Investigation about the correlation and propagation among meteorological, agricultural and groundwater droughts over humid and arid/semi-arid basins in China

Zhang, Hao; Ding, Jie; Wang, Yushi; De-xu, Zhou; Zhu, Qiankun

doi:10.1016/j.jhydrol.2021.127007

Cited by 85 publications

(23 citation statements)

References 72 publications

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“…Therefore, using appropriate methods and meteorological drought indices is necessary for predicting short-term and longterm droughts. Numerous studies have been performed to assess meteorological droughts in arid and semi-arid regions such as Zhang et al (2021) and Tayfur (2021). Iran locates in the sub-tropical high-pressure belt or horse latitudes.…”

Section: Introductionmentioning

confidence: 99%

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Lotfirad

Esmaeili-Gisavandani

Adib

2021

Journal of Water and Climate Change

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The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

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Section: Introductionmentioning

confidence: 99%

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Lotfirad

Esmaeili-Gisavandani

Adib

2021

Journal of Water and Climate Change

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“…The propagation time can be defined as the time length from dry-wet meteorological propagation to dry-wet soil (Ding et al, 2021;Han et al, 2019;Huang et al, 2015Huang et al, , 2017Xu et al, 2021;Zhang et al, 2021;Zhou et al, 2021bZhou et al, , 2021c. In this study, Pearson correlation coefficient (PCC) was selected to determine the propagation time of meteorological drought to soil drought.…”

Section: Propagation Time (Pt)mentioning

confidence: 99%

The Peer‐To‐Peer Type Propagation From Meteorological Drought to Soil Moisture Drought Occurs in Areas With Strong Land‐Atmosphere Interaction

Zhang

et al. 2022

Water Resources Research

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“…Van Loon et al, 2016;Mishra and Singh, 2010;Veldkamp et al, 2015), and/or on the impact of drought on specific user groups like agriculture (e.g. Meza et al, 2021;Zhang et al, 2021), public water supply (e.g. Ajami et al, 2008;Connell-Buck et al, 2011;Wendt et al, 2021), or electricity production (Byers et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Integrated drought risk assessment to support adaptive policymaking in the Netherlands

Mens

Rhee²,

Schasfoort

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Adaptive policymaking to prepare for current and future drought risks requires an integrated assessment of policy actions and combinations of those under changing conditions. This entails quantification of drought risks, integrating drought probability and socio-economic consequences for all relevant sectors that are potentially impacted by drought. The investment costs of proposed policy actions and strategies (various actions combined) can then be compared with the expected risk reduction to determine the cost-effectiveness. This paper presents a method to quantify drought risk in the Netherlands under changing future conditions and in response to policy actions. It illustrates how to use this information as part of a societal cost–benefit analysis and in building an adaptive long-term strategy. The method has been successfully applied to support decision making on the Netherlands' national drought risk management strategy as part of the National Delta Programme for climate change adaptation.

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Investigation about the correlation and propagation among meteorological, agricultural and groundwater droughts over humid and arid/semi-arid basins in China

Cited by 85 publications

References 72 publications

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

The Peer‐To‐Peer Type Propagation From Meteorological Drought to Soil Moisture Drought Occurs in Areas With Strong Land‐Atmosphere Interaction

Integrated drought risk assessment to support adaptive policymaking in the Netherlands

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