Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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The following should be pointed out when dealing with the climate change effects in more detail. Forecast of Climate Change Effect on River and Lake Ecosystems. Increasing “thermal load” on rivers and water reservoirs may accelerate eutrophication processes. Shifting in the species composition (groups) of phytoplankton toward species (groups) with higher temperature optimum (for example Cyanobacteria) poses a substantial risk for drinking water quality. Warming would affect fish resources. Uniform increase in water temperature in shallow water reservoirs would lead to weight loss of fish inhabiting cold water and cause plague of multiple bions. A disruption of the fish biocycle, peter-out of stenobiont fish from the fish fauna, change in species diversity and fish number and biomass are to be expected. Experts believe that, at present, systematized hydrobiological data are not available, therefore it is not possible, in a statistically credible and significant way, to record changes in structural parameters of aquatic organism communities in response to the effect of specific environmental factors, and to identify in particular climatic change impact. There is a need to commence long-term “high-frequency” hydrobiological parameter observations of most characteristic water bodies within a framework of research monitoring. Decreasing water levels in rivers and lakes is likely to increase 137 Cs and 90 Sr radio nuclide concentrations in surface water of the Dnieper and Pripyat basins located in Gomel and Mogilev Oblasts. The forecast of climate change effect on ground water level has demonstrated that if the annual temperature increases in Belarus by about 0.2 °С in the beginning of the 21 st century, this may result in the ground water level (GWL) recession of 0.02 m relative to the normal. If the temperature increases by 1.5 °C by 2025, this would lead to GWL recession by approximately 0.03-0.04 m relative to the normal. Spring GWL amplitudes are expected to decrease to such a low level, as was observed during the 5 years warm period in the late 1980s-early 1990s, or even lower. Risk of Inundation of Areas by Floods. The analysis of data of the flood events in 1845 and 1931 shows that more catastrophic floods and high water may form in Belarus in the future. Such a situation is possible with a higher anthropogenic load on the watershed area leading to substantial change in conditions of runoff formation in hydrological terms. Risk for Hydropower Engineering. All operating HEPP in the Belarusian power engineering system are categorized as small-sized units for which firm capacity is defined by the December runoff of design probability not lower than 95% in the year with low water. The firm capacity of Polotsk HEPP being currently designed and categorized as medium-sized is accepted based on the condition of 80-85% of the assured water supply. Control structures of small-sized HEPP comprise small reservoirs for day storage which are affected by climate to a great extent. The increase in mean monthly temperatures of the surface water layer would lead to additional evaporation and respective power generation loss. Winter warming, however, as observed in the last decades, improves the ice conditions on water reservoirs and rivers. - 182 - Risk for Water Transport. Climate factors may cause substantial variation in water discharge, both within a year and interannually. On average, 46 - 62% of the annual runoff occurs during spring. Approximately an average of 4-6% of the annual runoff falls within each of the 9 months of summer, fall and winter. In the years with low water in summer and winter months, the local flow may reduce down to 2-3 % of the annual one, thereby affecting the water level and operation of the water transport involved in freight and passenger traffic on the rivers of Pripyat, Dnieper, Berezina, Sozh and Dnieper-Bug canal. Adaptation Measures in Water Management. An increase in frequency and duration of dry spells would lead to the decline of water levels in rivers, lakes, and water storage reservoirs and, hence, would deteriorate the quality of the water. This would necessitate an upgrading treatment of waste water discharged into these sources and the relocation of polluters beyond the boundaries of water protection areas. Reduced water levels and consumption during the lowwater period would adversely affect the operation of the Belarusian inland water transport, HEPP and also radiation condition of surface water in Gomel and Mogilev Oblasts of Belarus. Aquatic flora and fauna are expected to change. Due to the above, adaptation of the water sector and aquatic systems should be aimed at mitigating climate warming-related adverse effects and contribute to sustainable development of the Republic of Belarus. Proposals on Most Critical Adaptation Measures. Major efforts in the water resources sphere are proposed to be focused on the following adaptation measures: (i) The Development of flood control actions primarily for the Polessie region, with specifics of the river runoff formation in Ukraine being accounted for; (ii) the development of a reliable hydrometeorological monitoring, extensive use of the radar and satellite data for assessing characteristics of the snow cover and planning water management, agricultural and forest protection measures; (iii) scheduled forest reclamation activity in the river basins as an efficient measure to control erosion water streams; (iv) substantiation of efficiency and feasibility of construction of underground water storage reservoirs in some regions of the country to regulate the water regime with the requirements of water users, i.e. to address the water supply problem, namely, increasing guaranteed water content of a source. Summary Requirements. Implementing water supply actions is time consuming, therefore large water management facilities need to be planned 25 years in advance and commissioned 10-15 years ahead of the water demand. The long-term planning of the economic activity should take into account the vulnerability of surface water and specific limitations of adaptation measures without reference being made to specific dates on the change onset. The adaptation of the economic activity should first of all include water conservation, extensive use of water-conservation processes, and more extensive use of agricultural land irrigation.
- 183 - Generating Synthetic Daily Weather Data for Modelling of Environmental Processes Leszek Kuchar Institute of Meteorology and Water Resources, ul. Parkowa 30, PL-51616 Wroclaw (Poland) Agricultural University of Wroclaw, Department of Applied Mathematics, ul. Grunwaldzka 53, PL-50357 Wroclaw (Poland) 1. Introduction For the needs of environmental models, particularly simulation models daily data of solar radiation, maximum and minimum temperature, and total precipitation are most often required. If there are no required data or they are missing, applications of models are very limited. The situation mentioned occurs if there is a lack of a meteorological station or new environmental conditions or when new records of data are not available. First methods generating data for the needs of agricultural modelling were constructed by Richardson, mainly for crop simulations for a new climate scenarios. 2. Methods Daily records of data were simulated by means of general climate information. Weather generators like many environmental statistical models, use Markov chains to determine occurrence of wet/dry days, and gamma or exponential probability distribution for amount of rainfall. Daily values of solar radiation, temperature maximum and minimum are considered as a weakly stationary process and generated by general linear model (GLM). Depending on location of future application more studies were related to choosing an appropriate probability distribution for each climate variable. Generated data series are required to have the same statistics as climate data including means, variations and cross, lag and lag-cross correlations of solar radiation and temperature. The amount of precipitation and its variation is also expected to be the same as from observed data. While means and variations of generated data (except variation of precipitation) sufficiently estimate moments of theoretical distribution, there are still poor fitting in precipitation variation, precipitation extremes and correlations between variables. 3. Results In Richardson’s weather generator, the cross, the lag and the cross-lag correlation illustrate seasonal and spatial relation between variables, are constant through locations and over the year. Recently, spatial and monthly course of correlation are introduced to the models by staircase function. Correlations differ from month to month and location, however constant within the given month. Transition probabilities and parameters of rainfall probability distribution are fixed monthly or biweekly as a set of 12 or 26 values or estimated by strait function. In this presentation new trends of data generating as parametrization of serial correlation, transition probability, and α parameter of Γ probability distribution will be presented. References Bruhn J.A., Fry W.E., Fick G.W., 1980: Simulation of Daily Weather Data Using Theoretical Probability Distributions. J. Appl. Meteorol., 19, 1029-1036. Gates W.L., 1985: The use of general circulation models in the analysis of the ecosystem impacts of climatic change. Clim. Change, 7, 267-284. Hayhoe H.N., 1998: Relationship between weather variables in observed and WXGEN generated time series. Agric. For. Meteorol., 90, 203-214. Hunt L.A., Kuchar L., Swanton C.J., 1998: Estimation of solar radiation for use in crop modelling. Agric. For. Meteorol., 91, 293-300. Kuchar L., 2004: Using WGENK to generate synthetic daily weather data for modelling of agricultural processes. Math. Comp. Simul., 65(2) /in print/. Larsen G., Pense R., 1982: Stochastic Simulation of Daily Climatic Data for Agronomic Models. Agron. J., 74, 510-514. Matalas N.C., 1967: Mathematical assessment of synthetic hydrology. Water Resources Res., 3(4), 937-945. Michaelson J., 1987: Cross-Validation in Statistical Climate Forecast Models. J. of Climate and Appl. Meteorol., 26, 1589-1600. Richardson C.W., Wright D.A., 1984: WGEN: A model for generating daily weather variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8, 83pp. Richardson C.W., 1985: Weather simulation for crop management models. Trans. ASAE., 28, 1602-1606. SAS Institute Inc., 1988: SAS/STAT User’s Guide. Release 6.03 Edition. Cary, North Carolina. Wilks D.S., 1992: Adapting stochastic weather generation alghoritms for climate change studies. Clim. Change, 22, 67-84.
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Fourth Stu
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Preface The 4 th Study</str
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- I - Table of Abstracts Title Auth
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- III - Sensitivity in Calculation
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- V - Parameter Estimation of the S
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- VII - The Realism of the ECHAM5.2
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Adam, W. K. .......................
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- 1 - Activities of the GEWEX Hydro
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eference site archive (Cabauw, Neth
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- 5 - Remote Sensing of Atmospheric
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minute averages around the satellit
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- 9 - Precipitation Type Statistics
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- 11 - Assimilation of New Land Sur
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Multichannel Microwave Radiometer f
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output has been processed in an equ
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height dependence of the Z-R-relati
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- 19 - CERES and Surface Radiation
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- 21 - Coastal Wind Mapping from Sa
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- 23 - Clouds and Water Vapor over
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- 25 - Broadband Cloud Albedo from
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- 27 - Observation of Clouds and Wa
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shows a ground-track of the vessel
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- 31 - Determination and Comparison
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- 33 - Spatial Variability of Snow
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- 35 - EVA-GRIPS: Regional Evaporat
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- 37 - LITFASS-2003 - A Land Surfac
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-39- Calibrated Surface Temperature
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- 41 - The Marine Boundary Layer -
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- 43 - Characteristics of the Atmos
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Figure 2. Surface stress from two d
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During the experiment the water was
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While the number of smallest drops
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SCANDIA will not be supported any l
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- 53 - Relationships Between Precip
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- 55 - Analysis of the Role of Atmo
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- 57 - Meteorological Peculiarities
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Baltic Sea Inflow Events Jan Piechu
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- 61 - The Different Baltic Inflows
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- 63 - Observations of Turbulent Ki
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- 65 - The Influence of Synoptic Si
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-67- Improved Method for the Determ
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-69- The Helicopter-Borne Turbulenc
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- 71 - CEOP Reference Site Data fro
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- 73 - The BALTEX Hydrological Data
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- 75 - Hydrological and Hydrochemic
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-77- Sea-level Monitoring at MARNET
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1 0.8 0.6 0.4 0.2 GO(CLD-M) ERA40 S
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Figure 2. Monhly mean values of lat
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In the case of an ideal fit, the co
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- 85 - Influence of Atmospheric For
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The largest inter-annual variabilit
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References Andrejev, O, Sokolov, A.
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On the other hand, if the stratific
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Cyberska 1989, Cyberska and Krzymin
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- 95 - Continental-Scale Water-Bala
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- 97 - ICTS (Inter-CSE Transferabil
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The subsurface flow calculation is
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functions only data with higher qua
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- 103 - Modelling the Impact of Ine
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- 105 - Objective Calibration of th
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- 107 - Validation of Boundary Laye
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- 109 - Simulated Dynamical Process
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spreads along isobaths (fig.2). As
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than the precipitation pattern of J
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Figure 2. Long-term variability in
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obvious from temperature charts. Ho
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shortening of the winter seasons by
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Maximum 5 day precipitation (mm) Nu
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scale parameters the correlation be
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similar: the locations of main mini
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- 127 - Storminess on the Western C
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- 129 - Trends in Wind Speed over t
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Page 155 and 156: Figure 2. Time series of Mean Sea L
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Page 161 and 162: - 145 - Baltic Sea Saltwater Inflow
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Page 169 and 170: - 153 - The Realism of the ECHAM5.2
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Page 177 and 178: conditions even more challenging th
Page 179 and 180: surface heat fluxes close to zero.
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Page 183 and 184: - 167 - Present-Day and Future Prec
Page 185 and 186: - 169 - Extreme Precipitation on a
Page 187 and 188: at the stations Pärnu (Estonia) an
Page 189 and 190: 6. Results There are many possible
Page 191 and 192: and vegetation, and to derive the h
Page 193 and 194: The structure of water bodies cadas
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Page 201 and 202: The evapotranspiration is affected
Page 203 and 204: Model parameters and coefficients:
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Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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