Suchergebnisse

Abstract. Floods in the Mediterranean region are often surface
water floods, in which intense precipitation is usually the main driver. Determining the link between the causes and impacts of
floods can make it easier to calculate the level of flood risk. However, up
until now, the limitations in quantitative observations for flood-related
damages have been a major obstacle when attempting to analyse flood risk in
the Mediterranean. Flood-related insurance damage claims for the last 20 years
could provide a proxy for flood impact, and this information is now
available in the Mediterranean region of Catalonia, in northeast Spain. This
means a comprehensive analysis of the links between flood drivers and
impacts is now possible. The objective of this paper is to develop and
evaluate a methodology to estimate flood damages from heavy precipitation in
a Mediterranean region. Results show that our model is able to simulate the
probability of a damaging event as a function of precipitation. The
relationship between precipitation and damage provides insights into flood
risk in the Mediterranean and is also promising for supporting flood
management strategies.

Quantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0° or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products. ; The authors thank EFFIS (European Forest Fire Information System of the European Commission Joint Research Centre, http://effis.jrc.ec.europa.eu) for providing access to fire series EFFIS. M.T. and E.T. have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 740073 (CLIM4CROP project) and grant agreement No. 748750 (SPFireSD project), respectively. The work of A.P. has been supported by the European Union's Horizon 2020 ECOPOTENTIAL project (grant agreement No. 641762).

Offline Land-Surface Model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/80-2013/14). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate Regional Climate Models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the later slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer, when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well, in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model. ; We are grateful to the French National Centre for Meteorological Research (CNRM UMR3539, Météo-France CNRS) for allowing us to use the code of the SAFRAN analysis system for our studies, the Spanish State Meteorological Agency (AEMET) for sharing their very valuable observational data with us and the European Centre for Medium-Range Weather Forecasts (ECMWF) for making their ERA-Interim product openly available. This is a contribution to the FP7 eartH2Observe project (http://www.earth2observer.eu), which received funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement no. 603608. This work has been funded by the Spanish Economy and Competitiveness Ministry and the European Regional Development Fund through grant CGL2013-47261-R. This work has been supported by the Metropolitan Area of Barcelona Project (no. 308321; flood evolution in the metropolitan area of Barcelona from a holistic perspective: past, present and future) and the Spanish Project HOPE (CGL2014-52571-R) supported by the Ministry of Economy and Competitiveness. This work is a contribution to the HyMeX program (Hydrological cycle in the Mediterranean EXperiment; http://www.hymex.org).

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction. ; We acknowledge the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for making the data available on their website http://www.esrl.noaa.gov/psd/. This work was partially funded by the Projects IMPREX (EU–H2020 PE024400) and SPECS (FP7-ENV-2012-308378). Marco Turco was supported by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). ; Peer Reviewed ; Postprint (published version)

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction. ; We acknowledge the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for making the data available on their website http://www.esrl.noaa.gov/psd/. This work was partially funded by the Projects IMPREX (EU–H2020 PE024400) and SPECS (FP7-ENV-2012-308378). Marco Turco was supported by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). ; Peer Reviewed ; Postprint (published version)

We present an application and validation of the SAFRAN meteorological analysis system for north-east Spain. SAFRAN is also compared to the SPAN analysis system and the meteorological model HIRLAM-HNR, both operational at AEMET. This application of SAFRAN is intended for hydrological studies. This is the first study that shows an application of SAFRAN outside of France and that compares it with SPAN. This is also the first article validating SPAN's rainfall values. Using one year of observational data, the results show that both SAFRAN and SPAN have a similar performance, which is also similar to SAFRAN's performance in France. Thus, SAFRAN and SPAN are both good tools to force land surface models at high resolution in the area of SAFRAN works under the assumption of the existence of climatically homogeneous zones. Two different sets of zones were tested, one based on the AEMET meteorological warning zones and another one based on hydrological catchments. Better results were obtained when using meteorological warning zones. However, the difference is small. In north-east Spain, SAFRAN has the same limitations that were previously shown in France: the spatial structure of the fields is not realistic enough and wind speed is underestimated. As expected, both SAFRAN and SPAN work better in flat areas than over areas of steep relief. This can be a problem in hydrological studies, especially for the Ebro river basin, where most of the runoff is generated in the Pyrenees. ; This project has received funding from the European Union's Seventh Program for research, technological development and demonstration under grant agreement No 603608.

Abstract. Flooding is one of the main natural hazards in the world and causes huge economic and human impacts. Assessing the flood damage in the Mediterranean region is of great importance, especially because of its large vulnerability to climate change. Most past floods affecting the region were caused by intense precipitation events; thus the analysis of the links between precipitation and flood damage is crucial. The main objective of this paper is to estimate changes in the probability of damaging flood events with global warming of 1.5, 2 and 3 ∘C above pre-industrial levels and taking into account different socioeconomic scenarios in two western Mediterranean regions, namely Catalonia and the Valencian Community. To do this, we analyse the relationship between heavy precipitation and flood-damage estimates from insurance datasets in those two regions. We consider an ensemble of seven regional climate model (RCM) simulations spanning the period 1976–2100 to evaluate precipitation changes and to drive a logistic model that links precipitation and flood-damage estimates, thus deriving statistics under present and future climates. Furthermore, we incorporate population projections based on five different socioeconomic scenarios. The results show a general increase in the probability of a damaging event for most of the cases and in both regions of study, with larger increments when higher warming is considered. Moreover, this increase is higher when both climate and population change are included.
When population is considered, all the periods and models show a clearly higher increase in the probability of damaging events, which is statistically significant for most of the cases. Our findings highlight the need for limiting global warming as much as possible as well as the importance of including variables that consider change in both climate and socioeconomic conditions in the analysis of flood damage.

DROP is a global land dataset to monitor meteorological drought that gathers an ensemble of observation-based datasets providing near-real time estimates with associated uncertainty using a probabilistic approach. Accurate and timely drought information is essential to move from post-crisis to pre-impact drought-risk management. A number of drought datasets is already available. They cover the last three decades and provide data in near-real time (using different sources), but they are all "deterministic" (i.e. single realisation), and input and output data partly differ between them. Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the Standardized Precipitation Index. Then, by applying an ensemble approach, mimicking weather/climate prediction studies, we develop DROP (DROught Probabilistic), a new global land gridded dataset, in which an ensemble of observations-based datasets is used to obtain the best near-real time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end-users. The high-quality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging. ; M.T. has received funding from the European Union's Horizon 2020 Research And Innovation Programme under the Marie Skłodowska-Curie Grant Agreement 740073 (CLIM4CROP project) and from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is cofinanced with the European Regional Development Fund (ERDF/FEDER). S.J. was supported by the Spanish Ministry of Science, Innovation and Universities through the project EASE (RTI2018-100870-A-I00), the Fundación Séneca—Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18) and by the Plan Propio de Investigación of the University of Murcia (Grant UMU-2017-10604). M.G.D. acknowledges funding by the Spanish Ministry of Science, Innovation and Universities Ramón y Cajal Grant Reference RYC-2017-22964. The authors thank the data providers listed in Table 1 for providing access to these datasets. Special thanks to Dr. Meng Zhao to provide the GRACE data and Dr. Hong Xuan Do for providing R scripts to read and process the GSIM data. ; Peer Reviewed ; Postprint (published version)

A record 500,000 hectares burned in Portugal during the extreme wildfire season of 2017, with more than 120 human lives lost. Here we analyse the climatic factors responsible for the burned area (BA) from June to October series in Portugal for the period 1980–2017. Superposed onto a substantially stationary trend on BA data, strong oscillations on shorter time scales were detected. Here we show that they are significantly affected by the compound effect of summer (June-July-August) drought and high temperature conditions during the fire season. Drought conditions were calculated using the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI). Then the extent to which the burned area has diverged from climate-expected trends was assessed. Our results indicate that in the absence of other drivers, climate change would have led to higher BA values. In addition, the 2017 extreme fire season is well captured with the model forced with climate drivers only, suggesting that the extreme fire season of 2017 could be a prelude to future conditions and likewise events. Indeed, the expected further increase of drought and high temperature conditions in forthcoming decades, point at a potential increase of fire risk in this region. The climate-fire model developed in this study could be useful to develop more skilled seasonal predictions capable of anticipating potentially hazardous conditions. ; M. Turco has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 740073 (CLIM4CROP project). S. Augusto was supported by FCT-MCTES (SFRH/BPD/109382/2015). RMT was supported by national funds through Fundação para a Ciência e a Tecnologia, Portugal (FCT) under project FireCast (PCIF/GRF/0204/2017). SJ was supported by the Plan Propio de Investigación of the University of Murcia (Grant No. UMU-2017-10604). Further support was provided under projects: (i) CGL2014-59677-R and CGL2017-87921-R also partially funded by FEDER; (ii) POCI-01-0145-FEDER-006939 (LEPABE -UID/EQU/00511/2013) funded by the European Regional Development Fund (ERDF), through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) and by national funds, through FCT - Fundação para a Ciência e a Tecnologia; (iii) NORTE-01-0145-FEDER-000005-LEPABE-2-ECO-NNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the ERDF; (iv) Investigador FCT contract IF/01101/2014 (Nuno Ratola); (v) the Fundación Séneca - Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18). We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu); the European Forest Fire Information System-EFFIS (http://effis.jrc.ec.europa.eu) of the European Commission Joint Research Centre for the fire data. ; Peer Reviewed ; Postprint (published version)

We present a long-term assessment of precipitation trends in Southwestern Europe (1850-2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage. ; This work was supported by the research projects CGL2017-82216-R, CGL2017-83866-C3-3-R and PCI2019-103631, financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X) - sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).