Flood quantiles scaling with upper soil hydraulic properties for different land uses at catchment scale

[EN] Changes in land use within a catchment are among the causes of non-stationarity in the flood regime, as they modify the upper soil physical structure and its runoff production capacity. This paper analyzes the relation between the variation of the upper soil hydraulic properties due to changes in land use and its effect on the magnitude of peak flows: (1) incorporating fractal scaling properties to relate the effect of the static storage capacity (the sum of capillary water storage capacity in the root zone, canopy interception and surface puddles) and the upper soil vertical saturated hydraulic conductivity on the flood regime; (2) describing the effect of the spatial organization of the upper soil hydraulic properties at catchment scale; (3) examining the scale properties in the parameters of the Generalized Extreme Value (GEV) probability distribution function, in relation to the upper soil hydraulic properties. This study considered the historical changes of land use in the Combeima River catchment in South America, between 1991 and 2007, using distributed hydrological modeling of daily discharges to describe the hydrological response. Through simulation of land cover scenarios, it was demonstrated that it is possible to quantify the magnitude of peak flows in scenarios of land cover changes through its Wide-Sense Simple Scaling with the upper soil hydraulic properties.This research was funded partially by the COLCIENCIAS 567 doctoral fellowship program, Universidad del Tolima project 1300213 and Universidad de Ibague (Colombia) project 12-262-COL00, and by Universitat Politecnica de Valencia (Spain) and by the Spanish Research Project ECO-TETIS (ref. CGL2011-28776-C02-01) and TETIS-MED (ref. CGL2014-58127-C3-3-R). Thanks to The Shuttle Radar Topography Mission NASA, IDEAM and IGAC in Colombia, for providing digital elevation model, streamflow, rainfall data, and soil study of the Tolima Region.Peña-Rojas, LE.; Barrios Peña, MI.; Francés, F. (2016). Flood quantiles scaling with upper soil hydraulic properties for different land uses at catchment scale. Journal of Hydrology. 541:1258-1272. https://doi.org/10.1016/j.jhydrol.2016.08.031S1258127254

Linking Pan-European data to the local scale for decision making for global change and water scarcity within water resources planning and management

[EN] This study focuses on a novel type of methodology which connects Pan-European data to the local scale in the field of water resources management. This methodology is proposed to improve and facilitate the decision making within the planning and management of water resources, taking into account climate change and its expected impacts. Our main point of interest is focused on the assessment of the predictability of extreme events and their possible effects, specifically droughts and water scarcity. Consequently, the Júcar River Basin was selected as the case study, due to the ongoing water scarcity problems and the last drought episodes suffered in the Mediterranean region. In order to study these possible impacts, we developed a modeling chain divided into four steps, they are: i) data collection, ii) analysis of available data, iii) models calibration and iv) climate impact analysis. Over previous steps, we used climate data from 15 different regional climate models (RCMs) belonging to the three different Representative Concentration Pathways (RCPs) coming from a hydrological model across all of Europe called E-HYPE. The data were bias corrected and used to obtain statistical results of the availability of water resources for the future (horizon 2039) and in form of indicators. This was performed through a hydrological (EVALHID), stochastic (MASHWIN) and risk management (SIMRISK) models, all of which were specifically calibrated for this basin. The results show that the availability of water resources is much more enthusiastic than in the current situation, indicating the possibility that climate change, which was predicted to occur in the future has already happened in the Júcar River Basin. It seems that the so called Effect 80 , an important decrease in water resources for the last three decades, is not well contemplated in the initial data.The authors thank the anonymous reviewers for their valuable comments, suggestions and positive feedback. All remaining errors, however, are solely the responsibility of the authors. We would also like to express our gratitude to the Jucar River Basin Authority - Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Fishery, Food and Environment) for providing data to develop this study. The authors wish to thank the Spanish Ministry of Economyand Competitiveness for its financial support through the NUTEGES project (CGL2012-34978) and ERAS project (CTM2016-77804-P). We also value the support provided by the European Community's Seventh Framework Program in financing the projects ENHANCE (FP7-ENV-2012, 308438), AGUAMOD (Interreg V-B Sudoe 2016), SWICCA (ECMRWF-Copernicus-FA 2015/C3S_441-LOT1/SMHI) and IMPREX (H2020-WATER-2014-2015, 641811).Suárez-Almiñana, S.; Pedro Monzonís, M.; Paredes Arquiola, J.; Andreu Álvarez, J.; Solera Solera, A. (2017). Linking Pan-European data to the local scale for decision making for global change and water scarcity within water resources planning and management. The Science of The Total Environment. 603-604:126-139. https://doi.org/10.1016/j.scitotenv.2017.05.259S126139603-60

Servicios urbanos integrados para las ciudades europeas: el ejemplo de Estocolmo

El concepto de servicio hidrometeorológico, climático y medioambiental urbano integrado ha sido propuesto por la OMM para satisfacer las necesidades futuras de sus Miembros, especialmente para lograr los Objetivos de Desarrollo Sostenible de las Naciones Unidas. UrbanSIS en Estocolmo es una excelente demostración de una iniciativa de integración de diversas disciplinas científicas de una forma holística e innovadora. Los modelos meteorológicos, de calidad del aire e hidrológicos se usan para proporcionar datos de alta resolución espacial (1 km) y temporal (15 minutos a 1 hora) para el diseño y la planificación urbanas de manera vanguardista y ecocéntrica. La iniciativa de la OMM se emprendió cooperativamente y en colaboración con otras ciudades –Bolonia y Rotterdam– para desarrollar y generalizar eficientemente su capacidad. La OMM está siguiendo la Guía para los servicios hidrometeorológicos, climáticos y medioambientales urbanos integrados, Parte 1: Concepto y Metodología con ejemplos adicionales de ciudades de muestra con la mayor diversidad económica, geográfica y de riesgos

Bivariate mixed distribution with a heavy-tailed component and its application to single-site daily rainfall simulation

This paper presents an improved bivariate mixed distribution, which is capable of modeling the dependence of daily rainfall from two distinct sources (e.g., rainfall from two stations, two consecutive days, or two instruments such as satellite and rain gauge). The distribution couples an existing framework for building a bivariate mixed distribution, the theory of copulae and a hybrid marginal distribution. Contributions of the improved distribution are twofold. One is the appropriate selection of the bivariate dependence structure from a wider admissible choice (10 candidate copula families). The other is the introduction of a marginal distribution capable of better representing low to moderate values as well as extremes of daily rainfall. Among several applications of the improved distribution, particularly presented here is its utility for single-site daily rainfall simulation. Rather than simulating rainfall occurrences and amounts separately, the developed generator unifies the two processes by generalizing daily rainfall as a Markov process with autocorrelation described by the improved bivariate mixed distribution. The generator is first tested on a sample station in Texas. Results reveal that the simulated and observed sequences are in good agreement with respect to essential characteristics. Then, extensive simulation experiments are carried out to compare the developed generator with three other alternative models: the conventional two-state Markov chain generator, the transition probability matrix model, and the semiparametric Markov chain model with kernel density estimation for rainfall amounts. Analyses establish that overall the developed generator is capable of reproducing characteristics of historical extreme rainfall events and is apt at extrapolating rare values beyond the upper range of available observed data. Moreover, it automatically captures the persistence of rainfall amounts on consecutive wet days in a relatively natural and easy way. Another interesting observation is that the recognized “overdispersion” problem in daily rainfall simulation ascribes more to the loss of rainfall extremes than the under-representation of first-order persistence. The developed generator appears to be a sound option for daily rainfall simulation, especially in particular hydrologic planning situations when rare rainfall events are of great importance

Regionalization of land-use impacts on streamflow using a network of paired catchments

Quantifying the impact of land use and cover (LUC) change on catchment hydrological response is essential for land-use planning and management. Yet hydrologists are often not able to present consistent and reliable evidence to support such decision-making. The issue tends to be twofold: a scarcity of relevant observations, and the difficulty of regionalizing any existing observations. This study explores the potential of a paired catchment monitoring network to provide statistically robust, regionalized predictions of LUC change impact in an environment of high hydrological variability. We test the importance of LUC variables to explain hydrological responses and to improve regionalized predictions using 24 catchments distributed along the Tropical Andes. For this, we calculate first 50 physical catchment properties, and then select a subset based on correlation analysis. The reduced set is subsequently used to regionalize a selection of hydrological indices using multiple linear regression. Contrary to earlier studies, we find that incorporating LUC variables in the regional model structures increases significantly regression performance and predictive capacity for 66% of the indices. For the runoff ratio, baseflow index, and slope of the flow duration curve, the mean absolute error reduces by 53% and the variance of the residuals by 79%, on average. We attribute the explanatory capacity of LUC in the regional model to the pairwise monitoring setup, which increases the contrast of the land-use signal in the data set. As such, it may be a useful strategy to optimize data collection to support watershed management practices and improve decision-making in data-scarce regions