Hydrological Analysis Using Satellite Remote Sensing Big Data and CREST Model | Zendy

Jun Ma | Zendy; Weiwei Sun | Zendy; Gang Yang | Zendy; Dianfa Zhang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Hydrological Analysis Using Satellite Remote Sensing Big Data and CREST Model

Author(s) -

Jun Ma,

Weiwei Sun,

Gang Yang,

Dianfa Zhang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2810252

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Hydrological modeling significantly contributes to the understanding of catchment water balance and water resource management and mitigates negative impacts of flooding. Considering the advantages of satellite remote sensing big data and the coupled routing and excess storage (CREST) model, this paper investigates the hydrological modeling in the Shehong basin during 2006-2013. The results show that humid Shehong basin has main rainfalls in summer (From May to September). For the monthly average rainfall and streamflow, there is a remarkable increase (+52%) in discharge and a smaller increase (+18%) in rainfall in the second period (2010-2013) relative to the first period (2006-2009). The CREST model was calibrated using China gauge-based daily precipitation analysis for the period of 2006-2009, followed by a favorable performance with Nash-Sutcliffe coefficient efficiency (NSCE) of 0.77, correlation coefficient (CC) up to 0.88 and -11% Bias. The model validation shows an error metric with NSCE of 0.74, CC of 0.87 and -11.7% Bias. In terms of water balance modeling results at Shehong basin, the runoff and rainfall estimates from CREST model coincide well with the gauge observations, indicating the model captures the appropriate signature of soil moisture variability. Therefore, the satellite-based precipitation product is feasible in hydrological prediction, and the CREST models the interaction between surface and subsurface water flow process in the Shehong basin.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research