Assessment of Conceptual Model Uncertainty for the Regional Aquifer Pampa del Tamarugal - North Chile

In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) ¿ North Chile using a novel and fully integrated multimodel approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, model weights used in BMA for multi-model aggregation are obtained from GLUE-based likelihood values. These model weights are based on model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUE-based cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding model weights used for multi-model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. Contribution of conceptual model uncertainty to the predictive uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual model uncertainty.JRC.H.7-Land management and natural hazard

Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements

In this work we benchmark, for the first time, the latest Standard Particle Swarm Optimisation algorithm (SPSO-2011) against the 28 test functions designed for the Special Session on Real-Parameter Single Objective Optimisation at CEC-2013. SPSO-2011 is a major improvement over previous PSO versions, with an adaptive random topology and rotational invariance constituting the main advancements. Results showed an outstanding performance of SPSO-2011 for the family of unimodal and separable test functions, with a fast convergence to the global optimum, while good performance was observed for four rotated multimodal functions. Conversely, SPSO-2011 showed the weakest performance for all composition problems (i.e. highly complex functions specially designed for this competition) and certain multimodal test functions. In general, a fast convergence towards the region of the global optimum was achieved, requiring less than 10E+03 function evaluations. However, for most composition and multimodal functions SPSO-2011 showed a limited capability to “escape” from sub-optimal regions. Despite this limitation, a desirable feature of SPSO-2011 was its scalable behaviour, which observed up to 50-dimensional problems, i.e. keeping a similar performance across dimensions with no need for increasing the population size. Therefore, it seems advisable that future PSO improvements be focused on enhancing the algorithm's ability to solve non-separable and asymmetrical functions, with a large number of local minima and a second global minimum located far from the true optimum. This work is the first effort towards providing a baseline for a fair comparison of future PSO improvements, using publicly-available software codes.JRC.H.1-Water Resource

Climate change and river floods in the European Union: Socio-economic consequences and the costs and benefits of adaptation

This study presents the first appraisal of the socio-economic impacts of river floods in the European Union in view of climate and socio-economic changes. The assessment is based on two trajectories: (a) no adaptation, where the current levels of protection are kept constant, and (b) adaptation, where the level of protection is increased to defend against future flooding events. As a basis for our analysis we use an ensemble-based pan-European flood hazard assessment for present and future conditions. Socioeconomic impacts are estimated by combining flood inundation maps with information on assets exposure and vulnerability. Ensemble-based results indicate that current expected annual population affected of ca. 200,000 is projected to increase up to 360,000 due to the effects of socio-economic development and climate change. Under the no adaptation trajectory current expected annual damages of s5.5 billion/year are projected to reach s98 billion/year by the 2080s due to the combined effects of socio-economic and climate change. Under the adaptation trajectory the avoided damages (benefits) amount to s53 billion/year by the 2080s. An analysis of the potential costs of adaptation associated with the increase in protection suggests that adaptation could be highly cost-effective. There is, however, a wide range around these central numbers reflecting the variability in projected climate. Analysis at the country level shows high damages, and by association high costs of adaptation, in the United Kingdom, France, Italy, Romania, Hungary and Czech Republic. At the country level, there is an even wider range around these central values, thus, pointing to a need to consider climate uncertainty in formulating practical adaptation strategies.JRC.H.7-Climate Risk Managemen

Assessment of future flood hazard in Europe using a large ensemble of bias-corrected regional climate simulations

We assess future flood hazard in view of climate change at pan-European scale using a large ensemble of climate projections. The ensemble consists of simulations from 12 climate experiments conducted within the ENSEMBLES project, forced by the SRES A1B emission scenario for the period 1961–2100. Prior to driving the hydrological model LISFLOOD, climate simulations are corrected for bias in precipitation and temperature using a Quantile Mapping (QM) method. For time slices of 30 years, a Gumbel distribution is fitted by the maximum likelihood method through the simulated annual maximum discharges. Changes in extreme river flows, here exemplified by the 100-year discharge (Q100), are then analyzed with respect to a control period (1961–1990). We assess the uncertainty arising from using alternative climate experiments to force LISFLOOD and from the fitting of extreme value distributions. Results show large discrepancies in the magnitude of change in Q100 among the hydrological simulations for different climate experiments, with some regions even showing an opposite signal of change. Due to the low signal-to-noise ratio in some areas the projected changes showed not all to be statistically significant. Despite this, western Europe, the British Isles and northern Italy show a robust increase in future flood hazard, mainly due to a pronounced increase in extreme rainfall. A decrease in Q100, on the other hand, is projected in eastern Germany, Poland, southern Sweden and, to a lesser extent, the Baltic countries. In these areas, the signal is dominated by the strong reduction in snowmelt induced floods, which offsets the increase in average and extreme precipitation.JRC.H.7-Climate Risk Managemen

Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

Six satellite-based rainfall estimates (SRFE)—namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMapMVK), and one reanalysis product [the interimECMWFRe-Analysis (ERA-Interim)]—were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba–Shabelle, and Baro–Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events.Validation was done at three spatially aggregated levels: point-topixel, subcatchment, and river basin for the period 2003–06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on amonthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKVwere the least accurate, andRFE 2.0 andTRMM3B42were themost accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.JRC.H.7-Climate Risk Managemen

Increasing stress on disaster risk finance due to large floods

Recent major flood disasters have shown that single extreme events can affect multiple countries simultaneously, which puts high pressure on trans-national risk reduction and risk transfer mechanisms. To date, little is known about such flood hazard inter-dependencies across regions, and the corresponding joint risks at regional to continental scales. Reliable information on correlated loss probabilities is crucial for developing robust insurance schemes and public adaptation funds, and for enhancing our understanding of climate change impacts. Here we show that large-scale atmospheric processes result in strongly correlated extreme discharges across European river basins. We present probabilistic trends in continental flood risk, and demonstrate that currently observed extreme flood losses could more than double in frequency by 2050 under future climate change and socioeconomic development. We suggest that risk management for these increasing losses is largely feasible, and we demonstrate that risk can be shared by expanding risk transfer financing, reduced by investing in flood protection, or absorbed by enhanced solidarity between countries. We conclude that these measures have vastly different efficiency, equity and acceptability implications, which need to be taken into account in broader consultation, for which our analysis provides a basis.JRC.H.7-Climate Risk Managemen