How well do meteorological indicators represent agricultural and forest drought across Europe?

Abstract

Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs' representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

Do Satellite Surface Soil Moisture Observations Better Retain Information About Crop‐Yield Variability in Drought Conditions?

“…Previous works on agricultural drought analysis also selected SPI‐3 (i.e., Bachmair et al, ; Lim et al, ; Sigdel & Ikeda, ), supporting our decision. For example, Bachmair et al () performed a correlation analysis between SPI at different time scales and remote sensing‐based vegetation indices for Europe. They showed that short accumulation periods of SPI (in particular three months) relate well to crop vegetation stress in most cases.…”

Drought Sensitiveness on Forest Growth in Peninsular Spain and the Balearic Islands

“…They also found that a higher percentage of forests from different biomes across the world correlated better with the SPEI than with the SPI. For its part, Bachmair et al [50] assessed the relationship between meteorological indicators and forests in Europe, suggesting slight differences between the SPEI and SPI. Nonetheless, they noted that-at shorter time scales-the SPEI shows a stronger response in the forests of southern Europe, a result that is in agreement with the findings of our study.…”

“…To accomplish this task, we linked seven widely used drought indices: Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), Standardized Palmer Drought Severity Index (SPDSI), and four Palmer-related drought indices (Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Palmer Z-Index (Z), and Palmer Modified Drought Index (PMDI)) with climatic, NDVI, and dendrochronological data for the IP and the Balearic Islands for the period 1981-2015. As a result we should be able to assess the validity of these drought indices to assessing and monitor the impacts of drought on forest growth and vitality [48][49][50].…”

Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change

“…Drought can be characterized based on impact information from socioeconomic sectors (Porter & Semenov, ), natural ecosystems (Bachmair, Tanguy, Hannaford, & Stahl, ; Camarero, Gazol, Sangüesa‐Barreda, Oliva, & Vicente‐Serrano, ), crops (Peña‐Gallardo et al, ; H. Wang et al, ), and hydrological systems (Barker, Hannaford, Chiverton, & Svensson, ; Peña‐Gallardo et al, ). Nevertheless, drought is more frequently quantified using meteorological variables that can be observed or modeled (Mukherjee et al, ).…”