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dc.contributor.author | Pansa, Alessandro | es_ES |
dc.contributor.author | Butera, Ilaria | es_ES |
dc.contributor.author | Gómez-Hernández, J. Jaime | es_ES |
dc.contributor.author | Vigna, Bartolomeo | es_ES |
dc.date.accessioned | 2023-10-19T18:01:45Z | |
dc.date.available | 2023-10-19T18:01:45Z | |
dc.date.issued | 2023-01 | es_ES |
dc.identifier.issn | 1436-3240 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/198415 | |
dc.description.abstract | [EN] Can the ensemble smoother with multiple data assimilation be used to predict discharge in an Alpine karst aquifer? The answer is yes, at least, for the Bossea aquifer studied. The ensemble smoother is used to fit a unit hydrograph simultaneously with other parameters in a hydrologic model, such as base flow, infiltration coefficient, or snow melting contribution. The fitting uses observed discharge flow rates, daily precipitations, and temperatures to define the model parameters. The data assimilation approach gives excellent results for fitting individual events. After the analysis of 27 such events, two average models are defined to be used to predict flow discharge from precipitation and temperature, one model for prediction during spring (when snow melting has an impact) and another one during autumn, yielding acceptable results, particularly for the fall rainfall events. The lesser performance for the spring events may indicate that the snow melting approximation needs to be revised. The results also show that the parameterization of the infiltration coefficient needs further exploration. Overall, the main conclusion is that the ensemble smoother could be used to define a characteristic "signature" of a karst aquifer to be used in forecast analyses. The reasons for using the ensemble smoother instead of other stochastic approaches are that it is easy to use and explain and provides an estimation of the uncertainty about the predictions. | es_ES |
dc.description.sponsorship | J. Jaime Gomez-Hernandez acknowledges grant PID2019109131RB-I00 funded by MCIN/AEI/10.13039/501100011033. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer-Verlag | es_ES |
dc.relation.ispartof | Stochastic Environmental Research and Risk Assessment | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject.classification | INGENIERIA HIDRAULICA | es_ES |
dc.title | Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s00477-022-02287-y | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109131RB-I00/ES/APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PRE2020-093145//APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports | es_ES |
dc.description.bibliographicCitation | Pansa, A.; Butera, I.; Gómez-Hernández, JJ.; Vigna, B. (2023). Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation. Stochastic Environmental Research and Risk Assessment. 37(1):185-201. https://doi.org/10.1007/s00477-022-02287-y | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s00477-022-02287-y | es_ES |
dc.description.upvformatpinicio | 185 | es_ES |
dc.description.upvformatpfin | 201 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 37 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.pasarela | S\470143 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.description.references | Anderson JL (2007) An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus A Dyn Meteorol Oceanogr 59:210–224 | es_ES |
dc.description.references | Anderson JL, Anderson SL (1999) A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon Weather Rev 127:2741–2758 | es_ES |
dc.description.references | Antonellini M, Nannoni A, Vigna B, Waele JD (2019) Structural control on karst water circulation and speleogenesis in a lithological contact zone: the Bossea cave system (Western Alps, Italy). Geomorphology 345:106832 | es_ES |
dc.description.references | Banzato C, Butera I, Revelli R, Vigna B (2017) Reliability of the VESPA index in identifying spring vulnerability level. J Hydrol Eng 22(6):04017008 | es_ES |
dc.description.references | Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438(7066):303–309 | es_ES |
dc.description.references | Bauser HH, Berg D, Klein O, Roth K (2018) Inflation method for ensemble Kalman filter in soil hydrology. Hydrol Earth Syst Sci 22:4921–4934 | es_ES |
dc.description.references | Bittner D, Richieri B, Chiogna G (2021) Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system. Hydrogeol J 29(7):2363–2379 | es_ES |
dc.description.references | Butera I, Gómez-Hernández JJ, Nicotra S (2021) Contaminant-source detection in a water distribution system using the ensemble Kalman filter. J Water Resour Plan Manag 147(7):04021029 | es_ES |
dc.description.references | Chen Z, Xu T, Gómez-Hernández JJ, Zanini A (2021) Contaminant spill in a sandbox with non-gaussian conductivities: simultaneous identification by the restart normal-score ensemble Kalman filter. Math Geosci 53(7):1587–1615 | es_ES |
dc.description.references | Civita M, Gregoretti F, Morisi A, Olivero G, Peano G, Vigna B, Villavecchia E, Vittone F (1990) Atti della stazione scientifica di della Grotta di Bossea, vol 23. Gruppo Speleologico Alpi Marittime C.A.I. Cuneo, Savigliano | es_ES |
dc.description.references | Emerick AA (2019) Analysis of geometric selection of the data-error covariance inflation for ES-MDA. J Pet Sci Eng 182:106168 | es_ES |
dc.description.references | Emerick AA, Reynolds AC (2013) Ensemble smoother with multiple data assimilation. Comput Geosci 55:3–15 | es_ES |
dc.description.references | Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using monte Carlo methods to forecast error statistics. J Geophys Res 99(C5):10143 | es_ES |
dc.description.references | Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367 | es_ES |
dc.description.references | Evensen G (2018) Analysis of iterative ensemble smoothers for solving inverse problems. Comput Geosci 22(3):885–908 | es_ES |
dc.description.references | Gassman P, Reyes M, Green C, Arnold J (2007) Soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250 | es_ES |
dc.description.references | Gómez-Hernández JJ, Xu T (2022) Contaminant source identification in aquifers: a critical view. Math Geosci 54(2):437–458 | es_ES |
dc.description.references | Hartmann A, Goldscheider N, Wagener T, Lange J, Weiler M (2015) Karst water resources in a changing world: review of hydrological modeling approaches. Rev Geophys 22(7):2018–242 | es_ES |
dc.description.references | Jódar J, González-Ramón A, Martos-Rosillo S, Heredia J, Herrera C, Urrutia J, Caballero Y, Zabaleta A, Antigüedad I, Custodio E, Lambán LJ (2020) Snowmelt as a determinant factor in the hydrogeological behaviour of high mountain karst aquifers: the garcés karst system, Central Pyrenees (Spain). Sci Total Environ 748:141363 | es_ES |
dc.description.references | Khaki M, Ait-El-Fquih B, Hoteit I (2020) Calibrating land hydrological models and enhancing their forecasting skills using an ensemble kalman filter with one-step-ahead smoothing. J Hydrol 584:124708 | es_ES |
dc.description.references | Kuichling E (1889) The relation between the rainfall and the discharge of sewers in populous districts. Trans ASCE 20:1–56 | es_ES |
dc.description.references | Li N, McLaughlin D, Kinzelbach W, Li W, Dong X (2015) Using an ensemble smoother to evaluate parameter uncertainty of an integrated hydrological model of Yanqi basin. J Hydrol 529:146–158 | es_ES |
dc.description.references | Liang X, Zheng X, Zhang S, Wu G, Dai Y, Li Y (2012) Maximum likelihood estimation of inflation factors on error covariance matrices for ensemble Kalman filter assimilation. Q J Royal Meteorol Soc 138(662):263–273 | es_ES |
dc.description.references | Lucianetti G, Penna D, Mastrorillo L, Mazza R (2020) The role of snowmelt on the spatio-temporal variability of spring recharge in a dolomitic mountain group, Italian Alps. Water 12(8):2256 | es_ES |
dc.description.references | Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290 | es_ES |
dc.description.references | Oaida CM, Reager JT, Andreadis KM, David CH, Levoe SR, Painter TH, Bormann KJ, Trangsrud AR, Girotto M, Famiglietti JS (2019) A high-resolution data assimilation framework for snow water equivalent estimation across the Western United States and validation with the airborne snow observatory. J Hydrometeorol 20(3):357–378 | es_ES |
dc.description.references | Rafiee J, Reynolds AC (2017) Theoretical and efficient practical procedures for the generation of inflation factors for ES-MDA. Inverse Probl 33(11):115003 | es_ES |
dc.description.references | Sherman L (1932) Stream flow from rainfall by the unit graph method. Engineering News Record 501–502 | es_ES |
dc.description.references | Shokri A, Walker J, van Dijk A, Pauwels V (2018) Performance of different ensemble kalman filter structures to assimilate grace terrestrial water storage estimates into a high-resolution hydrological model: a synthetic study. Water Resour Res 54(11):8931–8951 | es_ES |
dc.description.references | Sun Y, Bao W, Valk K, Brauer CC, Sumihar J, Weerts AH (2020) Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter. Water Resour Res 56(8):e2020WR027468 | es_ES |
dc.description.references | Todaro V, D’Oria M, Tanda MG, Gómez-Hernández JJ (2019) Ensemble smoother with multiple data assimilation for reverse flow routing. Comput Geosci 131:32–40 | es_ES |
dc.description.references | Uwamahoro S, Liu T, Nzabarinda V, Habumugisha JM, Habumugisha T, Harerimana B, Bao A (2021) Modifications to snow-melting and flooding processes in the hydrological model—a case study in Issyk-Kul, Kyrgyzstan. Atmosphere 12(12):1580 | es_ES |
dc.description.references | Van Leeuwen PJ, Evensen G (1996) Data assimilation and inverse methods in terms of a probabilistic formulation. Mon Weather Rev 124(12):2898–2913 | es_ES |
dc.description.references | Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J Atmos Sci 60(9):1140–1158 | es_ES |
dc.description.references | White WB (2003) Conceptual models for carbonate aquifers. Speleogenesis 50(2):180–186 | es_ES |
dc.description.references | Xu T, Gómez-Hernández JJ (2016) Joint identification of contaminant source location, initial release time and initial solute concentration in an aquifer via enseble Kalman filtering. Water Resour Res 52(8):6587–6595 | es_ES |
dc.description.references | Xu T, Gómez-Hernández JJ, Zhou H, Li L (2013) The power of transient piezometric head data in inverse modeling: an application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Adv Water Resour 54:100–118 | es_ES |