Spatial-temporal distribution characteristics and hazard assessment of millet drought disaster in Northern China under climate change

Yang, Yueting; Li, Kaiwei; Wei, Sicheng; Guga, Suri; Zhang, Jiquan; Wang, Chunyi

doi:10.1016/j.agwat.2022.107849

Cited by 15 publications

(3 citation statements)

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“…The extent of the impact of drought is closely related to the drought intensity grade expressed by the drought index and is also affected by the frequency and duration of drought [41,42]. There have been many studies focusing on the temporal trends and spatial distribution of drought [43][44][45][46], drought disaster prevention and prediction [47][48][49], or on analyzing the impact of drought disasters on the TGO based on crop models or statistical models [50][51][52]. However, there has been a lack of analysis on the changes in the GY and GPA, as well as on the impact of drought on the rate of change in the TGO.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations in Drought and Its Impact on Agriculture in China

Liu,

Zhang

2024

Water

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Drought, as a widespread natural calamity, leads to the most severe agricultural losses among all such disasters. Alterations in the yield of major global agricultural products are pivotal factors influencing food prices, food security, and land use decisions. China’s rapidly expanding demand for sustenance will persist over the forthcoming decades, emphasizing the critical need for an accurate assessment of drought’s impact on food production. Consequently, we conducted a comprehensive evaluation of the drought risk in China and its repercussions on agricultural output. Additionally, we delved into the underlying factors driving changes in yield for three primary grain crops (wheat, corn, and rice), which hold particular relevance for shaping effective strategies to mitigate future drought challenges. The findings divulge that both the number of drought months (DM) and the drought magnitude index (DMI) have displayed an upward trajectory over 60 years with a correlation coefficient of 0.96. The overall severity of meteorological drought has escalated across China, and it is particularly evident in regions such as the southwest and central parts of the Huang-Huai-Hai region, the northwestern middle region, and the Xinjiang region. Conversely, there has been some relief from drought conditions in southern China and the Yangtze River Delta. Shifts in the total grain output (TGO) during this period were compared: it underwent three stages, namely “fluctuating growth” (1961–1999), then a “sharp decline” (2000–2003), followed by “stable growth” (2004–2018). Similarly, changes in the grain planting area (GPA) experienced two stages, “continuous reduction” (1961–2003) succeeded by “stable growth” (2004–2018), while maintaining an upward trend for grain yield per unit area (GY) throughout. Furthermore, it was revealed that the drought grade serves as a significant constraint on continuous expansion within China’s grain output—where the drought damage rate’s influence on the TGO outweighs that from the GY. Our research outcomes play an instrumental role in deepening our comprehension regarding how drought impacts agricultural production within China while furnishing the scientific groundwork to devise efficacious policies addressing these challenges.

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Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations in Drought and Its Impact on Agriculture in China

Liu,

Zhang

2024

Water

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“…More scholars are realizing the importance of studying drought fighting. Due to the large time span of drought problems and the complex influencing factors, scholars have formulated effective strategies for the agricultural drought in the studied areas (Yang et al ., 2022; Zhang et al ., 2022; Liu et al ., 2021). It is necessary to apply a reasonable decision-making method to build a comprehensive decision model.…”

Section: Introductionmentioning

confidence: 99%

Two-stage grey cloud clustering model under the panel data and its application

Luo

Zhai

2023

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PurposeThe purpose of this paper is to establish a two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem with three-parameter interval grey number to evaluation of agricultural drought resistance grade of 18 cities in Henan Province.Design/methodology/approachThe clustering process is divided into two stages. In the first stage: Combining variance and time degree, the time weight optimization model is established. Applying the prospect theory, the index weight optimization model is established. Then, with the help of grey possibility function, the first stage of grey cloud clustering evaluation is carried out. In the second stage: the weight vector group of kernel clustering is constructed, and the grey class of the object is determined. A two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem is proposed.FindingsThis paper indicates that 18 cities in Henan Province are divided into four categories. The drought capacity in Henan province is high in the east and low in the west, high in the south and low in the north and the central region is relatively stable. The drought is greatly affected by natural factors. And the rationality and validity of this model is illustrated by comparing with other methods.Practical implicationsThis paper provides a practical method for drought resistance assessment, and provides theoretical support for farmers to grasp the drought information timely and improve the drought resistance ability.Originality/valueThe model in this paper solves the situation of ambiguity and randomness to some extent with the help of grey cloud possibility function. Moreover, the time weight of time degree and variance are used to reduce the volatility and the degree of subjective empowerment. Considering the risk attitude of the decision makers, the prospect theory is applied to make the index weight more objective. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

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“…Drought indices are calculated using databases of precipitation, soil moisture, crop yield, flows, and surface water and groundwater levels. Previous studies used Standardized Precipitation Evapotranspiration Index SPEI [ 10 ], Palmer Drought Severity Index PDSI [ 11 ], Modified Palmer Drought Severity Index MPDSI [ 12 ], Standardized Precipitation Requirement Index SPRI [ 13 ], Surface Water Supply Index SWSI [ 14 ], Standardized Soil Moisture Index SSMI [ 15 ], Standardized Runoff Index SRI [ 16 ], Standardized Water Supply and Demand Index SWSDI [ 17 ], Normalized Difference Vegetation Index NDVI [ 18 ] and Streamflow Drought Index SDI [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

Wicher-Dysarz

Dysarz

Jaskuła

2022

IJERPH

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The primary aim of this work is to assess the accuracy of the methods for spatial interpolation applied for the reconstruction of the spatial distribution of the Standardized Precipitation Index (SPI). The one-month version called SPI-1 is chosen for this purpose due to the known greatest variability of this index in comparison with its other versions. The analysis has been made for the territory of the entire country of Poland. At the same time the uncertainty related to the application of such computational procedures is determined based on qualitative and quantitative measures. The public data of two kinds are applied: (1) measurements of precipitation and (2) the locations of the meteorological stations in Poland. The analysis has been made for the period 1990–2020. However, all available observations since 1950 have been implemented. The number of available meteorological stations has decreased over the analyzed period. In January 1990 there were over one thousand stations making observations. In the end of the period of the study, the number of stations was below six hundred. Obviously, the temporal scarcity of data had an impact on the obtained results. The main tools applied were ArcGIS supported with Python scripting, including generally used modules and procedures dedicated to geoprocessing. Such an approach appeared crucial for the effective processing of the large number of data available. It also guaranteed the accuracy of the produced results and brought about drought maps based on SPI-1. The methods tested included: Inverse Distance Weighted, Natural Neighbor, Linear, Kriging, and Spline. The presented results prove that all the procedures are inaccurate and uncertain, but some of them provide satisfactory results. The worst method seems to be the interpolation based on Spline functions. The practical aspects related to the implementation of the methods led to removal of the Linear and Kriging interpolations from further use. Hence, Inverse Distance Weighted, as well as Natural Neighbor, seem to be well suited for this problem.

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Spatial-temporal distribution characteristics and hazard assessment of millet drought disaster in Northern China under climate change

Cited by 15 publications

References 45 publications

Temporal and Spatial Variations in Drought and Its Impact on Agriculture in China

Temporal and Spatial Variations in Drought and Its Impact on Agriculture in China

Two-stage grey cloud clustering model under the panel data and its application

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

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