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Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security

Authors :
Massimo Menenti
Xin Li
Li Jia
Kun Yang
Francesca Pellicciotti
Marco Mancini
Jiancheng Shi
Maria José Escorihuela
Chaolei Zheng
Qiting Chen
Jing Lu
Jie Zhou
Guangcheng Hu
Shaoting Ren
Jing Zhang
Qinhuo Liu
Yubao Qiu
Chunlin Huang
Ji Zhou
Xujun Han
Xiaoduo Pan
Hongyi Li
Yerong Wu
Baohong Ding
Wei Yang
Pascal Buri
Michael J. McCarthy
Evan S. Miles
Thomas E. Shaw
Chunfeng Ma
Yanzhao Zhou
Chiara Corbari
Rui Li
Tianjie Zhao
Vivien Stefan
Qi Gao
Jingxiao Zhang
Qiuxia Xie
Ning Wang
Yibo Sun
Xinyu Mo
Junru Jia
Achille Pierre Jouberton
Marin Kneib
Stefan Fugger
Nicola Paciolla
Giovanni Paolini
Source :
Remote Sensing, Vol 13, Iss 24, p 5122 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.7f80aa1c72ea43d494d2063aaa34ae8e
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13245122