Uncertainty of annual runoff projections in Lithuanian rivers under a future climate

Uncertainties of runoff projections arise from different sources of origin, such as climate scenarios (RCPs), global climate models (GCMs) and statistical downscaling (SD) methods. Assessment of uncertainties related to the mentioned sources was carried out for selected rivers of Lithuania (Minija, Nevėžis and Šventoji). These rivers reflect conditions of different hydrological regions (western, central and southeastern). Using HBV software, hydrological models were created for river runoff projections in the near (2021–2040) and far (2081–2100) future. The runoff projections according to three RCP scenarios, three GCMs and three SD methods were created. In the Western hydrological region represented by the Minija River, the GCMs were the most dominant uncertainty source (41.0–44.5%) in the runoff projections. Meanwhile, uncertainties of runoff projections from central (Nevėžis River) and southeastern (Šventoji River) regions of Lithuania were related to SD methods and the range of uncertainties fluctuates from 39.4% to 60.9%. In western Lithuania, the main source of rivers’ supply is precipitation, where projections highly depend on selected GCMs. The rivers from central and southeastern regions are more sensitive to the SD methods, which not always precisely adjust the meteorological variables from a large grid cell of GCM into catchment scale. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/nh.2019.004 ://iwaponline.com/hr/article-pdf/51/2/257/682088/nh0510257.pdf V. Akstinas (corresponding author) D. Jakimavičius D. Meilutytė-Lukauskienė J. Kriaučiūnienė D. Šarauskienė Lithuanian Energy Institute, Breslaujos str. 3, LT-44403 Kaunas, Lithuania E-mail: vytautas.akstinas@lei.lt


INTRODUCTION
The accuracy of runoff projections highly depends on a wide range of factors related to climate change. Application of different climate scenarios and modelling tools for calculation of runoff projections increases the spread in the ensemble. When projecting river runoff, it is important to assess the uncertainties of selected tools and input data.
Usually, the main sources of uncertainty are linked to global climate models (GCMs) and climate scenarios (RCPs). However, statistical downscaling (SD) methods can be regarded as an additional source of uncertainty as well.
The GCM in combination with RCP provides the basis for investigation of future climate change. On the other hand, they are also the primary sources of systematic errors.
There are large biases comparing GCM output data with historical observations. Therefore, SD methods are used for the reduction of mentioned biases. Latif () maintains that the primary uncertainty of projections is caused by the variability of natural hydro-meteorological processes. It is difficult to estimate such natural variability; hence, the assessment of uncertainties of GCMs is very important.
The uncertainty interpretation as the range of runoff projection was successfully applied in several studies (Dobler et al. ; Bosshard et al. ). These studies constitute a solid basis for the exploration of uncertainties in runoff projections. The mentioned studies were conducted in a variety of locations using different climate and hydrological cation of SD methods helps to correct projections of meteorological variables. This way, the projections of surface runoff and river hydrological regime in impact assessment studies can be improved (Hagemann et al. ; Hundecha et al. ). Some studies also analysed the advantages and disadvantages of different SD approaches (Teutschbein & Seibert ; Maraun ). The mentioned scientific studies can be used for the selection of SD methods to improve GCM outputs for a fine temporal and spatial scale.
In Lithuania, uncertainty analysis of river runoff projections is not widely discussed. Kriaucǐunienė et al. to select more precise GCMs, climate scenarios and downscaling methods for accurate projections of annual runoff.

STUDY AREA AND DATA
The Nemunas River is a major Lithuanian river. The total length of the Nemunas is 937 km, while the river's basin area covers 98,200 km 2 . Seventy-two per cent of Lithuanian territory falls within the Nemunas River basin. Lithuania falls within one climate zone. When the climate is homogeneous, the physico-geographical conditions have a larger influence on the formation of the rivers' runoff. Accordingly, the division into hydrological regions is done by the existing local physico-geographical conditions (relief, lithology, soils, land use, etc.), which differently transform precipitation into the surface and subsurface runoff.
Three river catchments (Minija -2,942 km 2 , Nevėžis -6,140 km 2 and Šventoji -6,888 km 2 ) were selected for this research. These catchments are from different hydrological regions of Lithuania (Western (LT-W), Central (LT-C) and Southeastern (LT-SE)) ( Figure 1). The main source of runoff generation in western Lithuania is precipitation.
The type of runoff generation in central Lithuania is mixed (snowmelt and rainfall). In southeastern Lithuania, the main feeding source is groundwater. Due to the previously mentioned physico-geographical factors and runoff generation patterns, the Lithuanian rivers from the same hydrological region have synchronic relations of the runoff.  (1986-1995) and validation (1996-2005).
The BC method corrects the projected raw daily data of GCM outputs in mean and variance (Ho et al. ; where V BC is a corrected meteorological variable of GCM        (Table 2)  In the near future, the variability of projections of annual runoff of the Nevė žis River (Dasiunai WGS) was as high as 60.9% using SD methods, while the influence of RCP scenarios was only 11.2% (Table 3)    In the Nevėžis River (LT-C), uncertainties were linked to SD methods (51.3% and 60.9%). In this region, the lowland topography has the opposite influence to uplands and the grid cell of GCMs is sufficiently large, so SD methods, in some cases, did not properly adjust the output of GCMs to local climatic conditions of the specific area. Especially, it is important for corrections of precipitation data; therefore, the selection of SD method causes the greatest uncertainties in LT-C. Due to a large part of rivers' feeding source as snowmelt, the floods in rivers of this region are usually caused by the thick cover of snow. Since in the future an increase in air temperature is projected, the period of snow accumulation will get shorter or will be absent in some years. Accordingly, the projections of river runoff had a wide range according to various scenarios during the winter and spring seasons. In another similar study, Lawrence & Haddeland () found that the estimated uncertainties in runoff projections of two river catchments which had generally been dominated by the spring snowmelt were mostly related to the SD methods (48% and 60%) as well.
In the Šventoji River (SE-LT), the influence of SD (46.2% and 39.4%) was established as well. This region is characterised by the widespread permeable sandy soils, which effectively absorb water from snow melting and later gradually release it, supplying rivers in the low-flow period. The annual discharge of rivers of southeastern Lithuanian is distributed rather equally. In the Šventoji River catchment, GCMs do not have a significant impact, therefore the importance of the SD methods increases since SDs determine the way meteorological data are adjusted for particular regional conditions. Results of Kriaucǐunienė et al. () established that the largest