A Distributed Time-Variant Gain Hydrological Model Based on Remote Sensing

doi:10.3969/j.issn.1674-764x.2010.03.005

Journal of Resources and Ecology ›› 2010, Vol. 1 ›› Issue (3): 222-230.DOI: 10.3969/j.issn.1674-764x.2010.03.005

• Water Topics • Previous Articles Next Articles

A Distributed Time-Variant Gain Hydrological Model Based on Remote Sensing

YE Aizhong^1*, DUAN Qingyun¹, ZENG Hongjuan², LI Lin³ and WANG Caiyun³

1 College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;
2 College of Geography, Beijing Normal University, Beijing 100875, China;
3 Tibet Institute of Plateau Atmospheric and Environmental Science Research, Lhasa 850001, China

Received:2010-08-24 Revised:2010-09-01 Online:2010-11-10 Published:2010-11-04
Contact: YE Aizhong

Abstract

Abstract: A Distributed Time-Variant Gain Hydrological Model (DTVGM) based on remote sensing (RS) is proposed. The model contains several sub-models, such as snowmelt model, runoff model. It produces outputs including snow cover, evaporation, runoff, etc. All inputs for the model are derived from remote sensing data. Data from the Lhasa River basin is used in this study, including USGS-SRTM DEM, TRMM precipitation and Modis-LST. More than eight years (2001-2008) of daily hydrological data set was selected to calibrate the model. Based on the comparison of the observed and estimated runoff, the model's averaged efficiency of daily runoff simulation is over 0.6. The error of water balance was less than 5%. Distributed modeling results are quite satisfactory. This study provided a promising approach to resolve hydrology and water resources problems in ungauged or sparsely gauged basins.

Key words: remote sensing, Distributed Time-Variant Gain Hydrological Model, TRMM, Modis-LST

YE Ai-Zhong, DUAN Qing-Yun, ZENG Hong-Juan, LI Lin, WANG Cai-Yun. A Distributed Time-Variant Gain Hydrological Model Based on Remote Sensing[J]. Journal of Resources and Ecology, 2010, 1(3): 222-230.

[1]	LI Ge, WANG Juanle, WANG Yanjie, WEI Haishuo. Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing [J]. Journal of Resources and Ecology, 2019, 10(6): 676-684.
[2]	ZHOU Yuke. Greenness Index from Phenocams Performs Well in Linking Climatic Factors and Monitoring Grass Phenology in a Temperate Prairie Ecosystem [J]. Journal of Resources and Ecology, 2019, 10(5): 481-493.
[3]	WANG Chunyu,SUN Xiaofang,WANG Meng,WANG Junbang,DING Qingfu. Chinese Cropland Quality and Its Temporal and Spatial Changes due to Urbanization in 2000-2015 [J]. Journal of Resources and Ecology, 2019, 10(2): 174-183.
[4]	BAI Yongqing, WANG Juanle, WANG Yujie, HAN Xuehua, Bair Z. Tsydypov, Altansukh Ochir, Davaadorj Davaasuren. Spatio-temporal Distribution of Drought in the Belt and Road Area During 1998-2015 Based on TRMM Precipitation Data [J]. Journal of Resources and Ecology, 2017, 8(6): 559-570.
[5]	NIU Ben, ZHANG Xianzhou, HE Yongtao, SHI Peili, FU Gang, DU Mingyuan, ZHANG Yangjian, ZONG Ning, ZHANG Jing, WU Jianshuang. Satellite-based Estimation of Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau: A Multi-model Comparison [J]. Journal of Resources and Ecology, 2017, 8(1): 57-66.
[6]	NIE Yong, YANG Chao, ZHANG Yili, LIU Linshan, XU Xia. Glacier Changes on the Qiangtang Plateau between 1976 and 2015: A Case Study in the Xainza Xiegang Mountains [J]. Journal of Resources and Ecology, 2017, 8(1): 97-104.
[7]	LI Na, XIE Gaodi, ZHOU Demin, ZHANG Changshun, JIAO Cuicui. Remote Sensing Classification of Marsh Wetland with Different Resolution Images [J]. Journal of Resources and Ecology, 2016, 7(2): 107-114.
[8]	LIU Wenchao, LIU Jiyuan, YAN Changzhen, QIN Yuanwei, YAN Huimin. Cropland Dynamics and Their Influence on the Productivity in Northern Shaanxi, China, for the Past 20 Years: Based on Remotely Sensed Data [J]. Journal of Resources and Ecology, 2014, 5(3): 272-279.
[9]	CHEN Xiaoyue. Short-interval Land Use Change Detection with Microwave Remote Sensing Data [J]. Journal of Resources and Ecology, 2014, 5(2): 185-192.
[10]	YU Quanzhou, WANG Shaoqiang, SHI Hao, HUANG Kun, ZHOU Lei. An Evaluation of Spaceborne Imaging Spectrometry for Estimation of Forest Canopy Nitrogen Concentration in a Subtropical Conifer Plantation of Southern China [J]. Journal of Resources and Ecology, 2014, 5(1): 1-10.
[11]	LIU Junzhi, ZHU A-Xing, DUAN Zheng. Evaluation of TRMM 3B42 Precipitation Product Using Rain Gauge Data in Meichuan Watershed, Poyang Lake Basin, China [J]. Journal of Resources and Ecology, 2012, 3(4): 359-366.
[12]	NGUYEN Lam-Dao, VIET Pham-Bach, MINH Nguyen-Thanh, MAI-THY Pham-Thi, PHUNG Hoang-Phi. Change Detection of Land Use and Riverbank in Mekong Delta, Vietnam Using Time Series Remotely Sensed Data [J]. Journal of Resources and Ecology, 2011, 2(4): 370-374.
[13]	ZHANG Jiping, ZHANG Yili, LIU Linshan, DING Mingjun, ZHANG Xueru. Identifying Alpine Wetlands in the Damqu River Basin in the Source Area of the Yangtze River Using Object-based Classification Method [J]. Journal of Resources and Ecology, 2011, 2(2): 186-192.

A Distributed Time-Variant Gain Hydrological Model Based on Remote Sensing

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 13

Recommended Articles

Metrics

Comments