The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China

https://doi.org/10.1016/j.agwat.2020.106479Get rights and content

Highlights

  • A long series of highly accurate soil moisture data was combined.

  • SWDI was improved and extended to the monthly scaled drought index.

  • Temporal features of SWDI were characterized in four geographical regions.

  • The relationship among drought indexes (SPI, SWDI and VCI) was explored.

Abstract

Drought monitoring and assessment are of great importance due to the costly damage caused by drought. Datasets, drought indexes and drought relationships are three critical areas of drought research. Satellite-retrieved soil moisture (SM) products derived from the European Space Agency Climate Change Initiative (ESA CCI) show application potential in drought monitoring. However, the products are missing certain data in some areas. The model-assimilated SM product derived from the Global Land Data Assimilation System (GLDAS) was used to supplement these missing data. The main goals of this paper are to characterize agricultural drought after the utility and applicability of the combined SM product and the monthly scaled soil water deficit index (SWDI) have been evaluated and to investigate the relationships among meteorological, agricultural and vegetation droughts. First, we provided a long series of highly accurate SM products through simple calculations. The drought index, SWDI, was extended to a monthly scale for long-term drought analysis by using the combined SM product. The probability of detection (POD) between the SWDI and in situ drought records performed fairly well. Half of the 566 stations had PODs higher than 0.9, and one-third of these stations had POD values equal to 1. Through correlation analysis and grey incidence analysis (GIA) between the standardized precipitation index (SPI) and SWDI, we found that the propagation time from meteorological drought to agricultural drought was shorter under drier conditions than wetter conditions, and at the regional scale, the response time ranged from 1 month to 2.5 months. Correlation analysis between the SWDI and vegetation condition index (VCI) indicated that there was no delay effect from agricultural to vegetation drought on a monthly scale in most parts of China except in several provinces distributed in the South; additionally, there was a significant time lag in forests, while grassland and agriculture were more inclined to have no time lag or the response time was less than 1 month.

Introduction

Drought, as the most damaging climate-related hazard, has a great impact on hydrology, meteorology, ecology and society (Wilhite and Glantz, 1985; Vicca et al., 2016). Drought may have a wide range of influences on human lives and result in large economic losses (Woodhouse and Overpeck, 1998; Mishra and Singh, 2010). China, including four geographical regions (the North, the South, the Northwest and the Qinghai-Tibet Plateau, shown in Fig. 1), has experienced frequent droughts over decades, and these drought events have caused serious losses (Zhao et al., 2017). For example, the 2011 summer drought over the middle and lower reaches of the Yangtze River basin influenced 30 million people and caused an economic loss of approximately 15 billion RMB (Yuan et al., 2015). The droughts that occurred in northern China in 2009 and in southwestern China from 2009 to 2010 affected approximately 300 million acres of farmland, and in 2009, the economic loss caused by droughts exceeded 100 billion RMB (National Bureau of Statistics of the People’s Republic of China, 2010). In China, despite the growing importance of industry, agriculture has a central role in ensuring the food security and welfare of the country’s population of 1.3 billion people (Piao et al., 2010). However, droughts affect approximately one-sixth of China’s cultivated land area each year and reduce the production of grain (Li et al., 2010). Therefore, understanding and assessing droughts, especially agricultural droughts, are of primary importance.

Drought is usually classified into four categories: meteorological drought, hydrological drought, agricultural drought and socio-economic drought (Wilhite and Glantz, 1985; Mishra and Singh, 2010). Many drought indexes have been proposed for these four drought types. For example, the PDSI (Palmer, 1965), SPI (Mckee et al., 1993) and SPEI (Vicente-Serrano et al., 2010) have been used for meteorological drought; the SRI (Shukla and Wood, 2008) and SWSI (Shafer and Dezman, 1982) have been used for hydrological drought; the CMI (Palmer, 1968) and SMI (Hunt et al., 2009; Kędzior and Zawadzki, 2017) have been for agricultural drought; and the improved Multivariate Standardized Reliability and Resilience Index (IMSRRI) (Guo et al., 2019) has been for socio-economic drought. Various drought indexes often reflect a single drought type. However, the causes of drought formation are extremely complex, and the physical factors that contribute to droughts are still poorly understood (van Dijk et al., 2013). The formation mechanisms of different drought types are different. Therefore, analysing the relationship among different types of drought is of great significance for drought warning (Hao et al., 2018) and disaster control. Wu et al. (2017) established a nonlinear function model to analyse the response of hydrological drought to meteorological drought and found that there was a clear nonlinear relationship between these drought types. Han et al. (2019) used cross-wavelet transformation to analyse the relationship between meteorological drought and groundwater drought in the Pearl River basin. It was found that there was an 8-month delayed response between these two drought types, and the response in spring and summer was shorter than that in autumn and winter. Xu et al. (2019) investigated the propagation from meteorological drought to hydrological drought by analysing the correlation between the SPI and SRI under the impact of human activities. However, the loss caused by drought is usually reflected in agricultural loss. Therefore, analysis of the relationship between agricultural drought and other types of drought is necessary to reduce the losses caused by drought.

The VCI (Kogan, 1990) is a drought index that is used to estimate the impact of drought on vegetation conditions and was proven to be useful in analysing the temporal and spatial evolution of regional drought as well as in qualitatively estimating crop production (Liu and Kogan, 1996). Therefore, the VCI can reflect the severity of agricultural drought through the condition of the vegetation to some extent. In addition, Martínez-Fernández et al. (2015) analysed a new approach to define the SWDI by using a satellite-based soil water series for agricultural drought monitoring. Soon thereafter, the utility of the satellite-based weekly scaled SWDI in analysing agricultural drought was evaluated, and the weekly SWDI was found to accurately track agricultural drought (Martínez-Fernández et al., 2016; Mishra et al., 2017; Bai et al., 2018). However, due to the short length of the satellite-based data (Soil Moisture Active Passive, SMAP) used in these studies, the SWDI was calculated from 2015. Therefore, it is necessary to extend the time length of satellite data for long-term agricultural drought analysis.

Traditional drought monitoring and assessments are usually based on in situ measurements, which require considerable resources and time and cannot meet the spatial resolution demands required for monitoring drought. Therefore, satellite-based data or assimilated data with larger spatial scales have been widely used in recent years (Luo and Wood, 2007; AghaKouchak et al., 2015; Nicolai-Shaw et al., 2017; Yuan et al., 2018; Du et al., 2019; Liu et al., 2019). Satellite-based data or assimilated data have the advantages of covering an entire region. In situ data are usually used to validate and improve the accuracy of these satellite-based or assimilated data.

The main goals of this study are to evaluate the utility and applicability of the combined SM product and the monthly scaled SWDI for monitoring agricultural drought characteristics in China and to investigate the relationships among meteorological, agricultural and vegetation droughts. The specific objectives are to (1) provide a long series of highly accurate SM data through simple calculation using combined ESA CCI SM products and GLDAS SM products; (2) determine whether the combined SM product can be used to accurately monitor agricultural drought by assessing the potential applicability of the monthly scaled SWDI in analysing long-term agricultural drought in China; (3) characterize agricultural drought in four geographical regions in different seasons; and (4) investigate the relationships among meteorological drought, agricultural drought and vegetation drought by comparing the relationships among the SPI, SWDI and VCI.

Section snippets

Ground observation datasets

The in situ precipitation data were derived from the China Meteorological Administration (CMA, http://data.cma.cn/). The dataset includes monthly precipitation from 1951 to 2019 from 613 rain gauges in China. In this study, we chose the data from 1998 to 2017 from 520 rain gauges, which had the best data quality, to calculate the SPI at different time scales (SPI-1 to SPI-12).

The in situ SM data were also derived from the CMA (http://data.cma.cn/). The CMA has 10-day SM data from 1998 to 2013

Soil water deficit index

The SWDI is considered to be an effective agricultural drought index that captures drought conditions by quantifying the associated SM deficit. It can be calculated through land surface SM and soil water parameters as shown in Eq. 1:SWDI=θ-0.8×θFCθAWC×10where θ represents the land surface SM (m3/m3); θFC and θAWC represent the SM (m3/m3) at field capacity (FC) and at available water capacity (AWC), respectively; and 0.8 is the reduction factor for θFC, which represents that drought occurs when θ

Evaluation of the combined SM data

The SM data were derived from 133 selected stations in the growing season (May, June, July and September) and used to evaluate the combined SM product. Some of the SM data in August and other non-growing-season months were missing and could not be used in the evaluation. The Pearson CC (R), RMSE and BIA values were calculated in May, June, July and September at each station from 2000 to 2013 (Table 3). The in situ and combined SM data series at four representative stations with different RMSEs

Advantages and disadvantages of combined SM products

In situ measurement is usually the most accurate method for drought monitoring. However, in situ datasets are limited by their spatial resolution, especially in remote areas. Therefore, this method is often used to validate the accuracy of other drought indexes or to improve the spatial resolution of other data (Zhao et al., 2017). The combined SM product has the advantages of 1) a long-term series, 2) acceptable accuracy and utility, and 3) covering the entire region, solving the problem of

Conclusions

The accuracy of drought indexes and the relationship among multiple droughts are two crucial areas of drought research. In this paper, the SM product, which was combined from the ESA CCI SM product and the GLDAS SM product, shows great application potential in analysing agricultural drought. The monthly scaled SWDI calculated using the combined SM product can accurately capture features of agricultural drought in most parts of China through comparison with in situ drought records. The poor

Declaration of Competing Interest

The authors report no declarations of interest.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51479130) and State Key Laboratory of Hydraulic Engineering Simulation and Safety Foundation (No. HESS1405).

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