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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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Unstable State of Hydrologic Regime and Grain Yields in Northern Kazakhstan Estimated with Tree-Ring Proxies
Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan’s wheat is produced. We reconstructed the Palmer Drought Severity Index (June–August PDSI) and average grain yields (with an explained variance of 48% and 44%, respectively) using five tree ring width chronologies. The extended history of the moisture variability and yields of spring wheat, oats, and barley shows the strong impact of hydrology, rather than the heat, on the grain production. We defined three distinctive hydrologic regimes in SKO: (1) 1886–1942, (2) 1943–1977, (3) 1978–2023. The early regime had fewer drought events, including some that covered a single year. Their duration increased up to 3 years in the second period. The latest regime is an extreme mode of hydrologic variability with events abruptly switching from extremely dry to extremely wet conditions (called “whiplash”). The 21st century regime signifies that the intensified and prolonged decade-long drought transitioned into pluvial condition. The new regime created sizable instability for grain producers. This crop yield reconstruction denotes the potential of the tree-ring proxy for understanding the impact of climate change on the agriculture and food security of Central Asia.
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- Award ID(s):
- 1917503
- PAR ID:
- 10529419
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Agriculture
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 2077-0472
- Page Range / eLocation ID:
- 790
- Subject(s) / Keyword(s):
- wheat oats barley tree-ring reconstruction Central Asia food security
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/agriculture14060790
