and Water Assessment Tool (SWAT) model to assess their reliability in streamflow simulations. Overall, monthly precipitation variability is well captured by the three SPPs. The IMERG_F exhibited a smaller systematic bias (RB = 7.14%) compared to the IMERG_E (RB = −10.42%) and IMERG_L (RB = −17.92%) in daily precipitation measurement. All the three SPPs (NSE = 0.66~0.71 and R 2 = 0.73~0.75) performed comparably well as precipitation gauges (NSE = 0.74 and R 2 = 0.79) in the daily streamflow simulation. However, the IMERG_E and IMERG_L showed a significant underestimation of daily streamflow by 27.6% and 36.3%, respectively. The IMERG_E and IMERG_F performed satisfactory in streamflow simulation during the 2014-2015 flood period, with NSE and R 2 values of 0.5~0.51 and 0.62~0.65, respectively. With a better peak flow capture ability, the IMERG_F outperformed the near real-time products in cumulative streamflow measurement. The study has also shown that the point-to-pixel or pixel-to-pixel comparison schemes gave comparable conclusions. Future work should focus on the development of a standardized GPM hydro-meteorological assessment framework, so that a fair comparison among IMERG validation studies can be conducted.

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Section: Discussionsupporting

confidence: 82%

Section: Meteorological Assessmentsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

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Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

et al. 2018

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“…Most of them confirmed that SPE forced hydrological simulations are hard to outperform or equal to gauge based hydrological simulations due to their seasonal and regional systematic biases and random errors [D Jiang & Wang, 2019] although their hydrologic utility may be acceptable. In addition, some studies discovered that the model recalibration by employing SPEs as rainfall forcing could increase the performance of the discharge simulation significantly (S Jiang et al, 2018;Z Wang et al, 2017;Yuan et al, 2018), while the model parameters may be unrealistic and thus limit the model's performance at the sub-basin scale (D Jiang & Wang, 2019;Maggioni & Massari, 2018). In addition, the hydrological assessments of SPEs over Mainland China are usually conducted in a small number of catchments, and therefore, it is not clear to what extent the results can be generalized (Beck et al, 2017).…”

Section: Implications Of the Resultsmentioning

confidence: 99%

The Assessment and Comparison of TMPA and IMERG Products Over the Major Basins of Mainland China

Lü

Ryu

et al. 2019

Earth and Space Science

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The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG) aims to deliver the "best" precipitation estimation from space and has attracted much attention. The Version 05 of IMERG products including the near-real-time "Early" and "Late" run products (IMERG-E and IMERG-L, respectively), and the post-real-time "Final" run IMERG product (IMERG-F) are assessed at both national and basin scales against gauge observations over Mainland China for a 4-year period (from April 2014 to March 2018). As control products for comparison, their predecessor Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products (i.e., TMPA-RT and TMPA-V7) are also employed. Components analysis confirms the best performance of IMERG-F among the five SPEs in three different categories. All five SPEs feature increasing bias and root mean square difference (RMSD) with increasing daily gauge total precipitation, and such issue is less pronounced for IMERG-F-as evidenced by the lowest bias and RMSD across all precipitation rates. Besides, compared to TMPA, IMERG products exhibit better accuracy in detecting real precipitation evens, especially for light-to-medium rain (<60 mm/day), but they do not demonstrate significant improvement in the assessment of severe over/underestimation. In the basin-scale comparison, all five SPEs catch the key variation feature of basin-averaged precipitation time series (except TMPA-RT over Continental River Basin). Overall, IMERG-F demonstrates the best performance over all nine basins despite the slight overestimation, followed by TMPA-V7. IMERG-E and IMERG-L show performance close to or even better than TMPA-V7 in terms of the correlation coefficient and RMSD.

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“…The Beijiang River Basin predominantly constitutes two cities and sixteen counties and features a subtropical monsoon climate with a multi-year average precipitation of 1800 mm. The flood season of the basin is from April to September, and the dry season is from October to March of the next year [68]. Approximately 70-80% of the annual rainfall is concentrated in the flood season with the rainfall fastigium from May to July [69].…”

Section: Study Areamentioning

confidence: 99%

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Wang

Bai

et al. 2018

Water

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Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

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Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility

Cited by 240 publications

References 36 publications

Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

The Assessment and Comparison of TMPA and IMERG Products Over the Major Basins of Mainland China

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

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