scholarly journals Uncertainty Quantification of Future Design Rainfall Depths in Korea

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 22 ◽  
Author(s):  
Kyungmin Kim ◽  
Jeonghyeon Choi ◽  
Okjeong Lee ◽  
Dong-Hyun Cha ◽  
Sangdan Kim
Keyword(s):  
Climate Change ◽  
Rainfall Intensity ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Scenarios ◽  
Model Combination ◽  
Future Design ◽  
Design Rainfall ◽  
Idf Curves ◽  
Intensity Duration Frequency

One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios. However, the projected future design rainfall intensity varies greatly depending on which climate model is applied. In this study, future rainfall Intensity–Duration–Frequency (IDF) curves are projected using various combinations of climate models. Future Ensemble Average (FEA) is calculated using a total of 16 design rainfall intensity ensembles, and uncertainty of FEA is quantified using the coefficient of variation of ensembles. The FEA and its uncertainty vary widely depending on how the climate model combination is constructed, and the uncertainty of the FEA depends heavily on the inclusion of specific climate model combinations at each site. In other words, we found that unconditionally using many ensemble members did not help to reduce the uncertainty of future IDF curves. Finally, a method for constructing ensemble members that reduces the uncertainty of future IDF curves is proposed, which will contribute to minimizing confusion among policy makers in developing climate change adaptation policies.

scholarly journals Evaluating quantile-based bias adjustment methods for climate change scenarios

2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Climate Change Signal ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Statistical Bias ◽  
Bias Adjustment ◽  
Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

scholarly journals Spectral representation of the annual cycle in the climate change signal

2011 ◽  
Vol 15 (9) ◽  
pp. 2777-2788 ◽  
Author(s):  
T. Bosshard ◽  
S. Kotlarski ◽  
T. Ewen ◽  
C. Schär
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Climate Impact ◽  
Climate Change Signal ◽  
Climate Change Scenarios ◽  
Regional Climate Model Output ◽  
Spectral Smoothing ◽  
Precipitation Changes

Abstract. The annual cycle of temperature and precipitation changes as projected by climate models is of fundamental interest in climate impact studies. Its estimation, however, is impaired by natural variability. Using a simple form of the delta change method, we show that on regional scales relevant for hydrological impact models, the projected changes in the annual cycle are prone to sampling artefacts. For precipitation at station locations, these artefacts may have amplitudes that are comparable to the climate change signal itself. Therefore, the annual cycle of the climate change signal should be filtered when generating climate change scenarios. We test a spectral smoothing method to remove the artificial fluctuations. Comparison against moving monthly averages shows that sampling artefacts in the climate change signal can successfully be removed by spectral smoothing. The method is tested at Swiss climate stations and applied to regional climate model output of the ENSEMBLES project. The spectral method performs well, except in cases with a strong annual cycle and large relative precipitation changes.

scholarly journals Uncertainties in Riverine and Coastal Flood Impacts under Climate Change

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1774
Author(s):  
Shuyi Wang ◽  
Mohammad Reza Najafi ◽  
Alex J. Cannon ◽  
Amir Ali Khan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Block Design ◽  
Coastal Flooding ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Storm Events ◽  
Idf Curves

Climate change can affect different drivers of flooding in low-lying coastal areas of the world, challenging the design and planning of communities and infrastructure. The concurrent occurrence of multiple flood drivers such as high river flows and extreme sea levels can aggravate such impacts and result in catastrophic damages. In this study, the individual and compound effects of riverine and coastal flooding are investigated at Stephenville Crossing located in the coastal-estuarine region of Newfoundland and Labrador (NL), Canada. The impacts of climate change on flood extents and depths and the uncertainties associated with temporal patterns of storms, intensity–duration–frequency (IDF) projections, spatial resolution, and emission scenarios are assessed. A hydrologic model and a 2D hydraulic model are set up and calibrated to simulate the flood inundation for the historical (1976–2005) as well as the near future (2041–2070) and far future (2071–2100) periods under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Future storm events are generated based on projected IDF curves from convection-permitting Weather Research and Forecasting (WRF) climate model simulations, using SCS, Huff, and alternating block design storm methods. The results are compared with simulations based on projected IDF curves derived from statistically downscaled Global Climate Models (GCMs). Both drivers of flooding are projected to intensify in the future, resulting in higher risks of flooding in the study area. Compound riverine and coastal flooding results in more severe inundation, affecting the communities on the coastline and the estuary area. Results show that the uncertainties associated with storm hyetographs are considerable, which indicate the importance of accurate representation of storm patterns. Further, simulations based on projected WRF-IDF curves show higher risks of flooding compared to the ones associated with GCM-IDFs.

scholarly journals Disaggregation of Future Regional Climate Model Data to Generate Future Rainfall Intensity-Duration-Frequency Curves to Assess Climate Change Impacts

2021 ◽  
Author(s):  
Hüsamettin Tayşi ◽  
Mehmet Ozger
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Model ◽  
Regional Climate ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Hourly Data ◽  
Idf Curves ◽  
Intensity Duration Frequency

Heavy increase in urbanization, industrialization and population is causing an increase in emissions of greenhouse gases (GHG) and this causes variations in atmosphere. Climate change causes extreme rainfall events and these events are expected to be enhanced in the future. Since flooding is influencing urban areas, controlling and management of flooding is a major necessity. Intensity-Duration-Frequency (IDF) curves play a huge role in representing rainfall characteristics by linking intensity, duration, and frequency of rainfall. Analysing short-duration rainfall is crucial for urban areas due to fast responses of drainage systems against heavy rainfall events. IDF curves were generated via the Gumbel method for rainfalls from 5-min to 24-h in this study. However, providing short-duration rainfall data is challenging due to the low capacity, costs and geographic conditions. Therefore, the HYETOS disaggregation model was applied to obtain sub-hourly data. IDF curves are stationary since they only consider historical events. However, IDF curves must be non-stationary and time varying based on preparation for upcoming extreme events. This study aims to generate IDF curves under climate change scenarios. The Regional Climate Model (RCM) HadGEM2-ES generated under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios and was used in the study to represent future rainfalls. Future daily rainfalls were disaggregated into sub-hourly using disaggregation parameters of corresponding station’s historical rainfall data since it is impossible to estimate parameters when hourly data is not available. With this new approach, future daily rainfall data is disaggregated into 5-min data by complying with historical rainfall patterns rather than complying with randomly selected rainfall characteristics. The study concluded that future rainfall intensities increases compared to historical IDF curves. RCP8.5 scenarios have higher rainfall intensities for all return periods compared to RCP4.5 scenarios for all stations except a station. In addition, the accuracy of the selected disaggregation model was verified.

scholarly journals Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1750 ◽  
Author(s):  
Muhammad Noor ◽  
Tarmizi Ismail ◽  
Eun-Sung Chung ◽  
Shamsuddin Shahid ◽  
Jang Sung
Keyword(s):  
General Circulation ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Peninsular Malaysia ◽  
Climate Change Scenarios ◽  
Rcp Scenarios ◽  
Maximum Rainfall ◽  
Idf Curves ◽  
Intensity Duration Frequency

This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006–2099 with that obtained using GCM hindcasts for the based period (1971–2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia.

scholarly journals Climate Change Impact on Flood Control Measures for Highly Populated Urban Watershed

2018 ◽  
Author(s):  
Carla Voltarelli Franco da Voltarelli ◽  
Andre Schardong ◽  
Joaquin I. B. Garcia ◽  
Cristiano de Padua Milagres Oliveira
Keyword(s):  
Climate Change ◽  
Flood Control ◽  
Climate Models ◽  
Climatic Factors ◽  
Drainage System ◽  
Global Climate Models ◽  
Control Measures ◽  
Climate Change Scenarios ◽  
Retention Measure ◽  
Idf Curves

Flooding and overflows are recurring problems in several Brazilian cities, which usually undergo disorderly development. Their causes vary from increased impervious surface areas, deficiency/inefficiency of drainage structures and their maintenance, siltation of rivers, channel obstructions, and climatic factors. This situation is aggravated in the major cities. The Anhangabau watershed lies in the central portion of the city of Sao Paulo – Brazil and covers a drainage area of 5.4 km². The region is highly urbanized and crossed by a major north-south road connection. During heavy rain events, portions of this interconnection passage become compromised, disrupting the flow of vehicles, creating a chaotic situation for the population, as well as losses to the national economy. Observed rainfall records and an existing IDF (intensity duration frequency) curve for the region are used to obtain design storms. To account for climate change, a well know procedure, the equidistance quantile matching method for updating IDF curves under climate change, was applied to the existing historical data. Several different global climate models (GCM) and one regional model were applied to obtain and update rainfall design storm. The GCMs and future scenarios used were from the IPCC Assessment Report 5 (AR5) and two future projections: RCP (representative concentration pathway) 4.5 and 8.5. Alternatives previously proposed to solve to flooding issue are briefly reviewed. On one of the latest studies [1], a few modern concepts of water resources management are presented, and the linear retention measure was found to offer higher potential to mitigate the flooding problem in the lower valley of the watershed. Therefore, this alternative was used to evaluate different design storms scenarios combined with return periods of 25 and 100-years as well as the updated IDF under climate change for RCP 4.5 and RCP 8.5. To model the complex network, representing both road and drainage systems and their interconnections, PCSWMM/SWMM software was applied. Results are presented as flooding maps and show the impacts of the proposed linear retention measure based on the existing IDF curves and the updated IDF curves under climate change for two different drainage system conditions, current and improved with the use of linear retention reservoirs. Results show that the prosed changes on the drainage system help reduce the risk and damage to flooding. The climate change scenarios, however, impose a significant threat and need immediate attention from city planners and stakeholders.

scholarly journals Disaggregation of future GCMs to generate IDF curves for the assessment of urban floods

2021 ◽  
Author(s):  
H. Tayşi ◽  
M. Özger
Keyword(s):  
Climate Change ◽  
Short Duration ◽  
Urban Areas ◽  
Climate Models ◽  
Global Climate ◽  
Drainage System ◽  
Global Climate Models ◽  
Urban Floods ◽  
Idf Curves ◽  
Intensity Duration Frequency

Abstract Urbanization and industrialization cause an increase in greenhouse gas emissions, which in turn causes changes in the atmosphere. Climate change is causing extreme rainfalls and these rainfalls are getting stronger day after day. Floods are threatening urban areas, and short-duration rainfall and outdated drainages are responsible for urban floods. Intensity–Duration–Frequency (IDF) curves are crucial for both drainage system design and assessment of flood risk. Once IDF curves are determined from historical data, they are assumed to be stationary. However, IDF curves must be non-stationary and time varying based on preparation for extreme events. This study generates future IDF curves with short-duration rainfalls under climate change. To represent future rainfall, an ensemble of four Global Climate Models generated under Representative Concentration Pathways (RCP) 4.5 and 8.5 were used in this study. A new approach to the HYETOS disaggregation model was applied to disaggregate daily future rainfall into sub-hourly using disaggregation parameters of hourly measured rainfalls. Hence, sub-hourly future rainfalls will be obtained capturing historical rainfall patterns instead of random rainfall characteristics. Finally, historical and future IDF curves were compared. The study concludes that increases in short-duration rainfalls will be highly intensified in both the near and distant futures with a high probability.

scholarly journals Spectral representation of the annual cycle in the climate change signal

2011 ◽  
Vol 8 (1) ◽  
pp. 1161-1192 ◽  
Author(s):  
T. Bosshard ◽  
S. Kotlarski ◽  
T. Ewen ◽  
C. Schär
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Climate Impact ◽  
Climate Change Signal ◽  
Climate Change Scenarios ◽  
Regional Climate Model Output ◽  
Spectral Smoothing ◽  
Precipitation Changes

Abstract. The annual cycle of temperature and precipitation changes as projected by climate models is of fundamental interest in climate impact studies. Its estimation, however, is impaired by natural variability. Using a simple form of the delta change method, we show that on regional scales relevant for hydrological impact models, the projected changes in the annual cycle are prone to sampling artefacts. For precipitation at station locations, these artefacts may have amplitudes that are comparable to the climate change signal itself. Therefore, the annual cycle of the climate change signal should be filtered when generating climate change scenarios. We test a spectral smoothing method to remove the artificial fluctuations. Comparison against moving monthly averages shows that sampling artefacts in the climate change signal can successfully be removed by spectral smoothing. The method is tested at Swiss climate stations and applied to regional climate model output of the ENSEMBLES project. The spectral method performs well, except in cases with a strong annual cycle and large relative precipitation changes.

scholarly journals A new dataset for systematic assessments of climate change impacts as a function of global warming

2012 ◽  
Vol 5 (4) ◽  
pp. 3533-3572 ◽  
Author(s):  
J. Heinke ◽  
S. Ostberg ◽  
S. Schaphoff ◽  
K. Frieler ◽  
C. Müller ◽  
...  
Keyword(s):  
Climate Change ◽  
Temperature Change ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Climate Change Scenarios ◽  
Political Debate ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract. In the ongoing political debate on climate change, global mean temperature change (ΔTglob) has become the yardstick by which mitigation costs, impacts from unavoided climate change, and adaptation requirements are discussed. For a scientifically informed discourse along these lines systematic assessments of climate change impacts as a function of ΔTglob are required. The current availability of climate change scenarios constrains this type of assessment to a narrow range of temperature change and/or a reduced ensemble of climate models. Here, a newly composed dataset of climate change scenarios is presented that addresses the specific requirements for global assessments of climate change impacts as a function of ΔTglob. A pattern-scaling approach is applied to extract generalized patterns of spatially explicit change in temperature, precipitation and cloudiness from 19 AOGCMs. The patterns are combined with scenarios of global mean temperature increase obtained from the reduced-complexity climate model MAGICC6 to create climate scenarios covering warming levels from 1.5 to 5 degrees above pre-industrial levels around the year 2100. The patterns are shown to sufficiently maintain the original AOGCMs' climate change properties, even though they, necessarily, utilize a simplified relationships between ΔTglob and changes in local climate properties. The dataset (made available online upon final publication of this paper) facilitates systematic analyses of climate change impacts as it covers a wider and finer-spaced range of climate change scenarios than the original AOGCM simulations.

Analysis of potential impacts of climate change on intensity–duration–frequency (IDF) relationships for six regions in the United States

2012 ◽  
Vol 3 (3) ◽  
pp. 185-196 ◽  
Author(s):  
Jianting Zhu ◽  
Mark C. Stone ◽  
William Forsee
Keyword(s):  
Climate Change ◽  
United States ◽  
Rainfall Intensity ◽  
Extreme Rainfall ◽  
The United States ◽  
Regional Patterns ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Projected Changes ◽  
Impacts Of Climate Change

Potential changes in climate are expected to lead to future changes in the characteristics of precipitation events, including extreme rainfall intensity in most regions. In order for government agencies and design engineers to incorporate these trends and future changes into assessment and design processes, tools for planning and design should be capable of considering nonstationary climate conditions. In this work, potential changes are investigated in intensity–duration–frequency (IDF) curves, which are often used for assessment of extreme rainfall events, using historic data and future climate projections. An approach is proposed for calculating IDF curves that incorporates projected changes in rainfall intensity at a range of locations in the United States. The results elucidate strong regional patterns in projected changes in rainfall intensity, which are influenced by the rainfall characteristics of the region. Therefore, impacts of climate change on extreme hydrologic events will be highly regional and thus such assessments should be performed for specific project locations.

Sign in / Sign up

Export Citation Format

Share Document