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  • 1
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    Online Resource
    Severity of drought and heatwave crop losses tripled over the last five decades in Europe
    IOP Publishing ; 2021
    In:  Environmental Research Letters Vol. 16, No. 6 ( 2021-06-01), p. 065012-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 16, No. 6 ( 2021-06-01), p. 065012-
    Abstract: Extreme weather disasters (EWDs) can jeopardize domestic food supply and disrupt commodity markets. However, historical impacts on European crop production associated with droughts, heatwaves, floods, and cold waves are not well understood—especially in view of potential adverse trends in the severity of impacts due to climate change. Here, we combine observational agricultural data (FAOSTAT) with an extreme weather disaster database (EM-DAT) between 1961 and 2018 to evaluate European crop production responses to EWD. Using a compositing approach (superposed epoch analysis), we show that historical droughts and heatwaves reduced European cereal yields on average by 9% and 7.3%, respectively, associated with a wide range of responses (inter-quartile range +2% to −23%; +2% to −17%). Non-cereal yields declined by 3.8% and 3.1% during the same set of events. Cold waves led to cereal and non-cereal yield declines by 1.3% and 2.6%, while flood impacts were marginal and not statistically significant. Production losses are largely driven by yield declines, with no significant changes in harvested area. While all four event frequencies significantly increased over time, the severity of heatwave and drought impacts on crop production roughly tripled over the last 50 years, from −2.2% (1964–1990) to −7.3% (1991–2015). Drought-related cereal production losses are shown to intensify by more than 3% yr −1 . Both the trend in frequency and severity can possibly be explained by changes in the vulnerability of the exposed system and underlying climate change impacts.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/abf004
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2255379-4
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  • 2
    Online Resource
    Online Resource
    Impacts of climate change on global food trade networks
    IOP Publishing ; 2022
    In:  Environmental Research Letters Vol. 17, No. 12 ( 2022-12-01), p. 124040-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 17, No. 12 ( 2022-12-01), p. 124040-
    Abstract: Countries’ reliance on global food trade networks implies that regionally different climate change impacts on crop yields will be transmitted across borders. This redistribution constitutes a significant challenge for climate adaptation planning and may affect how countries engage in cooperative action. This paper investigates the long-term (2070–2099) potential impacts of climate change on global food trade networks of three key crops: wheat, rice and maize. We propose a simple network model to project how climate change impacts on crop yields may be translated into changes in trade. Combining trade and climate impact data, our analysis proceeds in three steps. First, we use network community detection to analyse how the concentration of global production in present-day trade communities may become disrupted with climate change impacts. Second, we study how countries may change their network position following climate change impacts. Third, we study the total climate-induced change in production plus import within trade communities. Results indicate that the stability of food trade network structures compared to today differs between crops, and that countries’ maize trade is least stable under climate change impacts. Results also project that threats to global food security may depend on production change in a few major global producers, and whether trade communities can balance production and import loss in some vulnerable countries. Overall, our model contributes a baseline analysis of cross-border climate impacts on food trade networks.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/aca68b
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2255379-4
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  • 3
    Online Resource
    Online Resource
    LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description
    Copernicus GmbH ; 2018
    In:  Geoscientific Model Development Vol. 11, No. 4 ( 2018-04-12), p. 1343-1375
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 11, No. 4 ( 2018-04-12), p. 1343-1375
    Abstract: Abstract. This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    URL: Article
    DOI: 10.5194/gmd-11-1343-2018
    DOI: 10.5194/gmd-11-1343-2018-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2456725-5
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  • 4
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    Online Resource
    The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO〈sub〉2〈/sub〉, temperature, water, and nitrogen (version 1.0)
    Copernicus GmbH ; 2020
    In:  Geoscientific Model Development Vol. 13, No. 9 ( 2020-09-03), p. 3995-4018
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 13, No. 9 ( 2020-09-03), p. 3995-4018
    Abstract: Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    URL: Article
    DOI: 10.5194/gmd-13-3995-2020
    DOI: 10.5194/gmd-13-3995-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2456725-5
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  • 5
    Online Resource
    Online Resource
    LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation
    Copernicus GmbH ; 2018
    In:  Geoscientific Model Development Vol. 11, No. 4 ( 2018-04-12), p. 1377-1403
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 11, No. 4 ( 2018-04-12), p. 1377-1403
    Abstract: Abstract. The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land use change impacts on the terrestrial biosphere, agricultural production, and the water and carbon cycle. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and the potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., 2018c). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures and give hints on the strengths and shortcomings of the model to identify the need for further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration, while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied open source through https://gitlab.pik-potsdam.de/lpjml/LPJmL. We hope that this will lead to new model developments and applications that improve the model performance and possibly build up a new understanding of the terrestrial biosphere.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    URL: Article
    DOI: 10.5194/gmd-11-1377-2018
    DOI: 10.5194/gmd-11-1377-2018-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2456725-5
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  • 6
    Online Resource
    Online Resource
    Larger Drought and Flood Hazards and Adverse Impacts on Population and Economic Productivity Under 2.0 than 1.5°C Warming
    American Geophysical Union (AGU) ; 2020
    In:  Earth's Future Vol. 8, No. 7 ( 2020-07)
    Details
    In: Earth's Future, American Geophysical Union (AGU), Vol. 8, No. 7 ( 2020-07)
    Abstract: Annual runoff generally increases while terrestrial ecosystem water retention generally decreases under 2.0 than 1.5°C warming More areas would experience droughts (34.6%), floods (54.4%), droughts and floods (14.5%) under 2.0 than 1.5°C warming More people (85.9% totally) and GDP (85.9% totally) would be affected by droughts or/and floods under 2.0 than 1.5°C warming
    Type of Medium: Online Resource
    ISSN: 2328-4277 , 2328-4277
    URL: Article
    DOI: 10.1029/2019EF001398
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2020
    detail.hit.zdb_id: 2746403-9
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  • 7
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    Online Resource
    Financial Feasibility of Water Conservation in Agriculture
    American Geophysical Union (AGU) ; 2021
    In:  Earth's Future Vol. 9, No. 3 ( 2021-03)
    Details
    In: Earth's Future, American Geophysical Union (AGU), Vol. 9, No. 3 ( 2021-03)
    Abstract: A novel method compares the costs of water conservation measures with the added value that reallocation of water in agriculture generates Only 10%–20% of potential water savings would be realized in the Indo‐Gangetic plain if financial feasibility is taken into account Despite the modest expansion of irrigation it would accommodate, investing in water conservation can add significant profit to agriculture
    Type of Medium: Online Resource
    ISSN: 2328-4277 , 2328-4277
    URL: Article
    DOI: 10.1029/2020EF001726
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2021
    detail.hit.zdb_id: 2746403-9
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  • 8
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    Online Resource
    Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales
    American Geophysical Union (AGU) ; 2020
    In:  Earth's Future Vol. 8, No. 12 ( 2020-12)
    Details
    In: Earth's Future, American Geophysical Union (AGU), Vol. 8, No. 12 ( 2020-12)
    Abstract: We quantify the pure effect of climate change on the exposure to extreme climate impact events, for both historical and future time periods Global warming increases the global population exposure to river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves The largest increases in exposure are projected for tropical and subtropical regions
    Type of Medium: Online Resource
    ISSN: 2328-4277 , 2328-4277
    URL: Article
    DOI: 10.1029/2020EF001616
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2020
    detail.hit.zdb_id: 2746403-9
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  • 9
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    Online Resource
    Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields
    American Association for the Advancement of Science (AAAS) ; 2018
    In:  Science Advances Vol. 4, No. 11 ( 2018-11-02)
    Details
    In: Science Advances, American Association for the Advancement of Science (AAAS), Vol. 4, No. 11 ( 2018-11-02)
    Abstract: Testing our understanding of crop yield responses to weather fluctuations at global scale is notoriously hampered by limited information about underlying management conditions, such as cultivar selection or fertilizer application. Here, we demonstrate that accounting for observed spatial variations in growing seasons increases the variance in reported national maize and wheat yield anomalies that can be explained by process-based model simulations from 34 to 58% and 47 to 54% across the 10 most weather-sensitive main producers, respectively. For maize, the increase in explanatory power is similar to the increase achieved by accounting for water stress, as compared to simulations assuming perfect water supply in both rainfed and irrigated agriculture. Representing water availability constraints in irrigation is of second-order importance. We improve the model’s explanatory power by better representing crops’ exposure to observed weather conditions, without modifying the weather response itself. This growing season adjustment now allows for a close reproduction of heat wave and drought impacts on crop yields.
    Type of Medium: Online Resource
    ISSN: 2375-2548
    URL: Article
    DOI: 10.1126/sciadv.aat4517
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2018
    detail.hit.zdb_id: 2810933-8
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  • 10
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    Online Resource
    Global gridded crop models underestimate yield responses to droughts and heatwaves
    IOP Publishing ; 2022
    In:  Environmental Research Letters Vol. 17, No. 4 ( 2022-04-01), p. 044026-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 17, No. 4 ( 2022-04-01), p. 044026-
    Abstract: Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models’ capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models’ capacity to adequately respond to extreme weather events.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/ac592e
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2255379-4
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