Your search found 3 records
1 Chun, K. P.; He, Q.; Fok, H. S.; Ghosh, S.; Yetemen, O.; Chen, Q.; Mijic, A. 2020. Gravimetry-based water storage shifting over the China-India border area controlled by regional climate variability. Science of The Total Environment, 714:136360. [doi: https://doi.org/10.1016/j.scitotenv.2019.136360]
Water storage ; Climate change ; Precipitation ; Drought ; Temperature ; Monsoon climate ; Water depletion ; Satellite observation ; Gravimetry / China / India / Indus River / Ganges River / Brahmaputra River
(Location: IWMI HQ Call no: e-copy only Record No: H049784)
(1.73 MB)
The regional water storage shifting causes nonstationary spatial distribution of droughts and flooding, leading to water management challenges, environmental degradation and economic losses. The regional water storage shifting is becoming evident due to the increasing climate variability. However, the previous studies for climate drivers behind the water storage shifting are not rigorously quantified. In this study, the terrestrial water storage (TWS) spatial shifting pattern during 2002–2017 over the China-India border area (CIBA) is developed using the Gravity Recovery and Climate Experiment (GRACE), suggesting that the Indus-Ganges-Brahmaputra basin (IGBB) was wetting while the central Qinghai-Tibet Plateau (QTP) was drying. Similar drying and wetting patterns were also found in the precipitation, snow depth, Palmer Drought Severity Index (PDSI) and potential evaporation data. Based on our newly proposed Indian monsoon (IM) and western North Pacific monsoon (WNPM) variation indices, the water shifting pattern over the CIBA was found to be affected by the weakening of the variation of IM and WNPM through modulating the regional atmospheric circulation. The weakening of IM and WNPM variations has shown to be attributed to the decreasing temperature gradient between the CIBA and the Indian Ocean, and possibly related to increasing regional temperatures associated with the increasing global temperature. As the global warming intensifies, it is expected that the regional TWS shifting pattern over the CIBA will be further exaggerated, stressing the need of advancing water resources management for local communities in the region.
2 Chen, Q.; Timmermans, J.; Wen, W.; van Bodegom, P. M. 2022. A multi-metric assessment of drought vulnerability across different vegetation types using high resolution remote sensing. Science of the Total Environment, 832:154970. (Online first) [doi: https://doi.org/10.1016/j.scitotenv.2022.154970]
Drought ; Vulnerability ; Vegetation ; Remote sensing ; Frameworks ; Climate change ; Farmland ; Grasslands ; Ecosystems ; Precipitation ; Evapotranspiration ; Early warning systems / Netherlands / Belgium
(Location: IWMI HQ Call no: e-copy only Record No: H051125)
https://www.sciencedirect.com/science/article/pii/S0048969722020630/pdfft?md5=2146bf59795846d2ef5c878eb14ddac2&pid=1-s2.0-S0048969722020630-main.pdf
https://vlibrary.iwmi.org/pdf/H051125.pdf
https://vlibrary.iwmi.org/pdf/H051125.pdf
(4.83 MB) (4.83 MB)
Drought impact monitoring is of crucial importance in light of climate change. However, we lack an understanding of the concomitant responses of ecosystems to a variety of drought characteristics and the links between drought and ecosystem anomaly characteristics for a comprehensive set of vegetation types to provide needed information for water management. In response, this study presents a new framework that allows us to explore the relationship between drought and its impact on ecosystems in greater detail. Specifically, our framework focuses on estimating jointly the hydrological and ecosystem temporal evolution and anomalies around a drought event using four pairs of metrics: onset-onset, duration-duration, intensity-intensity, and severity-severity of drought and vegetation damage. Additionally, we incorporated a metric on vegetation vulnerability based on changes in damage severity along a gradient of increasing drought severity. Based on this framework, we evaluated drought vulnerability patterns of various vegetation types across the Netherlands and Belgium in 2018 at high spatiotemporal resolution. Our results reveal a differential vulnerability of vegetation between ecosystems with increasing drought severity, which could aid future drought impact predictions. In particular, mosaic grasslands and tree/shrub croplands are highly sensitive to increasing drought severity. Individual characteristics (onset, duration, intensity and severity) of drought and vegetation damage behave differently in various vegetation types. For instance, broadleaved forests respond faster than other forests, while mixed forests suffer less damage than other types. The early warning threshold to drought for most vegetation types is around a Standardized Precipitation Evapotranspiration Index (SPEI) value of -1. The characterization of a suite of drought response characteristics through our impact analysis framework can be used in a wide variety of regions to understand current and possible future responses to drought.
3 Chen, Q.; Fu, S. 2023. Quantitative analysis and management of sustainable development of ecological water resources and digital financial system based on an intelligent algorithm. Water Supply, ws2023152. [doi: https://doi.org/10.2166/ws.2023.152]
Sustainable development ; Water resources ; Digital technology ; Finance ; Water quality ; Water management ; Economic growth ; Machine learning ; Models ; Surface water ; Neural networks
(Location: IWMI HQ Call no: e-copy only Record No: H052011)
https://iwaponline.com/ws/article-pdf/doi/10.2166/ws.2023.152/1241761/ws2023152.pdf
https://vlibrary.iwmi.org/pdf/H052011.pdf
https://vlibrary.iwmi.org/pdf/H052011.pdf
(0.90 MB) (924 KB)
In the modern day, water is a crucial resource for advancing society and preserving ecological balance. Growth, which lessens poverty and increases equality, is often seen as inextricably linked to the effective use of water resources. Traditional water system management aims to optimize surface water and subsurface aquifers to meet conflicting needs. As a result, the special difficulties in water resource management (WRM) would be exacerbated by the added uncertainty brought on by climatic change. Managing the world's water supplies sustainably is crucial to the planet's continued existence and prosperity. However, ecological planning for sustainable water development is difficult because of complex impacts, random processes, and hydrological restrictions. The study was inspired to address the issues head-on by creating a hybrid AI algorithm for ecological water resource sustainability and digital finance (HAI-EWRS-DF) system for solving complex, multi-scale problems in WRM. Control mechanisms, including social, financial, and sustainability on ground-level and surface-level water resource facilities, are recommended to enhance WRM to increase the applicable revenue, promote community well-being, and pave the way for greater economic development.
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