MONERIS

General Information
Acronym of the model	MONERIS
Full name of the model	Modelling nutrient emissions in river systems
Model components	Subpart of Ecosystem
Supported platforms	Windows
Programming Language	C#
Still maintained	Yes, by: Institute of Freshwater Ecology and Inland Fisheries Berlin / RiSyM-Lab (Dr. Markus Venohr)
Most recent version	Version 3.2
Model structure
Executables are available
2D (horizontal)
Other: Fixed grid (Eulerian)
Model description
Model objective	– modelling temporal and spatial variability of nutrient emissions and loads in rivers systems, – quantification of pathway and sources of nutrients, – impacts of river basin management and global change
Specific application	– nutrient emissions and management, e.g. at the river Danube - modelling at regional (e.g. German model regions in the RESI-project) and continental (e.g. European catchments in the MARS-project) - Impact analysis and management options - Climate-change: BAUM, MARS, Innovate - Reconstruction of background concentrations in surface waters
Background knowledge needed to run model	- process understanding (physical, chemical and biological processes in river systems) for data / result interpretation - profound knowledge of standard software applications - standard software for data processing, specifically GIS software - data bases (Access, PostgreSQL)
Basic procedures	a) processing input data for modelling unit and for states / countries; optionally, runoff calibration if distributed values unavailable b) setup data base, optionally adjusting model constants (calibration) c) validation of model results against observed nutrient loads
MONERIS is a nutrient emission model to be used for regional, national and international studies of water quality in catchment areas. It was developed at IGB-Berlin, to address three goals: - Identification of the sources and pathways of nutrient emissions at the analytical unit (smallest calculation unit) level - Analysis of the transport and the retention of nutrients in river systems - Provision of a framework for examining management alternatives (scenarios) MONERIS is a very flexible system, and is therefore most suitable to cover these demands, and to support analysis at a variety of scales. Nutrient emissions of point and diffuse sources into surface waters are evaluated in the model. Point data (e.g. waste water treatment plants), areal information (e.g. soil data), and administrative information (like statistical data for districts), are integrated. The application of geographic information systems (GIS) is essential. Modelling scenarios allows calculation of the efficiency of management measures for reaching prescribed water quality standards (such as target concentrations of surface water quality). The MONERIS approach provides an assignment of the measures applied to the analytical units. In the model, suitable measures are pre-defined which can be selected by the user, either as single or combined measures. The measures can be based on analytical units or cover larger areas. Therewith, the resulting effect of measures on loads in the catchment can be tested. By integrating numerous of possible components into the system, complex analysis of effects of measures can be obtained in a short time.
Link to website/manual	“Webpage”: http://www.moneris.igb-berlin.de/index.php/homepage.html “Manual (Version 2.0):http://www.moneris.igb-berlin.de/tl_files/data_moneris/data_publikationen/Moneris%20Handbuch/Handbuch_englisch12_03.2010.pdf Research paper onf methods and background
Model characteristics
Input variables	Obligatory: – Spatial input data: 􀂾 river network 􀂾 catchment boundaries 􀂾 digital elevation model 􀂾 land-use 􀂾 Physico-chemical soil parameters (e.g. soil type, soil loss maps) 􀂾 precipitation + evaporation 􀂾 hydrogeological map 􀂾 hydrometeorology 􀂾 atmospheric deposition 􀂾 administrative areas 􀂾 population data (e.g. amount, density) 􀂾 location of tile drainages - for calculating point source emissions: 􀂾 inhabitant equivalent connected to WWTP 􀂾 volume of wastewater discharge (m³/a) 􀂾 effluent concentrations discharged into receiving waters (t/a) - water quality data of surface waters: 􀂾 Ammonia (NH4-N) 􀂾 Nitrite (NO2-N) 􀂾 Nitrate (NO3-N) 􀂾 Total Nitrogen (TN) 􀂾 Phosphates (PO4-P) 􀂾 Total Phosphorus (TP) - Administrative-statistical data: 􀂾 Inventory of waste water treatment plants 􀂾 Length of sewer system network 􀂾 population connected to wastewater treatment plants (WWTP) and sewers 􀂾 population connected to sewers only 􀂾 population without connection to sewers 􀂾 Share of tile drainages 􀂾 Nitrogen surplus of the soil nutrient balance 􀂾 Phosphorus accumulation in the soil - Monitoring data 􀂾 Data from water quality measuring points 􀂾 Data from water discharge measuring points Optional: – Stream inflow discharge, temperature and salinity and outflow discharge
Input file format	ASCII
Output variables	User-choice: Water temperature at user-defined depths Water velocity Salinity Turbulence Seiching Brunt-Väisälä frequency
Output file format	ASCII
Biogeochemical model components	None; Simstrat is a purely hydrodynamic model. But it can be coupled with biogeochemical modules using FABM.
Model structure/mathematical framework	K-epsilon, which solves 2 transport equations (PDEs)
Temporal resolution	Default setting is 10 minutes. Model become unstable for Lake Geneva below 1 minute.
Minimal spatial resolution	(Vertical) 1m and 0.75m have been tested & acknowledged for Lake Geneva.
Variables needing calibration	– 2 seiching parameters - Surface drag coefficient
Has successfully been used in
Climate Change Scenario	Perroud & Goyette (2010) Schwefel et al. (2016)
Shallow Lake/Reservoir	Stepanenko et al. (2013)
Deep lake/Reservoir	Perroud et al. (2009)
Countries in which the model has been applied	Switzerland (e.g. Schwefel et al., 2016), Germany (Stepanenko et al., 2013), Rwanda/Congo (Thiery et al., 2014), Macedonia (Matzinger et al., 2007), USA (Wisconsin and Lake Michigan) (Stepanenko et al., 2010), Finland (Stepanenko et al., 2014), Israel (Schmid et al., 2017)
Which institutes have applied the model	University of Geneva, Eawag, EPFL Lausanne
Has coding for	Ice dynamics, Internal waves
Accessibility
Open-Source
Available tools for pre- and post-processing	PEST for calibration
Support	There is a manual on Github
Can be coupled to the following models	Through FABM with AED or AQUASIM (Doan et al., 2015; Schmid et al., 2017).
How can someone get access to this model	Simstrat Github
Miscellaneous
Comments	The ice module is finished and available, but not yet in the Github version and the paper is in publication. Contact: Marjorie Perroud, University of Geneva.
Form was updated: 2018-06-09

Reference list:

Doan, P. T. K., Némery, J., Schmid, M., & Gratiot, N. (2015). Eutrophication of turbid tropical reservoirs: scenarios of evolution of the reservoir of Cointzio, Mexico. Ecological informatics, 29, 192-205.

Goudsmit, G. H., Burchard, H., Peeters, F., & Wüest, A. (2002). Application of k‐ϵ turbulence models to enclosed basins: The role of internal seiches. Journal of Geophysical Research: Oceans, 107(C12).

Matzinger, A., Schmid, M., Veljanoska-Sarafiloska, E., Patceva, S., Guseska, D., Wagner, B., … & Wüest, A. (2007). Eutrophication of ancient Lake Ohrid: Global warming amplifies detrimental effects of increased nutrient inputs. Limnology and Oceanography, 52(1), 338-353.

Perroud, M., Goyette, S., Martynov, A., Beniston, M., & Anneville, O. (2009). Simulation of multiannual thermal profiles in deep Lake Geneva: A comparison of one‐dimensional lake models. Limnology and Oceanography, 54(5), 1574-1594.

Perroud, M., & Goyette, S. (2010). Impacts of warmer climate on Lake Geneva water-temperature profiles. Boreal environment research, 15, 255-278.

Råman Vinnå, L., Wüest, A., Zappa, M., Fink, G., & Bouffard, D. (2018). Tributaries affect the thermal response of lakes to climate change. Hydrology and Earth System Sciences, 22(1), 31.

Schmid, M., Ostrovsky, I., & McGinnis, D. F. (2017). Role of gas ebullition in the methane budget of a deep subtropical lake: what can we learn from process‐based modeling?. Limnology and Oceanography, 62(6), 2674-2698.

Schwefel, R., Gaudard, A., Wüest, A., & Bouffard, D. (2016). Effects of climate change on deep‐water oxygen and winter mixing in a deep lake (Lake Geneva)–Comparing observational findings and modeling. Water Resources Research.

Stepanenko, V. M., Goyette, S., Martynov, A., Perroud, M., Fang, X., & Mironov, D. (2010). First steps of a Lake Model intercomparison project: LakeMIP. Boreal environment research, 15, 191-202.

Stepanenko, V. M., Martynov, A., Jöhnk, K. D., Subin, Z. M., Perroud, M., Fang, X., . . . Goyette, S. (2013). A one-dimensional model intercomparison study of thermal regime of a shallow, turbid midlatitude lake. Geoscientific Model Development, 6(4), 1337-1352. doi:10.5194/gmd-6-1337-2013

Stepanenko, V., Jöhnk, K. D., Machulskaya, E., Perroud, M., Subin, Z., Nordbo, A., … & Mironov, D. (2014). Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models. Tellus A: Dynamic Meteorology and Oceanography, 66(1), 21389.

Thiery, W. I. M., Stepanenko, V. M., Fang, X., Jöhnk, K. D., Li, Z., Martynov, A., … & Van Lipzig, N. P. (2014). LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set of one-dimensional lake models. Tellus A: Dynamic Meteorology and Oceanography, 66(1), 21390. |