Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Tian, Bingru; Chen, Hua; Wang, Jialing; Xu, Chong‐Yu

doi:10.2166/nh.2021.188

Cited by 11 publications

(13 citation statements)

References 26 publications

(39 reference statements)

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“…In an operational context, this performance skill is better than that found in a similar study in India using ECMWF data set as the forcing variable [48]. Categorical metrics are widely used to assess performance of streamflow and flood predictions [52,53,[55][56][57]. Similar to rainfall, we classified the streamflow of different percentiles and calculated CSI, POD and FAR for all catchments (an example shown in Figure A2) for different flow regimes to further understand the model's predictive capabilities.…”

Section: Performance Of Ensemble Meanmentioning

confidence: 90%

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Bari,

Hasan,

Amirthanathan

et al. 2024

Preprint

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The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau's Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF) and Poor Man's Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall-runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using the Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts and NSE, PCC, MAE, KGE, PBias and RMSE and three categorical metrics CSI, FAR and POD for deterministic forecasts. The skill scores CRPS, relative CRPS, CRPSS and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE and RMSE as well as the categorical metrics. Forecast performance also progressively decreased with the higher streamflow regime. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country – and were generally better in the high runoff generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia and mid-west of Western Australia. We did not find any association between model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resources management.

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Section: Performance Of Ensemble Meanmentioning

confidence: 90%

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Bari,

Hasan,

Amirthanathan

et al. 2024

Preprint

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“…Precipitation is the key process in the global water cycle, and its spatiotemporal variability is quite complex. Currently, obtaining accurate and timely precipitation data faces significant challenges [1] . Precipitation data are also used as the fundamental driving factor for various hydrological models [2] .…”

Section: Introductionmentioning

confidence: 99%

Exploring the source of GPM IMERG and GSMaP errors based on error component method over the Yellow River basin

Liu,

He,

Zhang

2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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Precise precipitation data is essential for effective hydrologic modeling, water resource management, and climate research. IMERG and GSMaP, which are high-resolution and wide-coverage satellite products, have great potential in monitoring precipitation. However, further research is required to understand the error characteristics of precipitation. This study focuses on the Yellow River Basin, results demonstrate that: (1) GPM IMERG and GSMaP display strong spatial patterns, with more precise detection in the east than the west. Hit bias dominates in the total bias, and each product overestimates the error. The GSMaP_Gauge product performs better in reducing hit bias and improving detection accuracy. (2) The Yellow River Basin is generally biased towards low-intensity precipitation events, with the GSMaP_Gauge product performing the best, followed by the IMERG_Final product with the smallest missed precipitation. (3) Altitude has a significant impact on the total bias and error components, mainly showing a negative correlation. As altitude increases, the total bias and error components gradually decrease. This study can facilitate the creation of quantitative bias models and the implementation of techniques to reduce bias in GPM IMERG and GSMaP by examining the structure and attributes of error components.

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“…In recent years, a suite of satellite-based precipitation products have become available at the global scale, such as the TMPA 3B42 [19,20], IMERG [21], CMORPH-CRT [22], and the PERSIANN-CDR [23]. Many researchers have evaluated the precision of these satellite precipitation products in specific countries and regions worldwide, including China [24][25][26][27][28][29][30][31][32][33][34]. Cai et al evaluated the quality of the TMPA 3B42-V7 data set over the Hun-Tai Basin at This study uses the DRIVE model, which couples the Variable Infiltration Capacity (VIC) land surface model [35,36] and the Dominant River Tracing (DRT)-based Routing (DRTR) model [37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Hydrological Evaluation of Satellite-Based Precipitation Products in Hunan Province

et al. 2022

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The quality of satellite-based precipitation products including TMPA 3B42, IMERG-early, IMERG-final, and CMORPH-CRT, is evaluated by comparing with gauge observations in Hunan province of China between 2017 and 2019. By using the outputs of the Dominant River Routing Integrated with VIC Environment (DRIVE) model, the hydrological applications of gauge- and satellite-based precipitation products are analyzed by comparing them with streamflow observations. Furthermore, we conduct a case study considering Typhoon Bailu. It is found that IMERG-final can produce better results compared to the other three satellite-based products against gauge-based precipitation. In terms of discharge simulations, the gauge-based precipitation provides the most accurate results, followed by IMERG-final. During Typhoon Bailu, the peak of the mean gauge-based precipitation in the rainfall center (24.5°N–26°N, 111°E–114°E) occurred on 25 August 2019, whereas the daily streamflow reached its peak one day later, suggesting the lagged impact of precipitation on streamflow. From the Taylor diagram, the gauge-based precipitation is the most accurate for estimating the streamflow during Typhoon Bailu, followed by IMERG-final, IMERG-early, TMPA 3B42, and CMORPH-CRT, respectively. Overall, gauge-based precipitation has the best performance in terms of hydrological application, whereas IMERG-final performs the best among four satellite-based precipitation products.

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Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Cited by 11 publications

References 26 publications

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Exploring the source of GPM IMERG and GSMaP errors based on error component method over the Yellow River basin

Hydrological Evaluation of Satellite-Based Precipitation Products in Hunan Province

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