Future fluvial flood risks in Central Vietnam assessed using global super-high-resolution climate model output

2014 ◽  
Vol 8 (3) ◽  
pp. 276-288 ◽  
Author(s):  
D.H. Nam ◽  
K. Udo ◽  
A. Mano
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Model Output ◽  
Flood Risks ◽  
Central Vietnam ◽  
Climate Model Output

Assessment of future flood intensification in Central Vietnam using a super-high-resolution climate model output

2013 ◽  
Vol 4 (4) ◽  
pp. 373-389 ◽  
Author(s):  
Do Hoai Nam ◽  
Keiko Udo ◽  
Akira Mano
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate Model ◽  
Annual Rainfall ◽  
Model Output ◽  
Correction Model ◽  
Central Vietnam ◽  
The Future ◽  
Climate Model Output

This paper presents an assessment of the changes in future floods. The ranked area-average heavy daily rainfall amounts simulated by a super-high-resolution (20 km mesh) global climate model output are corrected with consideration of the effects of the topography on heavy rainfall patterns and used as a basis to model design storm hyetographs. The rainfall data are then used as the input for a nearly calibration-free parameter rainfall–runoff model to simulate floods in the future climate (2075–2099) at the Upper Thu Bon River basin in Central Vietnam. The results show that although the future mean annual rainfall will not be considerably different compared to the present-day climate (1979–2003), extreme rainfall is projected to increase vigorously, leading to a similar order of intensification of future floods. It is very likely that the flood peak with a 25-year recurrence will increase approximately 42% relative to the present-day climate. The occurrence of floods with a 10-year recurrence may exceed those with a 25-year recurrence in the present-day climate. The projection results also exhibit insignificant uncertainties caused by an artificial neural network-based bias correction model. Additionally, the presented bias correction model shows advantages over a simple climatology scaling method.

scholarly journals Constructing High-Resolution, Bias-Corrected Climate Products: A Comparison of Methods

2021 ◽  
Vol 60 (4) ◽  
pp. 455-475
Author(s):  
Maike F. Holthuijzen ◽  
Brian Beckage ◽  
Patrick J. Clemins ◽  
Dave Higdon ◽  
Jonathan M. Winter
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Model Output ◽  
Quantile Mapping ◽  
Station Data ◽  
Climate Model Output ◽  
Mapping Techniques

AbstractHigh-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

scholarly journals The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model

2009 ◽  
Vol 6 (6) ◽  
pp. 7143-7178 ◽  
Author(s):  
T. L. A. Driessen ◽  
R. T. W. L. Hurkmans ◽  
W. Terink ◽  
P. Hazenberg ◽  
P. J. J. F. Torfs ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Western Europe ◽  
Hydrological Model ◽  
Climate Model ◽  
Reference Model ◽  
Regional Climate ◽  
Hydrological Regime ◽  
Model Output ◽  
Climate Model Output

Abstract. The Meuse is an important river in western Europe, and almost exclusively rain-fed. Projected changes in precipitation characteristics due to climate change, therefore, are expected to have a considerable effect on the hydrological regime of the river Meuse. We focus on an important tributary of the Meuse, the Ourthe, measuring about 1600 km2. The well-known hydrological model HBV is forced with three high-resolution (0.088°) regional climate scenarios, each based on one of three different IPCC CO2 emission scenarios: A1B, A2 and B1. To represent the current climate, a reference model run at the same resolution is used. Prior to running the hydrological model, the biases in the climate model output are investigated and corrected for. Different approaches to correct the distributed climate model output using single-site observations are compared. Correcting the spatially averaged temperature and precipitation is found to give the best results, but still large differences exist between observations and simulations. The bias corrected data are then used to force HBV. Results indicate a small increase in overall discharge for especially the B1 scenario during the beginning of the 21st century. Towards the end of the century, all scenarios show a decrease in summer discharge, partially because of the diminished buffering effect by the snow pack, and an increased discharge in winter. It should be stressed, however, that we used results from only one GCM (the only one available at such a high resolution). It would be interesting to repeat the analysis with multiple models.

scholarly journals The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model

2010 ◽  
Vol 14 (4) ◽  
pp. 651-665 ◽  
Author(s):  
T. L. A. Driessen ◽  
R. T. W. L. Hurkmans ◽  
W. Terink ◽  
P. Hazenberg ◽  
P. J. J. F. Torfs ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Western Europe ◽  
Hydrological Model ◽  
Climate Model ◽  
Reference Model ◽  
Regional Climate ◽  
Hydrological Regime ◽  
Model Output ◽  
Climate Model Output

Abstract. The Meuse is an important river in Western Europe, which is almost exclusively rain-fed. Projected changes in precipitation characteristics due to climate change, therefore, are expected to have a considerable effect on the hydrological regime of the river Meuse. We focus on an important tributary of the Meuse, the Ourthe, measuring about 1600 km2. The well-known hydrological model HBV is forced with three high-resolution (0.088°) regional climate scenarios, each based on one of three different IPCC CO2 emission scenarios: A1B, A2 and B1. To represent the current climate, a reference model run at the same resolution is used. Prior to running the hydrological model, the biases in the climate model output are investigated and corrected for. Different approaches to correct the distributed climate model output using single-site observations are compared. Correcting the spatially averaged temperature and precipitation is found to give the best results, but still large differences exist between observations and simulations. The bias corrected data are then used to force HBV. Results indicate a small increase in overall discharge, especially for the B1 scenario during the beginning of the 21st century. Towards the end of the century, all scenarios show a decrease in summer discharge, partially because of the diminished buffering effect by the snow pack, and an increased discharge in winter. It should be stressed, however, that we used results from only one GCM (the only one available at such a high resolution). It would be interesting to repeat the analysis with multiple models.

Continuous prediction of clay-bed stream erosion in response to climate model output for a small urban watershed

2018 ◽  
Vol 32 (8) ◽  
pp. 1104-1119 ◽  
Author(s):  
Colin P. Brennan ◽  
Parna Parsapour-Moghaddam ◽  
Colin D. Rennie ◽  
Ousmane Seidou
Keyword(s):  
Climate Model ◽  
Model Output ◽  
Urban Watershed ◽  
Climate Model Output ◽  
Stream Erosion

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

scholarly journals Climate Model Output Rewriter (CMOR)

2004 ◽  
Author(s):  
K Taylor ◽  
C Doutriaux ◽  
J Peterschmitt
Keyword(s):  
Climate Model ◽  
Model Output ◽  
Climate Model Output
Sign in / Sign up

Export Citation Format

Share Document