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

The IAHS Science for Solutions decade, with Hydrology Engaging Local People IN one Global world (HELPING)

The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions – whether it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes

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

From skill to value: Isolating the influence of end user behavior on seasonal forecast assessment

Recent improvements in initialization procedures and representation of large-scale hydrometeorological processes have contributed to advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, for which they can improve crop choices and irrigation scheduling. In this operational context, the accuracy associated with the forecast system setup does not necessarily yield proportional marginal benefit, as this is also affected by how forecasts are employed by end users. This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to the end users, which allows for the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both forecast system setup and end user behavioral factors. These analyses are supported by an evaluation framework demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; and the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that despite the gain in average conditions being negligible, informing the operations of Lake Como based on seasonal hydrological forecasts during intense drought episodes allows about 15 % of the farmers' profit to be gained with respect to a baseline solution not informed by any forecast. Moreover, our analysis suggests that behavioral factors capturing different perceptions of risk and uncertainty significantly impact the quantification of the benefit to the end users, whereby the estimated forecast value is potentially undermined by different levels of end user risk aversion. Lastly, our results show an intricate skill-to-value relation modulated by the underlying hydrologic conditions, which is well aligned over an exponential function in dry years, while the gains in profit are almost insensitive to the improvements in forecast skill in wet years

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

From seasonal forecast skill to end-user economic benefit: the case of the Lake Como

Recent increase in spatiotemporal model resolution, availability of data/monitored variables, improvement in initialization procedures, and more accurate representation of physical processes contributed in advancing the quality of weather and climate services. State-of-the-art meteorological and hydrological forecast services are becoming more and more skillful over seasonal timescales, potentially representing an asset for informing strategic decisions in different economic sectors. Such services can play a key role in irrigated agriculture for supporting crop choices and irrigation scheduling decisions, which strongly depend on the expected hydro-meteorological conditions. However, although the accuracy and reliability of forecast services depend on the set up of the models that generate the forecasts, their (added) value also depends on how decision makers use the provided information in operational contexts. In this work, we contribute a novel framework to assess the value of weather and climate services, by extending traditional forecast quality assessment methods with estimates of the potential end-user economic benefit from using forecast information. We also explore the sensitivity of the potential economic benefit on both the model set up and decision maker behavioral factors. The framework is demonstrated on the Lake Como system (Italy), a regulated lake primarily operated for flood protection and irrigation supply. Our framework relies on the following integrated modeling chain: 1) lake inflows are produced from bias adjusted ECMWF System 4 seasonal forecasts used as input to the continentally-calibrated E-HYPE hydrological model; 2) this information is then used for conditioning the daily lake operations; 3) the resulting lake releases finally feed an agricultural model to estimate the net profit of the farmers in the downstream irrigation district. The whole chain was run for a 12-year period running from 1996 to 2007, including a fairly balanced number of normal, wet, and dry agricultural seasons. Results suggest that, on average, informing the Lake Como operations based on ECMWF System 4 coupled with E-HYPE hydrological forecasts allows gaining about 4% of farmers’ profit with respect to a traditional operating policy conditioned on the modelled inflow climatology. This gain rises up to 16% during intense drought episodes. Moreover, this value is shown to be particularly sensitive to climate forcing inputs, but also on how the lake operator uses the forecast information depending on the different perceptions of risk and uncertaint

Isolating the Role of End-User Behavior in the Assessment of Seasonal Forecast Value

Recent improvements in model resolutions, initialization procedures, and representation of large scale hydro-meteorological processes contributed in advancing the accuracy of hydroclimatic forecasts, which are more and more skillful over the seasonal and longer timescales. These forecasts are potentially valuable for informing multisector strategic decisions, including irrigated agriculture, where they can improve crop choices and irrigation scheduling decisions. In this operational context, forecast accuracy is important but not necessarily proportional to the associated economic marginal benefit, which is also affected by how forecasts are employed by end-users. In this work, we contribute a novel framework to quantify the value of hydroclimatic forecasts by extending traditional quality assessments with estimates of the potential economic benefit of the forecasts to the end-user. We also explore the sensitivity of this benefit to both model set up and end-user behavioral factors. The approach is demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally-calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that, on average, informing the Lake Como operations based on E-HYPE hydrological forecasts allows gaining about 1% of the farmers’ profit with respect to a baseline solution not informed by any forecast. This gain rises up to about 15% during intense drought episodes. Moreover, our analysis suggests that this value can be largely attributed to the hydrological model and its initial conditions, while the role of meteorological forcing emerges only during dry seasons. Lastly, our results show a high sensitivity to behavioral factors capturing different perception of risk and uncertainty, with the estimated forecast value being potentially undermined if end-users are not able to properly extract the most valuable information from the forecast ensemble