In 1990 the SIMONA and the Complex agroecosystem model
developed in Lab. 170 of ARI was established on the mainframe
EC-1045 of the Institute of Mathematics and Cybernetics in Vilnius.
Meteorological data for 15 years had been collected. Visit to ARI
with acad. J. Mockus with plans to integrate SIMONA and global
optimization package OPTIMUM. Never completed. Adaptation of the
applied crop model (R.A. Poluektov, S.M.Fintushal, I.V. Oparina,
V.V.Terleev) to Lithuanain soil, plant and climate conditions:
Klaipeda University, 2014
Slide 3
Denisov V., et al. (1998). Simulation system of crop growth and
development, Biologija 3, 52-57.
Slide 4
Klaipeda University, 2014 Denisov V. Development of the Crop
Simulation System DIASPORA// Agronomy J., 2001. Vol. 93, N.3, p.
660- 666.
Slide 5
Klaipeda University, 2014 Juenko N. et al. (2001). Database of
integrated information modelling system of gran crops// Agric. Sci.
(ems kio mokslai), 3, 22-28. Metadata Meteorological data Soil
hydro physical parameters Parameters of mathematical models Crop
data
Slide 6
1. JUSHCHENKO, N.; DENISOV, V. Finite-difference methods for
solving inverse problems in agroecological modeling using field
experiments data. In: Finite difference schemes: theory and
applications. Vilnius: TEV, 2000, p. 117-123. 2. JUSHCHENKO, N.;
DENISOV, V. Software implementation of finite-difference method for
parameter identification in agroecological modelling. Mathematical
Modelling and Analysis, 2002, vol. 7, no. 1, p. 71-78. 3. VITRA,
D.; DENISOVAS, V.; JUENKO, N. Computer modelling of density
dynamics of single-species laboratory insects population.
Mathematical Modelling and Analysis, 2004, vol. 9, no. 4, p.
327-340. 4. DENISOVAS, V.; JUENKO, N. Grdini kultr ontogenezs
taikomj modeli identifikacijos algoritmas. Lietuvos matematikos
rinkinys, 2005, t. 45, spec. nr., p. 465-469. Klaipeda University,
2014
Slide 7
1. JUSHCHENKO, N.; DENISOV, V. Finite-difference methods for
solving inverse problems in agroecological modeling using field
experiments data. In: Finite difference schemes: theory and
applications. Vilnius: TEV, 2000, p. 117-123. 2. JUSHCHENKO, N.;
DENISOV, V. Software implementation of finite-difference method for
parameter identification in agroecological modelling. Mathematical
Modelling and Analysis, 2002, vol. 7, no. 1, p. 71-78. 3. VITRA,
D.; DENISOVAS, V.; JUENKO, N. Computer modelling of density
dynamics of single-species laboratory insects population.
Mathematical Modelling and Analysis, 2004, vol. 9, no. 4, p.
327-340. 4. DENISOVAS, V.; JUENKO, N. Grdini kultr ontogenezs
taikomj modeli identifikacijos algoritmas. Lietuvos matematikos
rinkinys, 2005, t. 45, spec. nr., p. 465-469. Klaipeda University,
2014
Slide 8
EVALUATION OF IMPACT OF CLIMATE CHANGE TO AGROECOLOGICAL
SYSTEMS IN LITHUANIA USING SIMULATION MODELLING (Partly presented
in 2010 Conference)
Slide 9
Klaipeda University, 2014 Crop yield change in Lithuania
calculated according to ECHAM5-B1 climate change scenario: (a) in
2030; (b) in 2060
Slide 10
Analysis of particular modelling results show that rising mean
temperature will stimulate increase in grain crop productivity (up
to 11 %) in the first half of 21st century While further
temperature rise and precipitation decrease in the second half of
the century make cereals highly dependent on soil moisture and
force to decrease their productivity by about 15 % This effect is
most noticeable in the sandy and sandy loam soils areas. Also water
supply stress for cereals will stimulate their growth rate
particularly at the beginning of the vegetation season. In case of
spring wheat, the time span between sowing and maturity shortens by
approximately 10 to 15 days Between 2030 and 2060 soil moisture
become a limiting factor, with essential soil water content
decrease in late June and July. So, it could be suggested to switch
to cultivation of winter crops, especially in the regions with
domination of sandy soils (f.e., South East Lithuania) Klaipeda
University, 2014
Slide 11
Research Laboratory of Plant Physiology is established in the
Faculty of Natural Sciences and Mathematics of KU. Climate chamber
with designed hydroponic system Some new results already achieved
and published: S. Valainait, A. imknas, V. Denisov. Leaf size
regularities in Festuca pratensis from the systemic viewpoint. //
Taylor & Francis, 2013, Plant Biosystems. Vol. 147, P. 629-637.
A. imknas, S. Valainait, V. Denisov, A. Salyte. Systemic view on
heading and overwintering: are they always opposed? //
Wiley-Blackwell, 2013, Journal of Agronomy and Crop Science. Vol.
199. P. 460465. Klaipeda University, 2014
Slide 12
Objective: examine the inter and intra-annual variability of
the Curonian Lagoon watershed and assess its long term tendency
under climate change using hydrological modelling and statistical
analysis methods. Goals: 1.Identify suitable model input parameters
for the study area, and apply the selected hydrological modelling
tool to create a model of the Curonian Lagoon watershed and its
elements; 2.Assess uncertainty, calibrate and validate the model to
adequately represent the study area; 3.Evaluate variability of the
Curonian Lagoon watershed under different possible climate change
scenarios. Klaipeda University, 2014
Slide 13
Curonian Lagoon: largest European coastal lagoon; Separated:
0,5 4 km sandy Curonian spit; Connected: through the fine Klaipeda
Strait Average depth: 3,8 m Max. natural depth: 5,8 m. Length: 93
km; Volume: 6,3 km 3 ; Surface area: 1584,03 km 2 ; Curonian lagoon
basin area: 100458 km 2 : 48% Belorussia; 46% Lithuania; 6%
Kaliningrad (Russia) and Poland. Nine rivers are discharging:
largest of them is Nemunas River Klaipeda University, 2014
Slide 14
Runoff of rivers to the lagoon: from 14 to 33 km 3 per year
(443,64 m 3 /s to 1045,73 m 3 /s); exhibits a strong seasonal
pattern
Slide 15
SWAT (Soil and Water Assessment Tool) is a river basin scale
model developed to quantify the impact of land management practices
in large, complex watersheds. SWAT can be considered a watershed
hydrological transport model Main components: Weather; Surface
runoff; Return flow; Percolation; Evapotranspiration; Transmission
losses; Pond and reservoir storage; Crop growth and irrigation;
Groundwater flow; Reach routing; Nutrient and pesticide loading;
Water transfer. Klaipeda University, 2014
Slide 16
Total area: 2200022.17 km 2 Minimum elevation: -5 m Maximum
elevation: 353 m Mean elevation: 130,42 m Cell size: 153x153 m
Klaipdos universitetas, 2014
Climate of watershed provides the moisture and energy inputs;
Required variables: -Daily precipitation; -Maximum and minimum air
temperature; -Wind speed; -Relative humidity; -Solar radiation.
Data obtained from Global Weather Data for SWAT service
http://globalweather.tamu.edu) Klaipdos universitetas, 2014
Climate change scenarios were developed within the patterns
defined by IPCC, Intergovernmental Panel on Climate Change 2013:
The global mean surface temperature is projected to increase
between 0.3 to 0.7C; Zonal mean precipitation will very likely
increase in high and some of the mid latitudes; Increases in
near-surface specific humidity over land are very likely. Scenario
number Increase in Temperature ( o C) Increase in Precipitation (%)
Decrease in relative Humidity (%) 1 DJF 1; MAM and SON 0,5; JJA
0,7. 2 DJF 7,5; MAM and SON 5; JJA 2,5. 3 All data points 1. P DJF
1; MAM and SON 0,5; JJA 0,7. DJF 7,5; MAM and SON 5; JJA 2,5. All
data points 1. O DJF 0,6; MAM and SON 0,4; JJA 0,3. DJF 3; MAM and
SON 1,5; JJA 0. All data points 1. Klaipeda University, 2014
Slide 24
Precipitation higher amount of runoff during all seasons; Air
Temperature river Nemunas Winter: increased runoff due to snow
melt; Spring: weaker floods; Autumn and summer: small impacts on
mean runoff; Air Temperature river Minija Winter: increased runoff;
Other seasons: decreased runoff, especially in summer. Humidity
almost no effect for Nemunas; Slight decrease of runoff during all
seasons for Minija. Klaipeda University, 2014
1.Results of climate assessment scenarios indicate that the
simulated hydrologic system is very sensitive to climatic
variations; 2.Potential impact on the Curonian Lagoon: salinity
change, sediment, biogeochemistry; 3.Need to perform a more
extensive assessment of potential climate change impacts on
hydrology and biogeochemistry of Curonian Lagoon basin by
simulating other factors changes (solar radiation, CO 2 emission,
etc.); 4.Improvement of the model is possible, provided additional
data is available; 5.Model limitations: high number of parameters,
difficult calibration, general performance. Klaipeda University,
2014
Slide 28
1.Next steps of Nemunas-SWAT model improvement: expand for
sediment and biogeochemistry. 2.Improvement of the landscape-scale
hydrological models of the Curonian lagoon watershed by integrating
them with dynamic crop simulation model. 3.The crop simulation
models needed should be capable to simulate biotic and abiotic
processes in different agroecosystems with an accuracy applicable
to daily time step of hydrological transport models, such as SWAT.
4.Crop model candidates: DIASPORA, APEX-AGROTOOL, DSSAT. 5.Further
models integration is envisaged to perform analysis and evaluation
of impact of changes in agricultural land use on coastal lagoon
ecosystem. Klaipeda University, 2014
Slide 29
Lithuanian Marine Valley Programme Klaipda University (KU),
Marine Science and Technology Centre (former Coastal Research and
Planning Institute) This work was partly supported by project
"Promotion of Student Scientific Activities"
(VP1-3.1-MM-01-V-02-003) from the Research Council of Lithuania.
Thank you! ! Klaipeda University, 2014