Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment - European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.This work was funded by the project AQUA-CLEW, which is part of ERA4CS (European Research Area for Climate Services), an ERANET (European Research Area Net-work) initiated by JPI Climate (Joint Programming Initiative) andfunded by Formas (Sweden); German Aerospace Center (DLR, Germany); Ministry of Education, Science and Research (BMBWF,Austria); Innovation Fund Denmark; Ministry of Economic Affairs and Digital Transformation (MINECO, Spain); and French National Research Agency with co-funding by the European Commission (grant no. 69046). The contribution of Philippe Lucas-Picher was supported by the French National Research Agency (future investment programme no. ANR-18-MPGA-0005). Rafael Pimentel acknowledges funding by the Modality 5.2 of the Programa Propio 2018 of the University of Córdoba and the Juan de la Cierva Incorporación programme of the Ministry of Science and Innovation (grant no. IJC2018-038093-I). Rafael Pimentel and María J. Polo are members of DAUCO (Unit of Excellence reference no. CEX2019-000968-M), with financial support from the Spanish Ministry of Science and Innovation and the Spanish State Research Agency, through the Severo Ochoa Centre of Excellence and María de Maeztu Unit of Excellence in research and development (R&D)

Analysis of the duration of sea states using segmentation of time series

125 σ.Μεταπτυχιακή Εργασία - - Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Διαπανεπιστημιακό Πρόγραμμα Μεταπτυχιακών Σπουδών "Ναυτική και Θαλάσσια Τεχνολογία και Επιστήμη"Ανάλυση διάρκειας καταστάσεων θάλασσας χρησιμοποιώντας αλγόριθμο κατάτμησης χρονοσειράς σημαντικού ύψους κύματος, αλλάζοντας το μέγιστο επιτρεπόμενο σφάλμα. Κατάτμηση της χρονοσειράς σημαντικού ύψους κύματος σε αναπτυσσόμενες, αποσβενύμενες και στάσιμες καταστάσεις θάλασσας.This work explores a time series segmentation algorithm by changing the maximum allowed error. The algorithm produces developing, decaying and stationary sea states.Χριστιάνα Σ. Φωτιάδο

Comparison of open access global climate services for hydrological data

There is a high demand for openly accessible hydroclimatic data for climate change adaptation. Different data sources are available, however, discrepancy between the data can confuse users and should be evaluated and explained. This study, investigates how climate impact indicators (CIIs) developed for global users in the Copernicus Climate Change Service (C3S) are comparable to other openly available global data for water and climate. We found that, for temperature, datasets are comparable and climate impacts are thus considered robust. Important discrepancies arise in the precipitation indicators. Of the CIIs analysed in this study, the hydrological CIIs differ most so they should be used with care. These differences are probably caused by model uncertainty (hydrological model, HM; global climate model, GCM), ensemble size and model selection. A HM ensemble, as well as a GCM ensemble combined with improved model performance and selection criteria, should be used to ensure high-quality global water and climate services.</p

How Does Seasonal Forecast Performance Influence Decision-Making? : Insights from a Serious Game

Abstract In a context that fosters the evolution of hydro-climate services, it is crucial to support and train users in making the best possible forecast-based decisions. Here, we analyze how decision-making is influenced by the seasonal forecast performance based on the Call For Water serious game in which participants manage a water supply reservoir. The aim is twofold: (1) train participants in the concepts of forecast sharpness and reliability, and (2) collect participants’ decisions to investigate the levels of forecast sharpness and reliability needed to make informed decisions. In the first game round, participants are provided with forecasts of varying reliability and sharpness, while in the second round, they have the possibility to pay for systematically reliable and sharp forecasts (improved forecasts). Exploitable answers were collected from 367 participants, predominantly researchers, forecasters and consultants in the water resources and energy sectors. Results show that improved forecasts led to better decisions, enabling participants to step out of purely conservative strategies and successfully take risks. Reliability levels of 60% are necessary for decision-making while both reliability levels above 70% and sharpness are required for informed risk-prone strategies. Improved forecasts are judged more valuable in extreme years, for instance when hedging against water shortage risks. Additionally, participants working in the energy, air quality and agriculture sectors, as well as traders, decision-makers and forecasters invested the most in forecasts. Finally, we discuss the potential of serious games to foster capacity development in hydro-climate services, and provide recommendations for forecast-based service development

Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin

An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a histori- cal period. The bias correction method is tailored to time scales relevant to flooding events in the basin. Large-scale circulation patterns (CPs) are obtained through Maximum Covariance Analysis using reanalysis sea level pressure and high-resolution precipitation observations. A bias cor- rection using these CPs is applied to winter and summer separately, acknowledging the seasonal variability of the circulation regimes in North Europe and their correlation with regional precipitation rates over the Rhine basin. Two different climate model ensemble outputs are explored: ESSENCE and CMIP5. The results of the CP-method are then compared to observations and uncorrected model out- puts. Results from a simple bias correction based on a delta factor (NoCP-method) are also used for comparison. For both summer and winter, the CP-method offers a statisti- cally significant improvement of precipitation statistics for subsets of data dominated by particular circulation regimes, demonstrating the circulation-dependence of the precipi- tation bias. Uncorrected, CP and NoCP corrected model outputs were used as forcing to a hydrological model to simulate river discharges. The CP-method leads to a larger improvement in simulated discharge in the Alpine area in winter than in summer due to a stronger dependence of Rhine precipitation on atmospheric circulation in winter. However, the NoCP-method, in comparison to the CP- method, improves the discharge estimations over the entire Rhine basin

ECA&D and E-OBS: High-resolution datasets for monitoring climate change and effects on viticulture in Europe

Climate change and climate variability profoundly affect the production of wine. When facing a changing climate, the characteristics of wine produced in each region will change while the natural year-to-year variations in climate will increase variability of income for wine businesses and therefore affect profitability and economic resilience. The challenge posed to the viticulture community is thus to closely monitor these changes to be able to adapt business practices. The European Climate Assessment and Dataset (ECA&D) and its gridded version E-OBS are tools to monitor the changing climatic conditions over Europe, with an emphasis on changes in extreme climatic conditions. In this paper, the potential of ECA&D and E-OBS for viticulture is demonstrated by a few examples. The examples include the changing areal suitable for Chardonnay cultivation, showing an expansion to areas which have been too cold only a few decades ago and retraction of optimal conditions from areas which have been suitable in the recent past. Other examples show the change in the diurnal temperature range in the latter stages of the ripening process of grapes and the variability in heavy precipitation events. Finally, first results of a new dataset for South America are presented

ECA&D and E-OBS: High-resolution datasets for monitoring climate change and effects on viticulture in Europe

Climate change and climate variability profoundly affect the production of wine. When facing a changing climate, the characteristics of wine produced in each region will change while the natural year-to-year variations in climate will increase variability of income for wine businesses and therefore affect profitability and economic resilience. The challenge posed to the viticulture community is thus to closely monitor these changes to be able to adapt business practices. The European Climate Assessment and Dataset (ECA&D) and its gridded version E-OBS are tools to monitor the changing climatic conditions over Europe, with an emphasis on changes in extreme climatic conditions. In this paper, the potential of ECA&D and E-OBS for viticulture is demonstrated by a few examples. The examples include the changing areal suitable for Chardonnay cultivation, showing an expansion to areas which have been too cold only a few decades ago and retraction of optimal conditions from areas which have been suitable in the recent past. Other examples show the change in the diurnal temperature range in the latter stages of the ripening process of grapes and the variability in heavy precipitation events. Finally, first results of a new dataset for South America are presented

Effect of model calibration strategy on climate projections of hydrological indicators at a continental scale

The effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection

Advances in the Definition of Needs and Specifications for a Climate Service Tool Aimed at Small Hydropower Plants' Operation and Management

The operation feasibility of small hydropower plants in mountainous sites is subjected to the run-of-river flow, which is also dependent on a high variability in precipitation and snow cover. Moreover, the management of this kind of system has to be performed with some particular operation conditions of the plant (e.g., turbine minimum and maximum discharge) but also some environmental flow requirements. In this context, a technological climate service is conceived in a tight connection with end users, perfectly answering the needs of the management of small hydropower systems in a pilot area, and providing a forecast of the river streamflow together with other operation data. This paper presents an overview of the service but also a set of lessons learnt related to the features, requirements, and considerations to bear in mind from the point of view of climate service developers. In addition, the outcomes give insight into how this kind of service could change the traditional management (normally based on past experience), providing a probability range of the future river flow based on future weather scenarios according to the range of future weather possibilities. This highlights the utility of the co-generation process to implement climate services for water and energy fields but also that seasonal climate forecasting could improve the business as usual of this kind of facility

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

International audienceAbstract Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes