Land Surface Energy Partitioning Dominates Dry‐Season Water Availability Uncertainties in Earth System Models

Abstract Accurately characterizing dry‐season water availability ( W d ) is critical for projecting terrestrial carbon exchange and global water security. W d is commonly calculated as the minimum value of precipitation minus evapotranspiration within each calendar year. However, Earth System Model (ESM) projected W d contains substantial uncertainties and can disagree on even the sign. Based on a newly proposed framework, we disentangle the uncertainty sources in ESM‐based W d projections. Results demonstrate that ESM‐based W d uncertainties are dominated by land surface energy partitioning (summarized by evaporation fraction, denoted as EF) instead of precipitation or available energy. As such, EF alone can explain more than 83% of inter‐ESM variability in historical and future W d projections. Compared against data‐driven benchmarks, ESMs tend to overestimate dry‐season EF—suggesting that W d is likely to be underestimated in ESMs. Our analysis indicates that the ET resistance parameterization is the central error source in ESM‐based EF, which should be constrained to enhance the reliability of EF, and by extension, W d projections.

Authors

Institutions

Publication Details

Journal
Water Resources Research
Published
2025-04-29
DOI
https://doi.org/10.1029/2024wr038000
Citations
2
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
2.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Land Surface Energy Partitioning Dominates Dry‐Season Water Availability Uncertainties in Earth System Models

Jianzhi Dong, Zheng Duan, Hongkai Gao, Yongqiang Zhang et al.
2 citations
Water Resources Research
Climate variability and models
2.21
article

Land Surface Energy Partitioning Dominates Dry‐Season Water Availability Uncertainties in Earth System Models

Jianzhi Dong, Zheng Duan, Hongkai Gao, Yongqiang Zhang, Jianhong Zhou, Man Ke Gao, Lingna Wei, Wade T. Crow, H. Zhou
article en
2 citations

Abstract

Abstract Accurately characterizing dry‐season water availability ( W d ) is critical for projecting terrestrial carbon exchange and global water security. W d is commonly calculated as the minimum value of precipitation minus evapotranspiration within each calendar year. However, Earth System Model (ESM) projected W d contains substantial uncertainties and can disagree on even the sign. Based on a newly proposed framework, we disentangle the uncertainty sources in ESM‐based W d projections. Results demonstrate that ESM‐based W d uncertainties are dominated by land surface energy partitioning (summarized by evaporation fraction, denoted as EF) instead of precipitation or available energy. As such, EF alone can explain more than 83% of inter‐ESM variability in historical and future W d projections. Compared against data‐driven benchmarks, ESMs tend to overestimate dry‐season EF—suggesting that W d is likely to be underestimated in ESMs. Our analysis indicates that the ET resistance parameterization is the central error source in ESM‐based EF, which should be constrained to enhance the reliability of EF, and by extension, W d projections.

Water Resources ResearchVol. 61(5)
Tianjin University (CN), Lund University (SE), Chinese Academy of Sciences (CN), Nanjing University of Information Science and Technology (CN), Beltsville Agricultural Research Center (US), Institute of Geographic Sciences and Natural Resources Research (CN), East China Normal University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 14%
Climate variability and models
2.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.