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  • Annual Reviews  (2)
  • Copernicus  (2)
  • American Association for the Advancement of Science (AAAS)  (1)
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  • 1
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    American Association for the Advancement of Science (AAAS)
    Publikationsdatum: 2019
    Beschreibung: 〈p〉Large-scale modes of climate variability can force widespread crop yield anomalies and are therefore often presented as a risk to food security. We quantify how modes of climate variability contribute to crop production variance. We find that the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), tropical Atlantic variability (TAV), and the North Atlantic Oscillation (NAO) together account for 18, 7, and 6% of globally aggregated maize, soybean, and wheat production variability, respectively. The lower fractions of global-scale soybean and wheat production variability result from substantial but offsetting climate-forced production anomalies. All climate modes are important in at least one region studied. In 1983, ENSO, the only mode capable of forcing globally synchronous crop failures, was responsible for the largest synchronous crop failure in the modern historical record. Our results provide the basis for monitoring, and potentially predicting, simultaneous crop failures.〈/p〉
    Digitale ISSN: 2375-2548
    Thema: Allgemeine Naturwissenschaft
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 1986-05-01
    Print ISSN: 0084-6597
    Digitale ISSN: 1545-4495
    Thema: Geologie und Paläontologie , Physik
    Publiziert von Annual Reviews
    Standort Signatur Erwartet Verfügbarkeit
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  • 3
    Publikationsdatum: 1986-01-01
    Print ISSN: 0084-6597
    Digitale ISSN: 1545-4495
    Thema: Geologie und Paläontologie , Physik
    Publiziert von Annual Reviews
    Standort Signatur Erwartet Verfügbarkeit
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  • 4
    Publikationsdatum: 2013-06-05
    Beschreibung: Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.
    Print ISSN: 1027-5606
    Digitale ISSN: 1607-7938
    Thema: Geographie , Geologie und Paläontologie
    Publiziert von Copernicus im Namen von European Geosciences Union.
    Standort Signatur Erwartet Verfügbarkeit
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  • 5
    Publikationsdatum: 2012-07-04
    Beschreibung: Snowmelt dominated streamflow of the Western Himalayan Rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in Northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in Northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper elevation stations and corresponding lower elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multi-collinearity of the predictor variables, cross-validated prediction skills of best subset were also compared with the prediction skills of Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra reservoir, a major energy and irrigation source in the region.
    Print ISSN: 1812-2108
    Digitale ISSN: 1812-2116
    Thema: Geographie , Geologie und Paläontologie
    Publiziert von Copernicus im Namen von European Geosciences Union.
    Standort Signatur Erwartet Verfügbarkeit
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