Alt lternativ ive ways s to def efin ine contrib ibutio ions - PowerPoint PPT Presentation

Narva va ri river nutrie trient t input: t: divi vision betw tween countrie ies Alt lternativ ive ways s to def efin ine contrib ibutio ions Water Management of the Narva River: harmonization and sustention ( NarvaWatMan Project) Natalia Oblomkova

About the Project • Project duration – 32 months (March 2019 – November 2021) • Lead partner - Tallinn University of Technology • Partners - FSBI “State Hydrological Institute”, SC "Mineral“ – Saint-Petersburg, Russia • Associated partners - Narva City Government, Administration of municipal formation «City Ivangorod Kingisepp municipal district of Leningrad Region»

Examples • Polish, German and Check Republic contributions to total Odra river nutrient input defined based on application of the MONERIS model. Poland has provided values on inputs from Czech Republic, and Germany has modeled their inputs to Oder taking 1 – capitols, 2 onto account retention using the MONERIS model. At the – state moment German share was assumed to be constant (5.5% for TN borders, 3 – and 3.1% for TP according to PLC Guideline ). Poland reports the the Baltic total input figures. Sea, 4 – the • For Torne river Swedish model has been applied. Finland International provided necessary information to Swedish experts, who Oder River modelled nutrient loads from the whole catchment area Basin District, considering retention (communication with Lars Sonesten). 5 – the Contributions have been assessed based on the 2006 data. Since International that time total input has been divided based on that constant proportion. Finnish share is 45 %, while Sweden responsible for Torne River 55 % of the load. Total input estimated according to the Basin District. monitoring data collected by countries. Average is computed of Finnish and Swedish data (Presentation by BNI). Values are almost similar. Picrure: Ibragimow, Aleksandra & Albrecht, Eerika & Albrecht, Moritz. (2019). The transboundary water management - Comparing policy translations of the Water Framework Directive in the international basin districts of the Oder River and the Torne River. Quaestiones Geographicae. 38. 29-39. 10.2478/quageo- 2019-0006.

Previous studies • No attempts were made to jointly simulate loading for the entire drainage basin of the Narva River. • In most cases modelling only for Peipsi drainage basin. • MESAW, EstModel (?) and Institute of Limnology Load Model were used. • For entire Narva catchment national estimates for HELCOM PLC can be utilized Ptot loss, kg/sq.km Ntot loss, kg/sq. km 25,00 1200,00 1000,00 20,00 800,00 15,00 600,00 10,00 400,00 5,00 200,00 0,00 0,00 2017 2010-2013 2017 2009 2006-20102011-2015 2005 2006-20102011-2015 2017 2010-2013 2017 2009 2006-2010 2011-2015 2005 2006-2010 2011-2015 EST_Narva_tot RUS_Narva_tot EST_Peipsi RUS_Peipsi EST_Narva_tot RUS_Narva_tot EST_Peipsi RUS_Peipsi Nutrient losses per square kilometer of the catchment according to the different modelling activities in Peipsi Lake and whole Nerva river catchment Area specific loss of N and P, calculated based on results from previous modelling, showed that loads from Estonian side are higher than Russian ones for corresponding period (especially for nitrogen) and that there is decreasing dynamics in inputs to the Peipsi Lake during recent decades. Such difference for entire Narva river catchment can be partly explained by the fact that Russia and Estonia use different total input data and consider only current share of load when calibrating the models. Thus, current approach to allocate loads according to the catchment area seems to be rather rough.

Recent modelling for HELCOM PLC • Following conclusions can be drawn based on the analysis: • Russia (ILLM) and Estonia (EstModel) use mainly emission coefficients-based models; • Significant difference originate from method how to connect calculated loads with monitoring data: Estonia estimates diffuse losses as the remaining part of the monitored load after subtracting input from point source and considering retention in inland surface waters , while Russia use reservoir retention for the same purpose; • Natural background Ntot losses almost 2 times higher according to Russian data, while Ptot loss is almost equal. • Atmospheric Ptot deposition is 2 times higher according to Estonian data, it should be noted that for Ntot Russia assume zero load compared to Estonian estimates which coincide 440 kg per square kilometer; • Load from scattered settlements calculated differently: Estonia use per capita coefficients while in Russia this source included in runoff from urban areas; • Big difference in retention estimates: Estonia calculate total retention based on the equation , while Russia used similar equation only for riverine part prior big reservoirs, the remaining retention calculated by subtracting total load from sum of diffuse and point sources load.

• to estimate burden based on proportion of agricultural area within the catchment or use per capita estimates According to per capita approach Russian Other methods contribution should be 59% (based on 2002 data) and Estonian is 41% (2011 data). Agricultural areas were in proportion: 53% in Russia and 47% in Estonia [Frumin, 2013]. • to compare potential nutrient reduction of the anthropogenic loads based on fulfillment of the HELCOM Recommendations, BAT, BEP etc.

Retention Source Ntot Ptot Area retention, % retention, % • Retention is one of the most Nõges et al., 2003 Peipsi Lake 50-70 complicated issues to consider due to specific features of the Narva Lozovik, 2018 Peipsi Lake 53 52 river lake system and unevenly RusNIP II report, 2015 Entire Narva river catchment 56 37 distributed anthropogenic pressures. Peipsi Lake 56 Frumin, 2013 Entire Narva river catchment 53 46 Stålnacke et al., 2015 Entire Narva river catchment 56 Peipsi Lake 49 To increase reliability of the estimates the spatial distribution of the sources as minimum should be considered. It would be beneficial to reduce possible mistakes related with retention when defining shares in nutrient input as far as it has no influence for planning measures at local level.

Advantages and disadvantages Approach Input data demand Approbation Advantages Disadvantages MESAW model Rather high (but low compared to Good for Baltic Sea 1. Adjusted in accordance with 1. Rather complicated to use semi-physical models) catchment area monitoring data; 2. Rather high data demand ESTMODEL Good for Estonian part 2. 2. Considers spatial distribution of ILLM based approach Good for Russian part sources Doesn’t - Per agricultural area approach Low Very simple to use 1. consider spatial - Per capita approach Low Very simple to use distribution 2. Doesn’t consider technological aspects (water treatment quality etc.) Potential reduction approach Moderate Low (only in several 1. Considers current level of pollution 1. Good quality data are needed; Russian studies) and possibility to reduce it 2. Potential from agriculture are 2. Allows to address reduction directly still rather uncertain; to source and elaborate corresponding measures; 3. Less uncertainties in calculation – no need to define natural background load

Follow-up based on discussion among Project team KEY POINTS: • Data demand for proposed models is extremely high and sometimes there is no possibility to get data (for e.g. not enough monitoring data). • Reliability of the modelling results might be low. • It will be hard to apply potential reduction method due to low data availability from Russian side. AGREED APPROACH: 1) To consider only pilot area – Narva river immediate catchment (There are several monitoring stations along the river); 2) To use balance method (point sources; diffuse sources (emission coefficients-based method); calculate riverine retention (for e.g Behrendt method) and to compare with load between two hydrochemical stations (outlet from Peipsi Lake and in the Narva river mouth); 3) Before step 2. - to test Estonian and Russian coefficients by calculation emissions using both values and follow-up comparing of the results of the balance calculation FOLLOW-UP: To collect data for pilot area starting from 2006 (tbc) to 2019 during summer 2020

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