scholarly journals Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1747
Author(s):  
Shanlei Sun ◽  
Jiazhi Wang ◽  
Wanrong Shi ◽  
Rongfan Chai ◽  
Guojie Wang
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Precipitation Amount ◽  
Climate Data ◽  
Validation Metrics ◽  
Huai River Basin ◽  
Huai River ◽  
Precipitation Estimation ◽  
Precipitation Changes ◽  
Linear Trends

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.

scholarly journals Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

2019 ◽  
Vol 11 (15) ◽  
pp. 1805 ◽  
Author(s):  
Sun ◽  
Zhou ◽  
Shen ◽  
Chai ◽  
Chen ◽  
...  
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Poor Performance ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Climate Data ◽  
Rainfall Events ◽  
Huai River Basin ◽  
Daily Precipitation Data ◽  
Huai River

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.

scholarly journals Spatiotemporal Changes and Frequency Analysis of Multiday Extreme Precipitation in the Huai River Basin during 1960 to 2014

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yixing Yin ◽  
Xin Pan ◽  
Xiuqin Yang ◽  
Xiaojun Wang ◽  
Guojie Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Extreme Precipitation ◽  
Abrupt Change ◽  
Upward Trend ◽  
Huai River Basin ◽  
Daily Precipitation Data ◽  
Significant Downward Trend ◽  
Huai River ◽  
The Huai River Basin

Floods and droughts are more closely related to the extreme precipitation over longer periods of time. The spatial and temporal changes and frequency analysis of 5-day and 10-day extreme precipitations (PX5D and PX10D) in the Huai River basin (HRB) are investigated by means of correlation analysis, trend and abrupt change analysis, EOF analysis, and hydrological frequency analysis based on the daily precipitation data from 1960 to 2014. The results indicate (1) PX5D and PX10D indices have a weak upward trend in HRB, and the weak upward trend may be due to the significant downward trend in the 21st century, (2) the multiday (5-day and 10-day) extreme precipitation is closely associated with flood/drought disasters in the HRB, and (3) for stations of nonstationary changes with significant upward trend after the abrupt change, if the whole extreme precipitation series are used for frequency analysis, the risk of future floods will be underestimated, and this effect is more pronounced for longer return periods.

scholarly journals Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Gebdang Biangbalbe Ruben ◽  
Zhenxin Bao ◽  
...  
Keyword(s):  
Error Correction ◽  
River Basin ◽  
Regional Scale ◽  
Mekong River ◽  
Precipitation Data ◽  
Climate Data ◽  
Hydrological Simulation ◽  
Mekong River Basin ◽  
Precipitation Estimation ◽  
Simulation Results

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.

Impact of Water Projects on River Flow Regimes and Water Quality in Huai River Basin

2009 ◽  
Vol 24 (5) ◽  
pp. 889-908 ◽  
Author(s):  
Yongyong Zhang ◽  
Jun Xia ◽  
Tao Liang ◽  
Quanxi Shao
Keyword(s):  
Water Quality ◽  
River Basin ◽  
River Flow ◽  
Flow Regimes ◽  
Water Projects ◽  
Huai River Basin ◽  
Huai River

scholarly journals Changes of flow regimes and precipitation in Huai River Basin in the last half century

2010 ◽  
Vol 25 (2) ◽  
pp. 246-257 ◽  
Author(s):  
Yongyong Zhang ◽  
Quanxi Shao ◽  
Jun Xia ◽  
Stuart E. Bunn ◽  
Qiting Zuo
Keyword(s):  
River Basin ◽  
Flow Regimes ◽  
Half Century ◽  
Huai River Basin ◽  
Huai River
Sign in / Sign up

Export Citation Format

Share Document