TY - JOUR A1 - Hundecha, Yeshewatesfa A1 - Sunyer, Maria A. A1 - Lawrence, Deborah A1 - Madsen, Henrik A1 - Willems, Patrick A1 - Bürger, Gerd A1 - Kriauciuniene, Jurate A1 - Loukas, Athanasios A1 - Martinkova, Marta A1 - Osuch, Marzena A1 - Vasiliades, Lampros A1 - von Christierson, Birgitte A1 - Vormoor, Klaus Josef A1 - Yuecel, Ismail T1 - Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe JF - Journal of hydrology N2 - The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km(2) in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35-60% of the total variance. (C) 2016 Elsevier B.V. All rights reserved. KW - Flooding KW - Statistical downscaling KW - Regional climate models KW - Climate change KW - Europe Y1 - 2016 U6 - https://doi.org/10.1016/j.jhydrol.2016.08.033 SN - 0022-1694 SN - 1879-2707 VL - 541 SP - 1273 EP - 1286 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Vormoor, Klaus Josef A1 - Lawrence, Deborah A1 - Schlichting, Lena A1 - Wilson, Donna A1 - Wong, Wai Kwok T1 - Evidence for changes in the magnitude and frequency of observed rainfall vs. snowmelt driven floods in Norway JF - Journal of hydrology N2 - There is increasing evidence for recent changes in the intensity and frequency of heavy precipitation and in the number of days with snow cover in many parts of Norway. The question arises as to whether these changes are also discernable with respect to their impacts on the magnitude and frequency of flooding and on the processes producing high flows. In this study, we tested up to 211 catchments for trends in peak flow discharge series by applying the Mann-Kendall test and Poisson regression for three different time periods (1962-2012, 1972-2012, 1982-2012). Field-significance was tested using a bootstrap approach. Over threshold discharge events were classified into rainfall vs. snowmelt dominated floods, based on a simple water balance approach utilizing a nationwide 1 x 1 km(2) gridded data set with daily observed rainfall and simulated snowmelt data. Results suggest that trends in flood frequency are more pronounced than trends in flood magnitude and are more spatially consistent with observed changes in the hydrometeorological drivers. Increasing flood frequencies in southern and western Norway are mainly due to positive trends in the frequency of rainfall dominated events, while decreasing flood frequencies in northern Norway are mainly the result of negative trends in the frequency of snowmelt dominated floods. Negative trends in flood magnitude are found more often than positive trends, and the regional patterns of significant trends reflect differences in the flood generating processes (FGPs). The results illustrate the benefit of distinguishing FGPs rather than simply applying seasonal analyses. The results further suggest that rainfall has generally gained an increasing importance for the generation of floods in Norway, while the role of snowmelt has been decreasing and the timing of snowmelt dominated floods has become earlier. (C) 2016 Elsevier B.V. All rights reserved. KW - Peak flow trends KW - Peak-over-threshold KW - Flood generating processes KW - Rainfall floods KW - Snowmelt floods KW - Climate change Y1 - 2016 U6 - https://doi.org/10.1016/j.jhydrol.2016.03.066 SN - 0022-1694 SN - 1879-2707 VL - 538 SP - 33 EP - 48 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Fer, Istem A1 - Tietjen, Britta A1 - Jeltsch, Florian T1 - High-resolution modelling closes the gap between data and model simulations for Mid-Holocene and present-day biomes of East Africa JF - Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences N2 - East Africa hosts a striking diversity of terrestrial ecosystems, which vary both in space and time due to complex regional topography and a dynamic climate. The structure and functioning of these ecosystems under this environmental setting can be studied with dynamic vegetation models (DVMs) in a spatially explicit way. Yet, regional applications of DVMs to East Africa are rare and a comprehensive validation of such applications is missing. Here, we simulated the present-day and mid-Holocene vegetation of East Africa with the DVM, LPJ-GUESS and we conducted an exhaustive comparison of model outputs with maps of potential modern vegetation distribution, and with pollen records of local change through time. Overall, the model was able to reproduce the observed spatial patterns of East African vegetation. To see whether running the model at higher spatial resolutions (10′ × 10′) contribute to resolve the vegetation distribution better and have a better comparison scale with the observational data (i.e. pollen data), we run the model with coarser spatial resolution (0.5° × 0.5°) for the present-day as well. Both the area- and point-wise comparison showed that a higher spatial resolution allows to better describe spatial vegetation changes induced by the complex topography of East Africa. Our analysis of the difference between modelled mid-Holocene and modern-day vegetation showed that whether a biome shifts to another is best explained by both the amount of change in precipitation it experiences and the amount of precipitation it received originally. We also confirmed that tropical forest biomes were more sensitive to a decrease in precipitation compared to woodland and savanna biomes and that Holocene vegetation changes in East Africa were driven not only by changes in annual precipitation but also by changes in its seasonality. KW - Dynamic vegetation models KW - Biome KW - Mid-Holocene KW - Leaf area index KW - Climate change KW - East Africa Y1 - 2016 U6 - https://doi.org/10.1016/j.palaeo.2015.12.001 SN - 0031-0182 SN - 1872-616X VL - 444 SP - 144 EP - 151 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Ayllon, Daniel A1 - Railsback, Steven Floyd A1 - Vincenzi, Simone A1 - Groeneveld, Juergen A1 - Almodoevar, Ana A1 - Grimm, Volker T1 - InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Current rates of environmental change are exceeding the capacity of many populations to adapt to new conditions and thus avoid demographic collapse and ultimate extinction. In particular, cold-water freshwater fish species are predicted to experience strong selective pressure from climate change and a wide range of interacting anthropogenic stressors in the near future. To implement effective management and conservation measures, it is crucial to quantify the maximum rate of change that cold-water freshwater fish populations can withstand. Here, we present a spatially explicit eco-genetic individual-based model, inSTREAM-Gen, to predict the eco-evolutionary dynamics of stream-dwelling trout under anthropogenic environmental change. The model builds on a well-tested demographic model, which includes submodels of river dynamics, bioenergetics, and adaptive habitat selection, with a new genetic module that allows exploration of genetic and life-history adaptations to new environments. The genetic module models the transmission of two key traits, size at emergence and maturity size threshold. We parameterized the model for a brown trout (Salmo trutta L.) population at the warmest edge of its range to validate it and analyze its sensitivity to parameters under contrasting thermal profiles. To illustrate potential applications of the model, we analyzed the population's demographic and evolutionary dynamics under scenarios of (1) climate change-induced warming, and (2) warming plus flow reduction resulting from climate and land use change, compared to (3) a baseline of no environmental change. The model predicted severe declines in density and biomass under climate warming. These declines were lower than expected at range margins because of evolution towards smaller size at both emergence and maturation compared to the natural evolution under the baseline conditions. Despite stronger evolutionary responses, declining rates were substantially larger under the combined warming and flow reduction scenario, leading to a high probability of population extinction over contemporary time frames. Therefore, adaptive responses could not prevent extinction under high rates of environmental change. Our model demonstrates critical elements of next generation ecological modelling aiming at predictions in a changing world as it accounts for spatial and temporal resource heterogeneity, while merging individual behaviour and bioenergetics with microevolutionary adaptations. KW - Individual-based model KW - Eco-genetic modelling KW - Eco-evolution KW - Climate change KW - Brown trout KW - Next-generation modelling Y1 - 2016 U6 - https://doi.org/10.1016/j.ecolmodel.2015.07.026 SN - 0304-3800 SN - 1872-7026 VL - 326 SP - 36 EP - 53 PB - Elsevier CY - Amsterdam ER -