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Institution

Potsdam Institute for Climate Impact Research

FacilityPotsdam, Germany
About: Potsdam Institute for Climate Impact Research is a facility organization based out in Potsdam, Germany. It is known for research contribution in the topics: Climate change & Global warming. The organization has 1519 authors who have published 5098 publications receiving 367023 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors quantify the amount of blue and green water (irrigation and precipitation water) needed to produce one unit of crop yield, for 11 of the world's major crop types.
Abstract: . The need to increase food production for a growing world population makes an assessment of global agricultural water productivities and virtual water flows important. Using the hydrology and agro-biosphere model LPJmL, we quantify at 0.5° resolution the amount of blue and green water (irrigation and precipitation water) needed to produce one unit of crop yield, for 11 of the world's major crop types. Based on these, we also quantify the agricultural water footprints (WFP) of all countries, for the period 1998–2002, distinguishing internal and external WFP (virtual water imported from other countries) and their blue and green components, respectively. Moreover, we calculate water savings and losses, and for the first time also land savings and losses, through international trade with these products. The consistent separation of blue and green water flows and footprints shows that green water globally dominates both the internal and external WFP (84 % of the global WFP and 94 % of the external WFP rely on green water). While no country ranks among the top ten with respect to all water footprints calculated here, Pakistan and Iran demonstrate high absolute and per capita blue WFP, and the US and India demonstrate high absolute green and blue WFPs. The external WFPs are relatively small (6 % of the total global blue WFP, 16 % of the total global green WFP). Nevertheless, current trade of the products considered here saves significant water volumes and land areas (~263 km3 and ~41 Mha, respectively, equivalent to 5 % of the sowing area of the considered crops and 3.5 % of the annual precipitation on this area). Relating the proportions of external to internal blue/green WFP to the per capita WFPs allows recognizing that only a few countries consume more water from abroad than from their own territory and have at the same time above-average WFPs. Thus, countries with high per capita water consumption affect mainly the water availability in their own country. Finally, this study finds that flows/savings of both virtual water and virtual land need to be analysed together, since they are intrinsically related.

208 citations

Journal ArticleDOI
TL;DR: A set of new temperature response functions are derived that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes, leading to higher skill of crop yield projections.
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

208 citations

Journal ArticleDOI
TL;DR: It is reported that drought stress will remain a key driver of yield losses in wheat and maize across Europe, and benefits from CO2 will be limited in low-yielding years.
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

207 citations

Journal ArticleDOI
TL;DR: Experimental results and security analysis show that the presented encryption algorithm has a good encryption effect and can resist various typical attacks.

207 citations

Journal ArticleDOI
TL;DR: In this paper, the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide was quantified, and the effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model LPJmL, yielding even larger uncertainties in discharge (average 90%).
Abstract: This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund‐Potsdam‐Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations.

207 citations


Authors

Showing all 1589 results

NameH-indexPapersCitations
Carl Folke133360125990
Adam Drewnowski10648641107
Jürgen Kurths105103862179
Markus Reichstein10338653385
Stephen Polasky9935459148
Sandy P. Harrison9632934004
Owen B. Toon9442432237
Stephen Sitch9426252236
Yong Xu88139139268
Dieter Neher8542426225
Johan Rockström8523657842
Jonathan A. Foley8514470710
Robert J. Scholes8425337019
Christoph Müller8245727274
Robert J. Nicholls7951535729
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023101
2022107
2021479
2020486
2019332
2018355