Cyclicity analysis of precipitation regimes in the Yangtze River basin, China

Becker, Stefan; Hartmann, Heike; Zhang, Q.; Wu, Ying; Jiang, Tong

doi:10.1002/joc.1572

Cited by 21 publications

(9 citation statements)

References 25 publications

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“…The data were provided by the National Climatic Centre (NCC) of the China Meteorological Administration (CMA), Beijing, PR China. The homogeneity of the precipitation data has been confirmed in a previous study by Becker et al (2007).…”

Section: Datasupporting

confidence: 81%

Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002.The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi‐annual oscillations.The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society

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Section: Datasupporting

confidence: 81%

Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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“…China. The homogeneity of the precipitation data was confirmed in a previous study by Becker et al (2008). The location of the climate stations is shown in figure 1.…”

Section: Datasupporting

confidence: 83%

“…Predicting water levels at the Yangtze River was the last step carried out in the context of the project "Teleconnections and their relevance for precipitation patterns in China: time series analyses as a base for an improved flood management in the Yangtze River basin," funded by the German Research Foundation (DFG). During this project, analyses of the climatic variability of different climatic factors as well as medium and long-term predictions of precipitation were undertaken (Becker et al 2007(Becker et al , 2008Hartmann et al 2008aHartmann et al , 2008bWang et al 2007;zHang et al 2007). The last step was to analyze the relation between rainfall and water levels in the Yangtze River basin and, through this, to extend the prewarning time for a flood.…”

Section: Introductionmentioning

confidence: 99%

Forecasting water levels at the Yangtze River with neural networks

Hartmann¹,

Becker²,

King³

et al. 2008

erd

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In the last ten years, the application of neural network models has become an emerging field of research in the field of hydrology. In the present study, three different neural network models, namely the Multilayer Perceptron (MLP), the Jordan net, and the Elman net were used for forecasting water levels at Cuntan station, located at the Yangtze River's upper reaches. The performances of the neural network models were compared with each other and with the results of a multiple linear regression (MLR) model. As input variables for the models, not only were precipitation data and antecedent water levels implemented, but also two climatic variables which are usually left out in the field of neural network modeling: evaporation and snow data. Before the models were adopted, the optimal lead time between the input variables and the model output was determined by means of a cross-correlation analysis. The highly significant correlation between the model input and output already indicated a highly linear relationship. Accordingly, the MLR model showed the best performance, even though the results of the other models are only slightly worse. The good capability of the Jordan net in forecasting high water levels should be investigated further. In predicting water levels in general, the integrated snow data improved the performance of the different models only marginally. However, the integration of evaporation data definitely improved the modeling results. Zusammenfassung: In den vergangenen zehn Jahren hat die Anwendung neuronaler Netze in der Hydrologie zunehmend an Bedeutung gewonnen. In der vorliegenden Studie wurden drei verschiedene neuronale Netzwerkmodelle, namentlich das Multilayer Perzeptron, das Jordan-und das Elman-Netz, eingesetzt, um die Wasserstände an der hydrologischen Station Cuntan (Yangtze-Oberlauf) zu simulieren. Die erzielte Modellgüte der unterschiedlichen Netztypen wurde sowohl untereinander als auch mit den Ergebnissen eines multiplen linearen Regressionsmodells verglichen. Als Eingangsvariablen wurden über die für neuronale Netzwerkmodelle üblichen Eingangsvariablen Niederschlag und Wasserstände der vorherigen Zeitschritte hinaus Verdunstungs-und Schneedaten integriert. Bevor die Modelle eingesetzt wurden, wurde mittels einer Kreuzkorrelationsanalyse der optimale Zeitabstand zwischen den Eingangvariablen und der Modellausgabe berechnet. Die Ergebnisse dieser Korrelationsanalyse zeigen eine hochsignifikante Korrelation und weisen damit auf eine lineare Relation hin. Aufgrund dieser linearen Relation zeigt das multiple lineare Regressionsmodel das beste Resultat, auch wenn die Ergebnisse der anderen Modelle nur als geringfügig schlechter einzuordnen sind. Die gute Leistungsfähigkeit des Jordan-Netzes bei der Simulation der hohen Wasserstände sollte in zukünftigen Studien vertieft untersucht werden. Bei der Simulation sämtlicher Wasserstände konnte durch die Integration der Schneedaten lediglich eine marginale Verbesserung der Modellgüte erzielt werden; mit der Implementierung der Verdunstungsdaten ...

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“…The trend need not be linear as testified by the extensive literature on climate cycles (e.g. Becker et al, 2008).…”

Section: Trend Extrapolationmentioning

confidence: 99%

A review of climate risk information for adaptation and development planning

Wilby¹,

Troni²,

Biot³

et al. 2009

Intl Journal of Climatology

254

155

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ABSTRACT:Although the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin-track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector-specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over-arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges.

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Cyclicity analysis of precipitation regimes in the Yangtze River basin, China

Cited by 21 publications

References 25 publications

Predicting summer rainfall in the Yangtze River basin with neural networks

Predicting summer rainfall in the Yangtze River basin with neural networks

Forecasting water levels at the Yangtze River with neural networks

A review of climate risk information for adaptation and development planning

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