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Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States
Hongxiang Yan1, Ning Sun2, Hisham Eldardiry2
1Pacific Northwest National Laboratory, Richland, WA, USA. hongxiang.yan@pnnl.gov.
Scientific Data
|April 6, 2023
Summary
This study characterizes hydrological parameter uncertainties in the Community Land Model Version 5 (CLM5) across the United States. Findings aid in understanding model limitations for water resource applications and identifying areas for future research.
Area of Science:
- Earth and Environmental Sciences
- Hydrology
- Climate Modeling
Background:
- Land surface models like the Community Land Model Version 5 (CLM5) are crucial for simulating terrestrial systems.
- However, uncertainties in CLM5's hydrological parameters and their impact on water resources remain under-explored.
Purpose of the Study:
- To conduct a comprehensive hydrological parameter uncertainty characterization of CLM5.
- To analyze these uncertainties across the hydroclimatic gradients of the conterminous United States.
- To provide datasets supporting CLM5 calibration and water resource application evaluations.
Main Methods:
- Utilized five distinct meteorological datasets for the uncertainty characterization.
- Performed sensitivity analyses for 28 hydrological metrics within CLM5.
- Generated large-ensemble outputs for CLM5 hydrological predictions.
Main Results:
- Developed a benchmark dataset of CLM5's default hydrological performance.
- Quantified parameter sensitivities for 28 hydrological metrics.
- Produced extensive CLM5 hydrological prediction outputs under uncertainty.
Conclusions:
- The generated datasets are valuable for CLM5 calibration and improving water resource management.
- Identified hydroclimatic conditions where parameter uncertainties significantly influence hydrological predictions.
- Highlighted the need for further research into the interaction of hydrological uncertainties with other Earth system processes.
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