Drought Reconstruction Over the Past Two Centuries in Southern Myanmar Using Teak Tree‐Rings: Linkages to the Pacific and Indian Oceans

Abstract

To develop a high-resolution paleoclimate proxy record facilitating adaptation measures on drought impacts, we constructed a regional composite tree-ring chronology of teak (Tectona grandis) by combining three local site chronologies from southern Myanmar. Our regional tree-ring record is strongly correlated with the November-April self-calibrated Palmer drought severity index and enabled us to reconstruct drought variability for the period 1802-2016. The reconstruction explained 46.5% of the actual self-calibrated Palmer drought severity index variance. Over the past two centuries, we detected one extreme (1812) and six severe drought years (1810, 1811, 1816, 1852, 1887, and 1980). Dry periods coincided with six major famines that occurred in India. Spatial correlation analyses revealed that drought occurrence is linked to broader-scale atmospheric circulations of the Pacific and Indian Oceans, especially during the positive phase of the El Niño-Southern Oscillation. Our reconstruction provides a useful historical perspective for assessing adaptation measures intent on mitigating future drought impacts in Myanmar. Plain Language SummaryWe developed a 226-year-long ring-width regional chronology of teak by combining three site chronologies from southern Myanmar. Teak growth is mainly controlled by moisture availability during the dry season, making teak a suitable species for assessing drought variation in southern Myanmar. We reconstructed a 215-year drought history and find prolonged droughts occurred in also found a strong association between drought variability in southern Myanmar and sea surface temperatures in the Pacific and Indian Oceans, as well as the El Niño-Southern Oscillation phenomenon. The results are useful to devise necessary policies and strategies for coping with the adverse impacts of drought and protecting the livelihoods of regional populations.

Seven centuries of reconstructed Brahmaputra River discharge demonstrate underestimated high discharge and flood hazard frequency

“…A limitation of our analyses regarding flood hazard is that we reconstruct Brahmaputra mean JAS monsoon season discharge and not flood years per se. Paleohydrology cross-proxy synthesis between tree-rings and other archives such as geomorphic field stratigraphy [74][75][76][77] and speleothems [78][79][80] , the documentation of tree-ring flood-scars that can precisely date past flood events [81][82][83] , and additional tree-ring sampling in the region of traditional 57,84 and non-traditional species [85][86][87] can help establish more skillful reconstructions of Brahmaputra discharge, its flooding history, and its flooding frequency in future work 88 . Additionally, we focus on the likelihood of high discharge as a proxy for flood hazard, and not on flood exposure and vulnerability 89,90 .…”

Sea surface temperature seasonality in the northern South China Sea during the middle Holocene derived from high resolution Sr/Ca ratios of Tridacna shells

“…Paleoclimate proxy records, such as ice cores (Dansgaard et al, 1993;Petit et al, 1999), stalagmites (Polyak et al, 2001;Cheng et al, 2018), loess (An et al, 1991), lake sediments (Eawag et al, 1992;Chen et al, 2020), and tree rings (Fritts et al, 1979;Roig et al, 2001;Tian et al, 2007;Zaw et al, 2020), are important supplements for modern instrumental data, but most of these proxies cannot be used to study past seasonal climate changes because of their relatively low temporal resolution. Thus, developing high-resolution paleoclimate records, which can capture full seasonal cycles, are essential for us to reconstruct and understand past seasonal climate changes at a level that we need for mitigation and adaptability.…”

Drought Analysis for the Seyhan Basin with Vegetation Indices and Comparison with Meteorological Different Indices

“…Various indices were used for this purpose in different geographical regions of the world. Main indices were developed on remote sensing data that find wide usage, especially to determine drought-e.g., the Enhanced Vegetation Index (EVI) [36][37][38], Evaporative Stress Index (ESI) [39][40][41], Normalized Difference Vegetation Index (NDVI) [42][43][44], Temperature Condition Index (TCI) [45], Vegetation Condition Index (VCI) [46][47][48], Vegetation Drought Response Index (VegDRI) [49], Vegetation Health Index (VHI) [50,51], Water Requirement Satisfaction Index (WRSI) [52], Normalized Difference Water Index (NDWI) [53], and Land Surface Water Index (LSWI) [54,55].…”