Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a multireservoir System

[EN] This paper presents a collaborative framework to couple historical records with expert knowledge and criteria in order to define a Decision Support System (DSS) to support the seasonal operation of the reservoirs of the Jucar river system. The framework relies on the co-development of a DSS tool that is able to explicitly reproduce the decision-making processes and criteria considered by the system operators. Fuzzy logic is used to derive the implicit operating rules followed by the managers based on historical decisions and expert knowledge obtained in the co-development process, combining both sources of information. Fuzzy regression is used to forecast future inflows based on the meteorological and hydrological variables considered by the system operators in their decisions on reservoir operation. The DSS was validated against historical records. The developed framework and tools offer the system operators a way to predefine a set of feasible ex ante management decisions, as well as to explore the consequences associated with any single choice. In contrast with other approaches, the fuzzy-based method used is able to embed inflow uncertainty and its effects in the definition of the decisions on the system operation. Furthermore, the method is flexible enough to be applied to other water resource systems.The authors wish to acknowledge the Jucar River Basin Management Authority (Confederacion Hidrografica del Joecar, CHJ), especially its Operation Office's (Oficina de Explotacion) system operators Jose Maria Benlliure Moreno and Juan Fullana Montoro, for their contribution to the whole process, valuable suggestions, and provision of the necessary data to carry out the study. The study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economia y Competitividad) and FEDER funds. It has also received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (GA 641.811).Macian-Sorribes, H.; Pulido-Velazquez, M. (2017). Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a multireservoir System. Journal of Water Resources Planning and Management. 143(1). https://doi.org/10.1061/(ASCE)WR.1943-5452.0000712S143

Inferring efficient operating rules in multireservoir water resource systems: A review

[EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. Journal of Water Resources Planning and Management, 141(11), 04015029. doi:10.1061/(asce)wr.1943-5452.0000553Ahmad, A., El-Shafie, A., Razali, S. F. M., & Mohamad, Z. S. (2014). Reservoir Optimization in Water Resources: a Review. Water Resources Management, 28(11), 3391-3405. doi:10.1007/s11269-014-0700-5Ahmadi, M., Bozorg Haddad, O., & Mariño, M. A. (2013). Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules. Water Resources Management, 28(1), 131-147. doi:10.1007/s11269-013-0476-zAndreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-xAndreu, J., & Sahuquillo, A. (1987). Efficient Aquifer Simulation in Complex Systems. Journal of Water Resources Planning and Management, 113(1), 110-129. doi:10.1061/(asce)0733-9496(1987)113:1(110)Ashbolt, S. C., Maheepala, S., & Perera, B. J. C. (2016). Using Multiobjective Optimization to Find Optimal Operating Rules for Short-Term Planning of Water Grids. Journal of Water Resources Planning and Management, 142(10), 04016033. doi:10.1061/(asce)wr.1943-5452.0000675Ashbolt, S. C., & Perera, B. J. C. (2018). Multiobjective Optimization of Seasonal Operating Rules for Water Grids Using Streamflow Forecast Information. Journal of Water Resources Planning and Management, 144(4), 05018003. doi:10.1061/(asce)wr.1943-5452.0000902Azari, A., Hamzeh, S., & Naderi, S. (2018). Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resources Management, 32(6), 2061-2078. doi:10.1007/s11269-018-1917-5Becker, L., & Yeh, W. W.-G. (1974). Optimization of real time operation of a multiple-reservoir system. Water Resources Research, 10(6), 1107-1112. doi:10.1029/wr010i006p01107Bellman, R. E., & Dreyfus, S. E. (1962). Applied Dynamic Programming. doi:10.1515/9781400874651Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050Bessler, F. T., Savic, D. A., & Walters, G. A. (2003). Water Reservoir Control with Data Mining. Journal of Water Resources Planning and Management, 129(1), 26-34. doi:10.1061/(asce)0733-9496(2003)129:1(26)Bhaskar, N. R., & Whitlatch, E. E. (1980). Derivation of monthly reservoir release policies. Water Resources Research, 16(6), 987-993. doi:10.1029/wr016i006p00987Bianucci, P., Sordo-Ward, Á., Moralo, J., & Garrote, L. (2015). Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain. Water, 7(12), 956-974. doi:10.3390/w7030956Biglarbeigi, P., Giuliani, M., & Castelletti, A. (2018). Partitioning the Impacts of Streamflow and Evaporation Uncertainty on the Operations of Multipurpose Reservoirs in Arid Regions. Journal of Water Resources Planning and Management, 144(7), 05018008. doi:10.1061/(asce)wr.1943-5452.0000945Bolouri-Yazdeli, Y., Bozorg Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Evaluation of Real-Time Operation Rules in Reservoir Systems Operation. Water Resources Management, 28(3), 715-729. doi:10.1007/s11269-013-0510-1Borgomeo, E., Mortazavi-Naeini, M., Hall, J. W., O’Sullivan, M. J., & Watson, T. (2016). Trading-off tolerable risk with climate change adaptation costs in water supply systems. Water Resources Research, 52(2), 622-643. doi:10.1002/2015wr018164Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems. Journal of Water Resources Planning and Management, 143(1), 04016070. doi:10.1061/(asce)wr.1943-5452.0000716Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015). Development and Application of the Bat Algorithm for Optimizing the Operation of Reservoir Systems. Journal of Water Resources Planning and Management, 141(8), 04014097. doi:10.1061/(asce)wr.1943-5452.0000498Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Brown, C., Ghile, Y., Laverty, M., & Li, K. (2012). Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resources Research, 48(9). doi:10.1029/2011wr011212Brown, C. M., Lund, J. R., Cai, X., Reed, P. M., Zagona, E. A., Ostfeld, A., … Brekke, L. (2015). The future of water resources systems analysis: Toward a scientific framework for sustainable water management. Water Resources Research, 51(8), 6110-6124. doi:10.1002/2015wr017114Cai, X., McKinney, D. C., & Lasdon, L. S. (2001). Piece-by-Piece Approach to Solving Large Nonlinear Water Resources Management Models. Journal of Water Resources Planning and Management, 127(6), 363-368. doi:10.1061/(asce)0733-9496(2001)127:6(363)Cai, X., Vogel, R., & Ranjithan, R. (2013). Special Issue on the Role of Systems Analysis in Watershed Management. Journal of Water Resources Planning and Management, 139(5), 461-463. doi:10.1061/(asce)wr.1943-5452.0000341Cancelliere, A., Giuliano, G., Ancarani, A., & Rossi, G. (2002). Water Resources Management, 16(1), 71-88. doi:10.1023/a:1015563820136Caseri, A., Javelle, P., Ramos, M. H., & Leblois, E. (2015). Generating precipitation ensembles for flood alert and risk management. Journal of Flood Risk Management, 9(4), 402-415. doi:10.1111/jfr3.12203Castelletti, A., Galelli, S., Restelli, M., & Soncini-Sessa, R. (2010). Tree-based reinforcement learning for optimal water reservoir operation. Water Resources Research, 46(9). doi:10.1029/2009wr008898Castelletti, A., Pianosi, F., & Restelli, M. (2013). A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research, 49(6), 3476-3486. doi:10.1002/wrcr.20295Castelletti, A., Pianosi, F., & Soncini-Sessa, R. (2008). Water reservoir control under economic, social and environmental constraints. Automatica, 44(6), 1595-1607. doi:10.1016/j.automatica.2008.03.003Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks in water resource modelling and management. Environmental Modelling & Software, 22(8), 1073-1074. doi:10.1016/j.envsoft.2006.06.001Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software, 22(8), 1075-1088. doi:10.1016/j.envsoft.2006.06.003Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429-1443. doi:10.1016/j.advwatres.2009.06.008Celeste, A. B., Curi, W. F., & Curi, R. C. (2009). Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil. Pesquisa Operacional, 29(1), 223-234. doi:10.1590/s0101-74382009000100011Chandramouli, V., & Raman, H. (2001). Multireservoir Modeling with Dynamic Programming and Neural Networks. Journal of Water Resources Planning and Management, 127(2), 89-98. doi:10.1061/(asce)0733-9496(2001)127:2(89)Chang, L.-C., & Chang, F.-J. (2001). Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9), 1621-1634. doi:10.1002/hyp.226Chazarra, M., García-González, J., Pérez-Díaz, J. I., & Arteseros, M. (2016). Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research, 130, 67-77. doi:10.1016/j.epsr.2015.08.014Chen, D., Leon, A. S., Fuentes, C., Gibson, N. L., & Qin, H. (2018). Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System. Journal of Water Resources Planning and Management, 144(2), 04017088. doi:10.1061/(asce)wr.1943-5452.0000876Coerver, H. M., Rutten, M. M., & van de Giesen, N. C. (2018). Deduction of reservoir operating rules for application in global hydrological models. Hydrology and Earth System Sciences, 22(1), 831-851. doi:10.5194/hess-22-831-2018Côté, P., & Leconte, R. (2016). Comparison of Stochastic Optimization Algorithms for Hydropower Reservoir Operation with Ensemble Streamflow Prediction. Journal of Water Resources Planning and Management, 142(2), 04015046. doi:10.1061/(asce)wr.1943-5452.0000575Cui, L., & Kuczera, G. (2005). Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resources Research, 41(5). doi:10.1029/2004wr003517Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R., … Castelletti, A. (2016). A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resources Research, 52(9), 6751-6768. doi:10.1002/2015wr018253Cunha, M. C., & Antunes, A. (2012). Simulated annealing algorithms for water systems optimization. WIT Transactions on State of the Art in Science and Engineering, 57-73. doi:10.2495/978-1-84564-664-6/04Dariane, A. B., & Momtahen, S. (2009). Optimization of Multireservoir Systems Operation Using Modified Direct Search Genetic Algorithm. Journal of Water Resources Planning and Management, 135(3), 141-148. doi:10.1061/(asce)0733-9496(2009)135:3(141)Das, B., Singh, A., Panda, S. N., & Yasuda, H. (2015). Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy, 42, 527-537. doi:10.1016/j.landusepol.2014.09.012Davidsen, C., Liu, S., Mo, X., Rosbjerg, D., & Bauer-Gottwein, P. (2016). The cost of ending groundwater overdraft on the North China Plain. Hydrology and Earth System Sciences, 20(2), 771-785. doi:10.5194/hess-20-771-2016Ehteram, M., Karami, H., & Farzin, S. (2018). Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models. Water Resources Management, 32(7), 2539-2560. doi:10.1007/s11269-018-1945-1Eisel, L. M. (1972). Chance constrained reservoir model. Water Resources Research, 8(2), 339-347. doi:10.1029/wr008i002p00339European Commission(2007). Communication from the Commission to the European Parliament and the Council: Addressing the challenge of water scarcity and droughts in the European Union COM(2007) 414 final. Brussels Belgium.European Commission. (2012a). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: A Blueprint to Safeguard Europe's Water Resources COM(2012) 673 final. Brussels Belgium.European Commission. (2012b). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: Report on the Review of the European Water Scarcity and Droughts Policy COM(2012) 672 final. Brussels Belgium.Fallah-Mehdipour, E., Bozorg Haddad, O., & Mariño, M. A. (2012). Real-Time Operation of Reservoir System by Genetic Programming. Water Resources Management, 26(14), 4091-4103. doi:10.1007/s11269-012-0132-zFazlali, A., & Shourian, M. (2017). A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach. Water Resources Management, 32(1), 67-81. doi:10.1007/s11269-017-1791-6Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P.-O., Van Overloop, P.-J., & Jay-Allemand, M. (2016). Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts. Journal of Water Resources Planning and Management, 142(1), 05015005. doi:10.1061/(asce)wr.1943-5452.0000571Fu, Q., Li, T., Cui, S., Liu, D., & Lu, X. (2017). Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty. Water Resources Management, 32(4), 1261-1274. doi:10.1007/s11269-017-1868-2Galelli, S., Goedbloed, A., Schwanenberg, D., & van Overloop, P.-J. (2014). Optimal Real-Time Operation of Multipurpose Urban Reservoirs: Case Study in Singapore. Journal of Water Resources Planning and Management, 140(4), 511-523. doi:10.1061/(asce)wr.1943-5452.0000342Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016). Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations. Journal of Water Resources Planning and Management, 142(2), 04015050. doi:10.1061/(asce)wr.1943-5452.0000570Giuliani, M., Herman, J. D., Castelletti, A., & Reed, P. (2014). Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management. Water Resources Research, 50(4), 3355-3377. doi:10.1002/2013wr014700Giuliani, M., Li, Y., Castelletti, A., & Gandolfi, C. (2016). A coupled human-natural systems analysis of irrigated agriculture under changing climate. Water Resources Research, 52(9), 6928-6947. doi:10.1002/2016wr019363Giuliani, M., Quinn, J. D., Herman, J. D., Castelletti, A., & Reed, P. M. (2018). Scalable Multiobjective Control for Large-Scale Water Resources Systems Under Uncertainty. IEEE Transactions on Control Systems Technology, 26(4), 1492-1499. doi:10.1109/tcst.2017.2705162Grüne, L., & Semmler, W. (2004). Using dynamic programming with adaptive grid scheme for optimal control problems in economics. Journal of Economic Dynamics and Control, 28(12), 2427-2456. doi:10.1016/j.jedc.2003.11.002Guariso, G., Rinaldi, S., & Soncini-Sessa, R. (1986). The Management of Lake Como: A Multiobjective Analysis. Water Resources Research, 22(2), 109-120. doi:10.1029/wr022i002p00109Gundelach, J., & ReVelle, C. (1975). Linear decision rule in reservoir management and design: 5. A general algorithm. Water Resources Research, 11(2), 204-207. doi:10.1029/wr011i002p00204Guo, X., Hu, T., Zeng, X., & Li, X. (2013). Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Droughts. Journal of Water Resources Planning and Management, 139(2), 139-148. doi:10.1061/(asce)wr.1943-5452.0000241Haddad, O. B., Afshar, A., & Mariño, M. A. (2006). Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resources Management, 20(5), 661-680. doi:10.1007/s11269-005-9001-3Hadka, D., Herman, J., Reed, P., & Keller, K. (2015). An open source framework for many-objective robust decision making. Environmental Modelling & Software, 74, 114-129. doi:10.1016/j.envsoft.2015.07.014Haguma, D., & Leconte, R. (2018). Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization. Water Resources Management, 32(5), 1725-1739. doi:10.1007/s11269-017-1900-6Haguma, D., Leconte, R., & Côté, P. (2018). Evaluating Transition Probabilities for a Stochastic Dynamic Programming Model Used in Water System Optimization. Journal of Water Resources Planning and Management, 144(2), 04017090. doi:10.1061/(asce)wr.1943-5452.0000883Houck, M. H. (1979). A Chance Constrained Optimization Model for reservoir design and operation. Water Resources Research, 15(5), 1011-1016. doi:10.1029/wr015i005p01011Ji, C., Zhou, T., & Huang, H. (2014). Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression. Water Resources Management, 28(9), 2435-2451. doi:10.1007/s11269-014-0610-6Karamouz, M., & Houck, M. H. (1982). Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resources Research, 18(5), 1337-1344. doi:10.1029/wr018i005p01337Karamouz, M., & Houck, M. H. (1987). COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION. Journal of the American Water Resources Association, 23(1), 1-9. doi:10.1111/j.1752-1688.1987.tb00778.xKaramouz, M., & Vasiliadis, H. V. (1992). Bayesian stochastic optimization of reservoir operation using uncertain forecasts. Water Resources Research, 28(5), 1221-1232. doi:10.1029/92wr00103Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Kelman, J., Stedinger, J. R., Cooper, L. A., Hsu, E., & Yuan, S.-Q. (1990). Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research, 26(3), 447-454. doi:10.1029/wr026i003p00447Keshtkar, A. R., Salajegheh, A., Sadoddin, A., & Allan, M. G. (2013). Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment). Ecological Modelling, 268, 48-54. doi:10.1016/j.ecolmodel.2013.08.003Kim, T., Heo, J.-H., Bae, D.-H., & Kim, J.-H. (2008). Single-reservoir operating rules for a year using multiobjective genetic algorithm. Journal of Hydroinformatics, 10(2), 163-179. doi:10.2166/hydro.2008.019Koutsoyiannis, D., & Economou, A. (2003). Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resources Research, 39(6). doi:10.1029/2003wr002148Kumar, D. N., & Reddy, M. J. (2006). Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resources Management, 20(6), 879-898. doi:10.1007/s11269-005-9012-0Nagesh Kumar, D., & Janga Reddy, M. (2007). Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resources Planning and Management, 133(3), 192-201. doi:10.1061/(asce)0733-9496(2007)133:3(192)Kumar, K., & Kasthurirengan, S. (2018). Generalized Linear Two-Point Hedging Rule for Water Supply Reservoir Operation. Journal of Water Resources Planning and Management, 144(9), 04018051. doi:10.1061/(asce)wr.1943-5452.0000964Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2016). Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty. Environmental Modelling & Software, 86, 168-183. doi:10.1016/j.envsoft.2016.09.017Labadie, J. W. (2004). Optimal Operation of Multireservoir Systems: State-of-the-Art Review. Journal of Water Resources Planning and Management, 130(2), 93-111. doi:10.1061/(asce)0733-9496(2004)130:2(93)Labadie J. W. Baldo M. &Larson R.(2000).MODSIM: Decision support system for river basin management. Documentation and user manual.Lee, J.-H., & Labadie, J. W. (2007). Stochastic optimization of multireservoir systems via reinforcement learning. Water Resources Research, 43(11). doi:10.1029/2006wr005627Lei, X., Tan, Q., Wang, X., Wang, H., Wen, X., Wang, C., & Zhang, J. (2018). Stochastic optimal operation of reservoirs based on copula functions. Journal of Hydrology, 557, 265-275. doi:10.1016/j.jhydrol.2017.12.038Lerma, N., Paredes-Arquiola, J., Andreu, J., & Solera, A. (2013). Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58(4), 797-812. doi:10.1080/02626667.2013.779777Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A., & Sechi, G. M. (2015). Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems. Environmental Modelling & Software, 69, 425-436. doi:10.1016/j.envsoft.2014.09.024Li, Y., Giuliani, M., & Castelletti, A. (2017). A coupled human–natural system to assess the operational value of weather and climate services for agriculture. Hydrology and Earth System Sciences, 21(9), 4693-4709. doi:10.5194/hess-21-4693-2017Lin, N. M., & Rutten, M. (2016). Optimal Operation of a Network of Multi-purpose Reservoir: A Review. Procedia Engineering, 154, 1376-1384. doi:10.1016/j.proeng.2016.07.504Liu, P., Cai, X., & Guo, S. (2011). Deriving multiple near-optimal solutions to deterministic reservoir operation problems. Water Resources Research, 47(8). doi:10.1029/2011wr010998Loucks, D. P. (1970). Some Comments on Linear Decision Rules and Chance Constraints. Water Resources Research, 6(2), 668-671. doi:10.1029/wr006i002p00668Loucks

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

Improving operating policies of large-scale surface-groundwater systems through stochastic programming

[EN] The management of large-scale water resource systems with surface and groundwater resources requires considering stream-aquifer interactions. Optimization models applied of large-scale systems have either employed deterministic optimization (with perfect foreknowledge of future inflows, which hinders their applicability to real-life operations) or stochastic programming (in which stream-aquifer interaction is often neglected due to the computational burden associated with these methods). In this paper, stream-aquifer interaction is integrated in a stochastic programming framework by combining the Stochastic Dual Dynamic Programming (SDDP) optimization algorithm with the Embedded Multireservoir Model (EMM). The resulting extension of the SDDP algorithm, named Combined Surface-Groundwater SDDP (CSG-SDDP), is able to properly represent the stream-aquifer interaction within stochastic optimization models of large-scale surface-groundwater resources systems. The algorithm is applied to build a hydroeconomic model for the Jucar River Basin (Spain), in which stream-aquifer interactions are essential to the characterization of water resources in the system. Besides the uncertainties regarding the economic characterization of the demand functions, the results show that the economic efficiency of the operating policies under the current system can be improved by better management of groundwater and surface resourcesThe data used in this study was obtained from the references included. This study was partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economia y Competitividad) and FEDER funds. It also received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement: 641.811). The authors want to thank the editor, the associated editor and the reviewers for their comments and suggestions in order to increase the quality of the paper. Readers interested in requesting data about the results of the study may send an e-mail to [email protected], H.; Tilmant, A.; Pulido-Velazquez, M. (2017). Improving operating policies of large-scale surface-groundwater systems through stochastic programming. Water Resources Research. 53(2):1407-1423. https://doi.org/10.1002/2016WR019573S1407142353

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

Definition of efficient scarcity-based water pricing policies through stochastic programming

[EN] Finding ways to improve the efficiency in water usage is one of the most important challenges in integrated water resources management. One of the most promising solutions is the use of scarcity-based pricing policies. This contribution presents a procedure to design efficient pricing policies based in the opportunity cost of water at the basin scale. Time series of the marginal value of water are obtained using a stochastic hydro-economic model. Those series are then post-processed to define step pricing policies, which depend on the state of the system at each time step. The case study of the Mijares River basin system (Spain) is used to illustrate the method. The results show that the application of scarcitybased pricing policies increases the economic efficiency of water use in the basin, allocating water to the highest-value uses and generating an incentive for water conservation during the scarcity periods. The resulting benefits are close to those obtained with the economically optimal decisions.This study has been partially funded by the European Union's Seventh Framework Program (FP7) ENHANCE (number 308.438). In addition, the authors acknowledge the editor and reviewers for their helpful and constructive comments.Macian-Sorribes, H.; Pulido-Velazquez, M.; Tilmant, A. (2015). Definition of efficient scarcity-based water pricing policies through stochastic programming. Hydrology and Earth System Sciences. 19(9):3925-3935. https://doi.org/10.5194/hess-19-3925-2015S39253935199Alvarez-Mendiola, E.: Diseño de una política eficiente de precios del agua integrando costes de oportunidad del recurso a escala de cuenca, PhD dissertation, Universitat Politècnica de València, Valencia, Spain, 2012 (in Spanish).Andreu, J., and Sahuquillo, A.: Efficient aquifer simulation in complex systems, J. Water Res. Pl.-ASCE, 113, 110–129, 1987.CHJ: Esquema provisional de Temas Importantes, Ministerio de Medio Ambiente y Medio Rural y Marino, Confederación Hidrográfica del Júcar, Valencia, Spain, 2009 (in Spanish).Dandy, G. C., McBean, E. A., and Hutchinson, B. G.: A model for constrained optimum water pricing and capacity expansion, Water Resour. Res., 20, 511–520, 1984.Dinar, A., Rosegrant, M. W., and Meinzen-Dick, R.: Water Allocation Mechanisms – Principles and Examples, Agriculture and Natural Resources Department, World Bank, Washington, DC, USA, 2007.European Commission: Directive 2000/60/Ec of the European Parliament and of the Council, of 23 October 2000, establishing a framework for community Action in the Field of Water Policy, Official Journal of the European Communities (OJL), 327, 1–73, 2000.Fisher, F., Huber-Lee, A., and Amir, I.: Liquid Assets: An Economic Approach for Water Management and Conflict Resolution in the Middle East and Beyond, RFF Press, Washington, DC, USA, 2005.Garrick, D., Siebentritt, M. A., Aylward, B., Bauer, C. J., and Purkey, A.: Water markets and freshwater ecosystem services: policy reform and implementation in the Columbia and Murray-Darling Basins, Ecol. Econ., 69, 366–379, 2009.Griffin, R. C.: Effective water pricing, J. Am. Water Resour. As., 37, 1335–1347, 2001.Griffin, R. C.: Water Resource Economics: The Analysis of Scarcity, Policies, and Projects, The MIT Press, Cambridge, USA, 402 pp., 2006.Gysi, M. and Loucks, D. P.: Some long run effects of water-pricing policies, Water Resour. Res., 7, 1371–1382, 1971.Harou, J. J., Pulido-Velazquez, M., Rosenberg, D. E., Medellín-Azuara, J., Lund, J. R., and Howitt, R. E.: Hydro-economic models: concepts, design, applications, and future prospects, J. Hydrol., 375, 627–643, 2009.Heinz, I., Pulido-Velazquez, M., Lund, J., and Andreu, J.: Hydro-economic modeling in river basin management: implications and applications for the European Water Framework Directive, Water Resour. Manag., 21, 1103–1125, 2007.Howe, C. W., Schurmeier, D. R., and Shaw Jr., W. D.: Innovative approaches to water allocation: the potential for water markets, Water Resour. Res., 22, 439–445, 1986.Johansson, R. C., Tsur, Y., Roe, T. L., Doukkali, R., and Dinar, A.: Pricing irrigation water: a review of theory and practice, Water Policy, 4, 173–199, 2002.Karamouz, M., Houck, M. H., and Delleur, J. W.: Optimization and simulation of multiple reservoir systems, J. Water Res. Pl.-ASCE, 118, 71–81, 1992.Kelman, K., Stedinger, J. R., Cooper, L. A., Hsu, E., and Yuan, S.-Q.: Sampling stochastic dynamic programming applied to reservoir operation, Water Resour. Res., 26, 447–454, 1990.Labadie, J. W.: Optimal operation of multireservoir systems: state-of-the-art review, J. Water Res. Pl.-ASCE, 130, 93–111, 2004.Lund, J. R. and Guzman, J.: Derived operating rules for reservoirs in series or in parallel, J. Water Res. Pl.-ASCE, 125, 143–153, 1999.Macian-Sorribes, H.: Utilización de Lógica Difusa en la Gestión de Embalses, Master Thesis dissertation, Universitat Politècnica de València, Valencia, Spain, 2012 (in Spanish).Macian-Sorribes, H. and Pulido-Velazquez, M.: Hydro-economic optimization under inflow uncertainty using the SDP-GAMS generalized tool, in: Evoling Water Resources Systems: Understanding, Predicting and Managing Water-Society Interactions, IAHS Press, Wallingford, UK, 410–415, 2014.Massarutto, A.: El precio de agua: herramienta básica para una política sostenible del agua?, Ingeniería del Agua, 10, 293–326, 2003 (in Spanish).Meinzen-Dick, R. and Mendoza, M.: Alternative water allocation mechanisms indian and international experiences, Econ. Polit. Weekly, 31, A25–A30, 1996.Mousavi, J. J., Ponnambalam, K., and Karray, F.: Reservoir operation using a dynamic programming fuzzy rule-based approach, Water Resour. Manag., 19, 655–672, 2005.Nandalal, K. D. W. and Bogardi, J. J.: Dynamic Programming Based Operation of Reservoirs: Applicability and Limits, Cambridge University Press, Cambridge, UK, 144 pp., 2007.Palomo-Hierro, S., Gomez-Limon, J. A., and Riesgo, L.: Water Markets in Spain: Performance and Challenges, Water 2015, 7, 652–678, 2015.Pulido-Velazquez, M., Jenkins, M., and Lund, J. R.: Economic values for conjunctive use and water banking in southern California, Water Resour. Res., 40, W03401, https://doi.org/10.1029/2003WR002626, 2004.Pulido-Velazquez, M., Andreu, J., Sahuquillo, A., and Pulido-Velazquez, D.: Hydro-economic river basin modelling: the application of a holistic surface-groundwater model to assess opportunity costs of water use in Spain, Ecol. Econ., 66, 51–65, 2008.Pulido-Velazquez, M., Alvarez-Mendiola, E., and Andreu, J.: Design of efficient water pricing policies integrating basinwide resource opportunity costs, J. Water Res. Pl.-ASCE, 139, 583–592, 2013.Riegels, N., Pulido-Velazquez, M., Doulgeris, C., Sturm, V., Jensen, R., Moller, F., and Bauer-Gottwein, P.: Systems analysis approach to the design of efficient water pricing policies under the EU Water Framework Directive, J. Water Res. Pl.-ASCE, 139, 574–582, 2013.Rogers, P., de Silva, R., and Bhatia, R.: Water is an economic good: How to use prices to promote equity, efficiency and sustainability, Water Policy, 4, 1–17, 2002.SCRM: Convenio de Bases para la Ordenación de las Aguas del río Mijares, Ministerio de Obras Públicas, Transportes y Medio Ambiente, Confederación Hidrográfica del Júcar, Valencia, Spain, 50 pp., 1974 (in Spanish).Stedinger, J. R., Sule, B. F., and Loucks, D. P.: Stochastic dynamic programming models for reservoir operation optimization, Water Resour. Res., 20, 1499–1505, 1984.Tejada-Guibert, J. A., Johnson, S. A., Stedinger, J. R.: Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming, Water Resour. Res., 29, 3969–3980, 1993.Tilmant, A. and Kelman, R.: A stochastic approach to analyze trade-offs and risks associated with large-scale water resources systems, Water Resour. Res., 43, W06425, https://doi.org/10.1029/2006WR005094, 2007.Tilmant, A., Pinte, D., and Goor, Q.: Assessing marginal water values in multipurpose multireservoir systems via stochastic programming, Water Resour. Res., 44, W12431, https://doi.org/10.1029/2008WR007024, 2008.Tilmant, A., Goor, Q., and Pinte, D.: Agricultural-to-hydropower water transfers: sharing water and benefits in hydropower-irrigation systems, Hydrol. Earth Syst. Sci., 13, 1091–1101, https://doi.org/10.5194/hess-13-1091-2009, 2009.Tilmant, A., Arjoon, D., and Fernandes Marques, G.: Economic value of storage in multireservoir systems, J. Water Res. Pl.-ASCE, 140, 375–383, 2014.US Army Corps of Engineers (USACE) Hydrologic Engineering Center (HEC): Developing Seasonal and Long-Term Reservoir System Operation Plans Using HEC-PRM, US Army Corps of Engineers, Hydrologic Engineering Center, Davis, California, USA, 1996.Ward, F. and Pulido-Velazquez, M.: Efficiency, equity and sustainability in a water quantity-quality optimization model in the Rio Grande basin, Ecol. Econ., 66, 23–37, 2008.Wurbs, R. A.: Reservoir-system simulation and optimization models, J. Water Res. Pl.-ASCE, 119, 455–472, 1993.Young, R. A.: Water economics, in: Water Resources Handbook, McGraw-Hill, New York, NY, USA, 3.1–3.57, 1996.Young, R. A.: Determining the Economic Value of Water: Concepts and Methods, RFF Press, Washington, DC, USA, 375 pp., 2005

System Dynamics Modeling for Supporting Drought-Oriented Management of the Jucar River System, Spain

[EN] The management of water in systems where the balance between resources and demands is already precarious can pose a challenge and it can be easily disrupted by drought episodes. Anticipated drought management has proved to be one of the main strategies to reduce their impact. Drought economic, environmental, and social impacts affect different sectors that are often interconnected. There is a need for water management models able to acknowledge the complex interactions between multiple sectors, activities, and variables to study the response of water resource systems to drought management strategies. System dynamics (SD) is a modeling methodology that facilitates the analysis of interactions and feedbacks within and between sectors. Although SD has been applied for water resource management, there is a lack of SD models able to regulate complex water resource systems on a monthly time scale and considering multiple reservoir operating rules, demands, and policies. In this paper, we present an SD model for the strategic planning of drought management in the Jucar River system, incorporating dynamic reservoir operating rules, policies, and drought management strategies triggered by a system state index. The DSS combines features from early warning and information systems, allowing for the simulation of drought strategies, evaluating their economic impact, and exploring new management options in the same environment. The results for the historical period show that drought early management can be beneficial for the performance of the system, monitoring the current state of the system, and activating drought management measures results in a substantial reduction of the economic impact of droughts.The data used in this study was obtained from the references included. We acknowledge the European Research Area for Climate Services consortium (ER4CS) and the Agencia Estatal de Investigacion for their financial support to this research under the INNOVA project (Grant Agreement: 690462; PCIN-2017-066). This study has also been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion y Universidades (MICIU) of Spain.Rubio-Martin, A.; Pulido-Velazquez, M.; Macian-Sorribes, H.; Garcia-Prats, A. (2020). System Dynamics Modeling for Supporting Drought-Oriented Management of the Jucar River System, Spain. Water. 12(5):1-19. https://doi.org/10.3390/w12051407S119125Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. doi:10.1016/j.jhydrol.2010.07.012Momblanch, A., Paredes-Arquiola, J., Munné, A., Manzano, A., Arnau, J., & Andreu, J. (2015). Managing water quality under drought conditions in the Llobregat River Basin. Science of The Total Environment, 503-504, 300-318. doi:10.1016/j.scitotenv.2014.06.069Van Loon, A. F., & Van Lanen, H. A. J. (2013). Making the distinction between water scarcity and drought using an observation-modeling framework. Water Resources Research, 49(3), 1483-1502. doi:10.1002/wrcr.20147Mishra, A. K., & Singh, V. P. (2011). Drought modeling – A review. Journal of Hydrology, 403(1-2), 157-175. doi:10.1016/j.jhydrol.2011.03.049Wilhite, D. A., Sivakumar, M. V. K., & Pulwarty, R. (2014). Managing drought risk in a changing climate: The role of national drought policy. Weather and Climate Extremes, 3, 4-13. doi:10.1016/j.wace.2014.01.002Marcos-Garcia, P., Lopez-Nicolas, A., & Pulido-Velazquez, M. (2017). Combined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Mediterranean basin. Journal of Hydrology, 554, 292-305. doi:10.1016/j.jhydrol.2017.09.028Estrela, T., & Vargas, E. (2012). Drought Management Plans in the European Union. The Case of Spain. Water Resources Management, 26(6), 1537-1553. doi:10.1007/s11269-011-9971-2Pedro-Monzonís, M., Solera, A., Ferrer, J., Estrela, T., & Paredes-Arquiola, J. (2015). A review of water scarcity and drought indexes in water resources planning and management. Journal of Hydrology, 527, 482-493. doi:10.1016/j.jhydrol.2015.05.003Zaniolo, M., Giuliani, M., Castelletti, A. F., & Pulido-Velazquez, M. (2018). Automatic design of basin-specific drought indexes for highly regulated water systems. Hydrology and Earth System Sciences, 22(4), 2409-2424. doi:10.5194/hess-22-2409-2018Carmona, M., Máñez Costa, M., Andreu, J., Pulido-Velazquez, M., Haro-Monteagudo, D., Lopez-Nicolas, A., & Cremades, R. (2017). Assessing the effectiveness of Multi-Sector Partnerships to manage droughts: The case of the Jucar river basin. Earth’s Future, 5(7), 750-770. doi:10.1002/2017ef000545PALLOTTINO, S., SECHI, G., & ZUDDAS, P. (2005). A DSS for water resources management under uncertainty by scenario analysis. Environmental Modelling & Software, 20(8), 1031-1042. doi:10.1016/j.envsoft.2004.09.012Sechi, G. M., & Sulis, A. (2010). Drought mitigation using operative indicators in complex water systems. Physics and Chemistry of the Earth, Parts A/B/C, 35(3-5), 195-203. doi:10.1016/j.pce.2009.12.001Svoboda, M. D., Fuchs, B. A., Poulsen, C. C., & Nothwehr, J. R. (2015). The drought risk atlas: Enhancing decision support for drought risk management in the United States. Journal of Hydrology, 526, 274-286. doi:10.1016/j.jhydrol.2015.01.006Buttafuoco, G., Caloiero, T., Ricca, N., & Guagliardi, I. (2018). Assessment of drought and its uncertainty in a southern Italy area (Calabria region). Measurement, 113, 205-210. doi:10.1016/j.measurement.2017.08.007Iglesias, A., & Garrote, L. (2015). Adaptation strategies for agricultural water management under climate change in Europe. Agricultural Water Management, 155, 113-124. doi:10.1016/j.agwat.2015.03.014Lewandowski, J., Meinikmann, K., & Krause, S. (2020). Groundwater–Surface Water Interactions: Recent Advances and Interdisciplinary Challenges. Water, 12(1), 296. doi:10.3390/w12010296Forrester, J. W. (1968). Industrial Dynamics—After the First Decade. Management Science, 14(7), 398-415. doi:10.1287/mnsc.14.7.398Sušnik, J., Molina, J.-L., Vamvakeridou-Lyroudia, L. S., Savić, D. A., & Kapelan, Z. (2012). Comparative Analysis of System Dynamics and Object-Oriented Bayesian Networks Modelling for Water Systems Management. Water Resources Management, 27(3), 819-841. doi:10.1007/s11269-012-0217-8Mirchi, A., Madani, K., Watkins, D., & Ahmad, S. (2012). Synthesis of System Dynamics Tools for Holistic Conceptualization of Water Resources Problems. Water Resources Management, 26(9), 2421-2442. doi:10.1007/s11269-012-0024-2Simonovic, S. (2002). World water dynamics: global modeling of water resources. Journal of Environmental Management, 66(3), 249-267. doi:10.1016/s0301-4797(02)90585-2Saysel, A. K., Barlas, Y., & Yenigün, O. (2002). Environmental sustainability in an agricultural development project: a system dynamics approach. Journal of Environmental Management, 64(3), 247-260. doi:10.1006/jema.2001.0488Winz, I., Brierley, G., & Trowsdale, S. (2008). The Use of System Dynamics Simulation in Water Resources Management. Water Resources Management, 23(7), 1301-1323. doi:10.1007/s11269-008-9328-7Nikolic, V. V., & Simonovic, S. P. (2015). Multi-method Modeling Framework for Support of Integrated Water Resources Management. Environmental Processes, 2(3), 461-483. doi:10.1007/s40710-015-0082-6Madani, K., & Mariño, M. A. (2009). System Dynamics Analysis for Managing Iran’s Zayandeh-Rud River Basin. Water Resources Management, 23(11), 2163-2187. doi:10.1007/s11269-008-9376-zGleick, P. H. (2000). A Look at Twenty-first Century Water Resources Development. Water International, 25(1), 127-138. doi:10.1080/02508060008686804Qaiser, K., Ahmad, S., Johnson, W., & Batista, J. (2011). Evaluating the impact of water conservation on fate of outdoor water use: A study in an arid region. Journal of Environmental Management, 92(8), 2061-2068. doi:10.1016/j.jenvman.2011.03.031Sušnik, J., Vamvakeridou-Lyroudia, L. S., Savić, D. A., & Kapelan, Z. (2012). Integrated System Dynamics Modelling for water scarcity assessment: Case study of the Kairouan region. Science of The Total Environment, 440, 290-306. doi:10.1016/j.scitotenv.2012.05.085Sehlke, G., & Jacobson, J. (2005). System Dynamics Modeling of Transboundary Systems: The Bear River Basin Model. Ground Water, 43(5), 722-730. doi:10.1111/j.1745-6584.2005.00065.xLi, L., & Simonovic, S. P. (2002). System dynamics model for predicting floods from snowmelt in North American prairie watersheds. Hydrological Processes, 16(13), 2645-2666. doi:10.1002/hyp.1064Ahmad, S., & Prashar, D. (2010). Evaluating Municipal Water Conservation Policies Using a Dynamic Simulation Model. Water Resources Management, 24(13), 3371-3395. doi:10.1007/s11269-010-9611-2Apperl, B., Pulido-Velazquez, M., Andreu, J., & Karjalainen, T. P. (2015). Contribution of the multi-attribute value theory to conflict resolution in groundwater management – application to the Mancha Oriental groundwater system, Spain. Hydrology and Earth System Sciences, 19(3), 1325-1337. doi:10.5194/hess-19-1325-2015Macian-Sorribes, H., & Pulido-Velazquez, M. (2017). Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a Multireservoir System. Journal of Water Resources Planning and Management, 143(1), 04016069. doi:10.1061/(asce)wr.1943-5452.0000712Escriva-Bou, A., Pulido-Velazquez, M., & Pulido-Velazquez, D. (2017). Economic Value of Climate Change Adaptation Strategies for Water Management in Spain’s Jucar Basin. Journal of Water Resources Planning and Management, 143(5), 04017005. doi:10.1061/(asce)wr.1943-5452.0000735Pulido-Velazquez, M. A., Sahuquillo-Herraiz, A., Camilo Ochoa-Rivera, J., & Pulido-Velazquez, D. (2005). Modeling of stream–aquifer interaction: the embedded multireservoir model. Journal of Hydrology, 313(3-4), 166-181. doi:10.1016/j.jhydrol.2005.02.026Sahuquillo, A. (1983). An eigenvalue numerical technique for solving unsteady linear groundwater models continuously in time. Water Resources Research, 19(1), 87-93. doi:10.1029/wr019i001p00087Estrela, T., & Sahuquillo, A. (1997). Modeling the Response of a Karstic Spring at Arteta Aquifer in Spain. Ground Water, 35(1), 18-24. doi:10.1111/j.1745-6584.1997.tb00055.xAndreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-xHaro-Monteagudo, D., Solera, A., & Andreu, J. (2017). Drought early warning based on optimal risk forecasts in regulated river systems: Application to the Jucar River Basin (Spain). Journal of Hydrology, 544, 36-45. doi:10.1016/j.jhydrol.2016.11.022Howitt, R. E. (1995). Positive Mathematical Programming. American Journal of Agricultural Economics, 77(2), 329-342. doi:10.2307/1243543Malard, J. J., Inam, A., Hassanzadeh, E., Adamowski, J., Tuy, H. A., & Melgar-Quiñonez, H. (2017). Development of a software tool for rapid, reproducible, and stakeholder-friendly dynamic coupling of system dynamics and physically-based models. Environmental Modelling & Software, 96, 410-420. doi:10.1016/j.envsoft.2017.06.053Vidal-Legaz, B., Martínez-Fernández, J., Picón, A. S., & Pugnaire, F. I. (2013). Trade-offs between maintenance of ecosystem services and socio-economic development in rural mountainous communities in southern Spain: A dynamic simulation approach. Journal of Environmental Management, 131, 280-297. doi:10.1016/j.jenvman.2013.09.03

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

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. Keywords: Causal reasoning, Theorem of Bayes, Temporal dependence propagation, Runoff time series, Water resources managemen

Integrated assessment of the impact of climate and land use changes on groundwater quantity and quality in the Mancha Oriental system (Spain)

[EN] Climate and land use change (global change) impacts on groundwater systems cannot be studied in isolation. Land use and land cover (LULC) changes have a great impact on the water cycle and contaminant production and transport. Groundwater flow and storage are changing in response not only to climatic changes but also to human impacts on land uses and demands, which will alter the hydrologic cycle and subsequently impact the quantity and quality of regional water systems. Predicting groundwater recharge and discharge conditions under future climate and land use changes is essential for integrated water management and adaptation. In the Mancha Oriental system (MOS), one of the largest groundwater bodies in Spain, the transformation from dry to irrigated lands during the last decades has led to a significant drop of the groundwater table, with the consequent effect on stream-aquifer interaction in the connected Jucar River. Understanding the spatial and temporal distribution of water quantity and water quality is essential for a proper management of the system. On the one hand, streamflow depletion is compromising the dependent ecosystems and the supply to the downstream demands, provoking a complex management issue. On the other hand, the intense use of fertilizer in agriculture is leading to locally high groundwater nitrate concentrations. In this paper we analyze the potential impacts of climate and land use change in the system by using an integrated modeling framework that consists in sequentially coupling a watershed agriculturally based hydrological model (Soil and Water Assessment Tool, SWAT) with a groundwater flow model developed in MODFLOW, and with a nitrate mass-transport model in MT3DMS. SWAT model outputs (mainly groundwater recharge and pumping, considering new irrigation needs under changing evapotranspiration (ET) and precipitation) are used as MODFLOW inputs to simulate changes in groundwater flow and storage and impacts on stream-aquifer interaction. SWAT and MODFLOW outputs (nitrate loads from SWAT, groundwater velocity field from MODFLOW) are used as MT3DMS inputs for assessing the fate and transport of nitrate leached from the topsoil. Three climate change scenarios have been considered, corresponding to three different general circulation models (GCMs) for emission scenario A1B that covers the control period, and short-, medium-and long-term future periods. A multi-temporal analysis of LULC change was carried out, helped by the study of historical trends (from remote-sensing images) and key driving forces to explain LULC transitions. Markov chains and European scenarios and projections were used to quantify trends in the future. The cellular automata technique was applied for stochastic modeling future LULC maps. Simulated values of river discharge, crop yields, groundwater levels and nitrate concentrations fit well to the observed ones. The results show the response of groundwater quantity and quality (nitrate pollution) to climate and land use changes, with decreasing groundwater recharge and an increase in nitrate concentrations. The sequential modeling chain has been proven to be a valuable assessment tool for supporting the development of sustainable management strategies.This study was partially funded by the EU FP7 GENESIS project (no. 226.536) on groundwater systems, the Plan Nacional de I+D+I 2008-2011 of the Ministry of Science and Innovation of Spain (projects CGL2009-13238-C02-01/02 on climate change impacts and adaptation), and the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economia y Competitividad) and Feder funds. We also want to thank SMHI for the climate scenarios provided in the context of the GENESIS project, as well as the anonymous reviewer and the handling editor, for the constructive and helpful review of the paper.Pulido-Velazquez, M.; Peña Haro, S.; García Prats, A.; Mocholí Almudéver, AF.; Henriquez-Dole, L.; Macian-Sorribes, H.; Lopez-Nicolas, A. (2015). Integrated assessment of the impact of climate and land use changes on groundwater quantity and quality in the Mancha Oriental system (Spain). Hydrology and Earth System Sciences. 19(4):1677-1693. https://doi.org/10.5194/hess-19-1677-2015S16771693194Abbaspour, K.: SWAT-CUP 2012: SWAT Calibration and Uncertainty Programs – A User Manual, available at: http://www.neprashtechnology.ca/Downloads/SwatCup/Manual/Usermanual_Swat_Cup.pdf, 2012.Apperl, B., Pulido-Velazquez, M., Andreu, J., and Karjalainen, T. P.: Contribution of the multi-attribute value theory to conflict resolution in groundwater management – application to the Mancha Oriental groundwater system, Spain, Hydrol. Earth Syst. Sci., 19, 1325–1337, https://doi.org/10.5194/hess-19-1325-2015, 2015.Arnold, J. G. and Williams, J. R.: SWRRB – A watershed scale model for soil and water resources management, in: Computer models of watershed hydrology, edited by: Singh, V. P., Water Resources Publications, 847–908, 1995.Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment part I: model development, J. Am. Water Resour. As., 34, 73–89, 1998.Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Van Liew, M. W., Kannan, N., and Jha, M. K.: SWAT: Model Use, Calibration, and Validation, T. ASABE, 55, 1491–1508, 2012.Calera, A., Medrano, J., Vela, A., and Castaño, S.: GIS tools applied to the sustainable management of hydric resources: application to the aquifer system 08-29, Agr. Water Manage., 40, 207–220, 1999.Calera, A., Jochum, A. M., Cuesta, A., Montoro, A., and Lopez, P.: Irrigation management from space: Towards user-friendly products, Irrig. Drain., 19, 337–353, 2005.Calera, A., Osann, A., D'Urso, G., Neale, C., and Moreno, J. M.: Earth Observation for irrigation and river basin management in an operational way: The SPIDER system, IAHS-AISH P., 352, 423–426, 2012.Caballero, S., Voirin-Morel, F., Habets, J., Noilhan, P., LeMoigne, A., and Lehenaff, A. B.: Hydrological sensitivity of the Adour-Garonne river basin to climate change, Water Resour. Res., 43, WO7448, https://doi.org/10.1029/2005WR004192, 2007.Candela, L., von Igel, W., Elorza, F. J., and Aronica, G.: Impact assessment of combined climate and management scenarios on groundwater resources and associated wetland (Majorca, Spain), J. Hydrol., 376, 510–527, 2009.Candela, L., Elorza, F. J., Jiménez-Martínez, J., and von Igel, W.: Global change and agricultural management options for groundwater sustainability, Comput. Electron. Agr., 86, 120–130, 2012.Castaño, S., Sanz, D., and Gómez-Alday, J. J.: Methodology for quantifying 13 groundwater abstractions for agricultura via remote sensing and GIS, Water Resour. Manage., 24, 795–814, 2010.Chaouche, K., Neppel, L., Dieulin, C., Pujol, N., Ladouche, B., Martin, E., Salas, D., and Caballero, Y.: Analyses of precipitation, temperatura and evapotranspiration in a French Mediterranean región in the context of climate change, C.R. Geosci., 342, 234–243, 2010.CHJ: Documento Técnico de Referencia: Evaluación del Estado de las Masas de Agua Superficial y Subterránea, Ámbito territorial de la Confederación Hidrográfica del Júcar, Ministerio de Medio Ambiente y Medio Rural y Marino. Confederación Hidrográfica del Júcar, Spain, 2009a (in Spanish).CHJ: Esquema provisional de Temas Importantes, Ministerio de Medio Ambiente y Medio Rural y Marino. Confederación Hidrográfica del Júcar, Spain, 2009b (in Spanish).CHJ: Memoria del Proyecto del Plan Hidrológico, Ministerio de Medio Ambiente y Medio Rural y Marino, Confederación Hidrográfica del Júcar, Spain, 2013 (in Spanish).Eastman, J. R.: IDRISI Andes, Guide to GIS and image processing Clark University, Worcester, MA, 2006.Ertürk, A., Ekdal, A., Gürel, M., Karakaya, N., Guzel, C., and Gönenç, E.: Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci. Total Environ., 499, 437–447, 2014.Feranec, J., Hazeu, G., Soukup, T., and Jaffrain, G.: Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data, Appl. Geogr., 30, 19–35, ISSN 0143-6228, 2010.Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G.: The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions, T. ASABE, 50, 1211–1250, 2007.Gassman, P. W., Sadeghi, A. M., and Srinivasan, R.: Applications of the SWAT model special section: overview and insights, J. Environ. Qual., 43, 1–8, https://doi.org/10.2134/jeq2013.11.0466, 2014.Henriquez-Dole, L. E.: Escenarios futuros de uso del suelo para el análisis del efecto del cambio global en los recursos hídricos aplicado al acuífero de la Mancha Oriental. Master Thesis dissertation. Universitat Politècnica de València, Spain, 2012 (in Spanish).Holman, I. P., Allen, D. M., Cuthbert, M. O., and Goderniaux, P.: Towards best practice for assessing the impacts of climate change on groundwater, Hydrogeol. J., 20, 1–4, 2012.IGME-DGA: Trabajos de la Actividad 4 "Identificación y caracterización de la interrelación entre aguas subterráneas, cursos fluviales, descargas por manantiales, zonas húmedas y otros ecosistemas naturales de especial interés hídrico", Encomienda de gestión para la realización de trabajos científico-técnicos de apoyo a la sostenibilidad y protección de las aguas subterráneas, Demarcación Hidrográfica del Júcar, Instituto Geológico y Minero de España (Ministerio de Ciencia e Innovación) y Dirección General del Agua (Ministerio de Medio y Medio Rural y Marino), 2010.INE (Instituto Nacional de Estadística): available at: http://www.ine.es, last access: December 2011.Jyrkama, I. M. and Sykes, J. F.: The impact of climate change on spatially varying groundwater recharge in the grand river watershed, J. Hydrol., 338, 237–250, 2007.Kim, N. W., Chung, I. M, Won, Y. S., and Arnold, J. G.: Development and application of the integrated SWAT-MODFLOW model, J. Hydrology, 356, 1–16, 2008.Kingston, D. G. and Taylor, R. G.: Sources of uncertainty in climate change impacts on river discharge and groundwater in a headwater catchment of the Upper Nile Basin, Uganda, Hydrol. Earth Syst. Sci., 14, 1297–1308, https://doi.org/10.5194/hess-14-1297-2010, 2010.Kjellstrom, E., Nikulin, G., Hasson, U., Strandberg, G., and Ullerstig, A.: 21st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations, Tellus A, 63, 24–40, 2011.Klijn, J. A., Vullings, L. A. E., van den Berg, M., van Meijl, H., van Lammeren, R., van Rheenen, T., Veldkamp, A., Verburg, P. H., Westhoek, H., and Eickhout, B.: The EURURALIS study: Technical document, Alterra, Wageningen, available at: http://www.eururalis.nl/background.htm (last access: 29 March 2014), 2005.Kløve, B., Ala-Aho, P., Bertrand, G., Gurdak, J. J., Kupfersberger, H., Kvœrner, J., Muotka, T., Mykrä, H., Preda, E., Rossi, P., Uvo, C. B., Velasco, E., and Pulido-Velázquez, M.: Climate Change Impacts on Groundwater and Dependent Ecosystems, J. Hydrol., 518, 250–266, 2014.Lopez-Gunn, E.: The Role of Collective Action in Water Governance: A Comparative 15 Study of Groundwater User Associations in La Mancha Aquifers in Spain, Water 16 International, 28, 367–378, 2003.López Urrea, R., López Córcoles, H., López Fuster, P., Montoro Rodríguez, A., Martín de Santa Olalla Mañas, F., and Calero Martínez, J. A.: Ensayos de programación de riegos en kenaf, trigo blando y bróculi, available at: http://www.itap.es/media/3279/4.programación riegos 2003.pdf (last access: February 2015), 2003.Ma, X., Lu, X. X., van Noordwijk, M., Li, J. T., and Xu, J. C.: Attribution of climate change, vegetation restoration, and engineering measures to the reduction of suspended sediment in the Kejie catchment, southwest China, Hydrol. Earth Syst. Sci., 18, 1979–1994, https://doi.org/10.5194/hess-18-1979-2014, 2014.Mango, L. M., Melesse, A. M., McClain, M. E., Gann, D., and Setegn, S. G.: Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management, Hydrol. Earth Syst. Sci., 15, 2245–2258, https://doi.org/10.5194/hess-15-2245-2011, 2011.Martin-Benlloch, A.: Obtención de Curvas de Producción y Lixiviado Mediante el Modelo Distribuído GEPIC en Escenarios de Cambio Climático, Degree Dissertation, Universitat Politècnica de València, 2012 (in Spanish).McDonald, M. G. and Harbaugh, A. W.: A modular three-dimensional finite difference groundwater flow model, US Geological Survey Techniques of Water-Resources Investigation Book 6, Chapter A1, 586 pp., 1988.Molina-Navarro, E., Trolle, D., Martínez-Pérez, S., Sastre-Merlín, A., and Jepsen, E.: Hydrological and water quality impact assessment of a Mediterranean limno-reservoir under climate change and land use change scenarios, J. Hydrol., 509, 354–366, 2014.Moratalla, A., Gómez-Alday, J. J., De las Heras, J., Sanz, D., and Castaño, S.: Nitrate in the Water-Supply Wells in the Mancha Oriental Hydrogeological System (SE Spain), Water Resour. Manage., 23, 1621–1640, https://doi.org/10.1007/s11269-008-9344-7, 2009.Nakicenovic, N. and Swart, R. (Eds.): Special Report on Emissions Scenarios, IPCC, Cambridge University Press, Cambridge, UK, 570 pp., 2000.Narula, K. K. and Gosain, A. K.: Modeling hydrology, groundwater recharge and non-point nitrate loadings in the Himalayan Upper Yamuna basin, Sci. Total Environ., 468–469, S102–S116, https://doi.org/10.1016/j.scitotenv.2013.01.022, 2013.Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., and Williams, J. R.: Soil and water assessment tool input/output file documentation (version 2005), Temple, Texas: Grassland, Soil and Water Research Laboratory, Agriculture Research Service, Blackland Research Center, Texas Agricultural Experiment Station, 2005.Nikulin, G., Kjellstrom, E., Hasson, U., Strandberg, G., and Ullerstig, A.: Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations, Tellus, 63A, 41–55, 2011.Oñate-Valdivieso, F. and Bosque Sendra, J.: Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling, J. Hydrol., 395, 256–263, 2010.Peña-Haro, S., Llopis-Albert, C., Pulido-Velazquez, M., and Pulido-Velazquez, D.: Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain, J. Hydrol., 392, 174–187, 2010.Peña-Haro, S., García-Prats, A., and Pulido-Velazquez, M.: Influence of soil and climate heterogeneity on the performance of economic instruments for reducing nitrate leaching from agriculture, Sci. Total Environ., 499, 510–519, https://doi.org/10.1016/j.scitotenv.2014.07.029, 2014.Pulido-Velazquez, D., García-Aróstegui, J. L., Molina, J. L., and Pulido-Velazquez, M.: Assessment of future groundwater recharge in semi-arid regions under climate change scenarios (Serral-Salinas aquifer, SE Spain). Could increased rainfall variability increase the recharge rate?, Hydrol. Process., 29, 828–844, https://doi.org/10.1002/hyp.10191, 2015.Reifen, C. and Toumi, R.: Climate projections: Past performance no guarantee of future skill?, Geophys. Res. Lett., 36, L13704, https://doi.org/10.1029/2009GL038082, 2009.Rienks, W. A. (Ed.): The future of rural Europe, An anthology based on the results of the Eururalis 2.0 scenario study. Wageningen UR and Netherlands Environmental Assesment Agency (MNP), Wageningen and Vilthoben, the Netherlands, 2007.Sanz, D.: Contribución a la caracterización geométrica de las unidades hidrogeológicas que integran el sistema de acuíferos de la Mancha oriental [Contribution to the geometrical characterization of the hydrogeological unit which forms the Mancha Oriental aquifers system], PhD Thesis, Univ. Complutense de Madrid, Spain, 2005 (in Spanish).Sanz, D., Gómez-Alday, J. J., Castaño, S., Moratalla, A., De las Heras, L., and Martínez Alfaro, P. P.: Hydrostratigraphic framework and hydrogeological behaviour of the Mancha Oriental System (SE Spain), Hydrogeol. J., 17, 1375–1391, 2009.Sanz, D., Castaño, S., Cassiraga, E., Sahuquillo, A., Gómez-Alday, J. J., Peña, S., and Calera, A.: Modeling aquifer-river interactions under the influence of groundwater abstraction in the Mancha Oriental System (SE Spain), Hydrogeol. J., 19, 475–487, 2011.Seiller, K. P. and Gat, J. R.: Groundwater Recharge from Run-off, Infiltration and Percolation. Series: Water Science and Technology Library, Vol. 55, 2007.Shrestha, B., Babel, M. S., Maskey, S., van Griensven, A., Uhlenbrook, S., Green, A., and Akkharath, I.: Impact of climate change on sediment yield in the Mekong River basin: a case study of the Nam Ou basin, Lao PDR, Hydrol. Earth Syst. Sci., 17, 1–20, https://doi.org/10.5194/hess-17-1-2013, 2013.Sophocleous, M. and Perkins, S. P.: Methodology and application of combined watershed and ground-water models in Kansas, J. Hydrol., 236, 185–201, 2000.Stuart, M. E., Goody, D. C., Bllomfield, J. P., and Williams, A. T.: A review of the impact of climate change on future nitrate concentrations in groundwater of the UK, Sci. Total Environ., 409, 2859–2873, 2011.Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456–457, 12–29, 2012.Westhoek, H. J., van den Berg, M., and Bakkes, J. A.: Scenario development to explore the future of Europe's rural areas, Agr. Ecosyst. Environ., 114, 7–20, 2006.Williams, J. R.: Flood routing with variable travel time or variable storage coefficients, T. ASAE, 12, 100–103, 1969.Xu, H., Taylor, R. G., and Xu, Y.: Quantifying uncertainty in the impacts of climate change on river discharge in sub-catchments of the Yangtze and Yellow River Basins, China, Hydrol. Earth Syst. Sci., 15, 333–344, https://doi.org/10.5194/hess-15-333-2011, 2011.Zheng, C. and Wang, P. P.: MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user's guide SERDP-99-1, Washington, DC, US Army Corps of Engineers, 1999