Assessment of Agricultural Drought Using Soil Water Deficit Index Based on ERA5-Land Soil Moisture Data in Four Southern Provinces of China

Abstract

It is important to accurately assess agricultural drought because of its harmful impacts on the ecosystem and economy. Soil moisture reanalysis datasets provide an important way to assess agricultural drought. In this study, the ERA5-Land surface and subsurface soil moisture was used to estimate the soil water deficit index (SWDI) in four southern provinces of China. The ERA5-Land dataset was evaluated with in situ soil moisture observations from agrometeorological stations. Agricultural drought was assessed for three climate zones at a weekly scale from 2017 to 2019 and was compared with the atmospheric water deficit (AWD). It was found that both ERA5-Land soil moisture and the derived SWDI have relatively high accuracy, and the wet bias in the ERA5-Land dataset can be reduced by the calculation of the SWDI. The subsurface layer has better performance than the surface layer in drought monitoring, though they are highly correlated. Different climate zones demonstrate different drought periods and drought severity, and the temperate climate zone with no dry season has less droughts. The most severe droughts with the largest spatial extent occurred in the early winter, especially in 2019. Differences in the SWDI and AWD are mainly shown in southwestern Yunnan. The results of this study have important reference values for drought risk management.

Understanding the distribution and drivers of PM2.5 concentrations in the Yangtze River Delta from 2015 to 2020 using Random Forest Regression

“…ERA5-Land data is a reanalysis dataset produced by the ECMWF ERA5 reanalysis model, which combines observations from around the world into a globally complete and consistent dataset using the laws of physics. This dataset includes 50 dynamic monthly indicators representing temperature, wind speed, precipitation, and vegetation since 1981, with a spatial resolution of 0.1° × 0.1° (about 9 × 9 km), which describes the past and the present climate conditions (Zhang et al, 2021 ). Detailed information, code, and summary statistics of all the variables are given in Tables S1 and S2 , respectively.…”

Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series

“…Furthermore, air temperature, humidity and pressure are corrected to account for the altitude differences between ERA5 and ERA5-Land grids 107 . Since its release, ERA5-Land has been extensively compared to similar datasets or in situ data [108][109][110] while other studies used ERA5-Land as hydrometeorological reference data for bias-correcting seasonal forecasts 111 , as driving data for modeling photovoltaic power 112 , or for deriving agricultural drought indicators 113,114 . Within a similar context, Zandler et al (2020) 115 compared the performance of ERA5-Land for assessing NDVI anomalies across peripheral conservation areas of Central Asia and concluded that such reanalysis-based datasets outperform gauge-or satellite-based products and their combinations as they are highly variable and may not be applicable in the analyzed regions.…”

Applicability evaluation of multiple sets of soil moisture data on the tibetan plateau

“…LAND is a re-analysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. LAND has been produced by replaying the land component of the ERA5 climate re-analysis (Nefabas et al, 2021;Ruqing Zhang et al, 2021). It combines the model data with observations from across the world into a globally complete and consistent dataset using the land surface model, which is based on the laws of physics and mathematical formulas.…”

“…Over the past few decades, researchers have developed many soil moisture products based on satellites or sensors, such as those from the Advanced SCATterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) satellite (Gloersen, 1981;Wagner et al, 1999;Paloscia et al, 2001;Bindlish et al, 2003;Kawanishi et al, 2003;Gaiser et al, 2004;Bartalis et al, 2007;Naeimi et al, 2009;Wagner et al, 2012;Al-Yaari et al, 2014;Zheng et al, 2018c;Liu et al, 2019;Zhu et al, 2019). In general, passive microwave soil moisture products have a greater temporal resolution and are less influenced by surface roughness disturbances, while active microwave products are more sensitive to soil moisture (Jiang et al, 2017;Li et al, 2018;Ruqing Zhang et al, 2021). In order to combine the advantages of both active and passive microwave products, the European Space Agency's soil moisture climate change initiative (ESA CCI soil moisture) uses a fusion algorithm to integrate soil moisture retrieved from various satellites into a soil moisture climate dataset (Alexander et al, 2019); ESA CCI soil moisture product v4.7, released in 2020, was used in this article.…”