Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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Meteorological Peculiarities of Maximum Rainfall-induced Runoff
Formation in Lithuania
Egidijus Rimkus and Jurgita Rimkuviene
Department of Hydrology and Climatology, Vilnius University, Ciurlionio 21/27, 2009 Vilnius, Lithuania
egidijus.rimkus@gf.vu.lt
1. Introduction
During warm season heavy or continuous rainfall is the
main cause in rainfall-induced runoff (further – RIR)
formation. RIR means relatively high water level
(discharge) in the rivers. Depending on meteorological
conditions of seasons RIR is formed from one to threefour
times a year in Lithuania. The study is focused on
one RIR during the warm season when the highest warm
season discharge occurs.
2. Data and methods
30 Lithuanian rivers or their parts were chosen for the
analysis. For the study purpose daily discharge data of 34
water gauging stations were used. The discharge database
for period 1968-1998 was created. Catchments areas
varies from 35 to 5320 km². Lithuania territory is divided
into four main hydrological regions: Baltic seashore,
Zemaiciai Highland, Middle Plain and South-East
(Fig. 1). RIR formation conditions analysis was made
only for three of them due to lack of data for small Baltic
seashore region.
Figure 1. Hydrological regions of Lithuania
Daily meteorological data from 37 weather stations were
used in this work (from 3 to 6 weather stations for every
catchment). Mean precipitation amount in catchments
areas were calculated using Inverse Distance Weighted
method (ArcView Spatial Analyst software was used).
Analysis of synoptic processes which leads RIR
formation was made. Synoptic processes were divided
into 5 types:
A – Western cyclones, moving from the Atlantic Ocean
and carrying wet marine air masses in their southern
parts;
B – Southern cyclones, developed in the wet subtropical
air masses;
C – Low pressure fields with stationary undulate cold
atmosphere fronts;
D – non-gradient pressure fields where intense
convective development are related to small area
cyclonogenetic processes;
E – complex of synoptic processes: long rainy periods
are influenced by different types of synoptic processes.
3. Precipitation amount
Reoccurrence of RIR cases differed in 3 hydrological
regions of Lithuania. RIR mostly occurs in May in the
Middle Plain and South-East hydrological region (33,8
and 34,8% accordingly). Rainfall in May could be
effectively transformed to surface runoff, because of low
evapotranspiration due to relatively low temperature and
the soil still saturated with water (after snow melting
process). Zemaiciai Highland is characterized by slightly
different reoccurrence of RIR. The most rainy months in
Western Lithuania are August and September (July is the
most rainy period in the major part of Lithuania).
Western cyclones frequently bring to the Lithuanian
territory relatively warm and wet air masses in
September, thus numerous rainfalls occur in Zemaiciai
Highland. For this reason more often RIR occurs in
September (28%).
During the warm period maximum RIR would usually be
formed by 41mm precipitation on average in the river
catchments in 1968-1998. Precipitation amount mostly
depends on geographic location (maximum RIR is
formed by 45 mm precipitation on average in Zemaiciai
Highland; while in the Middle Plain this amount reduces
to 39 mm). RIR was influenced by more intensive but
relatively less amount and short duration rainfall in the
small catchments. Nevertheless, the catchment area does
not influence amount of precipitation so much.
Does data from one rain gauge station can precisely
reflect precipitation amount in different scale catchment
areas were analyzed in this study. The data accuracy
decreases with the increase of the catchment area and the
distance to the center. Error size mostly depends on
catchment area (r=0,76) and less on the distance between
precipitation gauging station and catchment geometric
center (r=0,66). The authors for parameters integration
suggest to employ emphyrically based index k:
k = , where l – distance between precipitation
l F
1000
gauging station and catchment geometric center (km),
F – catchment area (km²). Correlation coefficient
between index k and average size of error (%) reaches
0,84 (Fig. 2).
Standard deviation of precipitation that falls on within the
river catchment is also the function of the catchment area
(r=0,88). Thus, precipitation dispersion coefficient
pd= σ (where σ - spatial standard deviation, F-
F
catchment area) was calculated for the purpose of
comparison of spatial dispersion in different size
catchments. The highest dispersion coefficient was