The isotopes of precipitation have climate change signal in arid Central Asia

Zhu, Guofeng; Liu, Yuwei; Wang, Lei; Sang, Liyuan; Zhao, Kan; Zhang, Zhuanxia; Lin, Xinrui; Qiu, Dongdong

doi:10.1016/j.gloplacha.2023.104103

Cited by 34 publications

(13 citation statements)

References 73 publications

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“…The development of the anticyclonic circulation reduces the power of the southeast wind, which in turn strengthen the summer monsoon season and rise warmer air into Bangladesh and ultimately causes an increase in rainfall 138 . Contrarily, the majority of the areas saw moderate to low mean convective precipitation rates, which to some degree reduced rainfall 147 . Overall, the number of low clouds has grown throughout the country and neighbor regions 27 .…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

Monir,

Rokonuzzaman,

Sarker

et al. 2023

Sci Rep

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Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

Monir,

Rokonuzzaman,

Sarker

et al. 2023

Sci Rep

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“…A little less than half of the flood pixels are found in soil group B, whereas the remaining 30.8% are found in soil group C. This dataset on rainfall was acquired from the National Meteorological Administration by analyzing the weather stations linked to the service. Rainfall is one of the leading predictors of flood phenomena, which is directly linked to the genesis of floods (Wu et al., 2022; Zhu et al., 2023). We used the spline method to interpolate the 23 rainfall values gathered from the weather station.…”

Section: Datamentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest‐PA‐WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest‐PA‐WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data‐scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

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“…Similar to most arid regions in the onslaught of climate-driven hydrologic disruptions [1,2], the Altiplano-Puna of South America faces prolonged drought [3], extreme precipitation events [4], and resulting uncertainty in the resilience of surface water bodies [5]. Surface water bodies in inland, arid, and endorheic basins are sources of water for local wildlife and thus sustain ecosystem diversity [6].…”

Section: Mainmentioning

confidence: 99%

Hydrological signatures in wetlands in the lithium-rich salar basins of the Andes, South America

McKnight,

Jenckes,

Boutt

et al. 2023

Preprint

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Despite the importance of surface water resilience to local ecosystems in arid and endorheic basins, the current understanding of surface water sensitivity to climate-driven hydrologic perturbations in such environments remains limited to basin-scale water budget analysis and often ignores the complex relationships between these surface waters and sources of recharge. We investigate the driving factors of surface water variability and drought vulnerability for the Altiplano-Puna region of the Andes in South America with satellite imagery via the Landsat-based JRC Surface Water Product and the Sentinel mission. We further identify areas of surface water-adjacent groundwater discharge through the normalized difference vegetation index and compare fluctuations in vegetated area to variability in surface water inundation. Regression analysis between surface water inundation and monthly average temperature through FLDAS and precipitation through Terraclimate also provides an opportunity to assess climate-driven sensitivity. Surface waters throughout the Altiplano-Puna show resilience to a prolonged region-wide drought which occurred between 2003 and 2012. However, high-elevation (>4,000 masl) surface water bodies exhibit statistically significant declines in surface water inundation, but these same surface water bodies show the highest post-drought rebounds. Increased sensitivity in vegetated area fluctuation adjacent to areas of higher surface water drought sensitivity indicates that surface water bodies are reliant on groundwater discharge. An exploration of basin attributes further suggests that higher-elevation basins experience higher variability in groundwater and surface water fluctuations, suggesting not only that both systems are closely coupled but also that increasing precipitation at higher elevations introduces more variability in the hydrologic pathways to local surface waters. We present the first regional study of arid, endorheic basins to define mechanisms of surface water resilience and determine the importance of groundwater discharge dynamics in such environments. This study further provides a framework for assessing surface water resilience for both the Altiplano-Puna and other similarly arid regions (e.g. the Tibetan Plateau, the Great basin, etc.).

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The isotopes of precipitation have climate change signal in arid Central Asia

Cited by 34 publications

References 73 publications

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

Hydrological signatures in wetlands in the lithium-rich salar basins of the Andes, South America

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