Trend analysis in Turkish precipitation data

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.     

Quality control and homogeneity of Turkish precipitation data

Outlier trimming and homogeneity checking/correction were performed on the monthly precipitation time series of various lengths from 267 stations in Turkey. Outlier values are usually found during dry summer months, and are concentrated mostly over the southern parts of the country, where the dry period is most pronounced, implying natural extremes rather than wrong measurements. Homogeneity analysis was done using the Standard Normal Homogeneity Test, on an individual monthly basis, which led to many non‐testable series due to lack of reference stations, especially during summer months. Yet, remaining testable months were usually helpful for the assessment of homogenity, revealing a well distributed set of stations that proved to be homogeneous. There were still a number of stations which either could not be tested efficiently, or were classified as inhomogeneous. Lack of metadata is argued to be largely responsible for inefficient homogeneity testing. Copyright © 2008 John Wiley & Sons, Ltd.     

Analysis of Turkish precipitation data: homogeneity and the Southern Oscillation forcings on frequency distributions

The homogeneity of newly compiled 212 precipitation records in Turkey for the period 1973‐2002 was checked by the Standard Normal Homogeneity Test (SNHT) and Pettitt Test. Stations were considered inhomogeneous if at least one of the tests rejects the homogeneity. As a result, 43 out of 212 stations were found to be inhomogeneous. In addition, the previously detected Southern Oscillation (SO)‐related precipitation anomalies by the authors were quantified at each station using the gamma distribution. The observed SO‐related shifts in the median precipitation amounts expressed as gamma percentiles may be considered as a typical SO response of that station. The results of this study confirm the wet responses of Turkish precipitations to El Nino events, whereas those for La Nina events seem to be masked by sampling variations within the study period. Copyright © 2007 John Wiley & Sons, Ltd.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

High‐ and low‐flow variations in annual runoff and their response to climate change in the headstreams of the Tarim River,Xinjiang, China

Based on the hydrological and meteorological data recorded for the northern and southern headstreams of the Tarim River over the last 50 years, this paper analyses the variation characteristics of high‐flow and low‐flow indexes of annual runoff, air temperature and precipitation using a non‐parametric test. Additionally, this paper also studies the correlations between these three time series on multiple time scales for both northern and southern headstreams employing wavelet analysis. The results show the following: (i) the annual runoff and air temperature had significant increasing trends, whereas precipitation had a non‐significant increasing trend for the northern and southern headstreams. (ii) Abrupt changes appeared in precipitation in the north and south in 1990 and 1986, as well as in high‐flow and low‐flow indexes of annual runoff in 1993 and in air temperature in 1996. (iii) In the case of the northern headstreams, there was significant periodicity of 6 years in both high‐flow and low‐flow indexes and air temperature and of 3 and 8 years in precipitation. In the case of the southern headstreams, there was significant periodicity of 3 and 9 years in high‐flow and low‐flow indexes, 5 years in air temperature, and 5 and 8 years in precipitation. (iv) The high‐flow and low‐flow indexes in the headstreams of the Tarim River are closely related to the air temperature and precipitation. Copyright © 2012 John Wiley & Sons, Ltd.     

Standard normal homogeneity test as a tool to detect change points in climate-related river discharge variation: case study of the Kupa River Basin

ABSTRACT

The study addresses homogeneity testing of annual discharge time series for eight hydrological stations and five annual climate time series for one weather station in the Kupa River Basin, between Slovenia and Croatia, and global annual average surface temperature time series for the period 1961–2010. The standard normal homogeneity test (SNHT) was used to detect both abrupt and gradual linear trend homogeneity breaks. The results reveal natural change points at the beginning of the 1980s. Absolute homogeneity testing of average annual weather station-level air pressure, annual precipitation, differences between precipitation totals and potential evapotranspiration and surface runoff from the independent observation time series confirmed an abrupt shift, also at the beginning of the 1980s. The trend of local air temperature for 1985–2000, which partly coincides with global surface temperature trend for 1974–2005, strengthened the river discharge regime shift since the beginning of the 1980s. These results could improve climate variation monitoring and estimation of the impact of climate variation on the environment in the area. Generally, an indication of climate regime change points and an assessment of their duration could provide significant benefits for the society.     

Evaluation of efficiency of different estimation methods for missing climatological data

Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001–2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.     

Trends in hydrological and climatic variables affected by four variations of the Mann-Kendall approach in Urmia Lake basin,Iran

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     

Frequency based imputation of precipitation

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method.     

Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey)

The purpose of this study is to determine the possible trends in annual total precipitation series by using the non-parametric methods such as the wavelet analysis and Mann-Kendall test. The wavelet trend (W-T) analysis is for the first time presented in this study. Using discrete wavelet components of measurement series, we aimed to find which periodicities are mainly responsible for trend of the measurement series. We found that some periodic events clearly affect the trend of precipitation series. 16-yearly periodic component is the effective component on Bal?kesir annual precipitation data and is responsible for producing a real trend founded on the data. Also, global wavelet spectra and continuous wavelet transform were used for analysis to precipitation time series in order to clarify time-scale characteristics of the measured series. The effects of regional differences on W-T analysis are checked by using records of measurement stations located in different climatic areas. The data set spans from 1929 to 1993 and includes precipitation records from meteorological stations of Turkey. The trend analysis on DW components of the precipitation time series (W-T model) clearly explains the trend structure of data.     

The links between the categorised Southern Oscillation indicators and climate and hydrologic variables in Turkey

Some previous global and regional studies have indicated teleconnection between the extreme phases of the Southern Oscillation (SO) and Turkish climate and hydrologic variables; however, they failed to suggest a strong correlation structure. In this study, categorised Southern Oscillation index (SOI) and Multivariate ENSO (El Nino Southern Oscillation) index (MEI) series were used to examine the far‐reaching effects of the SO on temperature, precipitation and streamflow patterns in Turkey. These SO indicators were categorised into five subgroups according to their empirical distributions. Correlations between the categorised SO indicators and three analysis variables were computed using the Spearman's rho from lag‐0 to lag‐4. Significance of calculated correlations was tested at the 0·01 level for station‐based analysis and at the 0·05 level for regional analysis. Temperature records demonstrated significant correlations with the categorised SOI and MEI in nearly half of the entire stations. For some categories, precipitation and streamflow were found to be correlated with the SO indicators in some stations mainly in western Turkey. Regional analyses of temperature and precipitation revealed a clear and strong correlation structure with the categorised SO indicators on a large portion of Turkey. This was not concluded by the earlier pertinent studies. Besides, this study showed that significant correlations were obtained not only for the SO extreme phases (namely, El Nino and La Nina) but also for neutral and moderate phases of the SO. Plausible explanations for the observed teleconnection are presented. Copyright © 2009 John Wiley & Sons, Ltd.     

Impacts of Urbanization and Station-relocation on Surface Air Temperature Series in Anhui Province, China

Since the 1990s, many meteorological stations in China have passively “entered” cities, which has led to frequent relocation and discontinuity in observational records at many stations. To study the impacts of urbanization on surface air temperature series, 52 meteorological stations in Anhui Province were chosen based firstly on a homogeneity test of the time series, and then their surrounding underlying surfaces during different decades were identified utilizing Landsat Multispectral Scanner images from the 1970s, Landsat Thematic Mapper images from 1980s and 1990s, and Enhanced Thematic Mapper images after 2000, to determine whether or not the station “entered” city, and then these stations were categorized into three groups: urban, suburban, and rural using Landsat-measured land use/land cover (LULC) around the station. Finally, variations in annual mean air temperature (T _mean), maximum air temperature (T _max), and minimum air temperature (T _min) were analyzed in urban-type stations and compared to their surrounding rural-type stations. The results showed that, in Anhui Province over the past two decades, many rural stations experienced urbanization and changed into urban or suburban locations. This process is referred as the “city-entering” phenomena of stations. Consequently, many of the latest stations were relocated and moved to currently rural and suburban areas, which significantly influenced the continuity of observational records and the homogeneity of long-term trends. Based on homogeneous data series, the averaged annual T _mean, T _max, and T _min over Anhui Province increased at a rate of 0.407, 0.383 and 0.432 °C decade^?1 from 1970 to 2008. The strongest effect of urbanization on annual T _mean, T _max, and T _min trends occurred at urban stations, with corresponding contributions of 35.824, 14.286, and 45.161 % to total warming, respectively. This work provides convincing evidences that (1) urban expansion has important impacts on the evaluation of regional climate change, (2) high spatial resolution images of Landsat are very useful for selecting reference climate stations for evaluating the potential urban bias in the surface air temperature data in certain regions of the continents, and (3) meteorological observation adjustments of station-relocation-induced inhomogeneities are essential for the study of regional or global climate change.     

Monotonic trend and abrupt changes for major climate variables in the headwater catchment of the Yellow River basin

On the basis of the mean air temperature, precipitation, sunshine duration and pan evaporation at 23 meteorological stations in the headwater catchment of the Yellow River basin from 1960 to 2001, the long‐term monotonic trend and abrupt changes for major climate variables have been investigated. The plausible monotonic trend of annual climatic time series are detected using a non‐parametric method. The abrupt changes have been investigated in terms of a 5 year moving averaged annual series, using the moving t‐test (MTT) method, Yamamoto method and Mann–Kendall method. The results showed that the annual air temperature has increased by 0·80 °C in the headwater catchment of the Yellow River basin during the past 42 years. One obvious cold period and one warm period were detected. The warmest centre was located in the northern part of the basin. The long‐term trend for annual precipitation was not significant during the same period, but a dry tendency was detected. According to the Kendall slope values, the declining centre for annual precipitation was located in the eastern part and the centre of the study area. The long‐term monotonic trend for annual sunshine duration and pan evaporation were negative. The average Kendall slopes are ? 29·96 h/10 yr and ? 39·63 mm/10 yr, respectively. The tests for abrupt changes using MTT and Yamamoto methods show similar results. Abrupt changes occurred in the mid 1980s for temperature, in the late 1980s for precipitation and in the early 1980s for sunshine duration and pan evaporation. It can be seen that the abrupt changes really happened in the 1980s for the climate variables. Different results are shown using the Mann–Kendall method. Both the abrupt changes of temperature and precipitation took place in the early 1990s, and that of pan evaporation occurred in the 1960s. The only abrupt change in sunshine duration happened during the similar period (in the 1980s) with the results detected by the MTT and Yamamoto methods. The abrupt changes which occurred in the 1990s and 1960s are not detectable using the MTT and Yamamoto methods because of the data limitation. However, the results tested by the MTT and Yamamoto methods exhibited great consistency. Some of the reasons may be due to the similar principles for these two methods. Different methods testing the abrupt climatic changes have their own merits and limitations and should be compared based on their own assumption and applicable conditions when they are used. Copyright © 2008 John Wiley & Sons, Ltd.     

Spatiotemporal analysis of precipitation trends in the Yangtze River catchment

Precipitation trends in the Yangtze River catchment (PR China) have been analyzed for the past 50 years by applying the Mann-Kendall trend test and geospatial analyses. Monthly precipitation trends of 36 stations have been calculated. Significant positive trends at many stations can be observed for the summer months, which naturally show precipitation maxima. They were preceded and/or followed by negative trends. This observation points towards a concentration of summer precipitation within a shorter period of time. The analysis of a second data set on a gridded basis with 0.5° resolution reveals trends with distinct spatial patterns. The combination of classic trend tests and spatially interpolated precipitation data sets allows the spatiotemporal visualization of detected trends. Months with positive trends emphasize the aggravation of severe situation in a region, which is particularly prone to flood disasters during summer. Reasons for the observed trends were found in variations in the meridional wind pattern at the 850 hPa level, which account for an increased transport of warm moist air to the Yangtze River catchment during the summer months.     

Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

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.     

Stream Temperature Trends in Turkey

Stream temperature is an important property of water and affects most other water quality constituents. It is also a property which is very much influenced by exogenous factors like air temperature and stream flow. This study investigates long‐term trends in stream temperatures measured at various stream monitoring stations in Turkey to better understand links with climate change. It was found by statistical trend analysis that more streams have experienced decreasing trends than increasing ones. Moreover, stream temperatures show a rising tendency in most stations over Turkey. Flow‐adjusted temperatures were computed to eliminate flow dependency and these show more positive than negative trends. Management plans of streams and watersheds need to take this into account and incorporate the implications into plans.     

Statistical characteristics of detectable inhomogeneities in observed meteorological time series

Statistical characteristics of detectable inhomogeneities [IHs] in more than 600 observed meteorological time series have been investigated using 16 objective homogenisation methods. Forty and 100 year long series of monthly or annual characteristics of surface air temperature, precipitation total and relative air humidity from the Czech Republic and Hungary were examined. The area of the part of the Czech observing network used here is smaller, and the density of sites is larger, than in the Hungarian network, resulting in higher spatial correlations among data in the Czech dataset relative to the Hungarian dataset. Time series with low number of gaps were supplied with interpolated data. Before homogenisation relative time series were created, using weighted averages of time series from the same geographical region as reference series. For ease of comparison, the magnitudes of the detected IHs are normalised with the standard deviation of the noise in the relative time series. Results show that observed meteorological time series usually contain large number of small IHs, and that the magnitude distribution of IHs from different data segments are surprisingly similar. Effects of different spatial coherences on the results are discussed.     

Long‐term trend of precipitation in China and its association with the El Niño–southern oscillation

The plausible long‐term trend of precipitation in China and its association with El Niño–southern oscillation (ENSO) are investigated by using non‐parametric techniques. It is concluded that a greater number of decreasing trends are observed than are expected to occur by chance. Geographically, the decreasing trend was concentrated in most parts of China, including the Songliao River, Hai River, Huai River, Yellow River, Zhujiang River, and southern part of the Yangtze River basins, whereas an increasing trend appeared primarily in the western and middle parts of China, mainly including the Inland River basin, and the northern part of the Yangtze River basins. Monthly mean precipitation for the summer and early autumn months generally decreased, with the greatest decrease occurring in August. The precipitation in spring from January to April and later autumn, including September and October, tended to increase. The teleconnection between precipitation and ENSO has been investigated by using the non‐parametric Kendall's τ. The correlation coefficients between the southern oscillation index (SOI) and precipitation show the areas with positive or negative associations. Approximately 20% of the stations exhibit statistically significant correlations between SOI and precipitation, of which 70% show a negative correlation, with most of them appearing in southeast China and several appearing in northwest and northeast China. Similar regional patterns are also observed when the precipitation records are further subdivided into El Niño, La Niña, and neutral periods. Statistical tests for the three kinds of time series were carried out using the non‐parametric Wilcoxon rank‐sum test, and it is noted that the stations with significant differences in precipitation averages are mainly marked in the Yellow River basin and south China. The frequencies of below‐ and above‐average precipitation that occurred during the El Niño, La Niña, and neutral periods are estimated as well. The result shows that greater precipitation may be associated with El Niño episodes in south China, but drought may easily occur during El Niño episodes in the Yellow River basin. Copyright © 2006 John Wiley & Sons, Ltd.     

Long‐term trend analysis for major climate variables in the Yellow River basin*

Some previous studies have shown that drying‐up of the lower Yellow River resulted from decreasing precipitation and excessive industrial and agricultural consumption of water from the middle and downstream regions of the Yellow River. On the basis of average air temperature, precipitation, and pan evaporation data from nearly 80 gauging stations in the Yellow River basin, the monotonic trends of major climate variables over the past several decades are analysed. The analysis was mainly made for 12 months and the annual means. The isograms for annual and typical months are given in the paper. The result shows that the average temperature in the study area exhibits an increasing trend, mainly because of the increase of temperature in December, January and February. The largest trend is shown in December and the smallest is in August. There are 65 of 77 stations exhibiting a downward trend for annual precipitation. In all seasons except summer, there is a similar trend in the upstream region of the Yellow River, south of latitude 35°N. It is interesting to note that the pan evaporation has decreased in most areas of the Yellow River basin during the past several decades. April and July showed the greatest magnitude of slope, and the area from Sanmenxia to Huayuankou as well as the Yiluo River basin exhibited the strongest declining trend. The conclusion is that the decreasing pan evaporation results from complex changes of air temperature, relative humidity, solar radiation, and wind speed, and both climate change and human activities have affected the flow regime of the Yellow River during the past several decades. Copyright © 2007 John Wiley & Sons, Ltd.     

近台资料对近震相对定位算法的影响

从地震定位的基本原理出发,分析认为近台资料可用于双差定位算法,但不能用于其他相对定位方法.在此基础上,通过分析和模拟计算表明,应用近台资料时,双差法有可能给出一定精度的地震绝对位置.但仅采用近台资料时,相对位置的误差会比使用远台资料时有所增加.近台和远台资料的联合使用,有利于得到较为精确的定位结果.当震源深度远小于震中距时,如果没有深度震相的参与,只能得到误差较小的震中相对分布,深度的相对位置仍有较大的误差.对2003年新疆伽师地震余震序列中部分余震的重新定位试验,验证了近台资料对双差定位算法的上述影响.