Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg

Adaptation to an increasingly dry regional climate requires spatially explicit information about current and future risks. Existing drought risk studies often rely on expert-weighted composite indicators, while empirical evidence on impact-relevant factors is still scarce. The aim of this study is t...

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Bibliographic Details
Published in:Natural hazards and earth system sciences 2024-11, Vol.24 (12), p.4237-4265
Main Authors: Brill, Fabio, Alencar, Pedro Henrique Lima, Zhang, Huihui, Boeing, Friedrich, Hüttel, Silke, Lakes, Tobia
Format: Article
Language:English
Subjects:
Agricultural drought
Agricultural production
Agriculture
Artificial intelligence
Climate change
Crops
Drought
Droughts
Empirical analysis
Environmental impact
Environmental risk
Extreme values
Indicators
Investigations
Land surface temperature
Mapping
Normalized difference vegetative index
Precipitation
Regional climates
Remote sensing
Soil quality
Surface temperature
Vegetation index
Weather hazards
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Summary:Adaptation to an increasingly dry regional climate requires spatially explicit information about current and future risks. Existing drought risk studies often rely on expert-weighted composite indicators, while empirical evidence on impact-relevant factors is still scarce. The aim of this study is to investigate to what extent hazard and vulnerability indicators can explain observed agricultural drought impacts via data-driven methods. We focus on the German federal state of Brandenburg, 2013–2022, including several consecutive drought years. As impact indicators we use thermal–spectral anomalies (land surface temperature (LST) and the normalized difference vegetation index (NDVI)) on the field level, and empirical yield gaps from reported statistics on the county level. Empirical associations to the impact indicators on both spatial levels are compared. Extreme gradient boosting (XGBoost) models explain up to about 60 % of the variance in the yield gap data (best R2 = 0.62). Model performance is more stable for the drought years and when using all crops for training rather than individual crops. Meteorological drought in June and soil quality are selected as the strongest impact-relevant factors. Rye is empirically found to be less vulnerable to drought than wheat, even on poorer soils. LST / NDVI only weakly relates to our empirical yield gaps. We recommend comparing different impact indicators on multiple scales to proceed with the development of empirically grounded risk maps.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-24-4237-2024