Long-term mean river discharge estimation with multi-source grid-based global datasets

Liu, Suning1,2,3; Shi, Haiyun1,2; Sivakumar, Bellie4

2021-11-01

10.1007/s00477-021-02128-4

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT   影响因子和分区

1436-3240

1436-3259

Estimation of long-term river discharge can be made from various sources that may have different levels of accuracy in different regions. Based on three grid-based global datasets from runoff map, model simulations, and reanalysis data, respectively, we first evaluate each dataset at the annual scale to determine the datasets with the highest and lowest accuracy for each river basin; and then derive a dataset that integrates only the highest-accuracy dataset for each river basin. We use the relative error (RE) and the mean relative error (MARE) as performance indicators. Results indicate that (1) the datasets from runoff map and model simulations have higher accuracy than that from the reanalysis data; and (2) the overall performance of the integrated dataset at the annual scale (i.e., MARE = 15%) is better than that of any of the three original datasets. We also evaluate the daily performance of two river discharge datasets from model simulations and reanalysis data in data-scarce regions (i.e., source regions and small river basins), and find that the dataset from reanalysis data has better performance. The outcomes can provide new avenues to recognize the most appropriate dataset for a given river basin and enhance the comprehensive utilization of multi-source grid-based global river discharge datasets.

Global river discharge Grid-based datasets Gauge observations Runoff map Model simulations Reanalysis data

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China[51909117]

Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources

Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources

WOS:000717369000001

SPRINGER

20214611154516

Rivers ; Watersheds

Flood Control:442.1 ; Surface Water:444.1

ENGINEERING

Web of Science

1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Guangdong, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Guangdong Prov Key Lab Soil & Groundwater Pollut, Shenzhen, Guangdong, Peoples R China
3.Inst Basic Sci, Ctr Climate Phys, Busan, South Korea
4.Indian Inst Technol, Dept Civil Engn, Mumbai, Maharashtra, India

Liu, Suning,Shi, Haiyun,Sivakumar, Bellie. Long-term mean river discharge estimation with multi-source grid-based global datasets[J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,2021,36:679-691.

Liu, Suning,Shi, Haiyun,&Sivakumar, Bellie.(2021).Long-term mean river discharge estimation with multi-source grid-based global datasets.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,36,679-691.

Liu, Suning,et al."Long-term mean river discharge estimation with multi-source grid-based global datasets".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 36(2021):679-691.