Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors

Zhuang, Xuan; Hao, Zengchao; Singh, Vijay P.; Zhang, Yu; Shi, Feng; Yang, Xu; Hao, Fang

doi:10.1016/j.scitotenv.2022.156021

Cited by 85 publications

(66 citation statements)

References 256 publications

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“…However, the time of drought occurrence in C5 was ahead of that in other clusters. Drought propagation effects with lag, attenuation, pooling and lengthening were obvious in the ARNA, which may have been caused by catchment factors such as vegetation [32]. The DFs for SPI -1, -3 and -12 were more frequent than those for SRI -1, -3 and -12, except in C2 and C5 at the 12-month scale (Figure 6e,f).…”

Section: Drought Characteristics Analysismentioning

confidence: 96%

Section: Drought Characteristics Analysismentioning

confidence: 99%

“…Statistics and hydrological models are two basic methods for drought propagation analysis [28], such as the MCC method [2], cross-wavelet analysis [29] and the variable infiltration capacity (VIC) model [30]. Effects of lag, attenuation, pooling and lengthening are common during the process of drought propagation [31,32]. Lag and attenuation are governed by catchment control, and pooling and lengthening are governed by both catchment control and climate control [31].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

Zhang

Yin

et al. 2022

Remote Sensing

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The characteristics of the drought propagation from meteorological drought (MD) to agricultural drought (AD) differ in various climatic and underlying surface conditions. However, how these factors affect the process of drought propagation is still unclear. In this study, drought propagation and influencing factors were investigated in an arid region of Northeast Asia (ARNA) during 1982–2014. Based on run theory, the drought characteristics were detected using the standardized precipitation index (SPI) and standardized soil moisture index (SMI), respectively. The propagation time from MD to AD was investigated, and the influence factors were identified. Results demonstrated that five clusters (C1–C5) based on land cover distribution were further classified by the K-means cluster algorithm to discuss the spatial and seasonal propagation variation. MD and AD in ARNA became more severe during the study period in all five clusters. The propagation times from MD to AD in all five clusters were shorter (1–3 months) in summer and autumn and longer (5–12 months) in spring and winter. This result suggested that the impact of vegetation on the seasonal drought propagation time was more obvious than that of the spatial drought propagation time. Precipitation and vegetation were the major impactors of AD in spring, summer and autumn (p < 0.05). The impact of precipitation on AD was more noticeable in summer, while vegetation mainly influenced AD in spring and autumn. The research also found that drought propagation time had a negative relationship (p < 0.05) with precipitation, evapotranspiration, soil moisture and NDVI in this region, which indicated that a rapid hydrological cycle and vegetation can shorten the propagation time from MD to AD. This study can help researchers to understand the drought propagation process and the driving factors to enhance the efficiency of drought forecasting.

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Section: Drought Characteristics Analysismentioning

confidence: 96%

Section: Drought Characteristics Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

Zhang

Yin

et al. 2022

Remote Sensing

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“…Beyond unrealistic simulated evapotranspiration, interactions between surface water and groundwater have been identified as a major contributing factor to non-stationarity in modeled rainfall-runoff relationships. In the MB, the groundwater component has a prominent role to satisfy water demands for agricultural, industrial, and domestic use, especially during drought periods (Carmona et al 2017;Zhuang et al 2022). In some cases (e.g., Grigg and Hughes 2018), specific adjustments on the adopted hydrological models were proposed to consider the groundwater impacts on streamflow, while in other cases (e.g., Deb et al 2019) multi-model frameworks coupling hydrological models with groundwater models were adopted.…”

Section: Hydrological and Impact Modelsmentioning

confidence: 99%

Climate Change in the Mediterranean Basin (Part II): A Review of Challenges and Uncertainties in Climate Change Modeling and Impact Analyses

Noto

Cipolla

Pumo

et al. 2023

Water Resour Manage

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The Mediterranean basin is particularly prone to climate change and vulnerable to its impacts. One of the most relevant consequences of climate change, especially for the southern Mediterranean regions, is certainly water scarcity as result of a reduction of surface runoff and groundwater levels. Despite the progress achieved in recent years in the field of climate change and its impact on water resources, results and outcomes should be treated with due caution since any future climate projection and derived implications are inevitably affected by a certain degree of uncertainty arising from each different stage of the entire modeling chain. This work offers a comprehensive overview of recent works on climate change in the Mediterranean basin, mainly focusing on the last ten years of research. Past and future trends on different components of the hydrological balance are discussed in a companion paper (Noto et al. 2022), while the present paper focuses on the problem of water availability and water scarcity. In addition, the work aims to discuss the most relevant sources of uncertainty related to climate change with the aim to gain awareness of climate change impact studies interpretation and reliability.

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“…We adopted the procedure by Bloomfield and Marchant (2013) originally outlined for groundwater response times, and used the lag time resulting in the highest cross-correlation between SPI and the sector-specific drought indices as proxy for the sector response times. Lag time is a common metric to characterize drought propagation (Zhang et al, 2022) and was here calculated for past conditions as well as for future conditions (2071-2100) under RCP2.6, RCP4.5 and RCP8.5. A faster response of a sector is, hence, indicated by a shorter response time of the sector-specific index to the SPI.…”

Section: Propagation Across Wefe Nexus Sectors (Response Times)mentioning

confidence: 99%

Future drought propagation through the water-energy-food-ecosystem nexus – A Nordic perspective

Teutschbein

Jonsson

Todorović

et al. 2023

Journal of Hydrology

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Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors

Cited by 85 publications

References 256 publications

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

Climate Change in the Mediterranean Basin (Part II): A Review of Challenges and Uncertainties in Climate Change Modeling and Impact Analyses

Future drought propagation through the water-energy-food-ecosystem nexus – A Nordic perspective

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