Fenntartható vízgyűjtőgazdálkodás adatszegény környezetben / Sustainable catchment management in data-poor environments

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Surface waters, from streams to oceans, are increasingly exposed to a wide range of anthropogenic impacts. Human activities alter water flow, the morphology of individual water bodies and that of the whole river network, as well as water quality of the affected ecosystems. At the same time, humanity relies on ecosystem services provided by both stagnant and flowing surface waters, and these services deteriorate with growing human impacts. Besides direct anthropogenic pressure, climate change imposes another threat to the present state and services of surface waters. In the Anthropocene, most surface waters on the Earth will  face a significant change in the following few decades.
The ever-growing complexity of multi-layered political, administrative or economic decision-making concerning the management, use and protection of natural resources calls for enhanced decision-support tools. This applies particularly to freshwater systems where the interaction among multiple pressures, drivers and actors creates uncertainties for decision-makers with potentially long-term ramifications on the sustainability of aquatic resources. The prevailing management paradigm for freshwater systems is integrated water resources management (IWRM). IWRM emphasizes the manifold aspects of water resources management and requires that the complex natural, economic and social unit of river basins become the basis of management. This implies that decision-making should be able to handle such complex systems. Environmental decisions and their consequences are not repeatable in a statistical manner, that is there is no way to replicate the exact decision situation on the very same system to explore the role of pure or apparent randomness, the analysis of decision consequences is most often carried out virtually, with the help of mathematical models.
However, mathematical models of the environment most often need a solid, site-specific database for their inputs and calibration, which is lacking in many developing countries, where pressure on water resources and the related ecosystems is most intensively growing. To this end, simplified, management-oriented model systems with limited data demand are the key tools of IWRM in many regions.
The proposed PhD research focuses on the further development and validation of a stream water quality and eutrophication model system targeted at data-poor regions. The candidate has to adapt the existing PhosFate model to subtropical climate, extend with lake water quality modules and apply to the Blue Nile catchment of Ethiopia including Lake Tana. A GIS database has to be compiled from mainly remote sensing data repositories and occasional field sampling performed on site. The modeling system has to be a basis for sustainable development scenarios for the region (considering both climate change and socio-economic development), targeting the issue of landuse, sanitation, operation of the Great Ethiopian Renaissance Dam among others. A comparative study should be done with the PhosFate model along a gradient of data availability and consistency (Blue Nile – a few Turkish rivers – a few transboundary rivers in Hungary – a few domestic rivers in Hungary) to explore the robustness of model outcomes.
A téma meghatározó irodalma: 
    1. Reichert, P., S.D. Langhans, J. Lienert, N. Schuwirth, 2015. The conceptual foundation of environmental decision support, Journal of Environmental Management, 154: 316-332. doi: 10.1016/j.jenvman.2015.01.053
    2. Pidd, M., 2003. Tools for Thinking, Modelling in Management Science, Second ed. John Wiley and Sons, Chichester
    3. McIntosh, B.S., Jeffrey, P., Lemon, M., Winder, N., 2005. On the design of computer-based models for integrated environmental science. Environmental Management 35, pages 741-752.
    4. GWP: Integrated Water Resources Management, Tech. Rep. TAC Background Paper no. 4, GWP, http://www. gwp.org/Global/ToolBox/Publications/Backgroundpapers/ 04IntegratedWaterResourcesManagement(2000)English.pdf, 2000 
    5. Istvánovics, V., Honti, M., Kovács, Á., Kocsis, G., and Stier, I.: Phytoplankton growth in relation to network topology: time- averaged catchment-scale modelling in a large lowland river, Freshwater Biol., 59, 1856–1871, doi:10.1111/fwb.12388, 2014. 
A téma hazai és nemzetközi folyóiratai: 
1. Hydrology and Earth System Sciences
2. Water
3. Water Resources Research
4. Science of the Total Environment
5. Water Science and Technology
A témavezető utóbbi tíz évben megjelent 5 legfontosabb publikációja: 
1. Honti, M., Hahn, S., Hennecke, D., Junker, T., Shrestha, P., and Fenner, K. (2016) Bridging across OECD 308 and 309 Data in Search of a Robust Biotransformation Indicator. Environmental Science & Technology 50 (13): 6865–6872. doi: 10.1021/acs.est.6b01097
2. Honti, M., Istvánovics V., Staehr, P. A., Brighenti, L.S., Zhu M-Y. and Zhu G-W. (2016) Robust estimation of lake metabolism by coupling high frequency dissolved oxygen and chlorophyll fluorescence data in a Bayesian framework. Inland Waters 6(4): 608-621. doi:10.5268/IW-6.4.877
3. Honti M., Nele Schuwirth, Jörg Rieckermann, and Christian Stamm. (2017) Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development? Hydrol. Earth Syst. Sci. 21: 1593-1609, doi:10.5194/hess-21-1593-2017
4. Honti, M., Bischoff, F., Moser, A., Stamm, C., Baranya, S., & Fenner, K. (2018). Relating degradation of pharmaceutical active ingredients in a stream network to degradation in water‐sediment simulation tests. Water Resources Research, 54, 9207– 9223. doi:10.1029/2018WR023592
5. Honti, M. and Istvánovics, V. (2019), Error propagation during inverse modeling leads to spurious correlations and misinterpretation of lake metabolism. Limnol Oceanogr Methods, 17: 17-24. doi:10.1002/lom3.10293
A témavezető fenti folyóiratokban megjelent 5 közleménye: 
1. Honti M., N. Schuwirth, J. Rieckermann, and C. Stamm. (2017) Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development? Hydrol. Earth Syst. Sci. 21: 1593-1609, doi:10.5194/hess-21-1593-2017
2. Honti, M., Gao C., Istvánovics V., and Clement A. (2020) Lessons Learnt from the Long-Term Management of a Large (Re)constructed Wetland, the Kis-Balaton Protection System (Hungary). Water 12(3): 659. doi:10.3390/w12030659
3. Honti M., Istvánovics V. and Kovács Á. (2010) Balancing between retention and flushing in river networks – optimizing nutrient management to improve trophic state. Science of the Total Environment 408(20): 4712-4721.
4. Honti, M., C. Stamm, and P. Reichert (2013) Integrated uncertainty assessment of discharge predictions with a statistical error model, Water Resources Research 49:4866–4884, doi:10.1002/wrcr.20374.
5. Kovács Á.S., M. Honti, A. Clement (2008) Design of best management practice applications for diffuse phosphorus pollution using interactive GIS. Water Sci Technol 57 (11): 1727–1733. doi:10.2166/wst.2008.264 

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