Preprints
https://doi.org/10.5194/egusphere-2024-4182
https://doi.org/10.5194/egusphere-2024-4182
30 Jan 2025
 | 30 Jan 2025
Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

Toward merging MOPEX and CAMELS hydrometeorological datasets: compatibility and statistical comparison

Katharine Owen Sink and Tom Brikowski

Abstract. This study compares two large hydrometeorological datasets, the Model Parameter Estimation Experiment (MOPEX), and the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS), focusing on 47 shared watersheds within the continental United States. The evaluation spans daily, monthly, seasonal, and annual scales for the overlapping water years of 1981 to 2000. Spatial aggregations are conducted based on Köppen-Geiger climate regions along with annual Budyko evaporative and aridity indices. Results indicate significant differences between the datasets at daily timesteps, highlighting the challenge of high temporal resolution data reconciliation; however, compatibility markedly improves with temporal aggregation at monthly, seasonal, and annual scales. While MOPEX shows a warm bias for temperature and CAMELS shows a wet bias for precipitation, statistical analyses demonstrate that both datasets are representative of climatic conditions and extreme events. Our findings validate the results of previous research employing either dataset. Furthermore, this study serves as a foundation for the merging and extension of MOPEX and CAMELS datasets.

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Katharine Owen Sink and Tom Brikowski

Status: open (until 13 Mar 2025)

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Katharine Owen Sink and Tom Brikowski
Katharine Owen Sink and Tom Brikowski

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Short summary
This study compares two prominent hydrometeorological datasets across 47 shared watersheds in the United States to assess their compatibility, using R programming language. While daily temperature and precipitation data showed notable discrepancies, agreement improved at monthly, seasonal, and annual scales. The findings validate both datasets for previous hydrological studies and justification for merging them into a unified dataset, enhancing water resource management nationwide.
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