scholarly journals Anthropogenic climate change has changed frequency of past flood during 2010-2013

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yukiko Hirabayashi ◽  
Haireti Alifu ◽  
Dai Yamazaki ◽  
Yukiko Imada ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Radiative Forcing ◽  
Water Cycle ◽  
Flood Frequency ◽  
Anthropogenic Climate Change ◽  
Climate Change Signal ◽  
Northern Eurasia ◽  
Large Ensemble ◽  
The Impact

AbstractThe ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

scholarly journals On the attribution of the impacts of extreme weather events to anthropogenic climate change

2021 ◽  
Author(s):  
Sarah E Perkins-Kirkpatrick ◽  
Daithi Stone ◽  
Dann M. Mitchell ◽  
Suzanne M. Rosier ◽  
Andrew David King ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Anthropogenic Impacts ◽  
Extreme Weather ◽  
Anthropogenic Climate Change ◽  
Extreme Weather Events ◽  
Climate Change Signal ◽  
Care Needs ◽  
Weather Events ◽  
The Impact

Abstract Investigations into the role of anthropogenic climate change in extreme weather events are now starting to extend into analysis of anthropogenic impacts on non-climate (e.g. socio-economic) systems. However, care needs to be taken when making this extension, because methodological choices regarding extreme weather attribution can become crucial when considering the events’ impacts. The fraction of attributable risk (FAR) method, useful in extreme weather attribution research, has a very specific interpretation concerning a class of events, and there is potential to misinterpret results from weather event analyses as being applicable to specific events and their impact outcomes. Using two case studies of meteorological extremes and their impacts, we argue that FAR is not generally appropriate when estimating the magnitude of the anthropogenic signal behind a specific impact. Attribution assessments on impacts should always be carried out in addition to assessment of the associated meteorological event, since it cannot be assumed that the anthropogenic signal behind the weather is equivalent to the signal behind the impact because of lags and nonlinearities in the processes through which the impact system reacts to weather. Whilst there are situations where employing FAR to understand the climate change signal behind a class of impacts is useful (e.g. “system breaking” events), more useful results will generally be produced if attribution questions on specific impacts are reframed to focus on changes in the impact return value and magnitude across large samples of factual and counterfactual climate model and impact simulations. We advocate for constant interdisciplinary collaboration as essential for effective and robust impact attribution assessments.

scholarly journals On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 799 ◽  
Author(s):  
Lorenzo Sangelantoni ◽  
Barbara Tomassetti ◽  
Valentina Colaiuda ◽  
Annalina Lombardi ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Central Italy ◽  
Stress Index ◽  
Climate Change Signal ◽  
Correction Technique ◽  
The Impact

The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to −50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season.

scholarly journals Impact of air–sea coupling on the climate change signal over the Iberian Peninsula

2021 ◽  
Author(s):  
Alba de la Vara ◽  
William Cabos ◽  
Dmitry V. Sein ◽  
Claas Teichmann ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Mediterranean Sea ◽  
Large Scale ◽  
Regional Distribution ◽  
Coupled Model ◽  
Climate Change Signal ◽  
The North ◽  
The Mediterranean ◽  
The Impact

AbstractIn this work we use a regional atmosphere–ocean coupled model (RAOCM) and its stand-alone atmospheric component to gain insight into the impact of atmosphere–ocean coupling on the climate change signal over the Iberian Peninsula (IP). The IP climate is influenced by both the Atlantic Ocean and the Mediterranean sea. Complex interactions with the orography take place there and high-resolution models are required to realistically reproduce its current and future climate. We find that under the RCP8.5 scenario, the generalized 2-m air temperature (T2M) increase by the end of the twenty-first century (2070–2099) in the atmospheric-only simulation is tempered by the coupling. The impact of coupling is specially seen in summer, when the warming is stronger. Precipitation shows regionally-dependent changes in winter, whilst a drier climate is found in summer. The coupling generally reduces the magnitude of the changes. Differences in T2M and precipitation between the coupled and uncoupled simulations are caused by changes in the Atlantic large-scale circulation and in the Mediterranean Sea. Additionally, the differences in projected changes of T2M and precipitation with the RAOCM under the RCP8.5 and RCP4.5 scenarios are tackled. Results show that in winter and summer T2M increases less and precipitation changes are of a smaller magnitude with the RCP4.5. Whilst in summer changes present a similar regional distribution in both runs, in winter there are some differences in the NW of the IP due to differences in the North Atlantic circulation. The differences in the climate change signal from the RAOCM and the driving Global Coupled Model show that regionalization has an effect in terms of higher resolution over the land and ocean.

Evaluation of the daily cycle in the simulation of the ClimEx large-ensemble

2021 ◽  
Author(s):  
Anne-Marie Begin
Keyword(s):  
Climate Change ◽  
Estimation Error ◽  
Natural Variability ◽  
Extreme Weather Events ◽  
Large Ensemble ◽  
Daily Cycle ◽  
Minimum Number ◽  
The Mean ◽  
Mean Cycle ◽  
The Impact

<p>To estimate the impact of climate change on our society we need to use climate projections based on numerical models. These models make it possible to assess the effects on climate of the increase in greenhouse gases (GHG) as well as natural variability. We know that the global average temperature will increase and that the occurrence, intensity and spatio-temporal distribution of extreme precipitations will change. These extreme weather events cause droughts, floods and other natural disasters that have significant consequences on our life and environment. Precipitation is a key variable in adapting to climate change.</p><p> </p><p>This study focuses on the ClimEx large ensemble, a set of 50 independent simulations created to study the effect of climate change and natural variability on the water network in Quebec. This dataset consists of simulations produced using the Canadian Regional Climate Model version 5 (CRCM5) at 12 km of resolution driven by simulations from the second generation Canadian Earth System Model (CanESM2) global model at 310 km of resolution.</p><p> </p><p>The aim of the project is to evaluate the performance of the ClimEx ensemble in simulating the daily cycle and representing extreme values.  To get there, 30 years of hourly time series for precipitation and 3 hourly for temperature are analyzed. The simulations are compared with the values from the simulation of CRCM5 driven by ERA-Interim reanalysis, the ERA5 reanalysis and Environment and Climate Change Canada (ECCC) stations. An evaluation of the sensitivity of different statistics to the number of members is also performed.</p><p> </p><p>The daily cycle of precipitation from ClimEx shows mainly non-significant correlations with the other datasets and its amplitude is less than the observation datas from ECCC stations. For temperature, the correlation is strong and the amplitude of the cycle is similar to observations. ClimEx provides a fairly good representation of the 95, 97, 99<sup>th</sup> quantiles for precipitation. For temperature it represents a good distribution of quantiles but with a warm bias in southern Quebec. For precipitation hourly maximum, ClimEx shows values 10 times higher than ERA5.  For temperature, minimum and maximum values may exceed the ERA5 limit by up to 20°C. For precipitation, the minimum number of members for the estimation of the 95 and 99<sup>th</sup><sup></sup>quantiles and the mean cycle is between 15 and 50 for an estimation error of less than 5%. For the 95, 99<sup>th</sup> quantiles of temperature, the minimum number of members is between 1 and 17 and for the mean cycle 1 to 2 members are necessary to obtain an estimation error of less than 0.5°C.</p>

scholarly journals Climate and hydrological variability: the catchment filtering role

2015 ◽  
Vol 19 (1) ◽  
pp. 379-387 ◽  
Author(s):  
I. Andrés-Doménech ◽  
R. García-Bartual ◽  
A. Montanari ◽  
J. B. Marco
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Peak Flow ◽  
Flood Frequency ◽  
Annual Maximum ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Maximum Peak ◽  
Hydrological Variability ◽  
The Impact

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change.

scholarly journals SWAT-Based Runoff Simulation and Runoff Responses to Climate Change in the Headwaters of the Yellow River, China

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 509
Author(s):  
Jingwen Wu ◽  
Haiyan Zheng ◽  
Yang Xi
Keyword(s):  
Climate Change ◽  
Assessment Tool ◽  
Water Cycle ◽  
Yellow River ◽  
Annual Runoff ◽  
The Yellow River ◽  
Runoff Simulation ◽  
Alpine Regions ◽  
Slight Upward Trend ◽  
The Impact

Runoff in snowy alpine regions is sensitive to climate change in the context of global warming. Exploring the impact of climate change on the runoff in these regions is critical to understand the dynamics of the water cycle and for the improvement of water resources management. In this study, we analyzed the long-term variations in annual runoff in the headwaters region of the Yellow River (HRYR) (a typical snowy mountain region) during the period of 1956–2012. The Soil and Water Assessment Tool (SWAT) with different elevation bands was employed to assess the performance of monthly runoff simulations, and then to evaluate the impacts of climate change on runoff. The results show that the observed runoff for the hydrological stations at lower relative elevations (i.e., Maqu and Tangnaihai stations) had a downward trend, with rates of 1.91 and 1.55 mm/10 years, while a slight upward trend with a rate of 0.26 mm/10 years was observed for the hydrological station at higher elevation (i.e., Huangheyan station). We also found that the inclusion of five elevation bands could lead to more accurate runoff estimates as compared to simulation without elevation bands at monthly time steps. In addition, the dominant cause of the runoff decline across the whole HRYR was precipitation (which explained 64.2% of the decrease), rather than temperature (25.93%).

scholarly journals Climate Change Impact on Flood Frequency and Source Area in Northern Iran under CMIP5 Scenarios

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 273 ◽  
Author(s):  
Fatemeh Fadia Maghsood ◽  
Hamidreza Moradi ◽  
Ali Reza Massah Bavani ◽  
Mostafa Panahi ◽  
Ronny Berndtsson ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Source Area ◽  
Flood Frequency ◽  
Northern Iran ◽  
Rcp Scenarios ◽  
Impact Of Climate Change ◽  
The Impact ◽  
Near Future ◽  
Flood Source

This study assessed the impact of climate change on flood frequency and flood source area at basin scale considering Coupled Model Intercomparison Project phase 5 General Circulation Models (CMIP5 GCMs) under two Representative Concentration Pathways (RCP) scenarios (2.6 and 8.5). For this purpose, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated and validated for the Talar River Basin in northern Iran. Four empirical approaches including the Sangal, Fill–Steiner, Fuller, and Slope-based methods were used to estimate the Instantaneous Peak Flow (IPF) on a daily basis. The calibrated SWAT model was run under the two RCP scenarios using a combination of twenty GCMs from CMIP5 for the near future (2020–40). To assess the impact of climate change on flood frequency pattern and to quantify the contribution of each subbasin on the total discharge from the Talar River Basin, Flood Frequency Index (FFI) and Subbasin Flood Source Area Index (SFSAI) were used. Results revealed that the projected climate change will likely lead to an average discharge decrease in January, February, and March for both RCPs and an increase in September and October for RCP 8.5. The maximum and minimum temperature will likely increase for all months in the near future. The annual precipitation could increase by more than 20% in the near future. This is likely to lead to an increase of IPF. The results can help managers and policy makers to better define mitigation and adaptation strategies for basins in similar climates.

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.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

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