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

2021 ◽  
Author(s):  
Eva Sebok ◽  
Hans Jørgen Henriksen ◽  
Ernesto Pastén-Zapata ◽  
Peter Berg ◽  
Guillume Thirel ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Model Performance ◽  
Climate Impact ◽  
Expert Elicitation ◽  
Model Structure ◽  
Hydrological Models ◽  
Model Ensemble ◽  
Impact Studies ◽  
Model Weighting

Abstract. 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 analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX 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 elicitations 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 reestablish a uniform weight between climate models.

scholarly journals Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

2018 ◽  
Vol 22 (4) ◽  
pp. 2163-2185 ◽  
Author(s):  
Stefan Liersch ◽  
Julia Tecklenburg ◽  
Henning Rust ◽  
Andreas Dobler ◽  
Madlen Fischer ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Model Performance ◽  
Climate Impact ◽  
Hydrological Models ◽  
Blue Nile ◽  
Projected Change ◽  
Model Ensembles

Abstract. Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970–1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q20 and Q80. On the other hand, bias-corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and characteristics and the effect of bias correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.

scholarly journals Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

2016 ◽  
Author(s):  
Stefan Liersch ◽  
Julia Tecklenburg ◽  
Henning Rust ◽  
Andreas Dobler ◽  
Madlen Fischer ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Model Performance ◽  
Climate Impact ◽  
Hydrological Models ◽  
Blue Nile ◽  
Projected Change ◽  
Model Ensembles

Abstract. Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side-effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias-correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth System Models and ten Regional Climate Models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias-correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970–1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q20 and Q80. On the other hand, bias-corrected simulations tend to overemphasise magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias-correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and characteristics, the effect of bias-correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.

scholarly journals Supplementary material to "Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies"

2021 ◽  
Author(s):  
Eva Sebok ◽  
Hans Jørgen Henriksen ◽  
Ernesto Pastén-Zapata ◽  
Peter Berg ◽  
Guillume Thirel ◽  
...  
Keyword(s):  
Climate Impact ◽  
Expert Elicitation ◽  
Hydrological Models ◽  
Impact Studies ◽  
Supplementary Material

scholarly journals Impact of climate change on water availability in the Oueme catchment at the outlet of the Save's bridge (Benin, West Africa)

2021 ◽  
Vol 384 ◽  
pp. 255-260
Author(s):  
Amedée Chabi ◽  
Esdras Babadjidé Josué Zandagba ◽  
Ezekiel Obada ◽  
Eliezer Iboukoun Biao ◽  
Eric Adéchina Alamou ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Availability ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Ghg Emissions ◽  
Model Performance ◽  
Hydrological Models ◽  
Impact Of Climate Change ◽  
The Impact

Abstract. One of the major threats to water resources today remains climate change. The objective of this study is to assess the impact of climate change on water availability in Oueme catchment at Savè. Precipitation provided by three regional climate models (RCMs) was analyzed. Bias in these data was first corrected using the Empirical Quantile Mapping (EQM) method be for etheir use as input to hydrological models. To achieve the objective, six hydrological models were used (AWBM, ModHyPMA, HBV, GR4J, SimHyd and Hymod). In projection, the results showed that the AWBM model appears to be the best. The multi-model approach further improves model performance, with the best obtained with combinations of the models AWBM-ModHyPMA-HBV. The AWBM model showed a fairly good capability for simulating flows in the basin with only HIRHAM5 climate model data as input. Therefore, the simulation with the HIRHAM5 data as inputs to the five (05) hydrological models, showed flows that vary at the horizons (2025, 2055 and 2085) under the scenarios (RCP4.5 and RCP8.5). Indeed, this variation is largely due to anthropogenic greenhouse gas (GHG) emissions.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

scholarly journals Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Joseph Leedale ◽  
Adrian M. Tompkins ◽  
Cyril Caminade ◽  
Anne E. Jones ◽  
Grigory Nikulin ◽  
...  
Keyword(s):  
Climate Change ◽  
Malaria Transmission ◽  
Climate Models ◽  
Climate Model ◽  
Eastern Africa ◽  
Future Climate Change ◽  
Climate Projections ◽  
Model Ensemble ◽  
East African Community ◽  
Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

A regime perspective on jet and blocking dynamics in CMIP6

2021 ◽  
Author(s):  
Joshua Dorrington
Keyword(s):  
Potential Vorticity ◽  
Climate Models ◽  
Rossby Waves ◽  
Climate Model ◽  
Atmospheric Dynamics ◽  
Interdecadal Variability ◽  
Model Ensemble ◽  
Atlantic Region ◽  
Non Linear ◽  
Jet Structure

<p>Weather over the Euro-Atlantic region during winter is highly variable, with rich and chaotic internal atmospheric dynamics. In particular, the non-linear breaking of Rossby waves irreversibly mixes potential vorticity contours and so triggers shifts in the latitude of the eddy driven jet and establishes persistent anticyclonic blocking events. The concept of atmospheric regimes captures the tendency for blocks – and for the jet – to persist in a small number of preferred locations. Regimes then provide a non-linear basis through which model deficiencies, interdecadal variability and forced trends in the Euro-Atlantic circulation can be studied.</p><p>A drawback of past regime approaches is that they were unable to easily capture both the dynamics of the jet and of blocking anticyclones simultaneously. In this work we apply a recently developed regime framework, which is able to capture both these important aspects while reducing sampling variability, to the CMIP6 climate model ensemble. We analyse both the historical variability and biases of blocking and jet structure in this latest generation of climate models, and make new estimates of the anthropogenic forced trend over the coming century.</p><p> </p>

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

scholarly journals How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change

2020 ◽  
Vol 163 (3) ◽  
pp. 1353-1377 ◽  
Author(s):  
Valentina Krysanova ◽  
Jamal Zaherpour ◽  
Iulii Didovets ◽  
Simon N. Gosling ◽  
Dieter Gerten ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Model Evaluation ◽  
Model Performance ◽  
Poor Performance ◽  
Climate Impact ◽  
Hydrological Models ◽  
Weighted Mean ◽  
Climate Impact Assessment ◽  
Ensemble Mean ◽  
The Mean

AbstractImportance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.

scholarly journals Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives

2019 ◽  
Vol 23 (3) ◽  
pp. 1409-1429 ◽  
Author(s):  
Sjoukje Philip ◽  
Sarah Sparrow ◽  
Sarah F. Kew ◽  
Karin van der Wiel ◽  
Niko Wanders ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Positive Influence ◽  
Anthropogenic Climate Change ◽  
Precipitation Event ◽  
Climate Change Signal ◽  
Hydrological Models ◽  
Additional Measure ◽  
Flooding Events

Abstract. In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 ∘C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95 % confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 ∘C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: we find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.

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