Optimization of soil hydraulic parameters within a constrained sampling space
Meijun Li,
Wei Shao,
Wenjun Yu,
Ye Su,
Qinghai Song,
Yiping Zhang,
Hongkai Gao,
Yonggen Zhang,
Jianzhi Dong
Affiliations
Meijun Li
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning, Ministry of Water Resources, School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China
Wei Shao
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning, Ministry of Water Resources, School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China; Qinghai Provincial Meteorological Disaster Prevention and Defense Technology Center, Sining Qinghai 810001, China; Corresponding author at: Key Laboratory of Hydrometeorological Disaster Mechanism and Warning, Ministry of Water Resources, School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Wenjun Yu
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning, Ministry of Water Resources, School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China
Ye Su
Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Albertov 6, 128 43 Prague 2, Czech Republic; Department of Physical Geography, and the Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden
Qinghai Song
Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, China
Yiping Zhang
Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, China
Hongkai Gao
School of Geographical Sciences, East China Normal University, Shanghai, China
Yonggen Zhang
Institute of Surface‐Earth System Science, Tianjin University, Tianjin, China
Jianzhi Dong
Institute of Surface‐Earth System Science, Tianjin University, Tianjin, China
The direct optimization of soil hydraulic parameters (SHP) in unconstrained parameter space introduces significant uncertainties in ecohydrological modeling, particularly when addressing the complex model structure of Richards’ equation combined with Penman-Monteith equation. Pedotransfer functions (e.g., the latest version of Rosetta 3), which have been extensively trained using abundant soil hydraulic data, could potentially provide a physical constraint for sampling SHP. This study integrates optimization algorithms (Particle Swarm Optimization, PSO; Markov Chain Monte Carlo, MCMC; Sequential Monte Carlo, SMC; Generalized Likelihood Uncertainty Estimation, GLUE) with two sampling strategies − direct optimization of SHP and indirect optimization of SHP derived from soil particle composition (SPC) using Rosetta 3 − to evaluate their performance in ecohydrological modeling under predefined soil conditions. The results demonstrated that indirect optimization of SHP significantly enhances the accuracy in recovering predefined true parameters and states, and reduces the uncertainty of ecohydrological modeling compared to direct optimization of SHP. Specifically, the mean quartile deviation of biases in soil water content and evaporation were reduced from 0.0347 m3/m3 and 0.0027 m/h to 0.0061 m3/m3 and 0.0010 m/h, respectively. Furthermore, integration of the Rosetta 3 diminished the dimensionality of inverse modeling, thereby significantly enhancing algorithm convergence speed and precision. It is recommended to integrate Rosetta 3 with various optimization algorithms to enhance the accuracy of ecohydrological modeling.
