ALBERT

Description: CONTEXT: Crop rotations considerably affect the hydrological regime of river basins used for agricultural production and are key for sustainable land and water management. However, eco-hydrological modelling usually neglects crop rotations.OBJECTIVE: In this paper, we present a Crop Generator to reproduce the stochastic characteristics of crop rotations at regional scale. METHODS: The Crop Generator emulates farmers’ decision making on crop rotation planning. We combined the Crop Generator with the eco-hydrological Soil and Water Integrated Model to show the hydrological relevance of considering crop rotations in a study region in central Europe including the Elbe River basin. RESULTS AND CONCLUSIONS: A spatial validation showed that the Crop Generator reproduced the given cropping patterns well. Higher daily discharge, runoff and groundwater seepage and lower evapotranspiration were simulated based on crop rotations compared with a simplified representation of cropping patterns. The Crop Generator is a solution to simulate more realistic cropping patterns in large-scale eco-hydrological modelling. It closes the gap between aggregated agricultural statistics and the requirement of representing crop rotations in a realistic way in eco-hydrological modelling.SIGNIFICANCE: The Crop Generator enables smart projections of future adjustments in crop rotations in view of climate and socio-economic changes as a basis for improving eco-hydrological projections and designing more sustainable agricultural systems.

Description: The 2018–2019 Central European drought was probably the most extreme in Germany since the early 16th century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km² including the cities of Berlin and Hamburg and contributing about 18 % to the German GDP. We employ meteorological, hydrological, and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40 % for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.

Description: ABSOLUT v1.2 is an adaptive algorithm that uses correlations between time-aggregated weather variables and crop yields for yield prediction. In contrast to conventional regression-based yield prediction methods, a very broad range of possible input features and their combinations are exhaustively tested for maximum explanatory power. Weather variables such as temperature, precipitation, and sunshine duration are aggregated over different seasonal time periods preceding the harvest to 45 potential input features per original variable. In a first step, this large set of features is reduced to those aggregates very probably holding explanatory power for observed yields. The second, computationally demanding step evaluates predictions for all districts with all of their possible combinations. Step three selects those combinations of weather features that showed the highest predictive power across districts. Finally, the district-specific best performing regressions among these are used for actual prediction, and the results are spatially aggregated. To evaluate the new approach, ABSOLUT v1.2 is applied to predict the yields of silage maize, winter wheat, and other major crops in Germany based on two decades of data from about 300 districts. It turned out to be absolutely crucial to not only make out-of-sample predictions (solely based on data excluding the target year to predict) but to also consequently separate training and testing years in the process of feature selection. Otherwise, the prediction accuracy would be over-estimated by far. The question arises whether performances claimed for other statistical modelling examples are often upward-biased through input variable selection disregarding the out-of-sample principle.

Description: Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness.

Description: Diverse agricultural land uses are a typical feature of multifunctional landscapes. The uncertain change in the drivers of global land use, such as climate, market and policy technology and demography, challenges the long-term management of agricultural diversification. As these global drivers also affect smaller scales, it is important to capture the traits of regionally specific farm activities to facilitate adaptation to change. By downscaling European shared socioeconomic pathways (SSPs) for agricultural and food systems, combined with representative concentration pathways (RCP) to regionally specific, alternative socioeconomic and climate scenarios, the present study explores the major impacts of the drivers of global land use on regional agriculture by simulating farm-level decisions and identifies the socio-ecological implications for promoting diverse agricultural landscapes in 2050. A hilly orchard region in northern Switzerland was chosen as a case study to represent the multifunctional nature of Swiss agriculture. Results show that the different regionalised pathways lead to contrasting impacts on orchard meadows, production levels and biodiversity. Increased financial support for ecological measures, adequate farm labour supplies for more labour-intensive farming and consumer preferences that favour local farm produce can offset the negative impacts of climate change and commodity prices and contribute to agricultural diversification and farmland biodiversity. However, these conditions also caused a significant decline in farm production levels. This study suggests that considering a broader set of land use drivers beyond direct payments, while acknowledging potential trade-offs and diverse impacts across different farm types, is required to effectively manage and sustain diversified agricultural landscapes in the long run.

Description: Many studies have explored farmers’ perspectives on biodiversity and ecosystem services, but fewer qualitative and cross-country comparisons exist. We develop a socio-ecological system to analyse agricultural landscape services, biodiversity, and drivers that have affected these services in recent decades. Via a systematic stakeholder mapping and 49 semi-structured interviews, we identify stakeholder perceptions of this system. We compare the perceptions across four regional case studies (Austria, Estonia, Germany, Switzerland), and two stakeholder groups (land managers and administrators). The case studies share certain commonalities in perceptions (e.g., provisioning and regulating services discussed in all of them) but also show differences (e.g., changes in biodiversity and landscape services more often perceived in the Swiss and German cases, but less in the Austrian and Estonian case studies). Across all case studies, typical land use change can be attributed to multiple drivers of various strengths, with climate change being the most often perceived driver directly affecting landscape services, followed by policies and market-based drivers, which affect services and biodiversity indirectly via land use. Compared to the administrators (e.g., decision-makers, scientists), the managers (e.g., farmers, NGOs) discuss more often the drivers, like various biodiversity and landscape service categories, as well as climate change, markets, and technologies. However, the administrators focus more on cultural services, policies as drivers, and consider more often links between drivers and landscape services and/or biodiversity. Hence, both of the groups’ (administrators and managers) perceptions partly complement each other. Since policy making should be based on the best knowledge of different stakeholder groups, active knowledge exchange between managers and administrators should be supported and outcome considered in decision making. The resulting regional differences in stakeholder perceptions of the drivers and their respective impact on agricultural landscapes suggest that future agricultural policies need regional targeting and the consideration of landscape-specific characteristics.

Description: This paper analyzes the influence of the interannual variability of climatic drought on ecological and hydrological droughts for a basin in the central Spanish Pyrenees using variables derived from observations and hydro-ecological simulation in order to determine the possible connection between meteorological, ecological and hydrological drought considering a cascading approach and encompassing different variables that give insights into water availability in the basin (e.g., soil moisture, streamflow, reservoir storages and releases). Using different climatic, ecological and hydrological standardized drought indices, we show the greater role of meteorological droughts in hydrological systems than in ecological systems, and the small influence of vegetation activity and growth in explaining the interannual variability of water resources in the basin. By contrast,hydrological droughts are strongly affected by precipitation variability with relationships characterized by seasonal differences and the role of different time-scales in the standardized drought metrics.