Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty for very high return period quantiles is considered acceptable, even for the PMF estimate. From the robustness analysis, the EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario. In this scenario and if there is an upper limit, the GEV quantile estimates are clearly unacceptable

Assessing the risk of vehicle instability due to flooding

[EN] Flooding can destabilize vehicles which might, in turn, exacerbate the negative effects of floods when vehicles are swept away by flows, and lead to economic loss and fatalities. In order to suitably manage floods, it is necessary to determine the risk of instability to which vehicles in flood-prone areas are subject. This paper develops a methodology to estimate this risk based on the characteristics of floods and the vehicle fleet located in potential flood-prone areas. This risk is determined by the statistical integral of the instability hazard and vehicles' vulnerability. The instability hazard was established by a stability function of partially submerged cars and flood frequency, while vulnerability was calculated by combining the susceptibility and exposure of cars. Our methodology was applied in the towns of Alfafar and Massanassa (Spain). It found that the annualized instability risk due to flooding could be relatively high on most streets and roads, with values reaching the order of 8.4 at-risk vehicles per hectare/year.Departamento Administrativo de Ciencia, Tecnologia e Innovacion COLCIENCIAS (Colombia) call, Grant/Award Number: 728-2015; Spanish Ministry of Science and Innovation through the research project TETISCHANGE, Grant/Award Number: RTI2018-093717-B-I00Bocanegra, RA.; Francés, F. (2021). Assessing the risk of vehicle instability due to flooding. Journal of Flood Risk Management. 14(4):1-15. https://doi.org/10.1111/jfr3.12738S11514

Review and analysis of vehicle stability models during floods and proposal for future improvements

This is the peer reviewed version of the following article: Bocanegra, RA, Vallés-Morán, FJ, Francés, F. Review and analysis of vehicle stability models during floods and proposal for future improvements. J Flood Risk Management. 2020; 13 ( Suppl. 1):e12551, which has been published in final form at https://doi.org/10.1111/jfr3.12551. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Flood water can affect vehicles significantly, which in turn can increase the negative effects of floods as vehicles are washed away by the flow and become a form of debris. In cities, most fatalities during floods occur inside vehicles. Consequently, it is necessary to establish thresholds for vehicle stability during this type of event to provide information necessary for flood risk management. This article analyses the available stability models developed over recent years to determine such thresholds. The stability models were grouped according to the way in which they approached car watertightness and the stability thresholds proposed by each of them were compared. It was found that these thresholds vary over a relatively wide range. Additionally, the experimental data were compared with the results provided by these studies leading to the conclusion that several of the stability models analysed do not fit measured data well. New research is required to overcome the simplifications made by the state-of-the-art models and to try to standardise the decision criteria which should be adopted to define stability thresholds for vehicles of different characteristics.Departamento Administrativo de Ciencia, Tecnologia e Innovacion COLCIENCIAS (Colombia) call 728-2015; Spanish Ministry of Science and Innovation through the research project TETISCHANGE, Grant/Award Number: RTI2018-093717-B-I00.Bocanegra, RA.; Vallés-Morán, FJ.; Francés, F. (2020). 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WRL Report No. 100. University of New South Wales. Sydney Australia.Cox R. J. Shand T. D. &Blacka M. J.(2010). Appropriate safety criteria for people in floods.Australian Rainfall and Runoff. WRL Research Report 240. Report for Institution of Engineers Australia.DROBOT, S., BENIGHT, C., & GRUNTFEST, E. (2007). Risk factors for driving into flooded roads. Environmental Hazards, 7(3), 227-234. doi:10.1016/j.envhaz.2007.07.003FitzGerald, G., Du, W., Jamal, A., Clark, M., & Hou, X.-Y. (2010). Flood fatalities in contemporary Australia (1997-2008). Emergency Medicine Australasia, 22(2), 180-186. doi:10.1111/j.1742-6723.2010.01284.xGordon A. D. &Stone P. B.(1973).Car stability on road causeways. WRL Technical Report No. 73/12. University of New South Wales. Sydney Australia.Jonkman, S. N., & Kelman, I. (2005). An Analysis of the Causes and Circumstances of Flood Disaster Deaths. Disasters, 29(1), 75-97. doi:10.1111/j.0361-3666.2005.00275.xKellar, D. M. M., & Schmidlin, T. W. (2012). 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Hydrological post-processing based on approximate Bayesian computation (ABC)

[EN] This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: (1) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and (2) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios.This study was partially supported by the Departamento del Huila Scholarship Program No. 677 (Colombia) and Colciencias, by the Spanish Research Project TETIS-MED (ref. CGL2014-58127-C3-3-R) and TETIS-CHANGE (ref.RTI2018-093717-B-I00). Also, G. Adelfio's research has been supported by the national grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN-2015 program, "Complex space-time modelling and functional analysis for probabilistic forecast of seismic events'. The authors also wish to thank the editor and the two anonymous reviewers for their thoughtful comments for the revision of the manuscript.Romero-Cuellar, J.; Abbruzzo, A.; Adelfio, G.; Francés, F. (2019). Hydrological post-processing based on approximate Bayesian computation (ABC). Stochastic Environmental Research and Risk Assessment. 33(7):1361-1373. https://doi.org/10.1007/s00477-019-01694-yS13611373337Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162(4):2025–2035Blackwell D, Dubins L (1962) Merging of opinions with increasing information. Ann Math Stat 33(3):882–886Bogner K, Liechti K, Zappa M (2016) Post-processing of stream flows in Switzerland with an emphasis on low flows and floods. Water 8(4):115Brown JD, Seo D-J (2010) A nonparametric postprocessor for bias correction of hydrometeorological and hydrologic ensemble forecasts. 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New Approach to Estimate Extreme Flooding Using Continuous Synthetic Simulation Supported by Regional Precipitation and Non-Systematic Flood Data

[EN] Stochastic weather generators combined with hydrological models have been proposed for continuous synthetic simulation to estimate return periods of extreme floods. Yet, this approach relies upon the length and spatial distribution of the precipitation input data series, which often are scarce, especially in arid and semiarid regions. In this work, we present a new approach for the estimation of extreme floods based on the continuous synthetic simulation method supported with inputs of (a) a regional study of extreme precipitation to improve the calibration of the weather generator (GWEX), and (b) non-systematic flood information (i.e., historical information and/or palaeoflood records) for the validation of the generated discharges with a fully distributed hydrological model (TETIS). The results showed that this complementary information of extremes allowed for a more accurate implementation of both the weather generator and the hydrological model. This, in turn, improved the flood quantile estimates, especially for those associated with return periods higher than 50 years but also for higher quantiles (up to approximately 500 years). Therefore, it has been proved that continuous synthetic simulation studies focused on the estimation of extreme floods should incorporate a generalized representation of regional extreme rainfall and/or non-systematic flood data, particularly in regions with scarce hydrometeorological records.This research was funded by the Spanish Ministry of Science and Innovation through the research projects TETISCHANGE (RTI2018-093717-B-100) and EPHIMED (CGL2017-86839-C3-1-R), both cofounded with FEDER European funds.Beneyto, C.; Aranda Domingo, JÁ.; Benito, G.; Francés, F. (2020). New Approach to Estimate Extreme Flooding Using Continuous Synthetic Simulation Supported by Regional Precipitation and Non-Systematic Flood Data. Water. 12(11):1-16. https://doi.org/10.3390/w12113174S1161211Stedinger, J. 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Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain. Hydrology and Earth System Sciences, 17(2), 479-494. doi:10.5194/hess-17-479-2013Boughton, W., & Droop, O. (2003). Continuous simulation for design flood estimation—a review. Environmental Modelling & Software, 18(4), 309-318. doi:10.1016/s1364-8152(03)00004-5Soltani, A., & Hoogenboom, G. (2003). Minimum data requirements for parameter estimation of stochastic weather generators. Climate Research, 25, 109-119. doi:10.3354/cr025109Verdin, A., Rajagopalan, B., Kleiber, W., & Katz, R. W. (2014). Coupled stochastic weather generation using spatial and generalized linear models. Stochastic Environmental Research and Risk Assessment, 29(2), 347-356. doi:10.1007/s00477-014-0911-6Cavanaugh, N. R., Gershunov, A., Panorska, A. K., & Kozubowski, T. J. (2015). The probability distribution of intense daily precipitation. 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Flood frequency analysis with systematic and historical or paleoflood data based on the two-parameter general extreme value models. Water Resources Research, 30(6), 1653-1664. doi:10.1029/94wr00154Stedinger, J. R., & Baker, V. R. (1987). Surface water hydrology: Historical and paleoflood information. Reviews of Geophysics, 25(2), 119. doi:10.1029/rg025i002p00119Simón, J. L., Pérez-Cueva, A. J., & Calvo-Cases, A. (2013). Tectonic beheading of fluvial valleys in the Maestrat grabens (eastern Spain): Insights into slip rates of Pleistocene extensional faults. Tectonophysics, 593, 73-84. doi:10.1016/j.tecto.2013.02.026Camarasa Belmonte, A. M., & Segura Beltrán, F. (2001). Flood events in Mediterranean ephemeral streams (ramblas) in Valencia region, Spain. CATENA, 45(3), 229-249. doi:10.1016/s0341-8162(01)00146-1Llasat, M. C., & Puigcerver, M. (1990). Cold air pools over Europe. Meteorology and Atmospheric Physics, 42(3-4), 171-177. doi:10.1007/bf01314823Herrera, S., Fernández, J., & Gutiérrez, J. M. (2015). Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: assessing the effect of the interpolation methodology. International Journal of Climatology, 36(2), 900-908. doi:10.1002/joc.4391Machado, M. J., Medialdea, A., Calle, M., Rico, M. T., Sánchez-Moya, Y., Sopeña, A., & Benito, G. (2017). Historical palaeohydrology and landscape resilience of a Mediterranean rambla (Castellón, NE Spain): Floods and people. Quaternary Science Reviews, 171, 182-198. doi:10.1016/j.quascirev.2017.07.014Francés, F., Vélez, J. I., & Vélez, J. J. (2007). Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332(1-2), 226-240. doi:10.1016/j.jhydrol.2006.06.032Papastathopoulos, I., & Tawn, J. A. (2013). Extended generalised Pareto models for tail estimation. 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Sample Uncertainty Analysis of Daily Flood Quantiles Using a Weather Generator

[EN] The combined use of weather generators (WG) and hydrological models (HM) in what is called synthetic continuous simulation (SCS) has become a common practice for carrying out flood studies. However, flood quantile estimations are far from presenting relatively high confidence levels, which mostly relate to the uncertainty of models¿ input data. The main objective of this paper is to assess how different precipitation regimes, climate extremality, and basin hydrological characteristics impact the uncertainty of daily flood quantile estimates obtained by SCS. A Monte Carlo simulation from 18 synthetic populations encompassing all these scenarios was performed, evaluating the uncertainty of the simulated quantiles. Additionally, the uncertainty propagation of the quantile estimates from the WG to the HM was analyzed. General findings show that integrating the regional precipitation quantile (XT,P) in the WG model calibration clearly reduces the uncertainty of flood quantile estimates, especially those near the regional XT,P. Basin size, climate extremality, and the hydrological characteristics of the basin have been proven not to affect flood quantiles¿ uncertainty substantially. Furthermore, it has been found that uncertainty clearly increases with the aridity of the climate and that the HM is not capable of buffering the uncertainty of flood quantiles, but rather increases it.The authors thank AEMET and the UC for the data provided to carry out this work (Spain02 dataset). This work was supported by the Spanish Ministry of Science and Innovation through the research projects TETISCHANGE (RTI2018-093717-B-100) and TETISPREDICT (PID2022-141631OBI00). Funding for the open-access charge has been provided by Universitat Politècnica de ValènciaBeneyto, C.; Vignes, G.; Aranda Domingo, JÁ.; Francés, F. (2023). Sample Uncertainty Analysis of Daily Flood Quantiles Using a Weather Generator. Water. 15(19):1-16. https://doi.org/10.3390/w15193489116151

Assessing Anthropogenic Dynamics in Megacities from the Characterization of Land Use/Land Cover Changes: The Bogotá Study Case

[EN] Usually, megacities expand without proper planning in a context of demographic growth and are increasingly dependent on the natural resources related to the occupied area. This is a major challenge for the sustainable management of these territories, justifying the need for a better knowledge of land use/land cover (LULC) distribution and characteristics to observe spatial anthropogenic dynamics. In this study, the Bogota river basin and the Bogota megacity were analyzed as a case study. The main objective of this work was to analyze the historical LULC dynamics from 1985 to 2014. Reliable forecasting scenarios were developed using the Land Change Modeler to support sustainable management and planning. Results show an expansion of the Bogota megacity toward the Northeast and an increase of urban areas within the basin. These changes implied a loss of 58% of forest surface, a strategic ecosystem, from 1985 to 2014. This dynamic is expected to continue, with a 50% increase of urban areas between 2012 to 2050, thus the megacity and neighbor cities potentially become an "urban continuum". A replacement of crop and pasture lands near the city is expected, even though Bogota lands are among the best agricultural lands in the Andean region of Colombia.This research was funded by the SANTO TOMAS UNIVERSITY (Colombia) and the Ministry of Science and Innovation of Spain through the research projects TETISMED (CGL2014-58127-C3-3-R) and TETISCHANGE (ref RTI2018-093717-B-I00).Romero, CP.; García-Arias, A.; Dondeynaz, C.; Francés, F. (2020). Assessing Anthropogenic Dynamics in Megacities from the Characterization of Land Use/Land Cover Changes: The Bogotá Study Case. Sustainability. 12(9):1-21. https://doi.org/10.3390/su12093884121129Aguilar, A. G., Ward, P. M., & Smith Sr, C. . (2003). Globalization, regional development, and mega-city expansion in Latin America: Analyzing Mexico City’s peri-urban hinterland. Cities, 20(1), 3-21. doi:10.1016/s0264-2751(02)00092-6Kourtit, K., Nijkamp, P., & Reid, N. (2014). The new urban world: Challenges and policy. Applied Geography, 49, 1-3. doi:10.1016/j.apgeog.2014.01.007Lin, Y.-P., Chu, H.-J., Wu, C.-F., & Verburg, P. H. (2011). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, 25(1), 65-87. doi:10.1080/13658811003752332Rothwell, A., Ridoutt, B., Page, G., & Bellotti, W. (2015). Feeding and housing the urban population: Environmental impacts at the peri-urban interface under different land-use scenarios. Land Use Policy, 48, 377-388. doi:10.1016/j.landusepol.2015.06.017Haas, J., & Ban, Y. (2014). Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta. International Journal of Applied Earth Observation and Geoinformation, 30, 42-55. doi:10.1016/j.jag.2013.12.012Tian, L., Li, Y., Yan, Y., & Wang, B. (2017). Measuring urban sprawl and exploring the role planning plays: A shanghai case study. Land Use Policy, 67, 426-435. doi:10.1016/j.landusepol.2017.06.002Veldkamp, A., & Fresco, L. O. (1996). CLUE: a conceptual model to study the Conversion of Land Use and its Effects. Ecological Modelling, 85(2-3), 253-270. doi:10.1016/0304-3800(94)00151-0Kok, K. (2004). The role of population in understanding Honduran land use patterns. Journal of Environmental Management, 72(1-2), 73-89. doi:10.1016/j.jenvman.2004.03.013Brown, L. A. (2014). The city in 2050: A kaleidoscopic perspective. Applied Geography, 49, 4-11. doi:10.1016/j.apgeog.2013.09.003Islam, M. S., & Ahmed, R. (1970). Land Use Change Prediction In Dhaka City Using Gis Aided Markov Chain Modeling. Journal of Life and Earth Science, 6, 81-89. doi:10.3329/jles.v6i0.9726Sangermano, F., Toledano, J., & Eastman, J. R. (2012). Land cover change in the Bolivian Amazon and its implications for REDD+ and endemic biodiversity. Landscape Ecology, 27(4), 571-584. doi:10.1007/s10980-012-9710-yHe, Y., Ai, B., Yao, Y., & Zhong, F. (2015). Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 38, 164-174. doi:10.1016/j.jag.2014.12.014Vásquez, D. L. A., Balslev, H., & Sklenář, P. (2015). Human impact on tropical-alpine plant diversity in the northern Andes. Biodiversity and Conservation, 24(11), 2673-2683. doi:10.1007/s10531-015-0954-0Alonso, D. L., Pérez, R., Okio, C. K. Y. A., & Castillo, E. (2020). Assessment of mining activity on arsenic contamination in surface water and sediments in southwestern area of Santurbán paramo, Colombia. Journal of Environmental Management, 264, 110478. doi:10.1016/j.jenvman.2020.110478Hofstede, R. G. M. (1995). The effects of grazing and burning on soil and plant nutrient concentrations in Colombian p�ramo grasslands. Plant and Soil, 173(1), 111-132. doi:10.1007/bf00155524Buytaert, W., Célleri, R., De Bièvre, B., Cisneros, F., Wyseure, G., Deckers, J., & Hofstede, R. (2006). Human impact on the hydrology of the Andean páramos. Earth-Science Reviews, 79(1-2), 53-72. doi:10.1016/j.earscirev.2006.06.002Bocarejo, J. P., Portilla, I., & Pérez, M. A. (2013). Impact of Transmilenio on density, land use, and land value in Bogotá. Research in Transportation Economics, 40(1), 78-86. doi:10.1016/j.retrec.2012.06.030Mas, J.-F., Kolb, M., Paegelow, M., Camacho Olmedo, M. T., & Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51, 94-111. doi:10.1016/j.envsoft.2013.09.010Pontius, R. (2018). Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance. Land, 7(3), 105. doi:10.3390/land7030105Jat, M. K., Choudhary, M., & Saxena, A. (2017). Application of geo-spatial techniques and cellular automata for modelling urban growth of a heterogeneous urban fringe. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 223-241. doi:10.1016/j.ejrs.2017.02.002Stellian, R., & Danna-Buitrago, J. P. (2017). Competitividad de los productos agropecuarios colombianos en el marco del tratado de libre comercio con los Estados Unidos: análisis de las ventajas comparativas. Revista de la CEPAL, 2017(122), 139-163. doi:10.18356/7fc7c097-esSAUNDERS, D. A., HOBBS, R. J., & MARGULES, C. R. (1991). Biological Consequences of Ecosystem Fragmentation: A Review. Conservation Biology, 5(1), 18-32. doi:10.1111/j.1523-1739.1991.tb00384.xFischer, J., & Lindenmayer, D. B. (2007). Landscape modification and habitat fragmentation: a synthesis. Global Ecology and Biogeography, 16(3), 265-280. doi:10.1111/j.1466-8238.2007.00287.xROJAS, I., BECERRA, P., GÁLVEZ, N., LAKER, J., BONACIC, C., & HESTER, A. (2011). Relationship between fragmentation, degradation and native and exotic species richness in an Andean temperate forest of Chile. Gayana. Botánica, 68(2), 163-175. doi:10.4067/s0717-66432011000200006Mendoza S., J. E., & Etter R., A. (2002). Multitemporal analysis (1940–1996) of land cover changes in the southwestern Bogotá highplain (Colombia). Landscape and Urban Planning, 59(3), 147-158. doi:10.1016/s0169-2046(02)00012-9Oviedo Hernandez, D., & Dávila, J. D. (2016). Transport, urban development and the peripheral poor in Colombia — Placing splintering urbanism in the context of transport networks. Journal of Transport Geography, 51, 180-192. doi:10.1016/j.jtrangeo.2016.01.00

Análisis integral del impacto del Cambio Climático enlos regímenes de agua, crecidas y sedimentos de una rambla mediterránea

El Cambio Climático y sus efectos en el Ciclo Hidrológico es un tema muy importante para todo el planeta, en la medida que estimar sus efectos tiene un papel preventivo. Esto además es especialmente crítico en las cuencas del arco mediterráneo español, dado que actualmente ya presentan un alto estrés hídrico y fuertes alteraciones antrópicas de su régimen. El caso de estudio es el de la Rambla de la Viuda. Dentro de la misma se encuentran los embalses de Mª. Cristina y Alcora, el primero de ellos con problemas de aterramiento y probable infra-dimensionamiento de su aliviadero, por lo que es prioritario el conocimiento de lo que pueda ocurrir con este embalse en el futuro. Los posibles efectos del Cambio Climático en la cuenca de la Rambla de la Viuda se han estimado utilizando el modelo hidrológico distribuido TETIS y su sub-modelo de sedimentos activado. La información meteorológica empleada proviene de escenarios climáticos regionalizados acordes con el quinto informe AR5 del IPCC, información que tuvo que ser corregida debido a errores relacionados con su resolución y/o hipótesis de regionalización. Los resultados de la modelización indican que en lo que respecta al clima, no hay una señal clara de cambios en la precipitación, tanto en cantidad como en torrencialidad, aunque si es evidente el futuro aumento de temperaturas que se traducirá en un incremento de la evapotranspiración de referencia. Tras la modelización, dicho incremento da lugar a una reducción de los recursos disponibles, tanto superficiales como subterráneos, pero al mismo tiempo a una disminución en los cuantiles de crecida, por lo que consecuentemente cabe esperar también en el futuro una disminución de los aportes de sedimentos al embalse de Mª Cristina