Spatial and Temporal Global Patterns of Drought Propagation

Fuentes, Ignacio; Padarian, José; Vervoort, W.

doi:10.3389/fenvs.2022.788248

Cited by 27 publications

(21 citation statements)

References 97 publications

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“…Then, the cross correlation between standardized indices was applied at each pixel. However, the different time series were previously prewhitened using an Autoregressive Integrated Moving Average (ARIMA) model to remove serial correlation (Fuentes et al, 2022). Thus, this analysis was applied to calculate the maximum correlation between lags of 0 and 24 months.…”

Section: Methodsmentioning

confidence: 99%

“…In many regions of the world, there is little difference between SPI and SPEI variability, as demonstrated in analyses of their historical record. This is particularly true in nonarid regions, where drought is driven mostly by rainfall variability (Fuentes et al, 2022). However, in a world with high temperature increases due to climate change, these two drought definitions might diverge further, as PET might play a larger role in some regions (Noureldeen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Different types of drought under climate change or geoengineering: Systematic review of societal implications

et al. 2022

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Climate change and solar geoengineering have different implications for drought. Climate change can “speed up” the hydrological cycle, but it causesgreater evapotranspiration than the historical climate because of higher temperatures. Solar geoengineering (stratospheric aerosol injection), on the other hand, tends to “slow down” the hydrological cycle while reducing potential evapotranspiration. There are two common definitions of drought that take this into account; rainfall-only (SPI) and potential-evapotranspiration (SPEI). In different regions of Africa, this can result in different versions of droughts for each scenario, with drier rainfall (SPI) droughts under geoengineering and drier potential-evapotranspiration (SPEI) droughts under climate change. However, the societal implications of these different types of drought are not clear. We present a systematic review of all papers comparing the relationship between real-world outcomes (streamflow, vegetation, and agricultural yields) with these two definitions of drought in Africa. We also correlate the two drought definitions (SPI and SPEI) with historical vegetation conditions across the continent. We find that potential-evapotranspiration-droughts (SPEI) tend to be more closely related with vegetation conditions, while rainfall-droughts (SPI) tend to be more closely related with streamflows across Africa. In many regions, adaptation plans are likely to be affected differently by these two drought types. In parts of East Africa and coastal West Africa, geoengineering could exacerbate both types of drought, which has implications for current investments in water infrastructure. The reverse is true in parts of Southern Africa. In the Sahel, sectors more sensitive to rainfall-drought (SPI), such as reservoir management, could see reduced water availability under solar geoengineering, while sectors more sensitive to potential-evapotranspiration-drought (SPEI), such as rainfed agriculture, could see increased water availability under solar geoengineering. Given that the implications of climate change and solar geoengineering futures are different in different regions and also for different sectors, we recommend that deliberations on solar geoengineering include the widest possible representation of stakeholders.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Different types of drought under climate change or geoengineering: Systematic review of societal implications

et al. 2022

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“…This is because there is enough subsurface flow to maintain streamflow (van Tiel et al, 2021). In addition, areas with more forest cover will witness longer meteorological to hydrological drought propagation (Fuentes et al, 2022).…”

Section: Propagationmentioning

confidence: 99%

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

et al. 2023

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Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias‐corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological‐hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought‐related consequences.

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“…Typically, meteorological drought is regarded as the beginning of a drought event; after the occurrence of meteorological drought, other drought phenomena occur, such as hydrological drought (Fendeková et al, 2018;Fuentes et al, 2022;Wang et al, 2021). However, there is a delay period from meteorological drought to hydrological drought (Ding et al, 2021;Xu et al, 2019;Onyutha, 2017;Cammalleri and Vogt, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…A change in the environment may lead to the nonstationarity of the relationship between hydrological series (for example, precipitation and runoff series), which also occurs in the Luanhe River basin (Wang et al, 2016(Wang et al, , 2018(Wang et al, , 2020. Traditional drought prediction methods need to be further improved to adapt to nonstationary conditions (Wang et al, 2022;Zhao et al, 2018;Chen et al, 2021). Ren et al (2017) found that the conditional distribution model using large-scale climatic indices as covariates can improve the accuracy of meteorological drought forecasting in the Luanhe River basin.…”

Section: Introductionmentioning

confidence: 99%

Hydrological drought forecasting under a changing environment in the Luanhe River basin

Zhang

Cao

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. Forecasting the occurrence of hydrological drought according to a forecasting system is an important disaster reduction strategy. In this paper, a new drought prediction model adapted to changing environments was constructed. Taking the Luanhe River basin in China as an example, first, nonstationarity analysis of hydrological sequences in the basin was carried out. Then, conditional distribution models with the human activity factor as an exogenous variable were constructed to forecast hydrological drought based on meteorological drought, and the results were compared with the traditional normal distribution model and conditional distribution model. Finally, a scoring mechanism was applied to evaluate the performance of the three drought forecasting models. The results showed that the runoff series of the Luanhe River basin from 1961 to 2010 were nonstationary; moreover, when human activities were not considered, the hydrological drought class tended to be the same as the meteorological drought class. The calculation results of the models involving HI as an exogenous variable were significantly different from the models that did not consider human activities. When the current drought class tended towards less severe or normal, the meteorological drought tended to turn into more severe hydrological drought with the increase in human index values. According to the scores of the three drought forecasting models, the conditional distribution models involving the human index can further improve the forecasting accuracy of drought in the Luanhe River basin.

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Spatial and Temporal Global Patterns of Drought Propagation

Cited by 27 publications

References 97 publications

Different types of drought under climate change or geoengineering: Systematic review of societal implications

Different types of drought under climate change or geoengineering: Systematic review of societal implications

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

Hydrological drought forecasting under a changing environment in the Luanhe River basin

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