AI Article Synopsis
- Climate data is essential for managing water resources and is utilized in various hydrological analyses beyond just climate change studies.
- Though climate models are becoming more accurate over time, their outputs often require adjustments to be applicable for local contexts.
- This study evaluates various statistical and machine learning methods for bias correction of climate data at different time scales, revealing distinct performance trends for correcting precipitation and temperature data.
Article Abstract
Climate data plays a crucial role in water resources management, which is becoming an increasingly relevant asset in all types of hydrological analysis not only for climate change studies but for various horizon forecasting. Though the ever-improving accuracy of climate models' spatial and temporal resolution has surged the validity of their outputs, the products of global and regional climate models need to be corrected to be reliably used for local purposes. Here, we propose a comprehensive analysis of statistical univariate and multivariate, as well as machine learning methods for bias correction, which are compared on different temporal scales, ranging from hourly time steps to monthly aggregations, in an environment of complex Alpine orthography, using ERA5-Land reanalysis data. The results reveal different trends in the performance of the bias correction methods for precipitation and temperature across the various time resolutions.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11625273 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e40352 | DOI Listing |
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