Using regional climate models to simulate extreme rainfall events in the Western Cape, South Africa

2015 ◽  
Vol 36 (2) ◽  
pp. 689-705 ◽  
Author(s):  
Babatunde J. Abiodun ◽  
Sabina Abba Omar ◽  
Chris Lennard ◽  
Chris Jack
Keyword(s):  
South Africa ◽  
Climate Models ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Western Cape ◽  
Extreme Rainfall Events ◽  
Rainfall Events

Precipitation extremes over southern South America and their synoptic environment in a set of CORDEX regional climate models

2021 ◽  
Author(s):  
Matias Ezequiel Olmo ◽  
Maria Laura Bettolli
Keyword(s):  
South America ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Circulation Types ◽  
Observational Uncertainty

<p>Southern South America (SSA) is a wide populated region exposed to extreme rainfall events, which are recognised as some of the major threats in a warming climate. These events produce large impacts on socio-economic activities, energy demand and health systems. Hence, studying this phenomena requires high-quality and high-resolution observational data and model simulations. In this work, the main features of daily extreme precipitation and circulation types over SSA were evaluated using a 4-model set of CORDEX regional climate models (RCMs) driven by ERA-Interim during 1980-2010: RCA4 and WRF from CORDEX Phase 1 and RegCM4v7 and REMO2015 from the brand-new CORDEX-CORE simulations. Observational uncertainty was assessed by comparing model outputs with multiple observational datasets (rain gauges, CHIRPS, CPC and MSWEP). </p><p>The inter-comparison of extreme events, characterized in terms of their intensity, frequency and spatial coverage, varied across SSA exhibiting large differences among observational datasets and RCMs, pointing out the current observational uncertainty when evaluating precipitation extremes, particularly at a daily scale. The spread between observational datasets was smaller than for the RCMs. Most of the RCMs successfully captured the spatial pattern of extreme rainfall across SSA, reproducing the maximum intensities in southeastern South America (SESA) and central and southern Chile during the austral warm (October to March) and cold (April to September) seasons, respectively. However, they often presented overestimations over central and southern Chile, and more variable results in SESA. RegCM4 and WRF seemed to well represent the maximum precipitation amounts over SESA, while REMO showed strong overestimations and RCA4 had more difficulties in representing the spatial distribution of heavy rainfall intensities. Focusing over SESA, differences were detected in the timing and location of extremes (including the areal coverage) among both observational datasets and RCMs, which poses a particular challenge when performing impact studies in the region. Thus, stressing that the use of multiple datasets is of key importance when carrying out regional climate studies and model evaluations, particularly for extremes. </p><p>The synoptic environment was described by a classification of circulation types (CTs) using Self-Organizing Maps (SOM) considering geopotential height anomalies at 500 hPa (Z500). Specific CTs were identified as they significantly enhanced the occurrence of extreme rainfall events in sectorized areas of SESA. In particular, a dipolar structure of Z500 anomalies that produced a marked trough at the mid-level atmosphere, usually located east of the Andes, significantly favoured the occurrence of extreme precipitation events in the warm season. The RCMs were able to adequately reproduce the SOM frequencies, although simplifying the predominant CTs into a reduced number of configurations. They appropriately reproduced the observed extreme precipitation frequencies conditioned by the CTs and their atmospheric configurations, but exhibiting some limitations in the location and intensity of the resulting precipitation systems.</p><p>In this sense, continuous evaluations of observational datasets and model simulations become necessary for a better understanding of the physical mechanisms behind extreme precipitation over the region, as well as for its past and future changes in a climate change scenario.</p>

scholarly journals Climate and Extreme Rainfall Events in the Mono River Basin (West Africa): Investigating Future Changes with Regional Climate Models

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 833
Author(s):  
Ernest Amoussou ◽  
Hervé Awoye ◽  
Henri S. Totin Vodounon ◽  
Salomon Obahoundje ◽  
Pierre Camberlin ◽  
...  
Keyword(s):  
River Basin ◽  
Climate Models ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Simulated Climate ◽  
Heavy Rainfall Events

This study characterizes the future changes in extreme rainfall and air temperature in the Mono river basin where the main economic activity is weather dependent and local populations are highly vulnerable to natural hazards, including flood inundations. Daily precipitation and temperature from observational datasets and Regional Climate Models (RCMs) output from REMO, RegCM, HadRM3, and RCA were used to analyze climatic variations in space and time, and fit a GEV model to investigate the extreme rainfalls and their return periods. The results indicate that the realism of the simulated climate in this domain is mainly controlled by the choice of the RCMs. These RCMs projected a 1 to 1.5 °C temperature increase by 2050 while the projected trends for cumulated precipitation are null or very moderate and diverge among models. Contrasting results were obtained for the intense rainfall events, with RegCM and HadRM3 pointing to a significant increase in the intensity of extreme rainfall events. The GEV model is well suited for the prediction of heavy rainfall events although there are uncertainties beyond the 90th percentile. The annual maxima of daily precipitation will also increase by 2050 and could be of benefit to the ecosystem services and socioeconomic activities in the Mono river basin but could also be a threat.

Finding homogeneous regions of extreme rainfall in reanalysis data

2020 ◽  
Author(s):  
Marjanne Zander ◽  
Frederiek Sperna Weiland ◽  
Albrecht Weerts
Keyword(s):  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Regional Frequency Analysis ◽  
Homogeneous Regions ◽  
Rainfall Statistics

<p>In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.</p><p>Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.</p><p>Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017).  For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).</p><p>Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009).  To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.</p><p>Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.</p><p>This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.</p><p>References:</p><p>Gabriele, S., & Chiaravalloti, F. (2013). “Searching regional rainfall homogeneity using atmospheric fields”. Advances in Water Resources, 53, 163–174. https://doi.org/https://doi.org/10.1016/j.advwatres.2012.11.002</p><p>Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., …, Zuo, H. (2018). “Operational global reanalysis: progress, future directions and synergies with NWP”, ECMWF.</p><p>Hosking, J. R. M., & Wallis, J. R. (2009). “Regional Frequency Analysis: An Approach Based on L-Moments”. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.</p><p>Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., … Wilkinson, J. M. (2017). “Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?” BAMS, 98(1), 79–93. https://doi.org/10.1175/BAMS-D-15-0004.1</p><p> Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). “A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.” Journal of Hydrology, 547(Supplement C), 413–427. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.02.019</p>

scholarly journals Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

2021 ◽  
Vol 24 (s1) ◽  
pp. 31-36
Author(s):  
Peter Valent ◽  
Roman Výleta
Keyword(s):  
Soil Erosion ◽  
Climate Models ◽  
Regional Climate ◽  
Multiple Linear Regression Model ◽  
Rain Gauge ◽  
Rainfall Erosivity ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
R Factor ◽  
Geostatistical Approach

Abstract Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (r 2 = 0.98) between monthly EI 30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site R-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create R-factor maps using a geostatistical approach. The results showed that the mean R-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.

scholarly journals Effects of Global Warming on Heavy Rainfall During the Baiu Season Projected by a Cloud-System-Resolving Model

2008 ◽  
Vol 3 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Masaomi Nakamura ◽  
◽  
Sachie Kaneda ◽  
Yasutaka Wakazuki ◽  
Chiashi Muroi ◽  
...  
Keyword(s):  
Global Warming ◽  
Heavy Rainfall ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Horizontal Resolution ◽  
Rainfall Events ◽  
Future Global Warming ◽  
Heavy Rainfall Events

Under the Kyosei-4 Project, unprecedented high resolution global and regional climate models were developed on the Earth Simulator to investigate the effect of global warming on tropical cyclones, baiu frontal rainfall systems, and heavy rainfall events that could not be resolved using conventional climate models.For the regional climate model, a nonhydrostatic model (NHM) with a horizontal resolution of 5 km was developed to be used in the simulation of heavy rainfall during the baiu season in Japan. Simulations in June and July were executed for 10 years in present and future global warming climates. It was found that, due to global warming, mean rainfall is projected to increase except in eastern and northern Japan, the frequency of heavy rainfall events would increase and its increment rate become higher for heavier rainfall, and return values for extreme rainfall would grow.Experiments using an NHM with a horizontal resolution of 1 km were conducted to study the effects of resolution. Compared to 5 km resolution, it expresses the organization of rainfall systems causing heavy rainfall and the appearance-frequency distribution of rainfall for variable intensities more realistically.

scholarly journals Estimation of the Changes in the Rainfall Erosivity in Hungary

2016 ◽  
Vol 9 (3-4) ◽  
pp. 43-48 ◽  
Author(s):  
Gábor Mezősi ◽  
Teodóra Bata
Keyword(s):  
Soil Erosion ◽  
Rainfall Intensity ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Rainfall Erosivity ◽  
Precipitation Data ◽  
Rainfall Events ◽  
Long Run ◽  
Erosion Effect

Abstract According to the forecasts of numerous regional models (eg. REMO, ALADIN, PREGIS), the number of predicted rainfall events decreases, but they are not accompanied by considerably less precipitation. It represents an increase in rainfall intensity. It is logical to ask (if the limitations of the models make it possible) to what extent rainfall intensity is likely to change and where these changes are likely to occur in the long run. Rain intensity is considered to be one of the key causes of soil erosion. If we know which areas are affected by more intense rain erosion, we can identify the areas that are likely to be affected by stronger soil erosion, and we can also choose effective measures to reduce erosion. This information is necessary to achieve the neutral erosion effect as targeted by the EU. We collected the precipitation data of four stations every 30 minute between 2000 and 2013, and we calculated the estimated level of intensity characterizing the Carpathian Basin. Based on these data, we calculated the correlation of the measured data of intensity with the values of the MFI index (the correlation was 0.75). According to a combination of regional climate models, precipitation data could be estimated until 2100, and by calculating the statistical relationship between the previous correlation and this data sequence, we could estimate the spatial and temporal changes of rainfall intensity.

scholarly journals Extreme Rainfall and Hydro-Geo-Meteorological Disaster Risk in 1.5, 2.0, and 4.0°C Global Warming Scenarios: An Analysis for Brazil

2021 ◽  
Vol 3 ◽  
Author(s):  
Jose A. Marengo ◽  
Pedro I. Camarinha ◽  
Lincoln M. Alves ◽  
Fabio Diniz ◽  
Richard A. Betts
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Extreme Rainfall ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Disaster Risk ◽  
Remarkable Change ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Metropolitan Regions

With the inclusion of demographic characteristics of the population living in vulnerable areas, a combination of empirical and climate models was used to project changes to climate and in hydro-geo-meteorological disasters in Brazil. This study investigated the effect of extreme rainfall changes and the risk of floods and landslides under 1.5, 2.0, and 4.0°C global warming levels (GWLs). Projections from a large ensemble of pre-CMIP6 models and different warming levels show a remarkable change in heavy precipitation. As a result, with increasing warming this enhances the risk of landslides and flash floods in the context of climate change. Comparisons of vulnerability and change in potential impacts of landslides and floods show that three regions, highly densely populated areas, are the most exposed to landslides and floods. The Southern and Southeastern of Brazil stand out, including metropolitan regions with high economic development and densely populated, which may be those where disasters can intensify both in terms of frequency and magnitude. The eastern portion of the Northeast is also signaled as one of the affected regions due to its high vulnerability and exposure since the present period, although the projections of future climate do not allow conclusive results regarding the intensification of extreme rainfall events in scenarios below 4°C. The main metropolitan regions and tourist resorts, and key infrastructure in Brazil are located in those regions. This study highlights the importance of environmental policies to protect human lives and minimize financial losses in the coming decades and reinforces the need for decision-making, monitoring, and early warning systems to better manage disasters as part of disaster risk reduction risk management.

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