Abstract
From 15 to 20 September 2016, precipitation extremes occurred in the middle and lower reaches of the Jinsha River, causing immense direct economic losses due to floods. The current research on extreme climate characteristics and the relationship between climate extremes and runoff extremes are based on a single data source. This is due to the uneven distribution of precipitation and temperature stations, which make it difficult to fully capture extreme climate events. In this paper, various internationally popular reanalysis datasets were introduced. Extreme climate indexes were computed using the merged datasets versus the meteorological station observations. The results showed that: (1) Comparative analysis of the extreme climate indexes of the reanalysis dataset and the data of traditional meteorological observation stations showed that most of the extreme precipitation indexes calculated by the various reanalysis of combined data exhibited good performances. Among the reanalyzed combined products, CMPA-H, CMADS, and GPM (IMERG) exhibited good performance while the performance of TRMM (TMPA) was slightly worse. The extreme temperature indexes, TXx and TNn, calculated based on the reanalysis of combined data showed a better consistency than the indexes calculated based on the observational data of meteorological stations. The CMADS temperature dataset exhibited a higher consistency with the data obtained from meteorological stations as well as the best accuracy (84% of the stations with the error value of TXx calculated from the CMADS dataset and observed data less than 3 °C). (2) The response of typical flood events to precipitation extremes were analyzed and evaluated; the spatial distribution of the precipitation in the combined dataset was used to quantitatively analyze the response of occurrence of typical flood events to precipitation extremes, and the typical flood events were found to be mainly caused by certain factors, such as lagging flood propagation in the upstream of the basin outlet. This study indicates that it is feasible to use the reanalyzed combined data products to calculate the extreme climate indexes of the Jinsha River Basin, especially in the upper reaches of the Yangtze River where there is a lack of meteorological observation stations.
Highlights
A rainfall event lasting from 15 to 20 September 2016, occurred in the west portion of the upper reaches of the Yangtze River
Referring to the percentile threshold method [2], the 90th percentile flow in the study basin as the reference threshold for typical flood events in the past decade is 10,600 m3/s
Atmosphere 2022, 13, 263 hydrological events in the Shule River Basin to extreme climate using daily temperature, precipitation, and evaporation data from ground-based meteorological stations, and the results showed that extreme precipitation events were the dominant factor in causing extreme flood events
Summary
A rainfall event lasting from 15 to 20 September 2016, occurred in the west portion of the upper reaches of the Yangtze River. Atmosphere 2022, 13, 263 hydrological events in the Shule River Basin to extreme climate using daily temperature, precipitation, and evaporation data from ground-based meteorological stations, and the results showed that extreme precipitation events were the dominant factor in causing extreme flood events. The objectives of this study were as follows: (1) Use the observed precipitation and temperature data of 31 ground-based meteorological stations in the Jinsha River Basin as reference true values to evaluate the accuracy of CMADS, GPM (IMERG), TRMM (TMPA), and 0.5◦ × 0.5◦ grid temperature dataset products in calculating extreme climate indexes in a large-scale and complex basin, and to evaluate the performance of each precipitation and temperature combined dataset. (2) Analyze the response of typical flood events to precipitation and temperature extremes within the large Jinsha River Basin where research stations are sparsely distributed, based on different reanalyzed combined precipitation datasets. The available data time range is from 1 January 2008 to the present day
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