2023
DOI: 10.1002/hyp.14894
|View full text |Cite
|
Sign up to set email alerts
|

Energy and vapour transfer in evaporation processes over saturated bare soil and water surface‐lysimeter studies

Abstract: Potential evaporation (PE) is a significant input in many hydrological models for the estimation of actual evaporation. Evaporation from water (PE w ) is generally considered equivalent to evaporation from saturated bare soils (PE s ). The influences of the underlying surface on PE as well as the energy and vapour transfer in potential evaporation processes over different surfaces are rarely discussed. In this research, lysimeter experiments were set up to measure the diurnal cycles of evaporation from two sat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 57 publications
0
1
0
Order By: Relevance
“…For the physical mechanism-constrained deep learning method proposed in this study, the sources of uncertainty are mainly the physical mechanism, the raw data itself and the model structure (Karniadakis et al, 2021). The uncertainty brought by the physical mechanisms is the parameter uncertainty, and for the three physical mechanisms of PIDL, the Richard equation involves some parameters of soil-and water-related properties, which are fitted by the VG formulation, and this fitting method can reduce the parameter uncertainty as low as possible when using a large amount of in situ monitoring data (Gao et al, 2019;Li et al, 2023;Ma et al, 2019Ma et al, , 2023Zhang et al, 2016;Zhao et al, 2020). Uncertainty from raw data, on the other hand, is due to the unavoidable noise in the data collection process and objective errors (truncation error, rounding error) during processing, which belong to chance uncertainty, and this part of uncertainty can usually be reduced by maximizing the accuracy of observations and quantity of data (Huang et al, 2022;Hüllermeier & Waegeman, 2021).…”
Section: Uncertainty Shortcomings and Outlookmentioning
confidence: 99%
“…For the physical mechanism-constrained deep learning method proposed in this study, the sources of uncertainty are mainly the physical mechanism, the raw data itself and the model structure (Karniadakis et al, 2021). The uncertainty brought by the physical mechanisms is the parameter uncertainty, and for the three physical mechanisms of PIDL, the Richard equation involves some parameters of soil-and water-related properties, which are fitted by the VG formulation, and this fitting method can reduce the parameter uncertainty as low as possible when using a large amount of in situ monitoring data (Gao et al, 2019;Li et al, 2023;Ma et al, 2019Ma et al, , 2023Zhang et al, 2016;Zhao et al, 2020). Uncertainty from raw data, on the other hand, is due to the unavoidable noise in the data collection process and objective errors (truncation error, rounding error) during processing, which belong to chance uncertainty, and this part of uncertainty can usually be reduced by maximizing the accuracy of observations and quantity of data (Huang et al, 2022;Hüllermeier & Waegeman, 2021).…”
Section: Uncertainty Shortcomings and Outlookmentioning
confidence: 99%