4 research outputs found

    Evoluci贸n de la precipitaci贸n en Galicia en el periodo 1961-2006

    Get PDF
    Ponencia presentada en: VI Congreso Internacional de la Asociaci贸n Espa帽ola de Climatolog铆a celebrado en Tarragona del 8 al 11 de octubre de 2008.[ES]El estudio de la variabilidad en precipitaci贸n muestra, a nivel global, resultados menos definidos que en el caso de la temperatura. Esta heterogeneidad se encuentra incluso a nivel nacional, debido a la complejidad de la distribuci贸n espacial de la cantidad y concentraci贸n temporal de la lluvia, haciendo necesarios estudios detallados a nivel local.[EN]Studies about precipitation variability and trends show results less clear than those about temperature. This heterogeneity is also found within Spain, because of the complex spatial distribution of the quantity and temporal concentration of the precipitation. For this reason, detailed local studies are necessary

    Refractivity and refractivity gradient estimation from radar phase data: a least squares based approach

    Get PDF
    Tropospheric refractivity, related to temperature, pressure, and humidity, is an interesting parameter for weather analysis, prediction, and study of climate trends. It has been shown to be useful for the detection and forecast of convective events. It has already been demonstrated that tropospheric refractivity can be estimated from radar phase measurements. In this article, a nonlinear least squares based approach for the estimation of the tropospheric refractivity that simultaneously provides the estimates of the refractivity vertical gradient is presented. A significant improvement of the presented technique is that it allows estimation of the refractivity over any terrain orography, flat, or hilly. Furthermore, the method developed can be implemented on klystron as well as on magnetron-based radars. Results for both radar types, at S- and C-bands, located over flat and hilly terrain show the potential of the method.European Climate, Infrastructure and Environment Executive Agency | Ref. Life16 Env/ES/000559Xunta de Galicia | Ref. GRC2019/02

    MERLIN: A new tool for flood hazard forecasting at the Galicia-Costa Hydrographic Demarcation

    Get PDF
    [EN] This article presents MERLIN, a tool for flood hazard evaluation, which forecasts discharges and water depths in flood prone areas of the Galicia Costa district. The warning system operates in two stages. During the hindcast stage, hydrological models of the basins included in the system assimilate hydro-meteorological data in order to characterize soil infiltration capacity. During the forecast stage, hydrological models are fed with meteorological predictions and discharge forecasts along the basins. Forecasted discharges define boundary conditions of hydraulic models, which compute the flood extent and the water depths over the upcoming days. The performance of MERLIN was evaluated in 4 areas using discharge data from the winter months of 2019-2020. Results proved MERLIN鈥檚 ability of predicting the discharges observed afterwards.[ES] Este art铆culo presenta MERLIN, una nueva herramienta para estimar el riesgo de inundaciones a partir de predicciones de caudales y calados en 脕reas de Riesgo Potencial Significativo de Inundaciones (ARPSIS) de la demarcaci贸n hidrogr谩fica Galicia-Costa. El sistema MERLIN opera en dos fases. Durante una primera fase de inicializaci贸n, modelos hidrol贸gicos de las cuencas incluidas en el sistema asimilan datos hidro-meteorol贸gicos para caracterizar la capacidad de infiltraci贸n del terreno. Durante la fase de predicci贸n, los modelos hidrol贸gicos previamente inicializados se alimentan con predicciones meteorol贸gicas para determinar los caudales esperados durante los pr贸ximos d铆as. Las predicciones de caudal alimentan a modelos hidr谩ulicos de las ARPSIS que determinan los calados y la extensi贸n de zonas inundadas. El funcionamiento de MERLIN se evalu贸 en 4 cuencas piloto a partir de los caudales registrados durante los temporales del invierno del 2019-2020, mostrando una buena capacidad de predecir los valores posteriormente observados.El desarrollo del sistema MERLIN y el resto de trabajos presentados en este art铆culo fue posible gracias a la financiaci贸n aportada por Augas de Galicia dentro del Convenio de colaboraci贸n entre Augas de Galicia e a Fundaci贸n de Enxe帽er铆a Civil de Galicia para a mellora do sistema de alerta temper谩 de risco de inundaci贸n na demarcaci贸n hidrogr谩fica Galicia-costa.Fraga, I.; Cea, L.; Puertas, J.; Mosqueira, G.; Quinteiro, B.; Botana, S.; Fern谩ndez, L.... (2021). MERLIN: Una nueva herramienta para la predicci贸n del riesgo de inundaciones en la demarcaci贸n hidrogr谩fica Galicia-Costa. Ingenier铆a del agua. 25(3):215-227. https://doi.org/10.4995/ia.2021.15565OJS215227253Alvarez-Garreton, C., Ryu, D., Western, A.W., Su, C.H., Crow, W.T., Robertson, E., Leahy, C. 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences, 19(4), 1659-1676. https://doi.org/10.5194/hess-19-1659-2015Arnell, N.W., Gosling, S.N. 2016. The impacts of climate change on river flood risk at the global scale. Climatic Change, 134(3), 387-401. https://doi.org/10.1007/s10584-014-1084-5Bennett, T.H., Peters, J.C. 2000. Continuous soil moisture accounting in the hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS). Building partnerships, 1-10.Berghuijs, W.R., Aalbers, E.E., Larsen, J.R., Trancoso, R., Woods, R.A. 2017. Recent changes in extreme floods across multiple continents. Environmental Research Letters, 12(11), 114035. https://doi.org/10.1088/1748-9326/aa8847Blad茅, E., Cea, L., Corestein, G., Escolano, E., Puertas, J., V谩zquez-Cend贸n, E., Dolz, J., Coll, A. 2014. Iber: herramienta de simulaci贸n num茅rica del flujo en r铆os. Revista Internacional de Metodos Numericos en Ingenier铆a, 30(1), 1-10. https://doi.org/10.1016/j.rimni.2012.07.004Carracedo, P. 2003. Acoplamiento de un modelo hidrodin谩mico de escala global con uno de escala regional para Galicia. Revista Real Academia Galega de Ciencias, 22, 85.Cea, L., Fraga, I. 2018. Incorporating antecedent moisture conditions and intraevent variability of rainfall on flood frequency analysis in poorly gauged basins. Water Resources Research, 54, 8774-8791. https://doi.org/10.1029/2018WR023194Cronshey, R. 1986. Urban hydrology for small watersheds. US Department of Agriculture Soil Conservation Service Engineering Division.Garc铆a-Feal, O., Gonz谩lez-Cao, J., G贸mez-Gesteira, M., Cea, L., Dom铆nguez, J., Formella, A. 2018. An accelerated tool for flood modelling based on Iber. Water, 10(10) 1459. https://doi.org/10.3390/w10101459Hossain, F., Siddique-E-Akbor, A.H.M., Yigzaw, W., Shah-Newaz, S., Hossain, M., Mazumder, L.C., Turk, F.J. 2014. Crossing the "valley of death": lessons learned from implementing an operational satellite-based flood forecasting system. Bulletin of the American Meteorological Society, 95(8), 1201-1207. https://doi.org/10.1175/BAMS-D-13-00176.1IPCC (2018). Global warming of 1.5掳C. An IPCC Special Report on the impacts of global warming of 1.5掳C above pre-industrial levels and related global greenhouse gas emission pathways in the context of strengthening the global response to the threat of climate change sustainable development and efforts to eradicate poverty. In Press.Jewell, S.A., Gaussiat, N. 2015. An assessment of kriging-based rain-gauge-radar merging techniques. Quarterly Journal of the Royal Meteorological Society, 141(691), 2300-2313. https://doi.org/10.1002/qj.2522Kasiviswanathan, K.S., He, J., Sudheer, K.P., Tay, J.H. 2016. Potential application of wavelet neural network ensemble to forecast streamflow for flood management. Journal of hydrology, 536, 161-173. https://doi.org/10.1016/j.jhydrol.2016.02.044Kellens, W., Vanneuville, W., Verfaillie, E., Meire, E., Deckers, P., De Maeyer, P. 2013. Flood risk management in Flanders: past developments and future challenges. Water Resources Management, 27(10), 3585-3606. https://doi.org/10.1007/s11269-013-0366-4Krajewski, W.F., Ceynar, D., Demir, I., Goska, R., Kruger, A., Langel, C., Small, S.J. 2017. Real-time flood forecasting and information system for the state of Iowa. Bulletin of the American Meteorological Society, 98(3), 539-554. https://doi.org/10.1175/BAMS-D-15-00243.1Kumar, M., Sahay, R.R. 2018. Wavelet-genetic programming conjunction model for flood forecasting in rivers. Hydrology Research, 49(6), 1880-1889. https://doi.org/10.2166/nh.2018.183Massari, C., Brocca, L., Tarpanelli, A., Moramarco, T. 2015. Data assimilation of satellite soil moisture into rainfall-runoff modelling: A complex recipe?. Remote Sensing, 7(9), 11403-11433. https://doi.org/10.3390/rs70911403McKay, M.D., Beckman, R.J., Conover, W.J. 1979 A Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239-245.Mure-Ravaud, M., Binet, G., Bracq, M., Perarnaud, J.J., Fradin, A., Litrico, X. 2016. A web based tool for operational realtime flood forecasting using data assimilation to update hydraulic states. Environmental Modelling and Software, 84, 35-49. https://doi.org/10.1016/j.envsoft.2016.06.002Naranjo, L., Taboada, J.J., Lage, A., Sals贸n, S., Montero, P., Souto, J.A., P茅rez-Mu帽uzuri, V. 2001. Estudio de las an贸malas condiciones meteorol贸gicas sobre Galicia durante el oto帽o de los a帽os 2000 y 2001. Revista Real Academia Galega de Ciencias, 20, 113-133Nguyen, P., Thorstensen, A., Sorooshian, S., Hsu, K., AghaKouchak, A., Sanders, B., Koren, V., Cui, Z., Smith, M. 2016. A high resolution coupled hydrologic-hydraulic model (HiResFlood-UCI) for flash flood modeling. Journal of Hydrology, 541, 401-420. https://doi.org/10.1016/j.jhydrol.2015.10.047Razmkhah, H. 2016. Comparing performance of different loss methods in rainfall-runoff modeling. Water resources, 43(1), 207-224. https://doi.org/10.1134/S0097807816120058Rosburg, T.T., Nelson, P.A., Bledsoe, B.P. 2017. Effects of urbanization on flow duration and stream flashiness: a case study of Puget Sound streams, western Washington, USA. Journal of the American Water Resources Association, 53(2), 493-507. https://doi.org/10.1111/1752-1688.12511Sanz-Ramos, M., Amengual, A., Blad茅 i Castellet, E., Romero, R., Roux, H. 2018. Flood forecasting using a coupled hydrological and hydraulic model (based on FVM) and highresolution meteorological model. Proceedings of River Flow 2018-Ninth International Conference on Fluvial Hydraulics (pp. 1-8) Lyon France. https://doi.org/10.1051/e3sconf/20184006028Scharffenberg, W.A, Fleming, M.J. 2006. Hydrologic modeling system HEC-HMS: User's manual. US Army Corps of Engineers Hydrologic Engineering Center.Shchepetkin, A.F., McWilliams, J.C. 2005. The regional oceanic modeling system (ROMS): a split-explicit free-surface topographyfollowing-coordinate oceanic model. Ocean Modelling, 9(4), 347-404. https://doi.org/10.1016/j.ocemod.2004.08.002Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., Powers, J.G. 2008. A description of the Advanced Research WRF version 3. NCAR Technical note-475+ STR.Sopelana, J., Cea, L., Ruano, S. 2018. A continuous simulation approach for the estimation of extreme flood inundation in coastal river reaches affected by meso and macro tides. Natural Hazards, 93(3) 1337-1358. https://doi.org/10.1007/s11069-018-3360-6Thielen, J., Bartholmes, J., Ramos, M. H., & Roo, A. D. 2009. The European flood alert system-part 1: concept and development. Hydrology and Earth System Sciences, 13(2), 125-140. https://doi.org/10.5194/hess-13-125-2009Thiemig, V., Bisselink, B., Pappenberger, F., Thielen, J. 2015. A pan-African medium-range ensemble flood forecast system. Hydrology and Earth System Sciences, 19(8), 3365-3385. https://doi.org/10.5194/hess-19-3365-2015U.S. Department of Agriculture, Natural Resources Conservation Service. 2010. National Engineering Handbook, Washington, DCVen芒ncio, A., Montero, P., Costa, P., Regueiro, S., Brands, S., Taboada, J. 2019. An Integrated Perspective of the Operational Forecasting System in R铆as Baixas (Galicia, Spain) with Observational Data and End-Users. In International Conference on Computational Science (pp. 229-239). Springer, Cham. https://doi.org/10.1007/978-3-030-22747-0_18Wallemarq, P., Below, R., McLean, D. 2018. UNISDR and CRED report: Economic Losses, Poverty & Disasters (1998-2017).Wanders, N., Karssenberg, D., Roo, A.D., De Jong, S.M., Bierkens, M.F.P. 2014. The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrology and Earth System Sciences, 18(6), 2343-2357. https://doi.org/10.5194/hess-18-2343-2014Weerts, A.H., Winsemius, H.C., Verkade, J.S. 2011. Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrology and Earth System Sciences, 15(1), 255-265. https://doi.org/10.5194/hess-15-255-2011Xia, X., Liang, Q., Ming, X. 2019. A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS). Advances in Water Resources, 132, 103392. https://doi.org/10.1016/j.advwatres.2019.10339

    Modelo matem谩tico para simulaci贸n num茅rica espacio-temporal de intensidades de lluvia en episodios torrenciales de car谩cter convectivo

    Full text link
    Los modelos hidrol贸gicos distribuidos requieren como entrada registros de lluvia de alta resoluci贸n, esto hace que exista un especial inter茅s por la disponibilidad de modelos espacio-temporales de lluvia, los cuales son capaces de generar, mediante simulaci贸n num茅rica, campos de intensidad de precipitaci贸n muy realistas. Cuando se trata de simular eventos de marcado car谩cter convectivo (de la clase de episodios que causan sistem谩ticas inundaciones en zonas como las del mediterr谩neo espa帽ol), los modelos espacio-temporales m谩s realistas y los que mejor se adaptan, son los basados en procesos de punteo. Estos modelos consideran la celda de lluvia como elemento fundamental. En esta tesis, tras una revisi贸n detallada de la literatura, se ha desarrollado un modelo que incorpora nuevas caracter铆sticas relevantes a la hora de describir el proceso de precipitaci贸n. Estas nuevas caracter铆sticas incluyen una funci贸n mejorada para el nacimiento de celdas en el tiempo (capaz de adaptarse a las distintas formas que presenta la curva normalizada acumulada a lo largo del episodio) y una funci贸n tipo gamma para representar la evoluci贸n temporal de la intensidad de la celda de lluvia, la cual describe de una forma realista las distintas fases por las que atraviesa una celda de lluvia convectiva: crecimiento, madurez y finalmente un decaimiento gradual. As铆 mismo, se han obtenido las expresiones te贸rias de los momentos de primer y segundo orden, las cuales permiten estimar los par谩metros del modelo y, por tanto, su uso en el contexto de las aplicaciones hidrol贸gicas. Los siete par谩metros de los que consta el modelo se han estimado, por el m茅todo de los momentos, para treinta episodios registrados por el SAIH (Sistema Autom谩tico de Informaci贸n Hidrol贸gica) de la Confederaci贸n Hidrol贸gica del J煤car (Valencia-Espa帽a) durante los a帽os 1991-2000. Estos episodios se detallan, tanto desde el punto de vista meteorol贸gico como desde el punto de vista de los efectos ocasionales, en un ap茅ndiceSals贸n Casado, S. (2001). Modelo matem谩tico para simulaci贸n num茅rica espacio-temporal de intensidades de lluvia en episodios torrenciales de car谩cter convectivo [Tesis doctoral no publicada]. Universitat Polit猫cnica de Val猫ncia. https://doi.org/10.4995/Thesis/10251/4622Palanci
    corecore