Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China

Ding, Yibo; Gong, Xinglong; Xing, Zhenxiang; Cai, Huanjie; Zhou, Zhaoqiang; Zhang, Doudou; Sun, Peng; Shi, Haiyun

doi:10.1016/j.agwat.2021.106996

Cited by 124 publications

(68 citation statements)

References 62 publications

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“…We used the Lindeman-Merenda-Gold (LMG) method to quantify the relative importance of factors such as precipitation and output value to the water expense coefficient [26][27][28]. This method distinguishes the relative contributions of different regressors through a multiple linear regression model, which can avoid the sequential effect of regression variables [29].…”

Section: Relative Contribution Of Influencing Factorsmentioning

confidence: 99%

Regression Model Selection and Assessment of Agricultural Water Price Affordability in China

Huang

Chen

et al. 2022

Water

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The agricultural water price depends on the agricultural water price affordability (AWPA) in each region. This study found that the logarithmic linear model had the best fitting effect through evaluating the grey situation decision model, which considered factors such as rainfall and output value per unit area. The contribution of each influencing factor was determined by the Lindeman–Merenda–Gold method. We established a new model to determine the water expenditure coefficient (WEC) by improving the way that the value of the WEC is assigned. Then, the AWPA in different regions was calculated. The results showed that the WEC was between 2.62% and 12.95%, and the AWPA price was between 0.058 and 0.52 yuan/m3 (0.0084 and 0.075 $/m3). The contribution of precipitation and output was 45.20% and 25.60%, respectively. The WEC and AWPA in northeast, northwest, and northern China are higher than those in southwest and southern China. The AWPA in the Yellow River Basin was higher than that in the Yangtze River Basin; however, the space for adjustment in the Yellow River Basin was slightly smaller than that in the Yangtze River Basin.

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Section: Relative Contribution Of Influencing Factorsmentioning

confidence: 99%

Regression Model Selection and Assessment of Agricultural Water Price Affordability in China

Huang

Chen

et al. 2022

Water

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“…Pearson correlation analysis (PCA) was used to figure out whether the SMOS-SM is robust enough for detecting drought. By using PCA, the correlation coefficient could be obtained, which reflects the degree of correlation of two variables [51]. In this study, the PCA is carried out between SMOS-SM and every in situ meteorological index, i.e., 1-, 3-, 6-, 9-and 12-month SPI (SPI-1, SPI-3, SPI-6, SPI-9 and SPI-12); 1-, 3-6-, 9-and 12-month SPEI (SPEI-1, SPEI-3, SPEI-6, SPEI-9 and SPEI-12); and sc-PDSI [22,52].…”

Section: Correlation Analysesmentioning

confidence: 99%

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Cheng

Hong

Huang

et al. 2021

Water

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Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

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“…Since the complete water cycle (precipitation, runoff, evapotranspiration, and soil moisture) Is considered in estimating scPDSI, scPDSI is capable in identifying different drought types. Hence, PDSI (or scPDSI) has also been used to characterize hydrological (Joetzjer et al 2013) and agricultural droughts (Ding et al 2021). To investigate the performance of SPEI, SPAEI, and scPDSI in identifying different drought types under the impact of climate warming, we estimated the Pearson's correlations between these three drought indices and univariate drought indices (SPI, SSIS, and SRIS) at 1-48 month scales.…”

Section: Evaluation Of Spei Scpdsi and Spaei In Identifying Different Drought Typesmentioning

confidence: 99%

Twenty-First Century Drought Analysis Across China using CMIP6 Under Climate Change

Zhang¹,

Gan²,

Su³

2021

Preprint

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Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices (the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)). Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the 21st century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in humid regions. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices are crucial in project representative future droughts and meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact.

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Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China

Cited by 124 publications

References 62 publications

Regression Model Selection and Assessment of Agricultural Water Price Affordability in China

Regression Model Selection and Assessment of Agricultural Water Price Affordability in China

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Twenty-First Century Drought Analysis Across China using CMIP6 Under Climate Change

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