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

Natalia Oblomkova Water Management of the Narva River: harmonization and sustention (NarvaWatMan Project)

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

• nto account retention using the MONERIS model. At the

moment German share was assumed to be constant (5.5% for TN and 3.1% for TP according to PLC Guideline ). Poland reports the total input figures.

• For Torne river Swedish model has been applied. Finland

provided necessary information to Swedish experts, who modelled nutrient loads from the whole catchment area considering retention (communication with Lars Sonesten). Contributions have been assessed based on the 2006 data. Since that time total input has been divided based on that constant

• proportion. Finnish share is 45 %, while Sweden responsible for

55 % of the load. Total input estimated according to the 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.

1 – capitols, 2 – state borders, 3 – the Baltic Sea, 4 – the International Oder River Basin District, 5 – the International Torne River Basin District.

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

0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 2017 2010-2013 2017 2009 2006-2010 2011-2015 2005 2006-2010 2011-2015 EST_Narva_tot RUS_Narva_tot EST_Peipsi RUS_Peipsi

Ntot loss, kg/sq. km

0,00 5,00 10,00 15,00 20,00 25,00 2017 2010-2013 2017 2009 2006-20102011-2015 2005 2006-20102011-2015 EST_Narva_tot RUS_Narva_tot EST_Peipsi RUS_Peipsi

Ptot loss, kg/sq.km

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 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.

Other methods

Retention

• Retention

is

• ne
• f

the most complicated issues to consider due to specific features of the Narva river lake system and unevenly distributed anthropogenic pressures.

Source Ntot retention, % Ptot retention, % Area

Nõges et al., 2003

50-70

Peipsi Lake Lozovik, 2018

53 52

Peipsi Lake RusNIP II report, 2015

56 37

Entire Narva river catchment

56

Peipsi Lake Frumin, 2013

53 46

Entire Narva river catchment Stålnacke et al., 2015

56

Entire Narva river catchment

49

Peipsi Lake

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 semi-physical models) Good for Baltic Sea catchment area

• 1. Adjusted

in accordance with monitoring data;

• 2. 2. Considers spatial distribution of

sources

• 1. Rather complicated to use
• 2. Rather high data demand

ESTMODEL Good for Estonian part ILLM based approach Good for Russian part Per agricultural area approach Low

• Very simple to use

1. Doesn’t consider spatial distribution

• 2. Doesn’t consider technological

aspects (water treatment quality etc.) Per capita approach Low

• Very simple to use

Potential reduction approach Moderate Low (only in several Russian studies)

• 1. Considers current level of pollution

and possibility to reduce it

• 2. Allows to address reduction directly

to source and elaborate corresponding measures;

• 3. Less uncertainties in calculation – no

need to define natural background load

• 1. Good quality data are needed;
• 2. Potential from agriculture are

still rather uncertain;

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

Thank you for attention!

https://www.narvawatman.com/