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The Po River Basin (PRB) is Italy’s largest river system and provides a vital water supply source for varying demands, including agriculture, energy (hydropower), and water supply. The current (2022) drought has been associated with low winter–early spring (2021–2022) snow accumulation in higher elevations (European Alps) and a lack of late spring–early summer (2022) precipitation, resulting in deficit PRB streamflow. Many local scientists are now estimating a 50- to 100-year (return period) drought for 2022. Given the importance of this river system, information about past (paleo) drought and pluvial periods would provide important information to water managers and planners. Annual streamflow data were obtained for thirteen gauges that were spatially located across the PRB. The Old World Drought Atlas (OWDA) provides annual June–July–August (JJA) self-calibrating Palmer Drought Severity Index (scPDSI) data for 5414 grid points across Europe from 0 to 2012 AD. In lieu of tree-ring chronologies, this dataset was used as a proxy to reconstruct PRB regional streamflow. Singular value decomposition (SVD) was applied to PRB streamflow gauges and gridded scPDSI data for two periods of record, referred to as the short period of record (SPOR), 1980 to 2012 (33 years), and the long period of record (LPOR), 1967 to 2012 (46 years). SVD serves as both a data reduction technique, identifying significant scPDSI grid points within the selected 450 km search radius, and develops a single vector that represents the regional PRB streamflow variability. Due to the high intercorrelations of PRB streamflow gauges, the SVD-generated PRB regional streamflow vector was used as the dependent variable in regression models for both the SPOR and LPOR, while the significant scPDSI grid points (cells) identified by SVD were used as the independent variables. This resulted in two highly skillful regional reconstructions of PRB streamflow from 0 to 2012. Multiple drought and pluvial periods were identified in the paleo record that exceed those observed in the recent historical record, and several of these droughts aligned with paleo streamflow reconstructions of neighboring European watersheds. Future research will utilize the PRB reconstructions to quantify the current (2022) drought, providing a first-time paleo-perspective of drought frequency in the watershed.
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Machine-Learning-Based Precipitation Reconstructions: A Study on Slovenia’s Sava River Basin
The Sava River Basin (SRB) includes six countries (Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Albania, and Montenegro), with the Sava River (SR) being a major tributary of the Danube River. The SR originates in the mountains (European Alps) of Slovenia and, because of a recent Slovenian government initiative to increase clean, sustainable energy, multiple hydropower facilities have been constructed within the past ~20 years. Given the importance of this river system for varying demands, including hydropower (energy production), information about past (paleo) dry (drought) and wet (pluvial) periods would provide important information to water managers and planners. Recent research applying traditional regression techniques and methods developed skillful reconstructions of seasonal (April–May–June–July–August–September or AMJJAS) streamflow using tree-ring-based proxies. The current research intends to expand upon these recent research efforts and investigate developing reconstructions of seasonal (AMJJAS) precipitation applying novel Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques. When comparing the reconstructed AMJJAS precipitation datasets, the AI/ML/DL techniques statistically outperformed traditional regression techniques. When comparing the SRB AMJJAS precipitation reconstruction developed in this research to the SRB AMJJAS streamflow reconstruction developed in previous research, the temporal variability of the two reconstructions compared favorably. However, pluvial magnitudes of extreme periods differed, while drought magnitudes of extreme periods were similar, confirming drought is likely better captured in tree-ring-based proxy reconstructions of hydrologic variables.
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- Award ID(s):
- 2152140
- PAR ID:
- 10532334
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Hydrology
- Volume:
- 10
- Issue:
- 11
- ISSN:
- 2306-5338
- Page Range / eLocation ID:
- 207
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/hydrology10110207
