Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China

2020 ◽  
Vol 585 ◽  
pp. 124760 ◽  
Author(s):  
Jiabo Yin ◽  
Shenglian Guo ◽  
Lei Gu ◽  
Shaokun He ◽  
Huanhuan Ba ◽  
...  
Keyword(s):  
Estimation Uncertainty ◽  
Flood Quantiles ◽  
Model Ensembles ◽  
Projected Changes

scholarly journals Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6

2021 ◽  
Vol 18 (13) ◽  
pp. 6760
Author(s):  
Isaac Kwesi Nooni ◽  
Daniel Fiifi T. Hagan ◽  
Guojie Wang ◽  
Waheed Ullah ◽  
Jiao Lu ◽  
...  
Keyword(s):  
Extreme Events ◽  
Coupled Model ◽  
Baseline Period ◽  
Model Intercomparison ◽  
Additional Information ◽  
Intercomparison Project ◽  
Key Drivers ◽  
Model Ensembles ◽  
Projected Changes ◽  
Near Future

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

Mediterranean climate change projections: an update from CMIP5 and CMIP6.

2021 ◽  
Author(s):  
Josep Cos ◽  
Francisco J Doblas-Reyes ◽  
Martin Jury
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Hot Spot ◽  
Weighting Scheme ◽  
Temperature And Precipitation ◽  
High Emission ◽  
The Mediterranean ◽  
Model Ensembles ◽  
Projected Changes ◽  
Model Weighting

<p>The Mediterranean has been identified as a climate change hot-spot due to increased warming trends and precipitation decline. Recently, CMIP6 was found to show a higher climate sensitivity than its predecessor CMIP5, potentially further exacerbating related impacts on the Mediterranean region.</p><p>To estimate the impacts of the ongoing climate change on the region, we compare projections of various CMIP5 and CMIP6 experiments and scenarios. In particular, we focus on summer and winter changes in temperature and precipitation for the 21st century under RCP2.6/SSP1-2.6, RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5 as well as the high resolution HighResMIP experiments. Additionally, to give robust estimates of projected changes we apply a novel model weighting scheme, accounting for historical performance and inter-independence of the multi-member multi-model ensembles, using ERA5, JRA55 and WFDE5 as observational reference. </p><p>Our results indicate a significant and robust warming over the Mediterranean during the 21st century irrespective of the used ensemble and experiments. Nevertheless, the often attested amplified Mediterranean warming is only found for summer. The projected changes vary between the CMIP5 and CMIP6, with the latter projecting a stronger warming. For the high emission scenarios and without weighting, CMIP5 indicates a warming between 4 and 7.7ºC in summer and 2.7 and 5ºC in winter, while CMIP6 projects temperature increases between 5.6 and 9.2ºC in summer and 3.2 to 6.8ºC in winter until 2081-2100 in respect to 1985-2005. In contrast to temperature, precipitation changes show a higher level of uncertainty and spatial heterogeneity. However, for the high emission scenario, a robust decline in precipitation is projected for large parts of the Mediterranean during summer. First results applying the model weighting scheme indicate reductions in CMIP6 and increases in CMIP5 warming trends, thereby reducing differences between the two ensembles.</p>

Modelling uncertainty of flood quantile estimations at ungauged sites by Bayesian networks

2013 ◽  
Vol 16 (4) ◽  
pp. 822-838 ◽  
Author(s):  
D. Santillán ◽  
L. Mediero ◽  
L. Garrote
Keyword(s):  
Bayesian Networks ◽  
Explanatory Variables ◽  
Modelling Uncertainty ◽  
Ungauged Sites ◽  
Estimation Uncertainty ◽  
Linear Relationships ◽  
Flood Quantiles ◽  
Quantile Estimations ◽  
Probabilistic Nature

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, some of these models assume linear relationships between variables and prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks (BNs) were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharge as a result. The proposed BN model can be applied to supply the estimation uncertainty in national flood discharge mappings. The methodology was applied to a case study in the Tagus basin in Spain.

scholarly journals Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

2010 ◽  
Vol 7 (4) ◽  
pp. 5413-5440 ◽  
Author(s):  
B. A. Botero ◽  
F. Francés
Keyword(s):  
Return Period ◽  
Robustness Analysis ◽  
Distribution Functions ◽  
Statistical Estimator ◽  
Systematic Information ◽  
Estimation Uncertainty ◽  
High Return ◽  
Probable Maximum Flood ◽  
Flood Quantiles

Abstract. This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty is considered acceptable, even for the PMF estimate. From the robustness analysis, EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario.

scholarly journals Calibrating large-ensemble European climate projections using observational data

2020 ◽  
Author(s):  
Christopher O'Reilly ◽  
Daniel Befort ◽  
Antje Weisheimer
Keyword(s):  
Observational Data ◽  
Climate Projections ◽  
Slight Reduction ◽  
Reference Period ◽  
Calibration Methods ◽  
Out Of Sample ◽  
Large Ensembles ◽  
Model Ensembles ◽  
Projected Changes ◽  
Gaussian Regression

<p>In this study methods of calibrating the output of large single model ensembles are examined. The methods broadly involve fitting seasonal ensemble data to observations over a reference period and scaling the ensemble signal and spread so as to optimize the fit over the reference period. These calibration methods are then applied to the future (or out-of-sample) projections. The calibration methods are tested and give indistinguishable results so the simplest of these methods, namely Homogenous Gaussian Regression, is selected. An extension to this method, applying it to dynamically decomposed data (in which the underlying data is separated into dynamical and residual components), is found to improve the reliability of the calibrated projections. The calibration methods were tested and verified using an “imperfect model” approach using the historical/RCP8.5 simulations from the CMIP5 archive. The verification indicates that this relatively straight-forward calibration produces more reliable and accurate projections than the uncalibrated (bias-corrected) ensemble for projections of future climate over Europe. When the two large ensembles are applied to observational data, the 2041-2060 climate projections for Europe for the RCP 8.5 scenario are more consistent between the two ensembles, with a slight reduction in warming but an increase in the uncertainty of the projected changes. </p>

scholarly journals The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections

2021 ◽  
Author(s):  
Josep Cos ◽  
Francisco Doblas-Reyes ◽  
Martin Jury ◽  
Raül Marcos ◽  
Pièrre-Antoine Bretonnière ◽  
...  
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Mediterranean Region ◽  
Model Performance ◽  
Warming Trend ◽  
Weighting Scheme ◽  
The Mediterranean ◽  
Model Ensembles ◽  
Projected Changes ◽  
Model Weighting

Abstract. The increased warming trend and precipitation decline in the Mediterranean region makes it a climate change hotspot. We compare projections of multiple CMIP5 and CMIP6 historical and scenario simulations to quantify the impacts of the already changing climate in the region. In particular, we investigate changes in temperature and precipitation during the 21st century following scenarios RCP2.6, SSP1-2.6, RCP4.5, SSP2-4.5, RCP8.5 and SSP5-8.5, as well as the HighResMIP high resolution experiments. A model weighting scheme is applied to obtain constrained estimates of projected changes, which accounts for historical model performance and inter-independence of the multi-model ensembles, using an observational ensemble as reference. Results indicate a robust and significant warming over the Mediterranean region along the 21st century over all seasons, ensembles and experiments. The Mediterranean amplified warming with respect to the global mean is mainly found during summer. The temperature changes vary between CMIPs, being CMIP6 the ensemble that projects a stronger warming. Contrarily to temperature projections, precipitation changes show greater uncertainties and spatial heterogeneity. However, a robust and significant precipitation decline is projected over large parts of the region during summer for the high emission scenario. While there is less disagreement in projected precipitation between CMIP5 and CMIP6, the latter shows larger precipitation declines in some regions. Results obtained from the model weighting scheme indicate increases in CMIP5 and reductions in CMIP6 warming trends, thereby reducing the distance between both multi-model ensembles.

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