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

Experimental study of the effects of grass vegetation and gravel bed on the turbulent flow using particle image velocimetry

Laboratory experiments are used to explore the effect of impermeable bed on the turbulent flow by using particle image velocimetry (PIV). The experiments were conducted in an open channel of 6.5 m length, 7.5 cm width and 25 cm height. Two different types of permeable bed (flexible vegetation with grass and gravel bed) with different height (2 and 6 cm) with the same porosity epsilon = 0.80 (volume of fluid over total porous medium volume) were used to represent the porous bed. These conditions can be commonly found in systems with sediment transport. Forty-eight (48) experiments were carried out for permeable beds, twenty-four (24) for flexible vegetation with grass and twenty-four (24) for gravel bed. Hydraulic characteristics such as distributions of velocities, turbulent intensities, turbulent kinetic energy and Reynolds stress are investigated. Measurements of velocity were taken for horizontal channel slope at different heights using the PIV. Results show that the kind of the bed type can significantly influence the turbulent characteristics of the flow

Shear stress estimation in the linear zone over impermeable and permeable beds in open channels

This paper investigates the shear stresses in the linear zone of open channel flows with permeable and impermeable bed. The permeable bed is simulated using a flexible vegetation of 2 cm thickness. Laboratory experiments were used for the calculation of the turbulent velocity profiles. The measurements were obtained using a two-dimensional (2D) particle image velocimetry (PIV). This optical method of fluid visualization is used to obtain instantaneous velocity measurements related properties in the fluids. The PIV method assumes that the particles of a fluid faithfully follow the flow dynamics; hence the motion of these seeding particles is used to calculate the dynamic characteristics of the flow. The measurements were conducted at a 12 x 10 cm(2) region located 4 m away from the channel's entrance, where the flow is considered fully developed. The uniformity of the flow was checked measuring the flow depth at two cross-sections (2 m distance between the two regions). The total discharge was estimated using a calibrated venture apparatus. Measurements of velocity were taken for the horizontal channel slope. Results showed that the type of bed can significantly influence the shear stress definition in the linear zone

The significance of spatial variability of rainfall on simulated runoff: an evaluation based on the Upper Lee catchment, UK

The significance of spatial variability of rainfall on runoff is explored as a function of catchment scale and type, and antecedent conditions via the continuous time, semi-distributed probability distributed model (PDM) hydrological model applied to the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments, and further assessed by artificially changing the catchment characteristics and translating these to model parameters (MPs) with uncertainty using model regionalisation. Dry and wet antecedent conditions are represented by ‘warming up’ the model under different rainfall time series. Synthetic rainfall events are introduced to directly relate the change in simulated runoff to the spatial variability of rainfall. Results show that runoff volume and peak are more sensitive to the spatial rainfall for more impermeable catchments; however, this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on runoff varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Parameter uncertainty analysis highlights the importance of accurately representing the spatial variability of the catchment properties and their translation to MPs when investigating the effects of spatial properties of rainfall on runoff

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of –mostly new for hydrology– concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13,000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale. We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency, while the simple method is shown to be mostly useful in identifying its lower limit. We then demonstrate that the assessed forecastability is strongly related to several descriptive features, including seasonality, entropy, (partial) autocorrelation, stability, (non)linearity, spikiness and heterogeneity features, among others. We further (i) show that, if such descriptive information is available for a monthly hydroclimatic time series, we can even foretell the quality of its future forecasts with a considerable degree of confidence, and (ii) rank the features according to their efficiency in explaining and foretelling forecastability. We believe that the obtained rankings are of key importance for understanding forecastability. Spatial forecastability patterns are also revealed through our experiments, with East Asia (Europe) being characterized by larger (smaller) monthly temperature time series forecastability and the Indian subcontinent (Australia) being characterized by larger (smaller) monthly precipitation time series forecastability, compared to other continental-scale regions, and less notable differences characterizing monthly river flow from continent to continent. A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible. Indeed, continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters (because of their essential differences in terms of descriptive features)