Precipitation Variations along the Bulgarian Coast from 1950 to 2009

Veneta Ivanova and Vesselin Alexandrov National Institute of Meteorology and Hydrology – BAS, [email protected]

Abstract

During the last years cases with increasing heavy precipitation have been observed at the Bulgarian Black Sea coast. An analysis of about 50-years period regarding the precipitation variations at the above region has been done. Data and information from 11 coastal meteorological stations were used. Several core precipitation indices characterizing precipitation intensity, frequency and distribution had been selected from the STARDEX project. Most of the applied precipitation indices show insignificant positive or negative changes for the period considered in the study. This refers not only for the seasonal but also for annual values as well as for the averaged values at all stations. A positive (statistically insignificant) trend was observed, for example, for the averaged annual values of the 90th percentile of the daily precipitation amounts in respect to the maximum 3- and 5-days precipitation sums. Opposite, a very little negative trend was observed for the numbers of days with precipitation higher or equal of 10mm as well as for the maximum number of consecutive wet days.

Keywords: precipitation variations, STARDEX, Mann-Kendall test

Introduction

Bulgaria is situated in the south part of the Balkan Peninsula and its eastern border is the Black sea. The climate is mostly continental, but the influence of Mediterranean and Black sea define the precipitation conditions over its southern and eastern parts and in particular along the coast. Severe hydro-meteorological extremes (e.g. floods, cold winter, drought) are observed during the last decades. Improvements of synoptic models are needed for weather forecasting in order to prevent large material and human losses. Investigation of the long-term variations and trend of precipitation extremes can help to understand climate variability. For the Bulgarian region many climatic indices have been already applied (Koleva et al., 2004; Alexandrov et al., 2006). In this work the STARDEX software was used to calculate some precipitation indices only for the Black sea coastal meteo stations. Their tendency was analyzed, as well.

Data and methods

Total monthly and annual precipitation sums for 11 coastal stations in Bulgaria, collected from the National Institute of Meteorology and Hydrology of Bulgarian Academy of Sciences for the period 1950 – 2009 are used (Table 1). To calculate climate indices the EU research project “STAtistical and Regional dynamical Downscaling of Extremes for European regions (STARDEX)” (http://www.cru.uea.ac.uk/projects/stardex) was explored. To provide an overall picture of precipitation variations along the coast, the average trend for every index is computed. To check the trends of precipitation indices used in this study the non-parametric Mann-Kendall test and the Sean’s method are implemented. A priority of this test is that the data needed not conform to any particular distribution and is robust to the effect of outliers in the series. It is applicable in cases when the data values xi of a time series can be assumed to obey the model Xi=f(ti)+εi (1) where f(t) is a continuous monotonic increasing or decreasing function of time and the residuals εi can be assumed to be from the same distribution with zero mean. It is therefore assumed that the variance of the distribution is constant in time.

The Sen’s method can be used in cases where the trend can be assumed to be linear. This means that f(t) in equation (1) is equal to f(t) = Qt + B (2)

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 1 where Q is the slope and B is a constant.

Table 1: Meteo stations and periods with precipitation data

Station Period of available data Durankulak 1950-2009 1954-2009 cape 1953-2009 1950-2009

Slanchev den 1950-2009

Varna 1959-2009

cape Emine 1965-2009 1950-1990 1950-2009 1971-2009 1953-2009

Four levels of significance of the Mann-Kendall test are used to identify the correct tendency of the analyzed indices corresponding to the next symbols:

Table 2: Symbols for significance applying in Mann-Kendall test

α = 0.001 α= 0.01 α= 0.05 α= 0.1

*** ** * +

For visual statistical results is used an Excel template – MAKESENS, developed in the Finnish Meteorological Institute.

Quality control and homogenization

To carry out a quality data control and homogenization of monthly and annual precipitation series a software package that is used is RHtestV3 (Wang and Feng 2010), based on the penalized maximal t test (Wang et al. 2007) and the penalized maximal F test (Wang 2008b). It is developed at the Meteorological Service of Canada and is based on two-phase regression model with a linear trend for the entire base series (Wang, 2003). The significant level here is α=0.05. In addition, this software can detect all changepoints that could be significant at this level even without metadata support (these are called Type-1 changepoints). All missing values are assume to be equally “-99.9”.

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 2 Results

Precipitation homogenization

In charts below with a red line is shown the location and magnitude of possible step of changes in the time series in station Varna, Bulgaria, for the period of 1959-2009. Moreover, the red line indicates the linear regression across the homogenous sections between the possible step-change points. It should be noted that not all of possible step changes are statistically significant. Sometimes, they even do not contain metadata.

Figure 1: Base series and regression fit of annual precipitation for station Varna, Bulgaria

Figure 2: Mean-adjusted base series of annual precipitation for station Varna, Bulgaria

Precipitation indices

The STARDEX package consists of around 80 indices, related to air temperature and precipitation. The calculated precipitation indices, used in this work, are shown in the Table 3:

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 3 Table 3: Precipitation indices, applied to determine changes

Index Definitions Units

pq90 90th percentile of rainday amounts mm/day pn10mm Numbers of days with precipitation >= 10mm days

pxcdd Max number consecutive dry days days pxcwd Max number consecutive wet days days

pdsav Mean dry spell lengths days

px3d Greatest 3-day total rainfall mm

px5d Greatest 5-day total rainfall mm

pav Mean climatological precipitation mm/day Tren ds in the indices for a region as a whole are shown in Figure 3. As previously mentioned, they are obtained as arithmetic average of the annual indices values at all stations.

a)

b)

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 4 c)

d)

e)

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 5 f)

g)

h)

Figure 3: The annual index of (a) 90th percentile of precipitation amounts, (b) pn10mm, (c) pxcdd, (d) pxcwd, (e) px3d, (f) px5d, (g) pdsav, and (h) pav. The dashed blue line is a linear trend.

It seems that all of the annual indices of the whole region of interest haven’t significant trend during the studied period. This largely is because the trends of individual stations have different sign, for the most part too small, as is the example shown in Figure 4:

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 6 a)

b)

Figure 4: Trend in annual maximum precipitation sums for 3 consequent days for (a) Durankulak and (b) Rezovo

All trends are checked for significance. The test of Mann-Kendall and Sen’s slope give us following important information.

Figure 5: Detecting trend of annual values of 90th percentile of precipitation amounts in the meteo station Shabla, Bulgaria

Follow the guidelines when working with the Excel template – MAKESENS is observed that the Mann- Kendall test statistic Z (normal approximation) has a negative value. That means downward trend. Using the symbols from Table 2 it has been concluded that the 90th percentile of the precipitation per day amounts have a negative tendency in the meteo station Shabla (situated on the north sea coast) and this trend is statistically significant at 99% level of confidence. The summarized results show that

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 7 significance of the trend of each index for each station is different. Results for annual indices for the entire region can be presented in a tabular form as follows:

Table 4: Summarized results for significance of precipitation indices along Bulgarian Black sea coast

pq90 pn10mm px3d px5d pxcdd pdsav pav

Durankulak * *

Shabla ** * + cape Kaliakra +

Kavarna Slanchev den +

Varna

cape Emine + *

Nesebar * * + **

Burgas Ahtopol + + ***

Rezovo + ***

Summarized results are the following: about of 2,6% of the values are statistically significant at the 0.1 % level about of 3,9% of the values are statistically significant at the 1% level about of 7,8% of the values are statistically significant at the 5% level about of 10,4% of the values are statistically significant with α=0.1 about of 77 % of the values are statistically significant with α›0.1

Conclusion

The aim in present work was to find tendency in precipitation variations along the Bulgarian Black sea coast. The period of 1950-2009 was investigated. Eight core precipitation indices included into the STARDEX software were calculated. Insignificant tendencies of precipitation variations were found. The trend results fit very well to the ones obtained by Alexander et al. (2006) The obtained results give some more details regarding the findings established by Alexandrov et al.(2006). The applied homogenization procedure of the time series provides reliability in the above results.

References

(1) Alexandrov,V., B. Dubuisson, J-M. Moisselin and E. Koleva, 2006. A case study on utilization of precipitation indices in Bulgaria. Proceedings of the International conference on Water Observation and Information System for Decision Support (BALWOIS), Ohrid, Macedonia, 23-26 May 2006, (CD) 18 pp.

(2) Alexander,L.V., et al., 2006. Global observed changes in daily climate extremes of temperature and precipitation. J. Geoph. Res., 111

(3) Haylock,M.R., 2003. Observed changes in European extremes, 1958–2000. Stardex report, University of East Anglia

(4) Koleva, E., N. Slavov and V. Alexandrov, 2004. Drought during the 20 Century. In: Knight, C. G., I. Raev and M. Staneva (eds.), 2004. Drought in Bulgaria: A contemporary analog for climate change. Aldershot, UK: Ashgate Publishing Limited, pp. 53-66.

BALWOIS 2012 – Ohrid, Republic of Macedonia - 28 May, 2 June 2012 8 (5) Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010. New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol.

(6) Wang, X. L., 2008. Penalized maximal F-test for detecting undocumented mean-shifts without trend-change. J. Atmos. Oceanic Tech., 25 (3): 368-384

(7) Wang, X. L., Q. H. Wen, and Y. Wu, 2007. Penalized maximal t test for detecting undocumented mean change in climate data series. J. Appl. Meteor. Climatol., 46 (6): 916-931

(8) http://www.cru.uea.ac.uk/projects/stardex (visited on 12.03.2011)

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