Estimation of uncertainty sources in the projections of ... - Springer Link

Stoch Environ Res Risk Assess (2013) 27:769–784 DOI 10.1007/s00477-012-0608-7

ORIGINAL PAPER

Estimation of uncertainty sources in the projections of Lithuanian river runoff Jurate Kriauciuniene • Darius Jakimavicius Diana Sarauskiene • Tadas Kaliatka

•

Published online: 20 June 2012 Springer-Verlag 2012

Abstract Particular attention is given to the reliability of hydrological modelling results. The accuracy of river runoff projection depends on the selected set of hydrological model parameters, emission scenario and global climate model. The aim of this article is to estimate the uncertainty of hydrological model parameters, to perform sensitivity analysis of the runoff projections, as well as the contribution analysis of uncertainty sources (model parameters, emission scenarios and global climate models) in forecasting Lithuanian river runoff. The impact of model parameters on the runoff modelling results was estimated using a sensitivity analysis for the selected hydrological periods (spring flood, winter and autumn flash floods, and low water). During spring flood the results of runoff modelling depended on the calibration parameters that describe snowmelt and soil moisture storage, while during the low water period—the parameter that determines river underground feeding was the most important. The estimation of climate change impact on hydrological processes in the Merkys and Neris river basins was accomplished through the combination of results from A1B, A2 and B1 emission scenarios and global climate models (ECHAM5 and HadCM3). The runoff projections of the thirty-year periods (2011–2040, 2041–2070, 2071–2100) were conducted applying the HBV software. The uncertainties introduced by hydrological model parameters, emission scenarios and global climate models were presented according to the magnitude of the expected changes in Lithuanian rivers runoff. The emission scenarios had much

J. Kriauciuniene (&) D. Jakimavicius D. Sarauskiene T. Kaliatka Lithuanian Energy Institute, Breslaujos Str. 3, 44403 Kaunas, Lithuania e-mail: [email protected]

greater influence on the runoff projection than the global climate models. The hydrological model parameters had less impact on the reliability of the modelling results. Keywords Lithuanian rivers Climate change HBV Model calibration Sensitivity and uncertainty analysis SUSA

1 Introduction Modelling of hydrological processes is becoming more and more important, since the developed hydrological models give an opportunity to make both short and long-term predictions of river runoff (Arduino et al. 2005). This becomes substantial when extreme river runoff parameters (floods and droughts) and possible climate change impact on river runoff are being forecasted (Bergstrom et al. 2001; Booij 2002; Veijalainen et al. 2010). River runoff formation models are very complex as they involve a large number of natural factors (temperature, precipitation, soil properties, relief, presence of forests, wetlands, lakes, etc.). However, in many cases available information is incomplete and inaccurate (Werner 2001; Di Baldassarre and Montanari 2009), which influences the reliability of modelling results. An increasing attention of researchers and practitioners is paid to the estimation of uncertainty in hydrological modelling (Montanari 2007; Lawrence and Haddeland 2011). Hydrologists identify the following main sources of uncertainty: (1) uncertainty in observations, which is the approximation in the measured hydrological variables, used as input or calibration/validation data (e.g. rainfall, temperature and river runoff) (Hwang et al. 2011); (2) parameter uncertainty, which is induced by imperfect calibration of hydrological

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models (Lawrence et al. 2009; Bergstrom et al. 2002); (3) model structural uncertainty, which is originated by the inability of hydrological models to perfectly schematize the physical processes involved in the rainfall-runoff transformation (Go¨tzinger and Ba´rdossy 2008); (4) modelling of climate change impact on hydrological processes (uncertainty of emission scenarios, global climate model and downscaling methods) (Lawrence and Haddeland 2011). The uncertainty analysis is widely used to identify issues regarding different hydrological objects: water quality modelling of coastal waters (Bryhn et al. 2008) and natural lagoons (Pastres and Ciavatta 2005), studies of water balance in fresh water reservoirs (Salah et al. 2005) or even in animal waste lagoons (Ham 2002), as well as modelling of projections of the river spring floods (Apel et al. 2004) and flash floods (Wagener et al. 2007). Methods for the uncertainty estimation are chosen depending on modelling objects and solving tasks. Generalized likelihood uncertainty estimation (GLUE) and Bayesian methods are the two most popular methods used for the quantification of uncertainty (Jin et al. 2010; Kriauciuniene et al. 2009). They can be considered as emergent methods in assessing the uncertainty in flood forecasting (Maskey 2007; Werner 2001; Wagener et al. 2007). In scientific literature Monte Carlo Markov Chain (MCMC) method is also mentioned as one of the main uncertainty analysis methods (Shbaita and Rode 2009). The bootstrap method, a non-Bayesian and a non-parametric technique recently used to quantify parameter uncertainties,

Stoch Environ Res Risk Assess (2013) 27:769–784

is characterised as easy to describe and implement in hydrological modelling as well (Li et al. 2010; Selle and Hannah 2010). Using HBV/IHMS software (Bergstrom 1995), hydrological models of the Merkys and Neris Rivers were created at the Laboratory of Hydrology of Lithuanian Energy Institute. The hydrological model calibration was performed using Software System for Uncertainty and Sensitivity Analysis (SUSA) (Krzykacz et al. 1994; Kloos and Hofer 2002; Kopustinskas et al. 2007). The aim of the present study was to estimate the influence of hydrological model parameters on the runoff modelling results, to perform the sensitivity analysis of the runoff projections as well as the contribution analysis of uncertainty sources (model parameters, emission scenarios and global climate models (GCM)) in the forecasting of Lithuanian river runoff.

2 Methodology and study area The estimation of uncertainty in catchment-scale modelling of climate change impact was performed as the analysis of different uncertainty sources: hydrological model parameters, emission scenarios and GCM (Fig. 1). The hydrological models of two Lithuanian rivers were prepared using the HBV software. The calibration of models was performed using hydrological model parameter sets generated by Monte Carlo method using the SUSA software. The runoff projections

Fig. 1 Contribution of uncertainty sources (model parameters, emission scenarios and global climate models) in Lithuanian river runoff projections

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of the Merkys and Neris Rivers were calculated using GCM and greenhouse gas emission scenarios. The contribution of uncertainties sources (hydrological model parameters, emission scenarios and GCM) were evaluated according to the magnitude of the difference between the simulated and measured discharge Dh in the river catchments.

The HBV software was created at SMHI (Swedish Meteorological Hydrological Institute) (Bergstrom 1995). This software is a rainfall-runoff modelling method, which encompasses conceptual numerical descriptions of hydrological processes at the catchment-scale. The modelling results can be used to forecast the impact of climate change on river runoff, floods and droughts, to ensure the dam security or to control water quality. The main HBV equation is described as follows (IHMS 2005): d ½SP þ UZ þ LZ þ V dt

ð1Þ

where P is the precipitation, E is the evapotranspiration, Q is the runoff, SP is the snow pack, UZ is the upper groundwater zone, LZ is the lower groundwater zone, V is the lake or dam volume. The model consists of subroutines for snow accumulation and melt, soil moisture accounting procedure, and routines for runoff generation. The calibration procedure consists of changing model calibration parameters and comparing calculated discharge values with the measured ones. There are three ways for the evaluation of the calibration results (IHMS 2005): (1) (2)

Visual comparison of hydrograms of computed and measured discharge values; Calculation of the accumulated difference between the simulated and measured discharge (Dh): Dh ¼ RðQC QR Þ Ct

RðQR QRmean Þ2 RðQc QR Þ2 RðQR QRmean Þ2

(3) (4)

A best estimate approach provides sufficient information about the behaviour of the physical processes and evaluates possible uncertainties. Nowadays a lot of methods exist, which are based on best estimate approach. In this study GRS method developed in the Gesellschaft fu¨r Anlagenund Reaktorsicherheit (GRS) (Hofer 1999; Krzykacz et al. 1994) is used. The GRS method considers the effect of uncertainties of model parameters, the options of modelling and the parameters of solution algorithms on the simulation results (Wickett et al. 1998). The method is based on a systematic identification of relevant physical processes and on a probabilistic quantification of the uncertainty of corresponding parameters. The main steps of this method are: • • •

• •

•

ð3Þ

where QRmean is the mean of the measured discharge over the calibration period.

The model parameters that control general runoff volume over the total calibration period were calibrated; The model parameters that describe snow accumulation and melting intensity were calibrated; The best values of indexes that characterize the moisture accumulated in soil were selected; The model parameters that define the extremes (river floods and droughts) of discharge hydrograms were calibrated.

2.2 GRS method for uncertainty and sensitivity analysis

ð2Þ

where QC is the simulated discharge, QR is the measured discharge, C is the coefficient transforming to mm over the basin, t is the time. The accumulated difference between the simulated and measured discharge has to be as small as possible. (3) Calculation of variance r2 (Nash–Sutcliffe efficiency criterion) (Nash and Sutcliffe 1970): r2 ¼

(1)

(2)

2.1 Structure of HBV/IHMS software

PEQ¼

The manual calibration was performed by changing the model parameters (Table 1) and comparing the calculated values of river discharge with the measured ones. Calibration process had 4 steps (IHMS 2005):

•

The identification of potentially relevant uncertainties. The definition of uncertainty ranges, i.e. minimum and maximum values or options of modelling. The specification of probability distributions over these ranges. Gaussian and uniform distributions are usually used in the absence of deeper knowledge on the model parameters. The identification and quantification of dependencies between parameters, if present. Generation of a random sample of size n for model parameters from their probability distributions by Monte Carlo method. Performing the corresponding simulation runs with the selected software. Each simulation run generates one possible solution of the model. All solutions together represent a sample from the unknown probability distribution of the model results. The calculation of quantitative uncertainty statements, e.g. 5 and 95 % quintiles or two-sided statistical

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Table 1 Calibration parameters (recommended (IHMS 2005) and estimated optimal values for the Merkys and Neris rivers catchments) Calibration parameters

Recommended values

Optimal calibration coefficients Merkys river

Neris river

General runoff volume parameters General precipitation correction factor

pcorr

0.8–1.2

1.010

0.981

Snowfall correction factor

sfcf

0.8–1.4

1.057

1.200

Rainfall correction factor

rfcf

0.9–1.3

0.789

0.750

Maximum soil moisture storage (mm)

FC

100–1,500a

120

140

Threshold temperature (C) below which precipitation is assumed to be snow

tt

-2 to ?2

-1.000

-1.000

Value to be added to tt to give the threshold temperature for snow melt (C)

dttm

-2 to ?2

1.510

1.530

Total length of the temperature interval in which the part of precipitation that is considered to be snow decreases linearly from 1.0 at the lower end of the interval to 0 at the upper end (C)

tti

0 to ?2.5

0.287

0.215

Snow melt factor (mm/C/day)

cfmax

2 to ?4.5

3.560

3.583

Limit for potential evaporation (mm)

LP

B1

0.749

0.744

Exponent in formula for drainage from soil

beta

1–4

0.860

0.859

Parameter of thornthwaites formula

athorn

0.2–0.3

0.217

0.217

Maximum capillary flow from upper response box to soil moisture zone (mm/day)

cflux

0–2

0.060

0.060

Snow routine parameters

Soil moisture routine parameters

Extreme river discharge parameters

a

•

•

Recession coefficient for lower response box

k4

0.001–0.1

0.006

0.007

Percolation capacity (mm/day) from upper to lower response box

perc

0.01–6

1.050

0.900

Recession coefficient for the upper box when water discharge is maximum

khq

0.005–0.2

0.036

0.041

Nonlinear coefficient

alfa

0.5–1.1

0.850

0.850

Depends on the region

tolerance limits like upper and lower limit values with 95 % probability content and 95 % confidence. The calculation of quantitative sensitivity measures (Spearman’s correlation coefficient) to identify those uncertain parameters which contribute most to the uncertainty of the results. The interpretation and documentation of results.

The main advantage of GRS method, based on proven statistical procedures, is that the number of calculations is independent of the number of uncertain parameters to be investigated. A simultaneous variation of all identified uncertain parameters is performed to determine the model parameter values for the simulation runs and their statistical evaluation. The number of calculations necessary for one-sided or two-sided tolerance intervals depends only on the required probability and the confidence level of the statistical tolerance limits. The relationship between these parameters is described by Wilks’ formula (1941):

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1 an b

ð4Þ

ð1 an Þ nð1 aÞan1 b

ð5Þ

Equation (4) is used for one-sided and Eq. (5)—for twosided statistical tolerance intervals, where (b 9 100 %) is the confidence level that the maximum code result will not be exceeded with the probability (a 9 100 %) of the corresponding output distribution, and n the number of calculations required. For a 95 % probability and a confidence level of 95 %, a number of calculations (n = 93) need to be performed. However, a total number of 100 calculations are typical for the application of uncertainty analysis. Sensitivity measures by using regression or correlation techniques from the sets of model parameters and from the corresponding calculation results allow the ranking of the uncertain model parameters in relation to their contribution to the output uncertainty. There are several techniques for


773

the evaluation of sensitivity. One of them is Spearman’s correlation coefficient. This coefficient is defined as the Pearson correlation coefficient between the ranked variables (Myers and Well 2003). The Xi, Yi are converted to ranks xi, yi, and Spearman’s correlation coefficient (q) is calculated as follows: Ri ðxi xÞðyi yÞ2 q ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ri ðxi xÞ2 Ri ðyi yÞ2

ð6Þ

This coefficient assesses how well the relationship between the two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of ?1 or -1 occurs when each of the variables is a perfect monotone function of the other. For the sensitivity analysis the coefficient of determination R2 is very important. In statistics, the R2 is used in the context of statistical models, which main purpose is the prediction of future outcomes on the basis of other related information. R2 is calculated by this equation (Steel and Torrie 1960): Pn ðyi yî Þ2 2 R ¼ 1 Pi¼1 ð7Þ n Þ2 i¼1 ðyi y where yî is the estimation of variables yi (where i = 1,…, n) calculated from the regression equation, y is the mean of the variables yi, n is the sample size (in our case is equal to the number of the sets generated for model parameters). The numerator of the equation reflects the scattering of the values of variables yi around the regression line. The denominator reflects the scattering of the values of variables yi around its mean. For example, if R2 = 0.85, it means that 85 % of the total variation of y can be explained by the linear relation (as described by the regression equation). The other 15 % of the total variation of y remains unexplained. Determination coefficient must be higher than 0.6 for the sensitivity analysis. If R2 is less, then the results of sensitivity analysis may be incorrect, because of too many unexplained y variations. The SUSA software is developed by GRS and provides a choice of statistical tools to be applied during the uncertainty and sensitivity analysis (Kloos and Hofer 2002). The SUSA software was developed to implement the GRS method. The generation of a random sample of size n for model parameters (by Monte Carlo method), calculation of quantitative uncertainty statements and quantitative sensitivity measures (Spearman’s correlation coefficient) are performed using this software. In this article GRS method of best estimate approach was employed for the analysis of process in hydrological systems. For this analysis the best estimate software HBV

was used for the calculation of river discharges. The SUSA software was used for the uncertainty and sensitivity analysis of calculation results. 2.3 Analysis of impact of the GCM and emission scenarios on the river runoff projections Climate change impact on hydrological processes in Lithuanian rivers has been estimated through the combination of results from A1B, A2 and B1 emission scenarios and GCM (ECHAM5 and HadCM3). Following the latest IPCC Fourth Assessment Report (AR4) issues and the GCM output data, climate change predictions were made for the territory of Lithuania (Kriauciuniene et al. 2008). Max Planck Institute atmosphere–ocean general circulation model ECHAM5 (Roeckner et al. 2003) and the Hadley Predictions Centre model HadCM3 (Gordon et al. 2000) were used for the assessment of climate change impact on water resources in Lithuania. Assumptions about future greenhouse gas emissions in Lithuania are based on A1B, A2, B1 greenhouse gas emission scenarios. Daily values of temperature and precipitation at the measurement sites (meteorological stations) are traditionally used as input data to hydrological models. Predictions of monthly meteorological variables of the 21st century were made for meteorological stations in the river catchment areas (Rimkus et al. 2007). A common method for the determination of climate change input to hydrological models is the delta change approach (Hay et al. 2000). The observed data of baseline period (1961–1990) were used as the background for the prediction of daily meteorological data by climate scenarios. Monthly relative precipitation and temperature changes predicted by climate model were used to modify the observed daily meteorological data for the baseline period. Six temperature and precipitation series for every 30 years of the period 2011–2100, representing two general circulation models (ECHAM5 and HadCM3) under three emission scenarios (A1B, A2, B1) were used as the input to the hydrological model. The daily discharge series of the rivers were calculated for 2011–2040, 2041–2070 and 2071–2100. Then the runoff projections were compared with the baseline period of 1961–1990. The average of Dh was calculated for the emission scenarios and GCM separately in every thirty-year period. 2.4 Study area and data The two catchments of the Merkys and Neris Rivers (Fig. 2) are the study areas selected for the analysis of uncertainty sources in the river runoff projection. The main catchment characteristics are presented in Table 2.

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Fig. 2 Catchments of the Merkys and Neris rivers Table 2 Characteristics catchments

of

the

Characteristics

Merkys

and

Nemunas

river

Catchments Merkys

Neris

Catchment area (km ) Average annual runoff (m3/s)

4416 32.4

24942 173

Specific discharge (l/s km2)

7.53

7.06

Average annual precipitation (mm)

695

675

Spring

29.1

31.1

Summer

28.8

27.2

Autumn

20.6

20.6

Winter

21.5

21.1

Lakes

0.9

2.5

Forests

40.0

28.0

Bogs

10.0

10.0

Other

49.1

59.5

2

Seasonal runoff (%)

Land cover (%)

Both rivers are effluents of the biggest Lithuanian river Nemunas. The Merkys river with the catchment area of 4,416 km2 is a naturally regulated river with a prevailing subsurface feeding. The permeable sandy soils, which are widespread here, effectively absorb snowmelt and later gradually release it, supplying rivers during the low water period. The annual runoff of the Merkys is distributed rather equally: a part of the spring flood runoff consists 29.1 % of the total annual runoff. The Neris river with the catchment area of 24,942 km2 is the second largest river in Lithuania and the biggest tributary of the Nemunas. Relatively permeable soils dominate in this catchment. A part

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of the spring flood runoff of the Neris river consists 31.1 % of the total annual runoff. The baseline period data (daily air temperature and precipitation) of two meteorological stations (Varena and Vilnius) in the Merkys catchment and six stations (Vilnius, Kaunas, Utena, Ukmerge, Dotnuva and Panevezys) in the Neris catchment were used as input data for the hydrological modelling. HBV software generalizes air temperature and precipitation in the catchment areas. Weighted coefficients evaluating the catchment area that is represented by each meteorological station were calculated using polygon method. The created hydrological models were used to calculate daily discharges of the Merkys and Neris Rivers. Daily discharge values from water gauging stations were the primary hydrological information for model calibration and validation (Fig. 2).

3 Results and discussion 3.1 Calibration of hydrological models of the Merkys and Neris river catchments using the uncertainty analysis Calibration of hydrological models was performed in two ways: manual model calibration and model calibration using GRS method. The model was calibrated manually and validated, considering the measured daily discharge data in Neris and Merkys catchments. The period of 1961–1975 was selected as the calibration period, and the period of 1976–1990 was used for model validation. The calibration process of the Merkys and Neris hydrological models covered 4 steps (described in Sect.


775

2.1). If Nash–Sutcliffe efficiency criterion r2 did not meet the requirements, all parameters would be calibrated in the same order repeatedly. During manual calibration of hydrological models, 16 parameters were used (Table 1). The optimal parameter values of the calibrated models for the Merkys and Neris catchments are presented in Table 1. Nash–Sutcliffe value was 0.77 for the Neris catchment and 0.72 for the Merkys catchment in the calibration period, and 0.70 and 0.69 respectively in the validation period. The success of manual model calibration depends on the qualification of a researcher. Application of the GRS method for the model calibration is another issue discussed in this the article. In this case the model calibration does not depend on the experience of a researcher. However, more calculations (according to 100 parameter sets) are required for uncertainty analysis of modelling results. Performing the uncertainty analysis the six most important model calibration parameters were selected from 16 parameters used for manual calibration (Lawrence and Haddeland 2011): beta—exponent in formula for drainage from soil; cfmax—snow melt factor; FC—maximum soil moisture storage; k4—recession coefficient for lower response box; perc—percolation capacity from upper to lower response box; sfcf—snowfall correction factor. The remaining ten calibration parameters were left unchanged (as defined during manual calibration). The statistical distributions (Gaussian or uniform) were applied for the 6 selected parameters. The distribution types, minimal and maximum values of model calibration parameters are presented in Table 3. Using the SUSA software, random sets for hydrological model parameters were generated. In this study our task was to analyse the uncertainty of the discharge calculation results. Therefore, the two-sided statistical tolerance interval was used and 100 sets of hydrological model parameters were generated. The Merkys and Neris Rivers daily discharges were modelled according to 100 sets in the selected period of 1978–1983 (Fig. 3). The simulated average discharge was similar to the measured one. The

measured river discharges were in the range of calculation results. 3.2 Sensitivity analysis of hydrological model parameters The impact of hydrological model parameters on the calculation results of the Merkys and Neris Rivers discharges was analysed in the period of 1978–1983. Using SUSA software the determination coefficient R2 was calculated for each day of this period (Fig. 4). In particular time periods R2 is less than 0.6 (R2 fluctuates from 0.16 to 1), and for these time periods the results of the sensitivity analysis may be incorrect. Spearman’s correlation coefficient was used to evaluate the influence of hydrological model parameters to the calculation results of Neris and Merkys rivers discharges. The greater absolute value of Spearman’s correlation coefficient means that model parameter has the greater impact on the calculation results. Calculated Spearman correlation coefficients for the each hydrological model parameter explain the influence of hydrological model parameters on the calculation results, i.e. Dh (the difference between the calculated and measured discharges) for Neris and Merkys Rivers (Fig. 5). Calculation of Dh was performed for the period of 1978–1983. As it was mentioned, in particular time periods the sensitivity analysis could be incorrect. Therefore, the sensitivity analysis of model parameters was performed for peak discharges in separate periods: spring flood, winter flash floods, autumn flash floods and low water. The information about maximum discharges of floods and flash floods is crucial for the construction of hydro technical structures and performing their risk analysis. Accurate modelling of the maximum discharges is essential for hydrological model calibration. The hydrological model must be calibrated in order to get the minimal differences between calculated and measured river discharges. In different periods particular model parameter groups had the greatest influence on the

Table 3 Distributions of the model calibration parameters in the Merkys and Neris rivers Model parameters

Range of hydrological model parameter Minimal value

Basic value

Distribution

Maximum value

Merkys river

Neris river

Merkys river

Neris river

Merkys river

Neris river

Sfcf FC, mm

0.74 70

0.80 90

1.40 170

1.60 190

1.075 120

1.20 140

Gaussian Uniform

Cfmax (mm/C/day)

3.00

3.00

4.00

4.00

3.56

3.583

Gaussian

Beta

0.55

0.55

1.20

1.20

0.86

0.859

Gaussian

K4

0.001

0.001

0.04

0.04

0.006

0.007

Uniform

Perc (mm/day)

0.65

0.60

1.45

1.30

1.05

0.90

Gaussian

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Fig. 3 Variations of the Merkys (a) and Neris (b) rivers discharges during the period of 1978–1983 according to 100 sets of hydrological model parameters

Fig. 4 Determination coefficients for the Neris and Merkys rivers in the modelling period (01-10-1978 to 30-09-1983)

modelling results. For example, during spring flood the results of runoff calculations depended on the calibration parameters that describe snowmelt and soil moisture storage, while during the low water period, the parameter that determines river underground feeding was the most important. Six hydrological model parameters were ranked according to the Spearman correlation coefficient value for the peak discharges in separate periods (spring flood,

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winter flash floods, autumn flash floods, low water) (Table 4). The hydrological model parameter which had the greatest impact on the modelling results received rank 1, whereas the parameter with the lowest impact received rank 6. Impact of hydrological model parameters on the calculation of discharges of Merkys and Neris Rivers was estimated during the periods of spring floods, autumn and winter flash floods and low water. The changes of six


777

Fig. 5 Influence of the six hydrological model parameters on the calculation results of Dh (the difference between the calculated and measured discharges) in the period of 01-10-1978 to 30-09-1983: a Merkys river, b Neris river

model parameters were described in Figs. 6 and 7. In time axis the number of days of the studied period (1,818 days from October 1, 1978 to September 30, 1983) is presented. For example, the first analysed spring flood in the Merkys catchment (Table 4) occurred from 164th to 254th day (from March 13 to May 24, 1979). The sensitivity analysis of the discharges during spring flood periods showed that the parameters sfcf and FC have the highest impact on the modelling results (Figs. 6a and 7a; Table 4). The parameter beta, which influences river runoff formation from underground water, is the least important. In the autumn flash flood periods the parameters beta and k4 have the greatest influence (Figs. 6b and 7b; Table 4), whereas the coefficients sfcf and cfmax are completely insignificant for calculation results. The parameters FC and k4 are the most important in the formation of winter flash floods, while cfmax have the least influence (Figs. 6c and 7c; Table 4). During the low water period of the Merkys and Neris Rivers, the parameters k4 and beta have the greatest impact on the discharge calculation (Figs. 6d, 7d) and that is related to ground water feeding, which forms a great part of low water runoff. The remaining parameters do not have a significant impact.

The results of sensitivity analysis showed that the parameter FC have the biggest impact on the calculation of Merkys and Neris Rivers discharges for the whole studied period of 5 years. The impact of other parameters varies depending on the period: in spring the parameter sfcf was important, in autumn—beta and k4, in winter—k4. The impact of the parameters beta and k4 that describe moisture migration in soil varies during a year due to the fact that moisture can easily move in soil in spring and the beginning of winter while the soil is still not frozen; later, from the middle of winter till late spring, the soil moisture turns to ice and the migration stops, which is why these parameters had the least importance then. The sensitivity analysis (estimation of hydrological model parameter influence on discharges in different peak periods) could be useful for the calibration of hydrological models of other river catchments. 3.3 Contribution analysis of uncertainty sources Model parameters, emission scenarios and GCM are uncertainty sources that were investigated in this study. Catchment-scale modelling of climate change impact on

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778 Table 4 The influence of hydrological model parameters on the runoff calculation of Merkys and Neris rivers


Peak date

Parameter impact (expressed by rank) 1

2

3

4

5

6

04–04–1979

FCa

Sfcf

cfmax

k4

perc

beta

15–04–1980

sfcf

FC

k4

cfmax

beta

perc

11–03–1981

FC

Sfcf

k4

perc

cfmax

beta beta

Merkys river Spring

Autumn

Winter

27–03–1982

sfcf

FC

k4

perc

cfmax

06–09–1979

beta

FC

k4

perc

sfcf

cfmax

02–09–1981

beta

FC

k4

perc

sfcf

cfmax perc

02–10–1982

k4

Beta

FC

sfcf

cfmax

06–08–1983

k4

Beta

sfcf

FC

perc

cfmax

24–12–1979

k4

FC

beta

perc

sfcf

cfmax

16–12–1980 06–01–1982

k4 FC

FC k4

sfcf perc

beta cfmax

cfmax beta

perc sfcf

03–02–1983

FC

k4

perc

sfcf

beta

cfmax

05–04–1979

sfcf

FC

cfmax

perc

k4

beta

Neris river Spring

Autumn

Winter

a

Parameters with negative impact are given in bold

10–04–1980

sfcf

FC

k4

perc

beta

cfmax

28–03–1981

sfcf

FC

k4

perc

cfmax

beta

27–03–1983

sfcf

FC

perc

k4

cfmax

beta

04–09–1979

beta

k4

FC

perc

sfcf

cfmax

07–09–1981

beta

k4

sfcf

cfmax

FC

perc

06–10–1982

beta

k4

sfcf

perc

cfmax

FC

01–08–1983

k4

beta

sfcf

FC

cfmax

perc

03–01–1980

k4

FC

beta

sfcf

perc

cfmax

17–12–1980

k4

sfcf

FC

beta

cfmax

perc

05–01–1982

k4

FC

beta

cfmax

perc

sfcf

02–02–1983

k4

FC

sfcf

beta

perc

cfmax

the Merkys and Neris Rivers runoff was performed for the 21st century according to two GCM (ECHAM5 and HadCM3), under three emission scenarios (A2, A1B, B1) (Fig. 7). The calculation results were compared with the runoff characteristics in the baseline period of 1961–1990, which is widely used and defined by WMO as a ‘‘normal’’ period (WMO 2007; Arguez and Vose 2011). It was estimated that the runoff in the studied rivers is going to decrease (Table 5). In the 21st century the runoff in the Merkys catchment area can decrease the most according to Echam5 A1B scenario (by 26.5 % comparing with the baseline period). HadCM3 B1scenario shows the smallest changes (by 11.1 %). In the Neris, as well as in the Merkys, catchment areas the most significant runoff decrease is expected according to Echam5 A1B scenario (by 27 %). The least runoff changes are expected by Echam5 B1 scenario. This scenario projects that the Neris runoff should decline by 12.7 % comparing with the ‘‘normal’’ period. According to 6 combinations of GCM and emission scenarios, the Merkys runoff can change from 28.6 to 23.7 m3/s, and the Neris runoff—from 145 to 121 m3/s on average.

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According to all emission scenarios, in the end of the 21st century (2071–2100), the runoff of the Merkys and Neris Rivers should increase in the winter season (Fig. 8), because high temperatures will cause the runoff increase (decrease of snow possibility and thickness of snow cover) (Kriauciuniene et al. 2008). Spring runoff should decline, since spring floods move to the winter season. The runoff will have tendencies to decrease in the summer and autumn seasons. The redistribution of river runoff in different seasons occurs because of the higher temperature and the change of precipitation amount. The contribution analysis was performed by weighting the impact of emission scenarios and GCM on the runoff projections of Merkys and Neris Rivers and impact of hydrological model parameters. In order to assess the impact of emission scenarios and GCM Dh were calculated as differences of the biggest and smallest Dh values of the emission scenarios and global models separately during every thirty years in 21st century. The average Dh calculated according to 100 generated sets of model parameters was 59.1 mm for the Neris river and 89.5 mm for the Merkys River. Figure 9 demonstrates the different


779

Fig. 6 Influence of hydrological model parameters on the calculation of discharges of Merkys river during the periods of spring flood (a), autumn flash floods (b), winter flash floods (c) and low water (d)

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780 Fig. 7 Impact of hydrological model parameters on the calculation of discharges of Neris river during the periods of spring flood (a), autumn flash floods (b), winter flash floods (c) and low water (d)

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Stoch Environ Res Risk Assess (2013) 27:769–784 Table 5 Projections of the Merkys and Neris rivers runoff according to different climatic scenarios of 2011–2100 (m3/s)

Period

781

1961–1990

Emission scenarios Echam5 A2

HadCM3 A1B

B1

A2

A1B

B1

Merkys 2011–2040

29.7

26.1

31.4

28.1

27.6

28.5

2041–2070

31.2

27.1

23.9

26.4

27.2

26.2

28.3

2071–2100

25.3

18.8

25.3

23.5

22.6

26.5

Neris 2011–2040

164

2041–2070 2071–2100

153

132

159

143

138

144

139 132

126 101

137 134

135 115

133 110

144 135

Fig. 8 Distribution of the Merkys (a) and Neris (b) discharge according to different climate scenarios in 2071–2100 and in the baseline period (1961–1990)

influence of the uncertainty sources (expressed by %) for the runoff projection in the selected 30 year periods. The accuracy of river runoff model projection mostly depends on the selected emission scenario (the expected tendencies of society development). Emission scenarios are created by

large groups of scientists from various research fields (the Fourth Reports of Intergovernmental Panel on Climate Change). Consequently, we can only choose the most reliable scenario for the Lithuanian conditions from available ones. The analysis of results reveals that the most

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782


4 Conclusions

Fig. 9 Sources of uncertainties of hydrological model calibration for the Merkys (M) and Neris (N) rivers: MP model parameters, GCM global climate models and ES emission scenarios

significant changes of the river runoff are expected under A1B scenario, less significant—under A2 and the least— under B1. The selected global climate model has less influence on the modelling results. The uncertainty magnitudes that come from the selected emission scenarios tend to increase, while the uncertainties caused by GCM are going to decrease from the period of 2011–2040 to 2071–2100 (Fig. 9). On average, the accuracy of runoff projections is determined by model parameters—7.2 %, emission scenarios—60.9 % and GCM— 32 % for the Merkys catchment and 5.6, 64.4 and 30 % respectively for the Neris catchment. The evaluation of uncertainty in the catchment-scale modelling of climate change impacts was carried out for the Norwegian rivers (Lawrence and Haddeland 2011). The investigated uncertainty sources were HBV model parameters, two emission scenarios, two GCM and two downscaling methods. Hydrological parameter uncertainty was less important for the Norwegian rivers Masi and Nybergsund (4 and 8 %, respectively), where the mean annual flood is generated by spring snowmelt. In this case, the range of the projections is greater. For the Norwegian catchments where rainfall makes the predominant contribution to the annual maximum flows, hydrological parameter uncertainty is significantly related to other uncertainty sources (from 21 to 45 %). Usually the floods of the Lithuanian rivers Merkys and Neris are generated from snowmelt. Therefore the influence of HBV parameters on the uncertainty results of runoff modelling is similar in the Lithuanian and Norway catchments.

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The assessment of the catchment-scale hydrological impacts of climate change is based on the uncertainties of hydrological model parameter set, emission scenario and global climate model (Fig. 1). The hydrological models of the two Lithuanian rivers were prepared using the HBV software. The quality and success of river hydrological model calibration depends on the correct selection of model parameters. Spearman’s correlation coefficient was used to indicate the general influence of hydrological model parameters on the calculation results of Neris and Merkys Rivers discharges. The sensitivity analysis of model parameters during the selected hydrological periods (spring flood, winter and autumn flash floods, low water) over a year was performed, because some parameters have greater influence on floods and flash floods and some can better describe the runoff during the low water period. During spring flood the results of hydrological model depended on the calibration parameters that describe snowmelt and soil moisture storage, while during the low water period—the parameter that determines river underground feeding was the most important. In this study the projections of climate change impacts on hydrological processes in two Lithuanian catchments were based on the scenarios from two GCM (ECHAM5, HadCM3), three emission scenarios for greenhouse gases (A2, A1B, B1) and the delta change approach method. The projection of the river runoff in the periods of 2011–2040, 2041–2070 and 2071–2100 was prepared by HBV software. The expected river runoff was compared with the runoff of the baseline period. The average annual runoff of the two rivers should decrease according to all studied climate scenarios in all selected periods. During the 21st century, the runoff of the Merkys and the Neris Rivers will decrease by 34 and 36 % respectively, according to the A1B emission scenario, because the air temperature will increase the most in comparison to the other scenarios. According to the A2 emission scenario, a smaller decrease of the runoff of these rivers is expected (only by 22 and 25 % during the 21st century, in comparison to the baseline period). Moreover, there are some differences in forecasting the river runoff according to different GCM. A significant uncertainty was determined in the expected changes of the Merkys and Neris runoff. The uncertainties introduced by hydrological model parameters, emission scenarios and GCM are presented according to the difference between the simulated and measured discharge Dh. The contribution analysis of the uncertainty sources revealed that the emission scenarios have much greater influence on the projection of the Merkys and Neris river runoff than the GCM. The selection of the model parameters has less impact on the reliability of the modelling results.

Stoch Environ Res Risk Assess (2013) 27:769–784 Acknowledgments The research described in this paper was supported by COST (European Cooperation in Science and Technology) action ES0901 ‘‘European procedures for flood frequency estimation’’. Special thanks to Prof. Sten Bergstrom (SMHI, Sweden) who allows us to use the HBV model for the evaluation of climate impact on the changes of the Lithuanian river runoff and to Dr. Deborah Lawrence (NVE, Norway), who gave us many useful advices on the understanding of the uncertainty in runoff modelling. The authors also thank Dr. Egidijus Rimkus and Dr. Justas Kazˇys (Vilnius University, Lithuania) for the data on climate scenarios of the Lithuanian territory.

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