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1.
Rainfall is the key climate variable that governs the spatial and temporal availability of water. In this study we identified monthly rainfall trends and their relation to the southern oscillation index (SOI) at ten rainfall stations across Australia covering all state capital cities. The nonparametric Mann–Kendall (MK) test was used for identifying significant trends. The trend free pre‐whitening approach (TFPW) was used to remove the effects of serial correlation in the dataset. The trend beginning year was approximated using the cumulative summation (CUSUM) technique and the influence of the SOI was identified using graphical representations of the wavelet power spectrum (WPS). Decreasing trends of rainfall depth were observed at two stations, namely Perth airport for June and July rainfall starting in the 1970s and Sydney Observatory Hill for July rainfall starting in the 1930s. No significant trends were found in the Melbourne, Alice Springs and Townsville rainfall data. The remaining five stations showed increasing trends of monthly rainfall depth. The SOI was found to explain the increasing trends for the Adelaide (June) and Cairns (April) rainfall data and the decreasing trends for Sydney (July) rainfall. Other possible climatic factors affecting Australian rainfall are also discussed. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

2.
Temporal and spatial variations of stable oxygen (18O) and hydrogen (2H) isotope measurements in precipitation act as important proxies for changing hydro‐meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ18O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann–Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend‐free pre‐whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

3.
Mann–Kendall (MK) test for trend detection must be modified when the data are serially correlated, to prevent the detection of false trends. Various approaches are developed for this purpose, such as prewhitening, trend‐free prewhitening, variance correction and block bootstrap. Each method has its own Type I and Type II errors. In this study, the errors of block bootstrapping MK test are estimated by a simulation study and compared with other methods. Optimal block length that minimizes the Type I error is determined as function of sample size and autocorrelation coefficient. It is shown that the power of block bootstrapping MK test is comparable with those of other modified MK tests. These tests are applied to some annual streamflow series with trend recorded in Turkish rivers, and their powers are compared. A modified form of the trend‐free prewhitening procedure is proposed that has a smaller Type I error. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

4.
The relationship between air (Ta) and water temperature (Tw) is very important because it shows how the temperature of a water body might respond to future changes in surface Ta. Mean monthly Tw records of three gauging stations (Bezdan, Bogojevo i Veliko Gradi?te) were analysed alongside mean monthly discharge (Q) for the same stations. Additionally, Ta series from two meteorological stations (Sombor and Veliko Gradi?te) were correlated with Tw variations over the period 1950–2012. Locally weighted scatter point smoothing (LOWESS) was used to investigate long‐term trends in the raw data, alongside the Mann–Kendall (MK) trend test. Trend significance was established using Yue–Pilon's pre‐whitening approaches to determine trends in climate data. Also, the rescaled adjusted partial sums (RAPS) method was used to detect dates of possible changes in the time series. Statistically significant warming trends were observed for annual and seasonal minimum and maximum Tw at all investigated sites. The strongest warming was observed at Bogojevo gauging station for seasonal maximum Tw, with +0.05 °C per year on average. RAPS established that the trend began in the 1980s. This behaviour is linked to climate patterns in the North and East Atlantic which determine the amount of heat advected onto mainland Europe. Statistically significant correlations were found for all Tw on an annual basis. Overall, the strongest correlations (p < 0.01) between Tw residuals and the North Atlantic Oscillation (NAO) were recorded for the winter period. These findings suggest possible predictability of Tw over seasonal time‐scales. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

5.
Trends in extreme rainfall in the state of New South Wales,Australia   总被引：1，自引：1，他引：0  
The trends in annual maximum rainfall (AMR) intensity data in New South Wales, Australia, were examined. Data from 60 stations were used covering three study periods, 1955–2010, 1965–2010 and 1978–2010. Mann-Kendall (MK) and Spearman’s rho (SR) tests were applied to assess trends at local stations. Pre-whitening (PW), trend-free pre-whitening (TFPW) and the variance correction (VC) tests were used to assess the effects of serial correlation on trend results. For regional trend analysis, the regional MK test was employed. The impacts of climatic variability modes on the observed trends in AMR intensity and seasonal maximum rainfall data were investigated. It was found that positive trends were more frequent than the negative ones. The PW, TFPW and VC tests resulted in a slight reduction in the count of stations exhibiting significant positive trends. The number of stations exhibiting significant trends decreased when the impact of climate variability modes was considered.  相似文献   

6.
A comprehensive evaluation of trends in annual instantaneous maximum flows (AIMF) from 153 gauge stations located in 26 river basins in Turkey is presented. Two traditional non-parametric trend tests, the Mann-Kendall (MK) and Spearman’s rho (SR), are used to quantify the significance of trends, while Sen’s slope method is applied to determine the magnitude of trends. The traditional tests indicate that the AIMF records of 57 stations showed statistically decreasing trends, while those of six stations showed an increasing trend. Sen’s trend method, which provides more detailed assessment of the trends in different clusters (low, medium and high), was applied to the AIMF series and the results were compared with traditional tests. Sen’s trend method indicated that all flow clusters at nine stations have increasing or decreasing trends, although no significant trend was detected by the MK and SR tests.  相似文献   

7.
In most studies, trend detection is performed under the assumption of a monotonic trend. However, natural processes and, in particular, hydro‐climatic variables may not conform to this assumption. This study performs a simultaneous evaluation of gradual and abrupt changes in Canadian low streamflows using a modified Mann–Kendall (MK) trend test and a Bayesian multiple change‐point detection model. Statistical analysis, using the whole record of observation (under a monotonic trend assumption), shows that winter and summer low flows are dominated by upward and downward trends, respectively. Overall, about 20% of low flows are characterized by significant trends, where ～80% of detected significant trends are upward (downward) for winter (summer) season. Change‐point analysis shows that over 50% of low‐flow time series experienced at least one abrupt change in mean or in direction of trend, of which ～50% occurred in 1980s with a mode in 1987. Analysis of segmented time series based on a common change‐point date indicates a reduced number of significant trends, which is attributed to first, the change in nonstationarity behaviour of low flows leading to less trend‐type changes in the last few decades; and second, the false detection of trends when the sample data are characterized by shifts in mean. Depending on whether the monotonic trend assumption holds, the on‐site and regional interpretation of results may vary (e.g. winter low flow) or even lead to contradictory conclusions (e.g. summer low flow). Trend analysis of last two decades of streamflows shows that (1) winter low flows are increasing in eastern Canada and southern British Columbia, whereas they are decreasing in western Canada; (2) summer low flows are increasing in central Canada, southern British Columbia and Newfoundland, whereas they are decreasing in Yukon and northern British Columbia and also in eastern Ontario and Quebec. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

8.
In the present study, the trends in the reference evapotranspiration (ETO) estimated through the Penman‐Monteith method were investigated over the humid region of northeast (NE) India by using the Mann‐Kendall (MK) test after removing the effect of significant lag‐1 serial correlation from the time series of ETO by pre‐whitening. During the last 22 years, ETO has been found to decrease significantly at annual and seasonal time scales for 6 sites in NE India and NE India as a whole. The seasonal decreases in ETO have, however, been more significant in the pre‐monsoon season, indicating the presence of an element of a seasonal cycle. The decreases in ETO are mainly attributed to the net radiation and wind speed, which are also corroborated by the observed trends in these two parameters at almost all the times scales over most of the sites in NE India. The steady decrease in wind speed and decline in net radiation not only balanced the impact of the temperature increases on ETO, but may have actually caused the decreases in ETO over the humid region of northeast India. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

9.
ABSTRACT

The trends in hydrological and climatic time series data of Urmia Lake basin in Iran were examined using the four different versions of the Mann-Kendall (MK) approach: (i) the original MK test; (ii) the MK test considering the effect of lag-1 autocorrelation; (iii) the MK test considering the effect of all autocorrelation or sample size; and (iv) the MK test considering the Hurst coefficient. Identification of hydrological and climatic data trends was carried out at monthly and annual time scales for 25 temperature, 35 precipitation and 35 streamflow gauging stations selected from the Urmia Lake basin. Mann-Kendall and Pearson tests were also applied to explore the relationships between temperature, precipitation and streamflow trends. The results show statistically significant upward and downward trends in the annual and monthly hydrological and climatic variables. The upward trends in temperature, unlike streamflow, are much more pronounced than the downward trends, but for precipitation the behaviour of trend is different on monthly and annual time scales. Furthermore, the trend results were affected by the different approaches. Specifically, the number of stations showing trends in hydrological and climatic variables decreased significantly (up to 50%) when the fourth test was considered instead of the first and the absolute value of the Z statistic for most of the time series was reduced. The results of correlations between streamflow and climatic variables showed that the streamflow in Urmia Lake basin is more sensitive to changes in temperature than those of precipitation. The observed decreases in streamflow and increases in temperature in the Urmia Lake basin in recent decades may thus have serious implications for water resources management under the warming climate with the expected population growth and increased freshwater consumption in this region.
Editor Z. W. Kundzewicz; Associate editor Q. Zhang  相似文献   

10.
This study developed a standard methodology for identifying spatial trends using satellite-based raster datasets. It involves the novelty of exploring the capabilities of a geographic information system in implementing the procedures of three trend tests, the Spearman rank order correlation (SROC) test, the Kendall rank correlation (KRC) test and the Mann-Kendall (MK) test, on raster datasets of the Tropical Rainfall Measuring Mission at 0.25° × 0.25° resolution. Comparative evaluation of the three tests revealed fair agreement of a major part of the test results for pre-, post- and non-monsoon and one-day maximum rainfall. Also, similar results from KRC and MK tests were obtained over a considerable area for annual, monsoon and monthly maximum rainfall. These findings suggest the importance of selecting the appropriate test depending on rainfall magnitudes at the chosen time scale and emphasize the robustness of the KRC and MK tests.  相似文献   

11.
J. Vaze  J. Teng  F. H. S. Chiew 《水文研究》2011,25(9):1486-1497
Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large‐scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south‐east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI‐ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO‐MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS‐AOM, MIROC‐M, NCAR‐PCM1, IAP and NCAR‐CCSM. However, the reduction in the combined score as we move from the best‐ to the worst‐performing GCMs is gradual, and there is no evident cut‐off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

12.
Climate change may affect magnitude and frequency of regional extreme events with possibility of serious impacts on the existing infrastructure systems. This study investigates how the current spatial and temporal variations of extreme events are affected by climate change in the Upper Thames River basin, Ontario, Canada. A weather generator model is implemented to obtain daily time series of three climate variables for two future climate scenarios. The daily time series are disaggregated into hourly to capture characteristics of intense and rapidly changing storms. The maximum annual precipitation events for five short durations, 6‐, 12‐, 24‐, 48‐, and 72‐h durations, at each station are extracted from the generated hourly data. The frequency and seasonality analyses are conducted to investigate the temporal and spatial variability of extreme precipitation events corresponding to each duration. In addition, this study investigates the impacts of increase in temperature using reliability, resilience, and vulnerability. The results indicate that the extreme precipitation events under climate change will occur earlier than in the past. In addition, episodes of extremely high temperature may last longer up to 19·7% than under the no‐change climate scenario. This study points out that the revision of the design storms (e.g. 100‐ or 250‐year return period) is warranted for the west and the south east region of the basin. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

13.
The aim of this study was to investigate rainfall–groundwater dynamics over space and annual time scales in a hard‐rock aquifer system of India by employing time series, geographic information system and geostatistical modelling techniques. Trends in 43‐year (1965–2007) annual rainfall time series of ten rainfall stations and 16‐year (1991–2006) pre‐monsoon and post‐monsoon groundwater levels of 140 sites were identified by using Mann–Kendall, Spearman rank order correlation and Kendall rank correlation tests. Trends were quantified by Kendall slope method. Furthermore, the study involves novelty of examining homogeneity of pre‐monsoon and post‐monsoon groundwater levels, for the first time, by applying seven tests. Regression analysis between rainfall and post‐monsoon groundwater levels was performed. The pre‐monsoon and post‐monsoon groundwater levels for four periods – 1991–1994, 1995–1998, 1999–2002 and 2003–2006 – were subjected to geographic information system‐based geostatistical modelling. The rainfall showed considerable spatiotemporal variations, with a declining trend at the Mavli rainfall station (p‐value < 0.05). The Levene's tests revealed spatial homogeneity of rainfall at α = 0.05. Regression analyses indicated significant relationships (r2 > 0.5) between groundwater level and rainfall for eight rainfall stations. Non‐homogeneity and declining trends in the groundwater level, attributed to anthropogenic and hydrologic factors, were found at 5–61 more sites in pre‐monsoon compared with post‐monsoon season. The groundwater declining rates in phyllite–schist, gneiss, schist and granite formations were found to be 0.18, 0.26, 0.21 and 0.14 m year?1 and 0.13, 0.19, 0.16 and 0.02 m year?1 during the pre‐monsoon and post‐monsoon seasons, respectively. The geostatistical analyses for four time periods revealed linkages between the rainfall and groundwater levels. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

14.
Trend analysis in Turkish precipitation data   总被引：9，自引：0，他引：9  
This study aims to determine trends in the long‐term annual mean and monthly total precipitation series using non‐parametric methods (i.e. the Mann–Kendall and Sen's T tests). The change per unit time in a time series having a linear trend was estimated by applying a simple non‐parametric procedure, namely Sen's estimator of slope. Serial correlation structure in the data was accounted for determining the significance level of the results of the Mann–Kendall test. The data network used in this study, which is assumed to reflect regional hydroclimatic conditions, consists of 96 precipitation stations across Turkey. Monthly totals and annual means of the monthly totals are formed for each individual station, spanning from 1929 to 1993. In this case, a total of 13 precipitation variables at each station are subjected to trend detection analysis. In addition, regional average precipitation series are established for the same analysis purpose. The application of a trend detection framework resulted in the identification of some significant trends, especially in January, February, and September precipitations and in the annual means. A noticeable decrease in the annual mean precipitation was observed mostly in western and southern Turkey, as well as along the coasts of the Black Sea. Regional average series also displayed trends similar to those for individual stations. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

15.
Parametric method of flood frequency analysis (FFA) involves fitting of a probability distribution to the observed flood data at the site of interest. When record length at a given site is relatively longer and flood data exhibits skewness, a distribution having more than three parameters is often used in FFA such as log‐Pearson type 3 distribution. This paper examines the suitability of a five‐parameter Wakeby distribution for the annual maximum flood data in eastern Australia. We adopt a Monte Carlo simulation technique to select an appropriate plotting position formula and to derive a probability plot correlation coefficient (PPCC) test statistic for Wakeby distribution. The Weibull plotting position formula has been found to be the most appropriate for the Wakeby distribution. Regression equations for the PPCC tests statistics associated with the Wakeby distribution for different levels of significance have been derived. Furthermore, a power study to estimate the rejection rate associated with the derived PPCC test statistics has been undertaken. Finally, an application using annual maximum flood series data from 91 catchments in eastern Australia has been presented. Results show that the developed regression equations can be used with a high degree of confidence to test whether the Wakeby distribution fits the annual maximum flood series data at a given station. The methodology developed in this paper can be adapted to other probability distributions and to other study areas. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

16.
Power law correlation properties of sign and magnitude series have been studied based on the series of observation records of flow of the River Yangtze. The results obtained give improved insight into and understanding of the linear and non‐linear processes of the water cycle. With the newly developed Delayed Vector Variance method and the surrogate test, the documented linkage between the sign series and the linear process, and that between the magnitude series and non‐linear process can be verified. The spectra estimated by detrended fluctuation analysis method show different properties of intra‐annual and inter‐annual correlations in both sign and magnitude series. The linear process behaves as an 1/f noise at a time scale less than about 60 days, but shows features of anti‐persistence in terms of long‐term fluctuation. The magnitudes are clustered in three ways mainly caused by non‐linear processes, i.e. periodic clustering, strong short‐term clustering of 1/f noise at time scales less than 20 days, and long‐term clustering with weak persistence. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

17.
This study is an attempt to determine the trends in monthly, annual and monsoon total precipitation series over India by applying linear regression, the Mann-Kendall (MK) test and discrete wavelet transform (DWT). The linear regression test was applied on five consecutive classical 30-year climate periods and a long-term precipitation series (1851–2006) to detect changes. The sequential Mann-Kendall (SQMK) test was applied to identify the temporal variation in trend. Wavelet transform is a relatively new tool for trend analysis in hydrology. Comparison studies were carried out between decomposed series by DWT and original series. Furthermore, visualization of extreme and contributing events was carried out using the wavelet spectrum at different threshold values. The results showed that there are significant positive trends for annual and monsoon precipitation series in North Mountainous India (zone NMI) and North East India (NEI), whereas negative trends were detected when considering India as whole.

EDITOR A. Castellarin ASSOCIATE EDITOR S. Kanae  相似文献   

18.
Miao Li  Zhi Chen  Dejuan Meng  Chongyu Xu 《水文研究》2013,27(20):2934-2943
Non‐parametric methods including Mann–Kendall (M–K) test, continuous wavelet transform (CWT) and discrete wavelet transform analysis are applied in this paper to detect the trend and periodic trait of precipitation data series in Beijing area where the data set spans nearly 300 years from 1724 to 2009. First, the trend of precipitation variables is elaborated by the M–K test (Sequential M–K test). The results show that there is an increasing trend (the value of this trend is 1.98) at the 5%‐significance level and there are not turning points in the whole data series. Then, CWT and wavelet variance are used to check for significant periodic characteristics of data series. In the plots of wavelet transform coefficients and figure of wavelet variance, some periodic events affect the trend of the annual total precipitation series in Beijing area. 85‐year, 35‐year and 21‐year periodic events are found to be the main periodic series of long‐term precipitation data, and they are all statistically significant. Moreover, the results of non‐parametric M–K test are exhibited on seven different combinations of discrete wavelet components. D5 (32‐year periodicity) periodic component is the effective and significant component on data. It is coincident with the result (35‐year periodic event as one part of main periodicity) by using CWT analysis. Moreover, approximation mode shows potential trend of the whole data set because it is the residuals as all periodicities are removed from data series. Thus, the mode A + D5 is responsible for producing a real basic structure of the trend founded on the data. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

19.
Identification of sub-trends from hydro-meteorological series   总被引：1，自引：1，他引：0  
In hydro-meteorological trend analysis, an alteration in the given variable is detected by considering the long-term series as a whole. Whereas the long-term trend may be absent, the significance of hidden (short-durational) sub-trends in the series may be important for environmental management practices. In this paper, a graphical approach of identifying trend or sub-trends using nonparametric cumulative rank difference (CRD) was proposed. To confirm the significance of the visualized trend, the CRD was translated from the graphical to a statistical metric. To assess its capability, the performance of the CRD method was compared with that of the well-known Mann–Kendall (MK) test. The graphical and statistical CRD techniques were applied to detect trends and sub-trends in the annual rainfall of 10 River Nile riparian countries (RNRCs). The co-occurrence of the trend evolutions in the rainfall with those of the large-scale ocean–atmosphere interactions was analyzed. The power of the CRD method was shown to closely agree with that of the MK test under the various circumstances of sample sizes, variations, linear trend slopes, and serial correlations. At the level of significance α = 5 %, the long-term trends were found present in 30 % of the RNRCs. However at α = 5 %, the main downward (upward) sub-trends were found significant in 30 (60 %) of the RNRCs. Generally at α = 1 %, linkages of the trend evolutions in the rainfall of the RNRCs were found to those of the influences from the Atlantic and Indian Oceans. At α = 5 %, influences from the Pacific Ocean on the rainfall trends of some countries were also evident.  相似文献   

20.
A statistical test on climate and hydrological series from different spatial resolution could obtain different regional trend due to spatial heterogeneity and its temporal variability. In this study, annual series of the precipitation heterogeneity indices of concentration index (CI) and the number of wet days (NW) along with annual total amount of precipitation were calculated based on at‐site daily precipitation series during 1962–2011 in the headwater basin of the Huaihe River, China. The regional trends of the indices were first detected based on at‐site series by using the aligned and intrablock methods, and field significance tests that consider spatial heterogeneity over sites. The detected trends were then compared with the trends of the regional index series derived from daily areal average precipitation (DAAP), which averages at‐site differences and thus neglects spatial heterogeneity. It was found that the at‐site‐based regional test shows increasing trends of CI and NW in the basin, which follows the test on individual sites that most of sites were characterized by increasing CI and NW. However, the DAAP‐derived regional series of CI and NW were tested to show a decreasing trend. The disparity of the regional trend test on at‐site‐based regional series and the DAAP‐derived regional series arises from a temporal change of the spatial heterogeneity, which was quantified by the generalized additive models for location, scale, and shape. This study highlights that compared with averaging indices, averaging at‐site daily precipitation could lead to an error in the regional trend inference on annual precipitation heterogeneity indices. More attention should be paid to temporal variability in spatial heterogeneity when data at large scales are used for regional trend detection on hydro‐meteorological events associated with intra‐annual heterogeneity.  相似文献   

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