- -

Automatic design of basin-specific drought indexes for highly regulated water systems

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Automatic design of basin-specific drought indexes for highly regulated water systems

Show simple item record

Files in this item

dc.contributor.author Zaniolo, M. es_ES
dc.contributor.author Giuliani, M. es_ES
dc.contributor.author Castelletti, A. es_ES
dc.contributor.author Pulido-Velazquez, M. es_ES
dc.date.accessioned 2018-11-21T21:05:17Z
dc.date.available 2018-11-21T21:05:17Z
dc.date.issued 2018 es_ES
dc.identifier.issn 1027-5606 es_ES
dc.identifier.uri http://hdl.handle.net/10251/112956
dc.description.abstract [EN] Socio-economic costs of drought are progressively increasing worldwide due to undergoing alterations of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, traditional drought indexes often fail at detecting critical events in highly regulated systems, where natural water availability is conditioned by the operation of water infrastructures such as dams, diversions, and pumping wells. Here, ad hoc index formulations are usually adopted based on empirical combinations of several, supposed-to-be significant, hydro-meteorological variables. These customized formulations, however, while effective in the design basin, can hardly be generalized and transferred to different contexts. In this study, we contribute FRIDA (FRamework for Index-based Drought Analysis), a novel framework for the automatic design of basin-customized drought indexes. In contrast to ad hoc empirical approaches, FRIDA is fully automated, generalizable, and portable across different basins. FRIDA builds an index representing a surrogate of the drought conditions of the basin, computed by combining all the relevant available information about the water circulating in the system identified by means of a feature extraction algorithm. We used the Wrapper for Quasi-Equally Informative Subset Selection (W-QEISS), which features a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The preferred variable subset is selected among the efficient solutions and used to formulate the final index according to alternative model structures. We apply FRIDA to the case study of the Jucar river basin (Spain), a drought-prone and highly regulated Mediterranean water resource system, where an advanced drought management plan relying on the formulation of an ad hoc "state index" is used for triggering drought management measures. The state index was constructed empirically with a trial-and-error process begun in the 1980s and finalized in 2007, guided by the experts from the Confederacion Hidrografica del Jucar (CHJ). Our results show that the automated variable selection outcomes align with CHJ's 25-year-long empirical refinement. In addition, the resultant FRIDA index outperforms the official State Index in terms of accuracy in reproducing the target variable and cardinality of the selected inputs set. es_ES
dc.description.sponsorship The work has been partially funded by the European Commission under the IMPREX project belonging to Horizon 2020 framework programme (grant no. 641811). The authors would like to thank the planning office of the Confederacion Hidrografica del Jucar (CHJ) for providing the data used in this study. es_ES
dc.language Inglés es_ES
dc.publisher EUROPEAN GEOSCIENCES UNION es_ES
dc.relation.ispartof HYDROLOGY AND EARTH SYSTEM SCIENCES es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Automatic design of basin-specific drought indexes for highly regulated water systems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.5194/hess-22-2409-2018 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/641811/EU/IMproving PRedictions and management of hydrological EXtremes/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Zaniolo, M.; Giuliani, M.; Castelletti, A.; 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. https://doi.org/10.5194/hess-22-2409-2018 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.5194/hess-22-2409-2018 es_ES
dc.description.upvformatpinicio 2409 es_ES
dc.description.upvformatpfin 2424 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\367705 es_ES
dc.contributor.funder European Commission es_ES
dc.description.references AghaKouchak, A.: Recognize anthropogenic drought, Nature, 524, p. 409, 2015a. a es_ES
dc.description.references AghaKouchak, A.: A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought, J. Hydrol., 526, 127–135, 2015b. a es_ES
dc.description.references Alcamo, J., Flörke, M., and Märker, M.: Future long-term changes in global water resources driven by socio-economic and climatic changes, Hydrolog. Sci. J., 52, 247–275, 2007. a es_ES
dc.description.references Andreu, J., Capilla, J., and Sanchís, E.: AQUATOOL, a generalized decision-support system for water-resources planning and operational management, J. Hydrol., 177, 269–291, 1996. a es_ES
dc.description.references Andreu, J., Ferrer-Polo, J., Pérez, M., and Solera, A.: Decision support system for drought planning and management in the Jucar river basin, Spain, in: 18th World IMACS/MODSIM Congress, Cairns, Australia, vol. 1317, 2009. a, b es_ES
dc.description.references Bowden, G. J., Dandy, G. C., and Maier, H. R.: Input determination for neural network models in water resources applications. Part 1 – Background and methodology, J. Hydrol., 301, 75–92, https://doi.org/10.1016/j.jhydrol.2004.06.021, 2005. a es_ES
dc.description.references Byun, H.-R. and Wilhite, D. A.: Objective quantification of drought severity and duration, J. Climate, 12, 2747–2756, 1999. a es_ES
dc.description.references Carmona, M., Máñez Costa, M., Andreu, J., Pulido-Velazquez, M., Haro-Monteagudo, D., Lopez-Nicolas, A., and Cremades, R.: Assessing the effectiveness of Multi-Sector Partnerships to manage droughts: The case of the Jucar river basin, Earth's Future, 5, 750–770, https://doi.org/10.1002/2017EF000545, 2017. a, b, c, d es_ES
dc.description.references Changnon, S. A.: Detecting drought conditions in Illinois, Circular (Illinois State Water Survey), 1–36, Illinois, USA, 1987. a es_ES
dc.description.references CHD: Plan Especial de Actuación en situaciones de alerta y eventual sequía, Plan Especial de Actuación en situaciones de alerta y eventual sequía en la cuenca del Duero, TYPSA, Valladolid, 2007. a es_ES
dc.description.references CHE: Plan especial de actuación en situaciones de alerta y eventual sequia en la cuenca hidrográfica del Ebro, MARM, Zaragoza, 2007. a es_ES
dc.description.references CHG: Plan especial de actuación en situaciones de alerta y eventual sequía de la cuenca hidrográfica del Guadalquivir, CHG, Seville, Spain, 2007. a es_ES
dc.description.references CHJ: Plan especial de alerta y eventual sequía en la confederación hidrográfica del Júcar, Confederación Hidrográfica del Júcar, Jucar River Basin Management Authority, Ministry of Agriculture, Food and Environment, Spanish Government, Valencia, Spain, 2007a (in Spanish). a, b, c, d es_ES
dc.description.references CHJ: Anejo2 – Plan especial de alerta y eventual sequía en la confederación hidrográfica del Júcar, Confederación Hidrográfica del Júcar, Jucar River Basin Management Authority, Ministry of Agriculture, Food and Environment, Spanish Government, Valencia, Spain, 2007b (in Spanish). a es_ES
dc.description.references Cunningham, P.: Dimension reduction, in: Machine learning techniques for multimedia, 91–112, Springer, Cognitive Technologies, Springer, Berlin, Heidelberg, 2008. a es_ES
dc.description.references Dracup, J. A., Lee, K. S., and Paulson, E. G.: On the definition of droughts, Water Resour. Res., 16, 297–302, https://doi.org/10.1029/WR016i002p00297, 1980. a es_ES
dc.description.references Estrela, T. and Vargas, E.: Drought management plans in the European Union. The case of Spain, Water Resour. Manag., 26, 1537–1553, 2012. a, b es_ES
dc.description.references EU: Water Scarcity and Droughts, Second Interim Report, Tech. rep., 2007. a es_ES
dc.description.references Falkenmark, M., Lundqvist, J., and Widstrand, C.: Macro-scale water scarcity requires micro-scale approaches, in: Natural resources forum, vol. 13, 258–267, Wiley Online Library, Blackwell Publishing Ltd, 1989. a es_ES
dc.description.references Galelli, S. and Castelletti, A.: Tree-based iterative input variable selection for hydrological modeling, Water Resour. Res., 49, 4295–4310, 2013. a es_ES
dc.description.references Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., and Gibbs, M. S.: An evaluation framework for input variable selection algorithms for environmental data-driven models, Environ. Modell. Softw., 62, 33–51, https://doi.org/10.1016/j.envsoft.2014.08.015, 2014. a es_ES
dc.description.references Garrote, L., Martin-Carrasco, F., Flores-Montoya, F., and Iglesias, A.: Linking drought indicators to policy actions in the Tagus basin drought management plan, Water Resour. Manag., 21, 873–882, 2007. a, b es_ES
dc.description.references Giorgi, F. and Lionello, P.: Climate change projections for the Mediterranean region, Global Planet. Change, 63, 90–104, 2008. a es_ES
dc.description.references Gómez, C. M. G. and Blanco, C. D. P.: Do drought management plans reduce drought risk? A risk assessment model for a Mediterranean river basin, Ecol. Econ., 76, 42–48, 2012. a es_ES
dc.description.references Gustard, A. and Demuth, S.: Operational Hydrology Report No. 50 German National Committee for the International Hydrological Programme (IHP) of UNESCO and the Hydrology and Water Resources Programme (HWRP) of WMO, Koblenz, 2009. a es_ES
dc.description.references Guyon, I.: An Introduction to Variable and Feature Selection, J. Mach. Learn. Res., 3, 1157–1182, 2003. a es_ES
dc.description.references Hadka, D. and Reed, P.: Borg: An auto-adaptive many-objective evolutionary computing framework, Evol. Comput., 21, 231–259, 2013. a es_ES
dc.description.references Hao, Z. and AghaKouchak, A.: Multivariate standardized drought index: a parametric multi-index model, Adv. Water Resour., 57, 12–18, 2013. a es_ES
dc.description.references Haro, D., Solera, A., Paredes, J., and Andreu, J.: Methodology for drought risk assessment in within-year regulated reservoir systems. application to the orbigo river system (Spain), Water Resour. Manag., 28, 3801–3814, 2014a. a, b es_ES
dc.description.references Haro, D., Solera, A., Pedro-Monzonís, M., and Andreu, J.: Optimal Management of the Jucar River and Turia River Basins under Uncertain Drought Conditions, Procedia Engineer., 89, 1260–1267, 2014b. a, b es_ES
dc.description.references Haro-Monteagudo, D., Solera, A., and Andreu, J.: Drought early warning based on optimal risk forecasts in regulated river systems: Application to the Jucar River Basin (Spain), J. Hydrol., 544, 36–45, 2017. a, b, c es_ES
dc.description.references Heim Jr., R. R.: A review of twentieth-century drought indices used in the United States, B. Am. Meteorol. Soc., 83, 1149–1165, 2002. a, b, c es_ES
dc.description.references Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K.: Extreme learning machine: theory and applications, Neurocomputing, 70, 489–501, 2006. a, b es_ES
dc.description.references Huang, G.-B., Zhou, H., Ding, X., and Zhang, R.: Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42, 513–529, 2012. a es_ES
dc.description.references Karakaya, G., Galelli, S., Ahipasaoglu, S. D., and Taormina, R.: Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach, IEEE Transactions on Cybernetics, PP, 1, https://doi.org/10.1109/TCYB.2015.2444435, 2015. a, b, c, d, e es_ES
dc.description.references Keyantash, J. and Dracup, J. A.: The quantification of drought: an evaluation of drought indices, B. Am. Meteorol. Soc., 83, 1167–1180, 2002. a es_ES
dc.description.references Keyantash, J. A. and Dracup, J. A.: An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage, Water Resour. Res., 40, 1–13, 2004. a es_ES
dc.description.references Kummu, M., Ward, P. J., de Moel, H., and Varis, O.: Is physical water scarcity a new phenomenon? Global assessment of water shortage over the last two millennia, Environ. Res. Lett., 5, 034006, 2010. a es_ES
dc.description.references Laaha, G., Gauster, T., Tallaksen, L. M., Vidal, J.-P., Stahl, K., Prudhomme, C., Heudorfer, B., Vlnas, R., Ionita, M., Van Lanen, H. A. J., Adler, M.-J., Caillouet, L., Delus, C., Fendekova, M., Gailliez, S., Hannaford, J., Kingston, D., Van Loon, A. F., Mediero, L., Osuch, M., Romanowicz, R., Sauquet, E., Stagge, J. H., and Wong, W. K.: The European 2015 drought from a hydrological perspective, Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, 2017. a es_ES
dc.description.references Lorenzo-Lacruz, J., Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., García-Ruiz, J. M., and Cuadrat, J. M.: The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain), J. Hydrol., 386, 13–26, https://doi.org/10.1016/j.jhydrol.2010.01.001, 2010. a es_ES
dc.description.references Macian-Sorribes, H. and Pulido-Velazquez, M.: Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a Multireservoir System, J. Water Res. Pl., 143, 04016069, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000712, 2017. a es_ES
dc.description.references MacKay, D. J.: Information theory, inference and learning algorithms, Cambridge university press, 2003. a es_ES
dc.description.references Marcos-Garcia, P., Lopez-Nicolas, A., and Pulido-Velazquez, M.: Combined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Mediterranean basin, J. Hydrol., 554, 292–305, 2017. a es_ES
dc.description.references McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, 179–183, American Meteorological Society Boston, MA, 1993. a, b es_ES
dc.description.references Ministerio del Medio Ambiente: Plan Hidrológico Nacional, ≪BOE≫ núm. 161, de 6 de julio de 2001, 24228–24250, Madrid, Espana, 2000. a es_ES
dc.description.references Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010. a, b, c, d es_ES
dc.description.references Narasimhan, B. and Srinivasan, R.: Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring, Agr. Forest Meteorol., 133, 69–88, https://doi.org/10.1016/j.agrformet.2005.07.012, 2005. a es_ES
dc.description.references Oki, T. and Kanae, S.: Global hydrological cycles and world water resources, Science, 313, 1068–1072, 2006. a es_ES
dc.description.references Palmer, W. C.: Meteorological drought, vol. 30, US Department of Commerce, Weather Bureau Washington, DC, 1965. a es_ES
dc.description.references Pedro-Monzonís, M., Ferrer, J., Solera, A., Estrela, T., and Paredes-Arquiola, J.: Water Accounts and Water Stress Indexes in the European Context of Water Planning: the Jucar River Basin, Procedia Engineer., 89, 1470–1477, 2014. a es_ES
dc.description.references Pedro-Monzonìs, M., Solera, A., Ferrer, J., Estrela, T., and Paredes-Arquiola, J.: A review of water scarcity and drought indexes in water resources planning and management, J. Hydrol., 527, 482–493, https://doi.org/10.1016/j.jhydrol.2015.05.003, 2015. a, b, c, d es_ES
dc.description.references Raskin, P., Gleick, P., Kirshen, P., Pontius, G., and Strzepek, K.: Water futures: Assessment of long-range patterns and problems, Comprehensive assessment of the freshwater resources of the world, SEI, 1997. a es_ES
dc.description.references Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., and Kollat, J. B.: Evolutionary multiobjective optimization in water resources: The past, present, and future, Adv. Water Resour., 51, 438–456, 2013. a es_ES
dc.description.references Rijsberman, F. R.: Water scarcity: fact or fiction?, Agr. Water Manage., 80, 5–22, 2006. a, b es_ES
dc.description.references Scott, D. W.: Multivariate density estimation and visualization, in: Handbook of Computational Statistics, edited by: Gentle, J., Härdle, W., and Mori Y., Springer Handbooks of Computational Statistics, Springer, Berlin, Heidelberg, 549–569, Springer, 2012. a es_ES
dc.description.references Shafer, B. and Dezman, L.: Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas, in: Proceedings of the western snow conference, vol. 50, pp. 164–175, Colorado State University Fort Collins, CO, 1982. a es_ES
dc.description.references Sharma, A.: Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 – A strategy for system predictor identification, J. Hydrol., 239, 232–239, 2000. a es_ES
dc.description.references Sharma, A. and Mehrotra, R.: An information theoretic alternative to model a natural system using observational information alone, Water Resour. Res., 50, 650–660, 2014. a, b es_ES
dc.description.references Spinoni, J., Naumann, G., Vogt, J., and Barbosa, P.: Meteorological Droughts in Europe, Publications Office of the European Union, ISBN-13: 978-92-79-55097-3, 2016. a, b, c, d, e es_ES
dc.description.references Stahl, K., Kohn, I., Blauhut, V., Urquijo, J., De Stefano, L., Acácio, V., Dias, S., Stagge, J. H., Tallaksen, L. M., Kampragou, E., Van Loon, A. F., Barker, L. J., Melsen, L. A., Bifulco, C., Musolino, D., de Carli, A., Massarutto, A., Assimacopoulos, D., and Van Lanen, H. A. J.: Impacts of European drought events: insights from an international database of text-based reports, Nat. Hazards Earth Syst. Sci., 16, 801–819, https://doi.org/10.5194/nhess-16-801-2016, 2016. a es_ES
dc.description.references Staudinger, M., Stahl, K., and Seibert, J.: A drought index accounting for snow, J. Hydrol., 6, 2108–2123, https://doi.org/10.1002/2012WR013085, 2014. a es_ES
dc.description.references Sullivan, C. A., Meigh, J. R., and Giacomello, A. M.: The water poverty index: development and application at the community scale, in: Natural Resources Forum, vol. 27, 189–199, Wiley Online Library, 2003. a es_ES
dc.description.references Tallaksen, L. M. and Van Lanen, H. A.: Hydrological drought: processes and estimation methods for streamflow and groundwater, vol. 48, Elsevier, Amsterdam, NL, 2004. a es_ES
dc.description.references Taormina, R., Galelli, S., Karakaya, G., and Ahipasaoglu, S.: An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models, J. Hydrol., 542, 18–34, 2016. a, b, c es_ES
dc.description.references Van Loon, A. F. and Van Lanen, H. A. J.: A process-based typology of hydrological drought, Hydrol. Earth Syst. Sci., 16, 1915–1946, https://doi.org/10.5194/hess-16-1915-2012, 2012. a, b es_ES
dc.description.references Van Loon, A. F. and Van Lanen, H. A. J.: Making the distinction between water scarcity and drought using an observation-modeling framework, Water Resour. Res., 49, 1483–1502, https://doi.org/10.1002/wrcr.20147, 2013. a es_ES
dc.description.references Vicente-Serrano, S. M. and López-Moreno, J. I.: Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin, Hydrol. Earth Syst. Sci., 9, 523–533, https://doi.org/10.5194/hess-9-523-2005, 2005.  a es_ES
dc.description.references Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index, J. Climate, 23, 1696–1718, 2010. a, b es_ES
dc.description.references Wanders, N., Van Lanen, H. A., and van Loon, A. F.: Indicators for drought characterization on a global scale, Tech. rep., Wageningen Universiteit, 2010. a es_ES
dc.description.references Witten, I. H. and Frank, E.: Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann, Cambridge, USA, 2005. a es_ES
dc.description.references Yang, H., Reichert, P., Abbaspour, K. C., and Zehnder, A. J.: A water resources threshold and its implications for food security, Environ. Sci. Technol., 37, 3048–3054, 2003. a es_ES
dc.description.references Yang, Y. and Pedersen, J. O.: A comparative study on feature selection in text categorization, International Conference of Machine Learning ICML, 97, 412–420, 1997. a es_ES
dc.description.references Zaniolo, M., Giuliani, M., Castelletti, A., and Pulido-Velàzquez, M.: Raw and processed hydro-meteorological variables of Jucar river basin for feature selection, https://doi.org/10.5281/zenodo.1185084, 2018. a, b es_ES
dc.description.references Zargar, A., Sadiq, R., Naser, B., and Khan, F. I.: A review of drought indices, Environ. Rev., 19, 333–349, 2011. a es_ES


This item appears in the following Collection(s)

Show simple item record