Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network

Gao, Chao; Gemmer, Marco; Zeng, Xiangyong; Liu, Bo; Su, Buda; Wen, Yuhua

doi:10.1007/s00477-009-0355-6

Cited by 62 publications

(21 citation statements)

References 40 publications

(45 reference statements)

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“…time-frequency relationships) in climatological datasets. Gao et al (2010) used the continuous wavelet transform likewise in order to assess the fluctuation of monthly observed and projected average stream flows (return periods of extremes) in the Huaihe River basin in China. Becker et al (2008) determined quasi-periodicities of extreme precipitation events in the Yangtze River basin by employing the continuous wavelet analysis.…”

Section: Power Spectrum and Continuous Wavelet Analysismentioning

confidence: 99%

Hydrological long-term dry and wet periods in the Xijiang River basin, South China

Fischer

Gemmer

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. In this study, hydrological long-term dry and wet periods are analyzed for the Xijiang River basin in South China. Daily precipitation data of 118 stations and data on daily discharge at Gaoyao hydrological station at the mouth of the Xijiang River for the period 1961-2007 are used. At a 24-month timescale, the standardized precipitation index (SPI-24) for the six sub-basins of the Xijiang River and the standardized discharge index (SDI-24) for Gaoyao station are applied. The monthly values of the SPI-24 averaged for the Xijiang River basin correlate highly with the monthly values of the SDI-24. Distinct long-term dry and wet sequences can be detected.The principal component analysis is applied and shows spatial disparities in dry and wet periods for the six subbasins. The correlation between the SPI-24 of the six subbasins and the first principal component score shows that 67 % of the variability within the sub-basins can be explained by dry and wet periods in the east of the Xijiang River basin. The spatial dipole conditions (second and third principal component) explain spatiotemporal disparities in the variability of dry and wet periods. All sub-basins contribute to hydrological dry periods, while mainly the northeastern sub-basins cause wet periods in the Xijiang River. We can also conclude that long-term dry events are larger in spatial extent and cover all sub-basins while long-term wet events are regional phenomena.A spectral analysis is applied for the SPI-24 and the SDI-24. The results show significant peaks in periodicities of 11-14.7 yr, 2.8 yr, 3.4-3.7 yr, and 6.3-7.3 yr. The same periodic cycles can be found in the SPI-24 of the six sub-basins but with some variability in the mean magnitude. A wavelet analysis shows that significant periodicities have been stable over time since the 1980s. Extrapolations of the reconstructed SPI-24 and SDI-24 represent the continuation of observed significant periodicities at given magnitudes until 2030. The projected hydrological long-term dry and wet periods can be used for planning purposes in water resources management. The applied methodologies prove to be able to identify spatial disparities, and to detect significant periodicities in hydrological long-term dry and wet periods in the Xijiang River basin.

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Section: Power Spectrum and Continuous Wavelet Analysismentioning

confidence: 99%

Hydrological long-term dry and wet periods in the Xijiang River basin, South China

Fischer

Gemmer

et al. 2013

Hydrol. Earth Syst. Sci.

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“…Its northern region is in the warm temperate zone, and its southern region is in the subtropical zone. The Huai River also bounds the subhumid and humid regions [28]. Because of these natural conditions, the study area has a diverse phenology.…”

Section: Study Sitesmentioning

confidence: 99%

Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

2015

Sustainability

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Abstract:Vegetation plays an important role in atmospheric, hydrologic and biochemical cycles and is an important indicator of the impact of climate and human factors on the environment. In this paper, a method, which combines the empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) methods, is applied to monitor the phenological characteristcs and spatial distribution of vegetation phenology in the middle part of the Huai River region. Based on the variance and EOF curves, the EOF provides the number of phenology modes, information which is the basis for an accurate temporal unmixing model. The TUA describes the temporal vegetation phenological details and spatial distribution. Importantly, this approach does not require assumptions, prior information or pre-defined thresholds. The vegetation phenology curves derived from the MODIS EVI data using the combined EOF and TUA methods display much more detail than the curves from Landsat TM using spectral mixture analysis (SMA). Additionally, the vegetation phenology spatial distribution from MODIS EVI is consistent with the field survey data. The combination method of EOF and TUA can be used to monitor vegetation phenology spatiotemporal change in a large area from time series of MODIS EVI data.

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“…Hence, the methods of linear regression, Mann-Kendall test, and Engle's test (on autocorrelation) are applied to the RX1 and RX5 of each station to identify stations exhibiting significant trends and autoregressive conditional heteroskedasticity (ARCH) effects in residuals at the 0.05 significance level (Gemmer et al 2004;Gao et al 2010;Duchesne 2006). Significant trends, cycles, and autocorrelation (heteroscedasticity) indicate nonstationarity.…”

Section: ) Indicatorsmentioning

confidence: 99%

Probability Distribution of Precipitation Extremes for Weather Index–Based Insurance in the Zhujiang River Basin, South China

Fischer

Luo

et al. 2012

Journal of Hydrometeorology

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In a changing climate, understanding the frequency of weather extremes is crucial to improving the management of the associated risks. The concept of weather index-based insurance is introduced as a new approach in weather risk adaptation. It can decrease the vulnerability to precipitation extremes that cause floods and economic losses in the Zhujiang River basin. The probability of precipitation extremes is a key input and the probability distribution of annual precipitation extremes is analyzed with four distribution functions [gamma 3, generalized extreme value (GEV), generalized Pareto, and Wakeby]. Three goodness-of-fit tests (Kolmogorov-Smirnov, Anderson-Darling, and Chi Squared) are applied to the distribution functions for annual time series of 192 meteorological stations. The results show that maximum precipitation and 5-day-maximum precipitation are best described by the Wakeby distribution. On a basin scale, the GEV is the most reliable and robust distribution for estimating precipitation indexes for an index-based insurance program in the Zhujiang River basin. However, each station has to be analyzed individually as GEV is not always the best-fitting distribution function. Based on the distribution functions, spatiotemporal characteristics of return periods for maximum precipitation and 5-day-maximum precipitation are determined. The return levels of the 25-and 50-yr return periods show similar spatial pattern: they are higher in the southeast and lower in the southwest of the basin. This spatial distribution is in line with the annual averages. The statistical distribution of precipitation indexes delivers important information for a theoretical weather index-based insurance program.

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Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network

Cited by 62 publications

References 40 publications

Hydrological long-term dry and wet periods in the Xijiang River basin, South China

Hydrological long-term dry and wet periods in the Xijiang River basin, South China

Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

Probability Distribution of Precipitation Extremes for Weather Index–Based Insurance in the Zhujiang River Basin, South China

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