Evaluation of the Performance of Multi-Source Precipitation Data in Southwest China

Xi, Jianchao; Liu, Yanli; Wu, Yongxiang; Wang, Gaoxu; Zhang, Xuan; Meng, Qingbo; Gu, Pengfei; Liu, Tao

doi:10.3390/w13223200

Cited by 8 publications

(3 citation statements)

References 32 publications

(79 reference statements)

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“…The NWL region is characterized by complex topography and significant elevation differences, ranging in elevation from 92 m to 5020 m [60]. The complex topography in this region may affect the accuracy of precipitation estimation, whereas the eastern regions of China, with higher precipitation intensity and relatively flatter topography, tend to have better precipitation estimation accuracy [31,45,61].…”

Section: Regional Differencesmentioning

confidence: 99%

Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Wang,

Jiang,

et al. 2024

Remote Sensing

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Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.

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Section: Regional Differencesmentioning

confidence: 99%

Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Wang,

Jiang,

et al. 2024

Remote Sensing

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“…However, due to the complex terrain and economic development lag in some areas of southwest China, rain gauge stations are often sparsely distributed and may not function. As a consequence, long-term continuous rainfall records are often insufficient or non-existent [4][5][6]. Satellite precipitation products (SPPs) based on remote sensing technology can provide rainfall data and model floods in these data-scarce areas [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

Lyu,

Li,

2024

Remote Sensing

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This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas.

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“…Precipitation is an essential meteorological factor in hydrologic processes, and accurate estimates are critical in hydrological and climate research. Rain gauges offer the most precise rain measurements [1][2][3][4][5][6][7][8][9]. Studies of hydrological and climate conditions need spatial rather than point inputs.…”

Section: Introductionmentioning

confidence: 99%

Bias Adjustment of Four Satellite-Based Rainfall Products Using Ground-Based Measurements over Sudan

Siddig

Ibrahim

Zhang

et al. 2022

Water

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Satellite-based rainfall estimates (SREs) represent a promising alternative dataset for climate and hydrological studies, where gauge observations are insufficient. However, these datasets are accompanied by significant uncertainties. Therefore, this study aims to minimize the systematic bias of Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Global Precipitation Climatology Project (GPCP) rainfall estimates using a quantile mapping (QM) method with climatic zones (CZs). The adjusted rainfall estimates were evaluated for the period from 2003–2017; data from 2003 to 2016 were used for calibration, and data from 2017 were used for validation. The results revealed significant improvements for the adjusted PERSIANN-CCS, PERSIANN-CDR, CHIRPS, and GPCP monthly time series in terms of all statistical measures and evaluation of overall CZs. In terms of Root Mean Square Errors (RMSEs), the adjusted CHIRPS did not show an improvement. This method successfully removed the mean bias of the daily time series for all SREs. The findings suggest that this method can be applied to correct the systematic bias of all SREs in the monthly time series in the future without the need for further gauge measurements over Sudan.

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Evaluation of the Performance of Multi-Source Precipitation Data in Southwest China

Cited by 8 publications

References 32 publications

Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

Bias Adjustment of Four Satellite-Based Rainfall Products Using Ground-Based Measurements over Sudan

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