Impact of the North Sea–Caspian pattern on meteorological drought and vegetation response over diverging environmental systems in western Eurasia

Abstract

Emerging drought stress on vegetation over western Eurasia is linked to varying teleconnection patterns. The North Sea-Caspian Pattern (NCP) is a relatively less studied Eurasian teleconnection pattern, which has a role on drought conditions and the consequence of changing conditions on vegetation. Between 1981 and 2015, we found that the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) have different trend patterns over various parts of western Eurasia. Specifically, the vegetation greenness is linked with wetter conditions over Scandinavia, and vegetation cover decreases over a drying central Asia. However, western Russia and Franceare paradoxically becoming greener under drier conditions.Using the Budyko framework, such paradoxical patterns are found in energy-limited environmental systems, where vegetation growth is primarily promoted by warmer temperatures. While most studies focused on the impacts of the North Atlantic Oscillation (NAO), we test whether the NCP explains better the variability of meteorological drought and vegetation response over western Eurasia. We hypothesised that the positive phases of the NCP are correlated to high pressure anomalies over the North Sea, which can be associated with weakening onshore moisture advection, leading to warmer and dryness conditions. These conditions are driving vegetation greening, as

Ecohydrological interactions during drought

“…The papers span a range of water‐dependent environments: aquatic (Kupferberg et al, 2022; Sabatino et al, 2022; Saltarelli et al, 2022; White et al, 2022), floodplain (Nguyen et al, 2022), terrestrial (Belmonte et al, 2022; Castellanos et al, 2022; Gao, Wang, Li, et al, 2022; Gao, Wang, Wang, et al, 2022; He et al, 2022; Lindh et al, 2022; Parolari & Paschalis, 2022; Xu et al, 2022), wetland and pool interface (Cartwright et al, 2022; Mattei et al, 2022), and regulated, perennial, and intermittent (Di Sabatino et al, 2022; Kupferberg et al, 2022; Saltarelli et al, 2022; Sarremejane et al, 2022; Wegscheider et al, 2022) systems across both modelled‐, field‐, and lab‐based studies to address process research, cascading effects, and novel methodological approaches in ecohydrological research.…”

“…The final paper of this special issue tackles drought at continental scale. He et al (2022) studied meteorological drought and subsequent response of vegetation across different ecozones in western Eurasia. He et al (2022) relied on the SPI for drought classification and the Normalized Difference Vegetation Index (NDVI) to evaluate vegetation response.…”

“…He et al (2022) studied meteorological drought and subsequent response of vegetation across different ecozones in western Eurasia. He et al (2022) relied on the SPI for drought classification and the Normalized Difference Vegetation Index (NDVI) to evaluate vegetation response. The trends between SPI and NDVI were different for different regions across the study area, most notably the authors found a greening of vegetation with increasing dryness in some parts of the study region as energy supply increased and water supply was not limited.…”

“…Based on the contributions to this special issue, it becomes clear that we need to work across scales to upscale from small‐scale ecohydrological drought process understanding (Nguyen et al, 2022) to large‐scale up to continental ecohydrological drought patterns (He et al, 2022). What is also urgently required in the future are meta and comparison studies (e.g., White et al, 2022) across different water dependent terrestrial and aquatic ecosystems that allow generalization and conceptualization of process based results, this includes uncertainty assessment and providing projections.…”

“…To achieve this, the ecohydrological community needs to leverage new measurement approaches, generate new data sets, and rely on novel modelling approaches (e.g., Wankmüller & Carminati, 2022) as well as taking advantage of longer term historical data to put ecohydrological drought in context (He et al, 2022). This includes—among others—relying on isotopic methods on specific ecohydrological contexts (e.g., Mattei et al, 2022), more widespread use of sensor technologies (Belmonte et al, 2022; Northup et al, 2022), novel remote sensing opportunities (Dugdale et al, 2022) and existing remotely sensed data (Mokhtar et al, 2022), and newly applied or developed statistical methods (Parolari & Paschalis, 2022), or machine learning/AI tools for processes that we currently do not incorporate well in physically‐based models of ecohydrological processes (Razavi et al, 2022).…”