CROSS-BORDER CONSEQUENCES and CONFLICTS of INTEREST in RIVER BASIN MANAGEMENT PLANNING the Case of the Tisza River (Ukraine, Romania, Hungary, Serbia)
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CROSS-BORDER CONSEQUENCES AND CONFLICTS OF INTEREST IN RIVER BASIN MANAGEMENT PLANNING The case of the Tisza River (Ukraine, Romania, Hungary, Serbia) Mark Honti & Vera Istvánovics MTA-BME Water Research Group, Hungarian Academy of Sciences Stream eutrophication • Eutrophication management relies on nutrient control • P control successful in lakes • Less obvious in streams • Interfering factors: • Hydromorphology (bedform) • Hydrodynamics (turbulence, WRT) • Stream network topology (reaches, reservoirs) • Algal development may occur 100’s of kms downstream, management requires basin-scale approach • WFD focuses on domestic water bodies Stream eutrophication: fuzzy relation with nutrients 60 * 30 45 25 20 30 15 * * 15 * 10 5 Number of sites * 0 Number of sites 0 other no trend P down N down no trend P or N up Chl down P and N up P or N down no trend Chl up P and N down Istvánovics & Honti (2012) Efficiency of nutrient management controlling eutrophication of running waters in the Middle Danube Basin. doi: 10.1007/s10750-012-0999-y Five countries share the Tisza catchment (UA,RO,SK,HU,SRB) EU member state Non-member state UKRAINE Tisza / Tisa Szamos / / Theiß Someș / Тиса Somesch HUNGARY Maros / Mure ș ROMANIA Danube 100 km SERBIA Hydrobiologia a 12 b 1 2500 200 60 20 400 20 ) ) 2000 -3 150 -3 -3 -3 ) ) 40 -1 -1 1500 s s 3 3 100 10 200 10 1000 Chl m 20 g Q (m Q (m 50 m mg Chl m 500 m (mg P TP Eutrophication status in the Tisza River m (mg P TP 0 0 0 0 0 0 1000 1100 1200 1300 1400 1500 0 50 100 150 200 250 • Tisza receives algae from 2 large tributaries1 • Tisza is too deep (up toL 10(km) m) to support meroplanktonic algal growth2 L (km) c 123Chl 45 d TP Maros 750 200 Szamos 200 30 3 1200 30 ) ) ] -3 -3 -3 150 150 -3 -3 ) ) -1 -1 500 20 2 800 20 s s 3 3 100 100 Chl m 250 10 1 400 10 g Q (m TP [mg P m Q (m mg Chl m 50 50 m TP (mg P m (mg P TP TP (mg P m (mg P TP 0 0 0 0 0 0 0 00 250250 500500 750750 1000 1000 0 50 100 150 200 L (km)L [km] L (km) 1: Istvánovics & Honti (2012) doi: 10.1007/s10750-012-0999-y 2: Honti et al. (2008) Assessing phytoplankton growth in River Tisza (Hungary). Verh. Internat. Verein. Limnol. 30 (1): 87-89. Fig. 8 Longitudinal profiles of median discharge (Q), TP and situated on the riverbank. Numbered arrows indicate large Chl in the period 1997–2006 in four rivers that exemplify four tributaries. a 1 Va´g (Vah) River, 2 Garam (Hron) River. different Chl response classes. a Danube; b Sajo´ River, c Tisza b 1 Herna´d (Horna´d) River. c 1 Szamos (Somes¸) River, 2 River, d Zagyva River; see Fig. 1. Dashed line Q, thick line TP, Bodrog River, 3 Sajo´ (Slana´) River, 4 Ko¨ro¨s (Cris¸) River, 5 closed symbol Chl. Arrows with circles indicate major cities Maros (Mures¸) River, dashed arrow indicates a major dam river. Longitudinal patterns in decadal (1997–2006) leach algae into the river. Class 2 response was median data in a few rivers of this study revealed four characteristic of several medium-sized and large rivers characteristic classes of trophic response to hydraulic of this study. properties and nutrient loads (Fig. 8). The Tisza River, the largest tributary of the Danube The Danube represented Class 1 response (Fig. 8a). represented Class 3 response (Fig. 8c). The highly In this large river the median biomass of phytoplankton discontinuous Chl profile contrasted the relatively was P-determined (cf. Figs. 5, 7), and thus, nutrient smooth downstream increase in Q and TP. Most of the criteria could be applied to further decrease Chl. Chl originated from the hypertrophic Szamos (Somes¸ The Sajo´ (Slana´ in Slovakian) River, a highly in Romanian) and Maros (Mures¸ in Romanian) Rivers. polluted tributary of the Tisza River (Somlyo´dy et al., Algae exported by the shallow Szamos were lost while 1999) exemplified Class 2 response (Fig. 8b). Q and advecting along the deep channel of the Tisza River. Chl increased relatively smoothly along the flow in This loss was further enhanced by the rapid sedimen- spite of elevated nutrient emission from a large tation of diatoms upstream of a large dam (Fig. 8c; industrial town along the riverbank (170,000 inhabit- Honti et al., 2008; Istva´novics et al., 2010). Notice- ants). Neither tributaries nor reservoirs caused dis- ably, the Szamos River was not significantly richer in continuity in nutrients or Chl that could be detected by nutrients than the Upper Tisza River, but its shallow the methods of this study. A reasonable nutrient channel favored the growth of meroplanktonic dia- criterion might aim at reducing the longitudinal toms (Istva´novics & Honti, 2011). The trophic status increase in TP (and DIN, not shown) along the Sajo´ of the Tisza River depended primarily on the status of River to eliminate the elevated nutrient concentrations its large, shallow tributaries (the Szamos and the in the recipient Tisza River (Fig. 8c). However, to Maros Rivers), so water quality management should reduce local Chl concentrations one should manage focus on these rivers to decrease Chl in the main the numerous upstream ponds and reservoirs that may channel. 123 SZAMOS / SOMEŞ Szamos & Maros Catchment area 18 000 km2 Population 1 200 000 Drinking water supply 74% Sewerage (& WWTP*) 65% (45%) Romania Hungary SzSzB SM MM SJ BN CJ Békés MS HR Csongrád AR AB SB HD MAROS / MUREŞ Catchment area 27 000 km2 Population 2 300 000 Drinking water supply 63% Sewerage (& WWTP*) 48% (30%) *tertiary treatment Conflicting development objectives along these international rivers • Downstream: improve • Upstream: improve water quality, incl. drinking water and trophic and sanitation infrastructure toxicological status • Downstream has only indirect influence on incoming water quality Approach • Objectives • Model eutrophication in the Szamos and Maros • Assess improvement strategies • Methods • Detailed modeling for the Szamos • Identify conflicts of interest • Propose compromise solution • Simplified modeling for the Maros (method testing) • Describe current status • Assess sensitivity / vulnerability Szamos: Methods Meteorology Hydrology Nutrient budget Algal growth • Nutrient budget on municipality-level • Point and diffuse sources • Unified catchment and water quality model • Embedded in a GIS environment • Modelled discharge, nutrient fluxes and algal growth in the entire stream network • Scenario analysis • Realistic and hypothetical states Districts Administrative & institutional differences Statistical data 2850000 Administrative boundaries ‣ ‣ Data collection on NUTS 5 level (RO: municipality, HU: settlement) 2800000 Y ‣ Different land use and crop categories N ‣ Institutions 2750000 ‣ RO: The Environmental Agency (Agenţia pentru Protecţia Mediului) doesn’t do routine method: Voronoi polygon interpolation water quality monitoring Vectorized catchment boundary: 1 km 2700000 accuracy ‣ RO: The Water Agency (Apele Române) 5250000 5300000 5350000 Land5400000 use 5450000 focuses on water quantity data X ‣ HU: United Environmental, Water and Nature Protection Agency (until 2012), now under Ministry of Internal Affairs N ‣ Water quality monitoring network ‣ HU: high spatial resolution, monthly data ‣ RO: minimum requirements from EU WFD, Data source: CORINE mostly NO3 49 NUTS_3 data, we calculated the area of land-use categories situated within the Somes catchment assuming a homogeneous land-use distribution across each county. Figure 13. Land-use map based on re-classified CORINE land-use categories. Picture 2. Examples of re-classified CORINE land-use types. (2.4.3. Agriculture + natural vegetation re-classified as arable land; 3.2.4. Transitional scrub re- classified as pasture.) 52 Low population density, extensive agriculture within the settlements. If so, the difference between the CORINE-based estimate and 2.4.3. 3.2.4. statistical data would represent a potentially intensely cultivated agricultural land (Picture 3) that remains unaccounted in this study in the lack of data. This area was equivalent to a mean 13.5% (range 8.8-22.7%) of the accounted arable land or 13.2% (range 7.0-19.3%) of the accounted grassland. Picture 3. Examples of rural settlement structure. Agricultural land in a village Marisel, centrum To calculate surface runoff and soil erosion, we further modified land-use categories that were used to assess nutrient status of agricultural soils (Table 7). The consideration behind this distinction was that runoff and erosion depend as much on vegetation phenology (canopy height, surface cover) as on land-use. Thus, the broad category grassland included dominantly grass-covered urban areas, meadows and pastures when modeling erosion. Simultaneously, shrubland was treated as a separate category. 3.8. Lakes and reservoirs We compiled information on the location and volume of lakes and reservoirs. Apele Romane (2009) listed 2 natural lakes and 11 reservoirs with surface area over 50 ha from the Somes catchment. Data for several other lakes and fishponds were found in various reports (Table 9). When only surface area and mean depth were reported, volume was taken equal to their product. We identified the coordinates of lakes and reservoirs that did not appear in the CORINE land-use map from Google Earth. Szamos: catchment modeling ‣ Agricultural ‣ Soil loss, diffuse ‣ Point sources nutrient budget nutrient loads ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● P ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●