Trends in Concentration and Flux of Total Suspended Matter in the Irrawaddy River

Author:

Zheng Zhuoqi12ORCID,Wang Difeng23ORCID,Fu Dongyang4ORCID,Gong Fang2,Huang Jingjing25ORCID,He Xianqiang2,Zhang Qing2

Affiliation:

1. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China

2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

3. Daya Bay Observation and Research Station of Marine Risks and Hazards, Ministry of Natural Resources, Hangzhou 310012, China

4. School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524025, China

5. Ocean College, Zhejiang University, Hangzhou 316021, China

Abstract

Large rivers without hydrological data from remote sensing observations have recently become a hot research topic. The Irrawaddy River is among the major tropical rivers worldwide; however, published hydrological data on this river have rarely been obtained in recent years. In this paper, based on the existing measured the total suspended matter flux (FTSM) and discharge data for the Irrawaddy River, an inversion model of the total suspended matter concentration (CTSM) is constructed for the Irrawaddy River, and the CTSM and FTSM from 1990 to 2020 are estimated using the L1 products of Landsat-8 OLI/TIRS and Landsat-5 TM. The results show that over the last 30 years, the FTSM of the Irrawaddy River decreased at a rate of 3.9 Mt/yr, which is significant at the 99% confidence interval. An increase in the vegetation density of the Irrawaddy Delta has increased the land conservation capacity of the region and reduced the inflow of land-based total suspended matter (TSM). The FTSM of the Irrawaddy River was estimated by fusing satellite data and data measured at hydrological stations. The research method employed in this paper provides a new supplement to the existing hydrological data for large rivers.

Funder

National Key R&D Program of China

Daya Bay Smart Ocean Intelligent Platform Project

National Natural Science Foundation of China

Publisher

MDPI AG

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