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  • 1
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    Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle (2017)
    Copernicus
    Details
    Publication Date: 2017-11-07
    Description: With the objective of tackling the problem of inaccurate long-term El Niño Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamic reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a correlation coefficient of approximately 0.80 and a mean absolute percentage error of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field, but the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in summer and those in winter is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.
    Print ISSN: 1812-0806
    Electronic ISSN: 1812-0822
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 2
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    Design water demand of irrigation for a large region using a high-dimensional Gaussian copula (2018)
    Copernicus
    Details
    Publication Date: 2018-05-23
    Description: Spatial and frequency distributions of precipitation should be considered in determining design water demand of irrigation for a large region. In Guangdong province, South China, as a study case, an eight-dimensional joint distribution of precipitation for agricultural sub-regions was developed. A design procedure for water demand of irrigation for a given frequency of precipitation of the entire region was proposed. Water demands of irrigation in the entire region and its sub-regions using three design methods, i.e. equalized frequency (EF), typical year (TY) and most-likely weight function (MLW), were compared. Results demonstrated that the Gaussian copula efficiently fitted the high-dimensional joint distribution of eight sub-regional precipitation values. The Kendall frequency was better than the conventional joint frequency to analyze the linkage between the frequency of the entire region and the joint frequency of sub-regions. For given frequencies of precipitation of the entire region, design water demands of irrigation of the entire region among the MLW, EF and TY methods slightly differed, but those of individual sub-regions of the MLW and TY methods fluctuated around the demand lines of the EF method. The alterations of design water demand in sub-regions were more complicated than those in the entire region. The design procedure using the MLW method in association with a high-dimensional copula, which simulated individual univariate distributions, captured their dependences for multi-variables, and built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended for design water demand of irrigation for a large region.
    Print ISSN: 1812-2108
    Electronic ISSN: 1812-2116
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
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    Discrete k-nearest neighbor resampling for simulating multisite precipitation occurrence and adaption to climate change (2018)
    Copernicus
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    Publication Date: 2018-10-12
    Description: Stochastic weather simulation models are commonly employed in water resources management and agricultural applications. The data simulated by these models, such as precipitation, temperature, and wind, are used as input for hydrological and agricultural models. Stochastic simulation of multisite precipitation occurrence is a challenge because of its intermittent characteristics as well as spatial and temporal cross-correlation. Employing a nonparametric technique, k-nearest neighbor resampling (KNNR), and coupling it with Genetic Algorithm (GA), this study proposes a novel simulation method for multisite precipitation occurrence. The proposed discrete version of KNNR (DKNNR) model is compared with an existing parametric model, called multisite occurrence model with standard normal variate (MONR). The datasets simulated from both the DKNNR model and the MONR model are tested using a number of statistics, such as occurrence and transition probabilities as well as temporal and spatial cross-correlations. Results show that the proposed DKNNR model can be a good alternative for simulating multisite precipitation occurrence. We also tested the model capability to adapt climate change. It is shown that the model is capable but further improvement is required to have specific variations of the occurrence probability due to climate change. Combining with the generated occurrence, the multisite precipitation amount can then be simulated by any multisite amount model.
    Print ISSN: 1991-9611
    Electronic ISSN: 1991-962X
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
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    Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle (2018)
    Copernicus
    Details
    Publication Date: 2018-04-24
    Description: With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.
    Print ISSN: 1812-0784
    Electronic ISSN: 1812-0792
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 5
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    Hydrological effects of climate variability and vegetation dynamics on annual fluvial water balance in global large river basins (2018)
    Copernicus
    Details
    Publication Date: 2018-07-26
    Description: The partitioning of precipitation into runoff (R) and evapotranspiration (E), governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assessing the water balance at global scale. It is widely acknowledged that the spatial variation in this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E0), we proposed a climate seasonality and asynchrony index (SAI) in terms of both phase and amplitude mismatch between P and E0. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 51 % of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation in n, explaining 67 % of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (Pe, E0 and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the Pe variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone. SAI, M and E0 have larger impacts on E than on R, whereas Pe has larger impacts on R.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 6
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    Hydrological effects of climate variability and vegetation dynamics on annual fluvial water balance at global large river basins (2018)
    Copernicus
    Details
    Publication Date: 2018-04-23
    Description: The partitioning of water and energy, governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assess the water balance at global scale. It is widely acknowledged that the spatial variation of this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E0), a climate seasonality and asynchrony index (SAI) were proposed in terms of both phase and amplitude mismatch between P and E0. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 46% of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation of n, explaining 67% of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (Pe, E0 and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the Pe variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone, E0 in the temperate maritime climate of Europe, and M in the temperate grassland zone of South America.
    Print ISSN: 1812-2108
    Electronic ISSN: 1812-2116
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 7
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    A discrete wavelet spectrum approach to identifying non-monotonic trend pattern of hydroclimate data (2017)
    Copernicus
    Details
    Publication Date: 2017-02-01
    Description: Hydroclimate system is changing non-monotonically and identifying its trend pattern is a great challenge. Building on the discrete wavelet transform theory, we develop a discrete wavelet spectrum (DWS) approach for identifying non-monotonic trend patterns in hydroclimate time series and evaluating their statistical significance. After validating the DWS approach using two typical synthetic time series, we examined the temperature and potential evaporation over China from 1961–2013, and found that the DWS approach detected both the warming and the warming hiatus in temperature, and the reversed changes in potential evaporation. Interestingly, the identified trend patterns showed stable significance when the time series was longer than 30 years or so (i.e., the widely defined climate timescale). Our results suggest that non-monotonic trend patterns of hydroclimate time series and their significance should be carefully identified, and the DWS approach has the potential for wide use in hydrological and climate sciences.
    Print ISSN: 1812-2108
    Electronic ISSN: 1812-2116
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 8
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    A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data (2018)
    Copernicus
    Details
    Publication Date: 2018-01-26
    Description: The hydroclimatic process is changing non-monotonically and identifying its trends is a great challenge. Building on the discrete wavelet transform theory, we developed a discrete wavelet spectrum (DWS) approach for identifying non-monotonic trends in hydroclimate time series and evaluating their statistical significance. After validating the DWS approach using two typical synthetic time series, we examined annual temperature and potential evaporation over China from 1961–2013 and found that the DWS approach detected both the “warming” and the “warming hiatus” in temperature, and the reversed changes in potential evaporation. Further, the identified non-monotonic trends showed stable significance when the time series was longer than 30 years or so (i.e. the widely defined “climate” timescale). The significance of trends in potential evaporation measured at 150 stations in China, with an obvious non-monotonic trend, was underestimated and was not detected by the Mann–Kendall test. Comparatively, the DWS approach overcame the problem and detected those significant non-monotonic trends at 380 stations, which helped understand and interpret the spatiotemporal variability in the hydroclimatic process. Our results suggest that non-monotonic trends of hydroclimate time series and their significance should be carefully identified, and the DWS approach proposed has the potential for wide use in the hydrological and climate sciences.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 9
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    More frequent flooding? Changes in flood frequency in the Pearl River basin, China, since 1951 and over the past 1000 years (2018)
    Copernicus
    Details
    Publication Date: 2018-05-03
    Description: Flood risks across the Pearl River basin, China, were evaluated using a peak flood flow dataset covering a period of 1951–2014 from 78 stations and historical flood records of the past 1000 years. The generalized extreme value (GEV) model and the kernel estimation method were used to evaluate frequencies and risks of hazardous flood events. Results indicated that (1) no abrupt changes or significant trends could be detected in peak flood flow series at most of the stations, and only 16 out of 78 stations exhibited significant peak flood flow changes with change points around 1990. Peak flood flow in the West River basin increased and significant increasing trends were identified during 1981–2010; decreasing peak flood flow was found in coastal regions and significant trends were observed during 1951–2014 and 1966–2014. (2) The largest three flood events were found to cluster in both space and time. Generally, basin-scale flood hazards can be expected in the West and North River basins. (3) The occurrence rate of floods increased in the middle Pearl River basin but decreased in the lower Pearl River basin. However, hazardous flood events were observed in the middle and lower Pearl River basin, and this is particularly true for the past 100 years. However, precipitation extremes were subject to moderate variations and human activities, such as building of levees, channelization of river systems, and rapid urbanization; these were the factors behind the amplification of floods in the middle and lower Pearl River basin, posing serious challenges for developing measures of mitigation of flood hazards in the lower Pearl River basin, particularly the Pearl River Delta (PRD) region.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 10
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    Design water demand of irrigation for a large region using a high-dimensional Gaussian copula (2018)
    Copernicus
    Details
    Publication Date: 2018-10-05
    Description: Spatial and frequency distributions of precipitation should be considered in determining design water demand of irrigation for a large region. In Guangdong Province, South China, as a study case, an eight-dimensional joint distribution of precipitation for agricultural sub-regions was developed. A design procedure for water demand of irrigation for a given frequency of precipitation of the entire region was proposed. Water demands of irrigation in the entire region and its sub-regions using three design methods, i.e., equalized frequency (EF), typical year (TY) and most-likely weight function (MLW), were compared. Results demonstrated that the Gaussian copula efficiently fitted the high-dimensional joint distribution of eight sub-regional precipitation values. The Kendall frequency was better than the conventional joint frequency to analyze the linkage between the frequency of precipitation of the entire region and individual sub-regions. For given frequencies of precipitation of the entire region, design water demands of irrigation of the entire region among the MLW, EF and TY methods slightly differed, but those of individual sub-regions of the MLW and TY methods fluctuated around the demand lines of the EF method. The alterations of design water demand in sub-regions were more complicated than those in the entire region. The design procedure using the MLW method in association with a high-dimensional copula, which simulated individual univariate distributions, captured their dependences for multi-variables, and built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended for design water demand of irrigation for a large region.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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