natalja Čerkasova, vitalij denisov, ligita venckuvienė klaipeda university, lithuania mathematical...

Natalja erkasova, Vitalij Denisov, Ligita Venckuvien Klaipeda University, Lithuania Mathematical models in theoretical ecology and agriculture Sankt-Petersburg, 2014. Klaipeda University, 2014

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

Denisov V., et al. (1998). Simulation system of crop growth and development, Biologija 3, 52-57.

Klaipeda University, 2014 Denisov V. Development of the Crop Simulation System DIASPORA// Agronomy J., 2001. Vol. 93, N.3, p. 660- 666.

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

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

EVALUATION OF IMPACT OF CLIMATE CHANGE TO AGROECOLOGICAL SYSTEMS IN LITHUANIA USING SIMULATION MODELLING (Partly presented in 2010 Conference)

Klaipeda University, 2014 Crop yield change in Lithuania calculated according to ECHAM5-B1 climate change scenario: (a) in 2030; (b) in 2060

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

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

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

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

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

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

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

Cell size: 230x230 m Dominant landuse types : -CRWO (Cropland, woodland mosaic) 64%; -CRDY (Dryland, cropland and pasture) 23%; -FOMI (Mixed forest) 6%. Dominant soil types: -Loam 33%; -Clay loam 10%; -Sandy loam 9%. 17 i 18 Klaipeda University, 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

Klaipeda University, 2014

Validation period: 2008-2012 Performance: NS = 0,75; R 2 = 0,79; (calibration performance: NS = 0,79; R 2 = 0,81) Klaipeda University, 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

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

Pessimistic scenario Optimistic scenario River Runoff change (%) WinterSpringSummerAutumn Minija+22-7-18+10 Nemunas+17-10-8+9 River Runoff change (%) WinterSpringSummerAutumn Minija+18-5-10+5 Nemunas+10-9-2+3 Klaipeda University, 2014

Annual runoff change Minija: P: +3%; O: +2%. Nemunas: P: +7%; O: +5%. 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

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

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

natalja Čerkasova, vitalij denisov, ligita venckuvienė klaipeda university, lithuania mathematical...

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