DETECTION OF CHANGES IN HYDROLOGICAL TIME SERIES …

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DETECTION OF CHANGES IN HYDROLOGICAL TIME SERIES DURING RECENT DECADES

Mária ĎURIGOVÁ1*, Kamila HLAVČOVÁ1, Jana POÓROVÁ2

Address

1 Slovak University of Technology in Bratislava, Faculty of Civil Engineering, Department of Land and Water Resources Management, Slovakia

2 Slovak hydrometeorogical institute, Jeséniova 17, Bratislava, Slovakia

* Corresponding author: [email protected]

Abstract

An analysis of a hydrological time-series data offers the pos-sibility of detecting changes that have arisen due to climate change or change in land use. This paper deals with the detec-tion of changes in the hydrological time data series. The trend analysis was applied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017. The Mann-Kendall test show a de-clining trends in the summer and a few rising trends in the win-ter in discharges. In the town of Banská Bystrica at a station on the Hron River, decades of discharges, air temperatures, and precipitation totals were analyzed. The five decades from the 1960s to the 2000s were used. The hydrological time data series were also analyzed by the Pettitt’s test, which is used to detect change points. The decadal analysis at the Banská Bystrica station shows an increase in the air temperature but insignificant changes in discharges and precipitation. Pettitt’s test identified many change points in the 1990s in the air tem-perature.

Key words

● Trend analysis, ● Discharge, ● Change point.

1 INTRODUCTION

Changes in natural phenomena, such as increasing sea levels, global warming, and more occurrences of extremes in hydrology and mete-orology, affect us and the environment. Studies directed at changes in hydrological regimes are of great importance, especially in the fields of water resources management, flood protection, and the revitalization of rivers; they concentrate on maintaining the quality of aquatic habi-tats or minimum discharges in the summer season (Barnett et al., 2005; Hlavčová et al., 2008; Škvarenina et al., 2010). Analysing the hydrolog-ical data of an instrumental period and identifying identifiers of changes in a runoff regime allow for determining changes in the statistical prop-erties of the data. This can be used in models of a hydrological regime´s evolution under climate change conditions (Szolgay et al., 2004).

Authors dealing with the issue of change detection, especially in hydrological data, can detect changes using various methods. Hal-

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DOI: 10.2478/sjce-2020-0016

Slovak Journal of Civil Engineering

mová and Pekárová (2013) evaluated minimum and maximum daily discharges from 1929 to 2011 using IHA hydrological software. Je-neiová et al. (2015) focused on detecting changes in long-term data series. The annual peak discharges from nine stations in southern Slo-vakia were used for the analysis. Changes in the flood regime of the Danube River in Slovakia were analysed by Pramuk et al. (2013). They evaluated changes in the amount, time of occurrence, and size of flood waves. Frequency analysis and long-term trend analysis were used. The results contained in the papers by the various Slo-vak authors above show a reduction in the runoff coefficient (Pra-muk et al., 2016), following by an increase in the frequency of floods but a reduction their duration (Pramuk et al., 2013). Halmová et al. (2019) used the Mann-Kendall test to analyze trends in mean monthly discharges and extreme discharges at Belá – Podbanské station and Váh – Liptovský Mikuláš station. Results showed that is necessary to analyse the longest series of observations at any station.


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Various German papers point to an upward trend in discharges on the Rhine River, which were caused by an increase in winter precip-itation (Bronstert et al., 2002; Schӧnwiese and Rapp, 1997). Petrow and Merz (2009) dealt with the frequency of floods in Germany, where increasing trends were also detected. Bawden et al. (2014) dealt with an analysis of the trends and variability of the hydrological regime in the Athabasca catchment and surrounding catchments in Canada. The study showed a decreasing tendency in the trends. Wong et al. (2006) identified the change points in a hydrological time series using the grey relational method. The method was applied at several stations on the Shunde River in China. Gautier et al. (2018) investigated the hydrologi-cal response to climate change at the Lena River in eastern Siberia. The locality is the coldest area of the northern hemisphere, and the authors focused on the development of the floods. They found an increase in spring floods and peak discharges and also determined the beginning of a flood is less predictable. Summer floods are more frequent and intense.

This paper deals with the detection of changes in mean monthly discharges using the Mann-Kendall test. The trend analysis was ap-plied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017. In Hron – Banská Bystrica gauging station, decades of mean monthly discharges, air temperatures and monthly precipitation totals related to the river basin were analyzed. These hydrological time data series were also an-alyzed by the Pettitt’s test, which is used to detect change points.

2 MATERIALS

Slovakia belongs in a northern temperate climate zone. The mean annual temperature is from 6˚C to 11˚C, and the mean annual rainfall total is from 500 mm to 2,000 mm (MINŽP, 2011). The observed time data series was provided by the Slovak Hydrometeorological Institute. The time data series were selected from 1962 to 2017 for all 58 stage-discharge gauging stations (Fig. 1, Tab. 1). The stations are of different sizes and at different altitudes of the catchment area. The aim of this selection was to have the most even coverage of all of Slovakia. The stations are unaffected or only slightly affected by water consumption or hydro-technical constructions (Danáčová et al., 2015). The time data series used are:

– the mean monthly discharges (the hydrological year, i.e., No-vember – October),

– the mean annual discharges (Qr),– the mean seasonal discharges (Qwin, Qsum), i.e., the winter sea-

son (November – April) and the summer season (May – October).

The upper Hron River basin to the Banská Bystrica profile has an area of 1766 km2. The minimum elevation of the basin is 340 m a.s.l, the maximum elevation is 2004 m a.s.l, and the mean elevation is 850 m a.s.l. The basin’s area is 70 % covered by forest, 10 % by grasslands, 17 % by agricultural land and 3 % urban areas (Hlavčová, 2008).

3 METHODS

The Mann-Kendall test is a non-parametric test that detects mono-tonic trends in time data series. This test is widely used for environ-mental, climate, or hydrological data. The null hypothesis says that the time data series comes from a population with independent realiza-tions and is identically distributed. The alternative hypothesis says that the time data series follows a monotonic trend (Pohlert, 2018; Kendall, 1957). The Mann-Kendall test statistic S is calculated according to:

(1)

where xj and xk are the time series observations in chronological order, and n is the length of the time series.

The Mann-Kendall test was applied to the time data series to analyze the mean monthly discharges, the mean annual discharges, and the mean seasonal discharges of the winter and summer seasons. The results of the trend analysis were spatially interpreted using the ArcGIS software.

For the Hron – Banská Bystrica gauging station (7160), decades of the mean monthly discharge, air temperatures, and monthly rainfall total were analyzed. The five decades from the 1960s to the 2000s were used. The time data series were obtained from the CarpatClim database (CarpatClim, 2013). The Thiessen Polygons method was used to calculate the precipitation and air temperature for the Hron River basin. The rainfall totals were calculated as a weighted average of the data from the individual grid points of the CarpatClim data-base. The weight of the area assigned to the relevant point within the river basin was considered as the weight (Keszeliová, 2019).

These hydrological time data series were also analyzed by Pet-titt’s test, which is used to detect change points. Pettitt’s test allows researchers to determine if a series can be considered as homogeneous or if abrupt changes have appeared over time. It is a widely used tool for detecting change points in hydrological processes. Pettitt’s test obtained the most probable location of the change point, and the sig-nificance of this change point was evaluated by the corresponding

Fig. 1. A map of the stage-discharge gauging stations and their IDs in Slovakia used


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p-value=0.1 (Pettitt, 1979). The test was evaluated with the RStudio statistical software. The test statistic is defined as:

(2)

(3)

where k=1,2,…,n and ri are the ranks of the observations Xi. The most probable change point is located where reaches its maximum value.

4 RESULTS

The first part of the results deals with the detection of changes in mean monthly discharges using the Mann-Kendall test. This trend anal-ysis was applied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017.

The second part of results deals with the decadal analysis in the Hron – Banská Bystrica gauging station. The five decades from the 1960s to the 2000s of mean monthly discharges, air temperatures, and monthly precipitation totals related to the river basin were analyzed. These hydrological time data series were also analyzed by the Pettitt’s test, which is used to detect change points.

4.1 Mann-Kendall test

The Mann-Kendall test was applied at mean monthly, annual and seasonal discharges, at 58 stage-discharge gauging stations. The total number of identified trends was 203, with a statistical significance of α=0.1. There are 178 declining trends and 25 rising trends. In the evaluation of the trends by months and seasons, most of the trends were identified in June, namely 36 trends (Fig. 2), all of which had a decreasing character. The lowest trend was identified in September,

Tab. 1. The list of stage-discharge gauging stations used

ID STATION RIVER CATCHMENT AREA [km2] ID STATION RIVER CATCHMENT

AREA [km2]5040 Moravský Ján Morava 24129.30 6690 Biskupice Bebrava 312.60

5100 Láb Močiarka 47.10 6710 Nadlice Bebrava 598.81

5140 Bratislava Dunaj 131331.1 6730 Nitrianska Streda Nitra 2093.71

5160 Pezinok Blatina 19.09 6820 Vieska n/Žitavou Žitava 295.46

5230 Bohdanovce Trnávka 115.02 7015 Brezno Hron 582.08

5310 Čierny Váh Ipoltica 87.07 7065 Mýto p/Ďumbierom Štiavnička 47.10

5311 Čierny Váh Čierny Váh 243.06 7070 Dolná Lehota Vajskovský p. 53.02

5330 Východná Biely Váh 105.64 7160 B.Bystrica Hron 1766.48

5400 Podbanské Belá 93.49 7290 Brehy Hron 3821.38

5550 Liptovský Mikuláš Váh 1107.21 7440 Holiša Ipeľ 685.67

5740 Podsuchá Revúca 217.95 7600 Plášťovce Litava 214.27

5790 Ľubochňa Ľubochnianka 118.39 7660 Dobšiná Dobšinský p. 31.97

5800 Lokca Biela Orava 359.96 7820 Lenartovce Slaná 1829.65

5810 Oravská Jasenica Veselianka 90.10 7860 Lehota n/Rimavicou Rimavica 148.95

5820 Zubrohlava Polhoranka 158.67 7930 Ždiar. Podspády Javorinka 34.89

5840 Trstená Oravica 129.95 8320 Chmelnica Poprad 1262.41

6130 Martin Turiec 827.00 8530 Stratená Hnilec 68.23

6150 Stráža Varínka 139.70 8840 Prešov Sekčov 352.80

6180 Čadca Kysuca 492.54 8870 Košické Olšany Torysa 1298.30

6200 Kys. N. Mesto Kysuca 955.09 8930 Ždaňa Hornád 4232.20

6300 Poluvsie Rajčianka 243.60 8970 Nižný Medzev Bodva 90.15

6450 Horné Srnie Vlára 341.79 9120 Koškovce Laborec 437.90

6470 Čachtice Jablonka 163.25 9170 Snina Cirocha 250.04

6480 Šaľa Váh 11217.61 9320 Lekárovce Uh 1989.41

6540 Nedožery Nitra 181.57 9410 Veľké Kapušany Latorica 2915.46

6550 Handlová Handlovka 40.18 9500 Hanušovce n/Topľou Topľa 1050.05

6570 Chalmová Nitra 601.11 9620 Jasenovce Oľka 173.94

6620 Liešťany Nitrica 136.08 9650 Horovce Ondava 2885.80

6640 Chynorany Nitra 1134.28 9670 Streda n/Bodrogom Bodrog 11474.25



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Fig. 2. The significant statistical trends of the mean monthly discharges for June. The red points represent stations with statistically significant decreasing trend from 1962 to 2017.

Fig.3. The significant statistical trends of mean seasonal discharges for the winter season. The red points represent stations with statistically significant decreasing trend, the blue points represent stations with increasing trend.

Fig. 4. The significant statistical trends of mean seasonal discharges for the summer season. The red points represent stations with statistically significant decreasing trend.



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namely, five decreasing trends. The red points represent stations with a statistically significant decreasing trend.

The total number of the statistically significant trends from No-vember to April, i.e., the winter season, was 67 trends. There were 47 decreasing trends and 20 increasing trends. In the summer season 83 trends were identified (79 decreasing trends and four rising trends). Fewer increasing trends were identified in the summer season than in the winter season. This phenomenon is also reflected in the results of the trend analysis of the mean seasonal discharges, where eight trends (one inclining and seven declining), were identified for the winter sea-son (Fig. 3) and 21 declining trends for the summer season (Fig. 4).

The Mann-Kendall test results were summarized by spatial analy-sis. In January and February, statistically significant increasing trends were found in Central and Eastern Slovakia. Most of the decreasing trends were found in Western Slovakia. Generally, fewer trends were found in Eastern Slovakia than in other parts of Slovakia. The trend analysis of the mean annual discharges can be seen in Fig. 5.

4.2 The analysis of the decades of the discharges, the rainfall total, and the air temperatures in the Hron catchment to Banská Bystrica gauging station

The results of the analysis of the decades at issue are expressed in graphs (Figs. 6, 7, 8 and 9). The first graph (Fig. 6) shows the dis-charge (yellow) and total rainfall (green) calculated for every decade on the left axis of the chart. The air temperature over the decades

Fig. 5. The significant statistical trends for the mean annual discharges. The red points represent stations with statistically significant decreasing trend.

Fig. 6. The results of the decadal analysis of the discharges, the rainfall total, and the air temperatures in the Hron catchment to Banská Bystrica gauging station.

Fig. 7. The decadal discharges from 1960s to 2000s in the Hron catchment to Banská Bystrica gauging station.

Fig. 8. The decadal rainfall total from 1960s to 2000s in the Hron catchment to Banská Bystrica gauging station.

Fig. 9. The decadal air temperature from the 1960s to the 2000s in the Hron catchment to Banská Bystrica gauging station.



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(black, blue, and red lines) is plotted on the right axis of the graph. The decades are plotted on the primary x-axis.

The analysis of the decades in the Hron River basin shows an apparent upward trend in the air temperature. The average difference between summer and winter air temperature is 12.5 °C. The rising air temperature caused the increasing evapotranspiration. The rainfall to-tal for the winter season is significantly lower than for the summer sea-son. The summer rainfall total was decreasing until the 1980s and then started to rise. The last decade of the summer season has the highest rainfall total, i.e., 90.3 mm. The winter total rainfall shows a similar course, but the first decade had the highest rainfall total (65.5 mm). The lowest rainfall total for every season was found in the 1980s.

The discharge over the decades declined over the 1980s. The 1980s and the following two decades show a continuous discharge. The rising air temperature and the increasing rainfall total at last de-cades caused the unchanging regime of discharge. The reason is prob-ably hidden in the increasing evapotranspiration in recent decades.

The decades were also evaluated by the months of the hydrological year. The first graph (Fig. 7) shows the mean discharge of each decade during the hydrological year. The discharges have a similar course; all of them have higher values in the spring with a peak in April. The 1960s in April had a much more significant maximum than in the other months.

The second graph (Fig. 8) shows the mean monthly rainfall totals for each decade. The minimum value mainly occurred in January and February. The maximum value especially occurred in the early sum-mer. Each decade has a varied pattern, but all of them follow a similar regime in their maximum and minimum values. The rainfall total in 1960s in April did not contributed to higher discharge.

The third graph (Fig. 9) shows the mean air temperature over the decades. The course of the temperatures during the hydrological year in every decade is very similar. The lowest air temperature oc-curred in January, and the highest air temperature occurred in July. The graph shows the course of the lower temperatures in the 1960s (dark blue color) to the course of the air temperatures in the 2000s (red color), which is represented by the upper line.

In addition to analyzing decades of the hydrological data from the Hron River basin, the change points were identified using Pettitt’s test. Change points for the mean monthly discharges were identified in 1996 in June and the mean annual discharge in 1981. The change points for the air temperature were identified in almost all the time data series. The change points occurred from 1970 to 1997. For the mean monthly rainfall total, only one change point in July, 1996, was identified.

5 CONCLUSIONS

The trend analysis shows a considerable number of declining trends versus inclining trends in 58 stations in Slovakia. The total number of identified trends was 203, with a statistical significance of

α=0.1. There are 178 declining trends and 25 rising trends. Generally, it can be argued that the discharges are declining to a large extent, especially in the summer months. Most of the trends were identified in June, namely 36 trends. The Mann-Kendall test shows statistically significant decreasing trends occurred less frequently in the winter months. The occurrence of rising trends is mainly in the winter. In January and February, statistically significant increasing trends were found in Central and Eastern Slovakia. Most of the decreasing trends were found in Western Slovakia. Generally, fewer trends were found in Eastern Slovakia than in other parts of Slovakia.

The analysis of the Banská Bystrica station by decades shows an increase in the air temperature and insignificant changes in the discharges and precipitation. The analysis of the decades in the Hron River basin shows an apparent upward trend in the air temperature. The rainfall total for the winter season is significantly lower than for the summer season. The summer rainfall total was decreasing until the 1980s and then started to rise. The winter total rainfall shows a similar course. The lowest rainfall total for every season was found in the 1980s. The discharge over the decades declined over the 1980s. The 1980s and the following two decades show a continuous dis-charge. The rising air temperature and the increasing rainfall total at last decades caused the unchanging regime of discharge. The reason is probably hidden in the increasing evapotranspiration in recent de-cades.

Pettitt’s test identified many change points in the 1990s in the air temperature. A low number of change points was identified in the discharges and total rainfalls. The knowledge gained about chang-es in hydrological time data series can be useful for modelling the behaviour of a hydrological regime under the conditions of climate change.

Acknowledgments

This work was supported by the Science Grant Agency (Slovakia) under VEGA Contract No. 1/0891/17.

Tab. 2. The change points identified by Pettitt’s test for discharges (Q), the air temperature (TEM) and precipitation (PREC) in the Hron catchment to Banská Bystrica gauging station.

Month XI XII I II III IV V VI VII VIII IX X

Q - - - - - - - 1996 - - - -

TEM - 1970 1987 1986 1971 1997 1992 1991 1986 1987 - -

PREC - - - - - - - - 1996 - - -

Period YEAR WIN SUM

Q 1981 - -

TEM 1987 1987 1991

PREC - - -



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