Hao Sun

Mr. Hao Sun

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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26 May 2023 in Agronomy
Verified
Journal Article
Open access
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BWBin Wang
Bin Wang
HSHao Sun
Hao Sun
SGShuaishuai Guo
Shuaishuai Guo
JHJinbai Huang
Jinbai Huang
ZWZhongbo Wang
Zhongbo Wang
XJXiaoli Jin
Xiaoli Jin
Physically-based parameter estimations are essential to improve the simulation performance of a hydrologic model and to produce physically reasonable parameters with spatial consistency. This study proposed a parameter derivation strategy to improve the Sacramento Soil Moisture Accounting (SAC-SMA) model simulation performance based on the publicly accessible Harmonized World Soil Database (HWSD). The HWSD soil properties were used to estimate the soil moisture characteristics, and the HWSD soil texture classifications and International Geosphere-Biosphere Programme (IGBP) land cover types were used to identify the Soil Conservation Service (SCS) runoff curve number (CN). After the soil moisture characteristics and CNs were identified, the major parameters of the SAC-SMA model were derived. The simulation results were evaluated using the Nash efficiency coefficient (NSEC), and Free Search (FS) algorithm was used to further adjust and calibrate the parameters. Compared with the simulation accuracy (NSEC = 0.66~0.88) and parameter transferability (NSEC = 0.22~0.83) obtained for the SAC-SMA model using directly calibrated parameters, the HWSD data-derived parameters allowed the SAC-SMA model to achieve a similar simulation accuracy (NSEC = 0.65~0.86) and a better transferability (NSEC = 0.61~0.85).

30 September 2026 in Journal of Environmental Management
Verified
Journal Article
Open access
RZRuixuan Zhao
Ruixuan Zhao
YQYanping Qu
Yanping Qu
TJTianliang Jiang
Tianliang Jiang
XYXiaojing Yang
Xiaojing Yang
HSHao Sun
Hao Sun