Understanding the influence of hydrologic parameter uncertainty on Community Water Model predictions
A diagnostic assessment through extensive ensemble simulations
Bibliographic Data
| ID | 15545205 |
|---|---|
| Authors | Junho Kim (0000-0002-8977-2925), Jun Ho Kim (0000-0002-3672-0136, Kongju National University, corresponding author), Kuk‐Hyun Ahn (0000-0001-8142-0813, Kongju National University) |
| Year | 2025 |
| Volume | 20 |
| Issue | 6 |
| Pages | 064001-064001 |
| Publication date | 2025-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/add27e |
| OpenAlex | W4410009017 |
| Language | EN |
| References cited | 65 |
Global hydrological models have been used to analyze Earth’s hydrological cycle and evaluate water scarcity risks. Despite their significance, a comprehensive investigation into the effects of parametric uncertainty on their hydrologic predictions across diverse regions and flow characteristics remains lacking. This study contributes by detailing how variations in the response of the Community Water Model (CWatM) can be linked to the uncertainty associated with hydrologic parameters. Relying on the default hydrologic model parameters in CWatM may pose a risk, potentially leading to inaccurate streamflow predictions and improper decision-making in subsequent inferences. To confirm this, we first assess the effectiveness of CWatM in predicting streamflow across 481 basins spanning the Eurasian continent, utilizing the commonly employed default hydrologic parameters. Subsequently, we evaluate CWatM simulations using a comprehensive range of parameter realizations, employing the Latin Hypercube Sampling-based approach, and evaluate the daily performance based on 10 error metrics. Our results confirm the presence of significant variations in CWatM predictions in specific regions and across selected error metrics. In particular, the baseline CWatM exhibits relatively poor streamflow prediction skill in certain Eurasian regions, such as the arid region over Central Asia. In addition, the results show that the complex nonlinear behaviors in streamflow predictions are evident not only due to the overarching uncertainty in hydrologic parameters but also arise from the influence of the most dominant parameters. Ultimately, this exploration of parameter realizations offers insights for enhancing CWatM’s predictive capabilities and refining parameter selection in future studies
Climatology · Econometrics · Ensemble forecasting · Hydrological modelling · Machine learning · Computer Science · Environmental Science · Hydrology and Watershed Management Studies · Mathematics · Geology
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| Citation velocity | historical |
|---|---|
| Highly cited | No |