Analysis of Drought Characteristics and Its Effects on Crop Yield in Xinjiang in Recent 60 Years

With global warming, the frequency of drought in China is gradually increasing. The study of drought characteristics and its impact on crop yield is of great significance to ecological construction and food security. Based on the SPEI index in Xinjiang from 1961 to 2020, the changing characteristics of drought and their responses to meteorological yield of three kinds of crops (cotton, wheat and corn) were analyzed. The results revealed that the SPEI in Xinjiang exhibited a decreasing trend. Drought occurred less during 1961–1996 and mainly occurred during 1997–2020. The annual variation trend of SPEI decreased regionally, and the arid trend increased from northwest to southeast. The seasonal variation in SPEI displayed an increasing trend only in winter, but a decreasing trend in spring, summer and autumn. The decreasing trend of the SPEI index in spring accounted for 90.91%, mainly distributed in the central and eastern part of Xinjiang; the decreasing trend in summer accounted for 81.82%, mainly located in the eastern part of Xinjiang; the decreasing trend in autumn accounted for 84.85%, mainly located in the south and central and eastern part of Xinjiang; the decreasing trend in winter accounted for only 33.33%, mainly located in the southwest, central and eastern part of Xinjiang; the per unit area yield of the three kinds of crops showed a significant increasing trend (p < 0.01). The meteorological yield of cotton showed an increasing trend, while that of wheat and corn showed a decreasing trend. Correlation analysis of climatic yield and SPEI, and regression analysis of standardized climatic yield residuals and SPEI in different seasons revealed that drought in spring and autumn had the greatest effect on the meteorological yield of cotton, while drought in spring and summer had the greatest effect on the meteorological yield of wheat and corn.

Framework for Monitoring the Spatiotemporal Distribution and Clustering of Drought Characteristics in Hunan Province

Drought is a widespread phenomenon in the context of global climate change. Owing to the geographical location of Hunan Province in the middle reaches of Yangtze River and the abundance of forests area in this region with a large population, there is a need to focus on the impacts of drought for devising policies. The spatiotemporal distribution scheme of a given area must be determined to plan water management and protect ecosystems effectively. This study proposes a framework for exploring the spatiotemporal distribution model of drought using comprehensive surveys of historical meteorological stations, which consists of two parts, namely the characteristics of drought extraction in the spatiotemporal distribution and drought models discovered by the clustering method. Firstly, we utilized the run theory to extract drought characteristics, such as drought duration, drought severity, and drought intensity. Secondly, the K-means clustering method was adopted to explore the distribution patterns on the basis of the drought characteristics. Lastly, the method was applied to Hunan Province. Results show that historical drought conditions can be monitored with their characteristics of spatiotemporal variability. Three drought distribution clusters exist in this region. Cluster 1 in western Hunan tends to be a long-term, low-intensity drought, cluster 2 in the southern part tends to be a short-term, high-intensity drought, and cluster 3 in the central part is prone to severe drought. The proposed framework is flexible as it allows parameters to be adjusted and extraction methods to achieve reasonable results for a given area.

Spatio-temporal evaluation of drought characteristics in the Dhasan basin

ABSTRACT. The meteorological drought characteristics including onset, departure, duration, severity as well as intensity have been evaluated mainly for monsoon season at all the three rain gauge stations located in Dhasan basin. The Standardized Precipitation Index (SPI) has been applied to understand and quantify the drought severity on multiple time scale (1, 3, 6, 12 and 24 months). The spatiotemporal analysis of drought based on 3-month SPI has also carried out to identify drought years and the regions of the study area which is under the grip of continuous drought events. Based on the 3-month SPI, major drought events have been identified. The maximum drought severity of -11.17 occurred during November 1991 to August 1992 having the longest duration of 10 months, in the area under Sagar rain gauging station. The onset of most of the drought events in the basin take place during the beginning of Kharif season and terminate by the end of August or September, so affect the agricultural crops severely. The spatial variation indicates that during June 2002, about 55.74% of basin area was experiencing severe drought conditions, followed by 35.29% area under moderate drought condition and only 8.97% area faced mild drought conditions. The inter-relationship among the drought duration, number of drought events, drought severity and time scale have been studied.

Evaluation and Forecasting Meteorological Drought, Case Study: Kohgilooyeh and Boyer Ahmad

The SPI is the most widely used drought index to provide an acceptable estimation of drought characteristics. The objective of this study was to compare different threshold levels effect on derived drought characteristics, assessment of the spatial variation of meteorological drought properties as well as drought frequency, duration, and value in Kohgilooyeh and Boyer Ahmad Province, Iran, using SPI for 1, 3, 6, 12, 24 and 48 months lead-times, and finally SPI forecasting using Artificial Neural Networks (ANNs). For the first threshold level (scenario), drought properties are extracted based on the standard level of zero, and for the second one, -1 is considered. Results showed that the frequency of drought and wet periods decreased from SPI-1 to 48 for both scenarios in all stations. Max drought duration of stations had an increasing trend from SPI-1 to 48. The average duration of dry periods changed as a function of the time scales; it increased from SPI-1 to 48. Spatial variation of the drought average duration was considerable for long-term drought. Max SPI value did not follow any spatial variation, as it was constant for all lead times in all stations. Average SPI values had a decreasing trend from SPI-1 to 9 but increased from SPI-9 to 48 in all stations. Max average of SPI value observed in short-term drought and min value in medium-term. SPI value general trend was similar in both scenarios, therefore drought threshold level did not affect the results. The third objective was to develop neural network models for drought forecasting. Different architectures are applied to find the best models to forecast SPI over various lead times. The best forecasting results for SPI-3 and 6, obtained from the Quasi-Newton training algorithm, when for SPI-1, 9, 12, 24, and 48, Levenberg-Marquardt was the best. There was an increasing trend in performance measure R2 from SPI-1 to 48 and a decreasing trend in Root Mean Square Error (RMSE). The best input lead-time for SPI-1 to 48 decreased from 11 to 1, the number of hidden layers decreased, but there was no significant trend in hidden neurons. Drought properties could be considered in water resources management to supply water for various demands.