Simulation of operating rules and discretional decisions using a fuzzy rule-based system integrated into a water resources management model

Oral presentation performed by Hector Macian-Sorribes at the EGU General Assembly 201

Historical upscaling of the socio-hydrological cycle: three cases from the Mediterranean Spain

Poster presentation performed by Hector Macian-Sorribes at the 2015 EGU General Assembly in Vienna (Austria

GENESIS project: Synthesis and Policy Recommendations:Deliverable D6.5: GENESIS, Work Package 6

The GENESIS project set out, principally, to review and develop scientific knowledge regarding groundwater systems, and to develop tools for better integrated management of these systems with related aquatic and terrestrial groundwater dependent ecosystems. The objectives of the project over its five year duration also included development of indicator methods, and of integrated model simulations applied to a series of representative European groundwater systems that incorporate new components on climate, land-use and pollution input changes. Special efforts have been made to link the project research to the ongoing process of implementing the Water Framework and Groundwater Directives (WFD and GWD respectively) – for example, examining the role of biogeochemical processes in pollutant degradation and the vulnerability of groundwater systems in the context of the GWD art.4(c) “appropriate investigation”. In addition, new methods were to be developed for assessing cost-effectiveness and the economic impacts resulting from changes in groundwater management practices across a range of the project case areas.This report aims to set out the main conclusions from each of the constituent work packages under which work has been done for the project. It will then go on to detail those conclusions that have relevance to policy making at the EU level, and those that are most relevant to decision makers at the Member State level as they seek to implement the WFD and GWD. Work Packages 1 and 7 have been excluded from this report as they were not concerned with substantive research work.<br/

Qualitative approach for assessing runoff temporal dependence through geometrical symmetry

Currently, noticeable changes in traditional hydrological patterns are being observed on the short and medium-term. These modifications are adding a growing variability on water resources behaviour, especially evident in its availability. Consequently, for a better understanding/knowledge of temporal alterations, it is crucial to develop new analytical strategies which are capable of capturing these modifications on its temporal behaviour. This challenge is here addressed via a purely stochastic methodology on annual runoff time series. This is performed through the propagation of temporal dependence strength over the time, by means of Causality, supported by Causal Reasoning (Bayes’ theorem), via the relative percentage of runoff change that a time-step produces on the following ones. The result is a dependence mitigation graph, whose analysis of its symmetry provides an innovative qualitative approach to assess time-dependency from a dynamic and continuous perspective against the classical, static and punctual result that a correlogram offers. This was evaluated/applied to four Spanish unregulated river sub-basins; firstly on two Douro/Duero River Basin exemplary case studies (the largest river basin at Iberian Peninsula) with a clearly opposite temporal behaviour, and subsequently applied to two watersheds belonging to Jucar River Basin (Iberian Peninsula Mediterranean side), characterised by suffering regular drought conditions.info:eu-repo/semantics/publishedVersio

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

[EN] With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 di erent combinations of a global climate model (GCM)¿regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041¿2070) and long-term (2071¿2100) periods with respect to the baseline (1983¿2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between di erent GCM¿RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.Sperotto, A.; Molina, J.; Torresan, S.; Critto, A.; Pulido-Velazquez, M.; Marcomini, A. (2019). Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks. Sustainability. 11(17):1-34. https://doi.org/10.3390/su11174764S1341117RES/70/1. Transforming our World: The 2030 Agenda for Sustainable Developmenthttps://sustainabledevelopment.un.org/post2015/transformingourworldPasini, S., Torresan, S., Rizzi, J., Zabeo, A., Critto, A., & Marcomini, A. (2012). Climate change impact assessment in Veneto and Friuli Plain groundwater. Part II: A spatially resolved regional risk assessment. Science of The Total Environment, 440, 219-235. doi:10.1016/j.scitotenv.2012.06.096Iyalomhe, F., Rizzi, J., Pasini, S., Torresan, S., Critto, A., & Marcomini, A. (2015). Regional Risk Assessment for climate change impacts on coastal aquifers. Science of The Total Environment, 537, 100-114. doi:10.1016/j.scitotenv.2015.06.111Bussi, G., Whitehead, P. G., Bowes, M. J., Read, D. S., Prudhomme, C., & Dadson, S. J. (2016). Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK). Science of The Total Environment, 572, 1507-1519. doi:10.1016/j.scitotenv.2016.02.109Huttunen, I., Lehtonen, H., Huttunen, M., Piirainen, V., Korppoo, M., Veijalainen, N., … Vehviläinen, B. (2015). Effects of climate change and agricultural adaptation on nutrient loading from Finnish catchments to the Baltic Sea. Science of The Total Environment, 529, 168-181. doi:10.1016/j.scitotenv.2015.05.055Carrasco, G., Molina, J.-L., Patino-Alonso, M.-C., Castillo, M. D. C., Vicente-Galindo, M.-P., & Galindo-Villardón, M.-P. (2019). Water quality evaluation through a multivariate statistical HJ-Biplot approach. Journal of Hydrology, 577, 123993. doi:10.1016/j.jhydrol.2019.123993Molina, J.-L., Zazo, S., & Martín, A.-M. (2019). Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers. Water, 11(5), 877. doi:10.3390/w11050877Beck, M., & Krueger, T. (2016). The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modeling. Wiley Interdisciplinary Reviews: Climate Change, 7(5), 627-645. doi:10.1002/wcc.415Kundzewicz, Z. W., Krysanova, V., Benestad, R. E., Hov, Ø., Piniewski, M., & Otto, I. M. (2018). Uncertainty in climate change impacts on water resources. Environmental Science & Policy, 79, 1-8. doi:10.1016/j.envsci.2017.10.008Parker, W. S. (2013). Ensemble modeling, uncertainty and robust predictions. Wiley Interdisciplinary Reviews: Climate Change, 4(3), 213-223. doi:10.1002/wcc.220Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1108. doi:10.1175/2009bams2607.1Ajami, N. K., Hornberger, G. M., & Sunding, D. L. (2008). Sustainable water resource management under hydrological uncertainty. Water Resources Research, 44(11). doi:10.1029/2007wr006736Larson, K., White, D., Gober, P., & Wutich, A. (2015). Decision-Making under Uncertainty for Water Sustainability and Urban Climate Change Adaptation. Sustainability, 7(11), 14761-14784. doi:10.3390/su71114761Power, M., & McCarty, L. S. (2006). Environmental Risk Management Decision-Making in a Societal Context. Human and Ecological Risk Assessment: An International Journal, 12(1), 18-27. doi:10.1080/10807030500428538Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3-4), 312-318. doi:10.1016/j.ecolmodel.2006.11.033Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Climatic Change, 139(3-4), 551-564. doi:10.1007/s10584-016-1803-1Tebaldi, C., & Knutti, R. (2007). The use of the multi-model ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857), 2053-2075. doi:10.1098/rsta.2007.2076Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., … Wolf, J. (2014). Multimodel ensembles of wheat growth: many models are better than one. Global Change Biology, 21(2), 911-925. doi:10.1111/gcb.12768Krishnamurti, T. N., Kishtawal, C. M., Zhang, Z., LaRow, T., Bachiochi, D., Williford, E., … Surendran, S. (2000). Multimodel Ensemble Forecasts for Weather and Seasonal Climate. Journal of Climate, 13(23), 4196-4216. doi:10.1175/1520-0442(2000)0132.0.co;2Xu, H., Brown, D. G., & Steiner, A. L. (2018). Sensitivity to climate change of land use and management patterns optimized for efficient mitigation of nutrient pollution. Climatic Change, 147(3-4), 647-662. doi:10.1007/s10584-018-2159-5Zuliani, A., Zaggia, L., Collavini, F., & Zonta, R. (2005). Freshwater discharge from the drainage basin to the Venice Lagoon (Italy). Environment International, 31(7), 929-938. doi:10.1016/j.envint.2005.05.004Facca, C., Ceoldo, S., Pellegrino, N., & Sfriso, A. (2014). Natural Recovery and Planned Intervention in Coastal Wetlands: Venice Lagoon (Northern Adriatic Sea, Italy) as a Case Study. The Scientific World Journal, 2014, 1-15. doi:10.1155/2014/968618Pesce, M., Critto, A., Torresan, S., Giubilato, E., Santini, M., Zirino, A., … Marcomini, A. (2018). Modelling climate change impacts on nutrients and primary production in coastal waters. Science of The Total Environment, 628-629, 919-937. doi:10.1016/j.scitotenv.2018.02.131Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., … Navarra, A. (2011). Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model. Journal of Climate, 24(16), 4368-4384. doi:10.1175/2011jcli4104.1Cattaneo, L., Zollo, A. L., Bucchignani, E., Montesarchio, M., Manzi, M. P., & Mercogliano, P. (2012). Assessment of COSMO-CLM Performances over Mediterranean Area. SSRN Electronic Journal. doi:10.2139/ssrn.2195524Sperotto, A., Molina, J. L., Torresan, S., Critto, A., Pulido-Velazquez, M., & Marcomini, A. (2019). A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading. Environmental Science & Policy, 100, 21-36. doi:10.1016/j.envsci.2019.06.004MADSEN, A. L., JENSEN, F., KJÆRULFF, U. B., & LANG, M. (2005). THE HUGIN TOOL FOR PROBABILISTIC GRAPHICAL MODELS. International Journal on Artificial Intelligence Tools, 14(03), 507-543. doi:10.1142/s0218213005002235Bromley, J., Jackson, N. A., Clymer, O. J., Giacomello, A. M., & Jensen, F. V. (2005). The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling & Software, 20(2), 231-242. doi:10.1016/j.envsoft.2003.12.021J. G. Arnold, D. N. Moriasi, P. W. Gassman, K. C. Abbaspour, M. J. White, R. Srinivasan, … M. K. Jha. (2012). SWAT: Model Use, Calibration, and Validation. Transactions of the ASABE, 55(4), 1491-1508. doi:10.13031/2013.42256Marcot, B. G. (2012). Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling, 230, 50-62. doi:10.1016/j.ecolmodel.2012.01.013http://www.landscapelogic.org.au/publications/Technical_Reports/No_9_BNs_for_Integrated_Catchment_Management.pdfMolina, J.-L., Zazo, S., Rodríguez-Gonzálvez, P., & González-Aguilera, D. (2016). Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks. Water, 8(11), 484. doi:10.3390/w8110484Pollino, C. A., Woodberry, O., Nicholson, A., Korb, K., & Hart, B. T. (2007). Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software, 22(8), 1140-1152. doi:10.1016/j.envsoft.2006.03.006Pesce, M., Critto, A., Torresan, S., Giubilato, E., Pizzol, L., & Marcomini, A. (2019). Assessing uncertainty of hydrological and ecological parameters originating from the application of an ensemble of ten global-regional climate model projections in a coastal ecosystem of the lagoon of Venice, Italy. Ecological Engineering, 133, 121-136. doi:10.1016/j.ecoleng.2019.04.011Bouraoui, F., Galbiati, L., & Bidoglio, G. (2002). Climate change impacts on nutrient loads in the Yorkshire Ouse catchment (UK). Hydrology and Earth System Sciences, 6(2), 197-209. doi:10.5194/hess-6-197-2002Panagopoulos, Y., Makropoulos, C., & Mimikou, M. (2011). Diffuse Surface Water Pollution: Driving Factors for Different Geoclimatic Regions. Water Resources Management, 25(14), 3635-3660. doi:10.1007/s11269-011-9874-2Molina, J.-L., Pulido-Velázquez, D., García-Aróstegui, J. L., & Pulido-Velázquez, M. (2013). Dynamic Bayesian Networks as a Decision Support tool for assessing Climate Change impacts on highly stressed groundwater systems. Journal of Hydrology, 479, 113-129. doi:10.1016/j.jhydrol.2012.11.03

Dynamic procedure for daily PM56 ETo mapping conducive to site-specific irrigation recommendations in areas covered by agricultural weather networks.

[EN] Modern agriculture is underpinned by actual meteorological data registered using automated meteorological stations forming networks specifically created for advising purposes. In many cases, those data used to be accessible online by means of APIs (Application Programming Interface). One of the most common cases is the irrigation-advice weather network implemented with the aim of obtaining ETo values to be used in irrigation recommendations. However, those punctual values of ETo scattered throughout the territory do not allow to produce specific irrigation recommendations for each farm. The only way of disposing site-specific values of ETo is by compiling maps that describe its spatial variation. With this objective, a new dynamic procedure based on an existing regression-based technique of interpolation was proposed. Using the meteorological data registered at the end of each day, maximum and minimum temperature, maximum and minimum relative humidity, wind velocity, and radiation maps were interpolated and then, an ETo map was derived. The proposed procedure demonstrated a special adaptation capacity to the synoptic pattern of each day using some geographical features or others, as appropriate to explain the spatial variability of the interpolated meteorological variable. In those months where radiation plays a key role in the ETo value (growing season), ETo maps obtained were especially fine-grained in areas with significant relief. This procedure improved other contrasted methodologies they were compared with. The impact of using the nearest-weather-station ETo vs interpolated value on a daily water needs was investigated and near 10% average value of error was encountered in the case study.This study has received funding from the eGROUNDWATER project (GA n. 1921) , part of the PRIMA program supported by the European Union 's Horizon 2020 research and innovation program, and the WATER4CAST project (PROMETEO/2021/074) , which is funded by the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Comunitat Valenciana.Meteorological data were provided by SIAR: " Sistema de Informacion Agroclimatica para el Regadio. Ministerio de Agricultura, Pesca y Alimentacion" . Special thanks to Carlos Garrido Garrido and Ivan Cilleros Fuentetaja for providing us an API-SIAR access. Thanks to Luis Bonet for giving us permission to use the picture of the IVIA-SIAR automated station.Garcia-Prats, A.; Carricondo-Antón, JM.; Jiménez Bello, MA.; Manzano Juarez, J.; López Pérez, E.; Pulido-Velazquez, M. (2023). Dynamic procedure for daily PM56 ETo mapping conducive to site-specific irrigation recommendations in areas covered by agricultural weather networks. Agricultural Water Management. 287:1-18. https://doi.org/10.1016/j.agwat.2023.10841511828

Qualitative Approach for Assessing Runoff Temporal Dependence Through Geometrical Symmetry

Currently, noticeable changes in traditional hydrological patterns are being observed on the short and medium-term. These modifications are adding a growing variability on water resources behaviour, especially evident in its availability. Consequently, for a better understanding/knowledge of temporal alterations, it is crucial to develop&nbsp; new analytical strategies which are capable of capturing these modifications on its temporal behaviour. This challenge is here addressed via a purely stochastic methodology on annual runoff time series. This is performed through the propagation of temporal dependence strength over the time, by means of Causality, supported by Causal Reasoning (Bayes’ theorem), via the relative percentage of runoff change that a time-step produces on the following ones. The result is a dependence mitigation graph, whose analysis of its symmetry provides an innovative qualitative approach to assess time-dependency from a dynamic and continuous perspective against the classical, static and punctual result that a correlogram offers. This was evaluated/applied to four Spanish unregulated river sub-basins; firstly on two Douro/Duero River Basin exemplary case studies (the largest river basin at Iberian Peninsula) with a clearly opposite temporal behaviour, and subsequently applied to two watersheds belonging to Jucar River Basin (Iberian Peninsula Mediterranean side), characterised by suffering regular drought conditions. Keywords: Causal reasoning, Theorem of Bayes, Temporal dependence propagation, Runoff time series, Water resources managemen

The value of scientific information on climate change: a choice experiment on Rokua esker, Finland

This article presents an application of the choice experiment method in order to provide estimates of economic values generated by water quantity improvements in the environment. More importantly, this is the first choice experiment study valuing scientific information and in particular scientific information on climate change. The case study of interest is Rokua in Northern Finland, a groundwater dependent ecosystem very sensitive to climate change and natural variability. The study deals with the uncertainty about the actual dynamics of the system and the effect of future climate change by exploring whether the public values sustained provision of resources for scientific research to better understand long-term environmental changes in Rokua. Data are analysed using a nested multinomial logit and an error component model. Evidence from this study suggests that individuals are willing to pay in order to assure scientific research so as to better understand long-term environmental changes. As a result, policy should consider investing in and supporting related research. Other aspects of water management policy valued by the public are water quantity, recreation, and total land income. We gratefully acknowledge the financial support from the European Union via the 7th Framework Program GENESIS: Groundwater and dependent ecosystems: New Scientific basis on climate change and land-use impact for the update of the EU Groundwater Directive; WP-6 Groundwater systems management: scenarios, risk assessment, cost-efficient measures and legal aspects. We finally thank two anonymous referees for constructive and insightful comments Koundouri, P.; Kougea, E.; Stithoua, M.; Ala-Ahob, P.; Eskelinenb, R.; Karjalainenc, T.; Klove, B.... (2012). The Value of Scientific Information on Climate Change: A Choice Experiment on Rokua esker, Finland. Journal of Environmental Economics and Policy. 1(1):85-102. doi:10.1080/21606544.2011.647450 Senia 85 102 1

Dynamic Bayesian Networks as a Decision Support Tool for assessing Climate Change impacts on highly stressed groundwater systems

Bayesian Networks (BNs) are powerful tools for assessing and predicting consequences of water management scenarios and uncertain drivers like climate change, integrating available scientific knowledge with the interests of the multiple stakeholders. However, among their major limitations, the non-transient treatment of the cause-effect relationship stands out. A Decision Support System (DSS) based on Dynamic Bayesian Networks (DBNs) is proposed here aimed to palliate that limitation through time slicing technique. The DSS comprises several classes (Object-Oriented BN networks), especially designed for future 5 years length time steps (time slices), covering a total control period of 30 years (2070-2100). The DSS has been developed for assessing impacts generated by different Climate Change (CC) scenarios (generated from several Regional Climatic Models (RCMs) under two emission scenarios, A1B and A2) in an aquifer system (Serral-Salinas) affected by intensive groundwater use over the last 30 years. A calibrated continuous water balance model was used to generate hydrological CC scenarios, and then a groundwater flow model (MODFLOW) was employed in order to analyze the aquifer behavior under CC conditions. Results obtained from both models were used as input for the DSS, considering rainfall, aquifer recharge, variation of piezometric levels and temporal evolution of aquifer storage as the main hydrological components of the aquifer system. Results show the evolution of the aquifer storage for each future time step under different climate change conditions and under controlled water management interventions. This type of applications would allow establishing potential adaptation strategies for aquifer systems as the CC comes into effectThis study has been partially supported by the European Community 7th Framework Project GENESIS (226536) on groundwater systems and from the subprogram Juan de la Cierva (2010) of the Spanish Ministry of Science and Innovation as well as from the Plan Nacional I+D+i 2008-2011 of the Spanish Ministry of Science and Innovation (Subprojects CGL2009-13238-C02-01 and CGL2009-13238-C02-02). T. Finally, the authors want to thank the Segura River Basin Agency (Confederacion Hidrografica del Segura) for the data and information facilitated, and to all the stakeholders who have collaborated in this research.Molina, JL.; Pulido Velázquez, D.; García-Arostegui, J.; Pulido-Velazquez, M. (2013). Dynamic Bayesian Networks as a Decision Support Tool for assessing Climate Change impacts on highly stressed groundwater systems. Journal of Hydrology. 479:113-129. https://doi.org/10.1016/j.jhydrol.2012.11.038S11312947