Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

Projected Changes in Precipitation Extremes Over Jiulongjiang River Basin in Coastal Southeast China

The southeast coastal region of China is susceptible to challenges related to extreme precipitation events; hence, projection of future climate extremes changes is crucial for sustainable development in the region. Using the Regional Climate Model Version 4 (RegCM4), the future changes of summer precipitation extremes have been investigated over the Jiulongjiang River Basin (JRB), a coastal watershed in Southeast China. Comparison between the RegCM4 simulated and observed rainy season precipitation over JRB suggests that the RegCM4 can reasonably reproduce the seasonal precipitation cycle, the frequency distribution of precipitation intensity, and the 50-year return levels of precipitation extremes over JRB. Furthermore, the model projects an increase in daily maximum rainfall (RX1day) mostly over the northern part of the basin and a decrease over other parts of the basin, while projecting a widespread decrease for maximum consecutive 5-day precipitation amount (RX5day) relative to the present day. In terms of the 50-year return level of RX1day (RL50yr_RX1day), a general increase is projected over most parts of the basin in the near and far future of the 21st century, but a decrease can be found in the northeast and southwest parts of the JRB in the mid-21st century. The future change of the 50-year return level of RX5day (RL50yr_RX5day) shows a similar spatial pattern with that of RL50yr_RX1day in the near and mid-21st century, but with a larger magnitude. However, a remarkable decrease in RL50yr_RX5day is found in the south basin in the far future. Meanwhile, the projected changes in the 50-year return level for both RX1day and RX5day differ between the first and second rainy seasons in JRB. Specifically, the future increase in RL50yr_RX5day over the north basin is mainly contributed by the changes during the first-half rainy season, while the decrease of RL50yr_RX5day in the south is mostly ascribed to the future changes during the second-half rainy season. All above results indicate that the future changes of precipitation extremes in JRB are complicated, which might differ from extreme indices, seasons, and future projected periods. These will thus be of practical significance for flood risk management, mitigation, and adaptation measures in Jiulongjiang River Basin.

Expected Recurrence of Extreme Winds in Northwestern Sahara and Associated Uncertainties

Estimating the probability of the occurrence of hazardous winds is crucial for their impact in human activities; however, this is inherently affected by the shortage of observations. This becomes critical in poorly sampled regions, such as the northwestern Sahara, where this work is focused. The selection of any single methodological variant contributes with additional uncertainty. To gain robustness in the estimates, we expand the uncertainty space by applying a large body of methodologies. The methodological uncertainty is constrained afterward by keeping only the reliable experiments. In doing so, we considerably narrow the uncertainty associated with the wind return levels. The analysis suggest that not necessarily all methodologies are equally robust. The highest 10-min speed (wind gust) for a return period of 50 years is about 45 ms−1 (56 ms−1). The intensity of the expected extreme winds is closely related to orography. The study is based on wind and wind gust observations that were collected and quality controlled for the specific purposes herein. We also make use of a 12-year high-resolution regional simulation to provide simulation-based wind return level maps that endorse the observation-based results. Such an exhaustive methodological sensitivity analysis with a long high-resolution simulation over this region was lacking in the literature.

Analysis of extreme rainfall and drought events using statistical and fractal methods: A case study of Mauritius

Due to climate change, extreme rainfall and drought events are becoming more and more frequent in several regions of the globe. We investigated the suitability of employing statistical and fractal (or scaling) methods to characterise extreme precipitation and drought events. The case of the island of Mauritius was considered, for which monthly mean rainfall data for the period January 1950 to December 2016 were analysed. The generalised extreme value distribution was used to extract the 10- and 20-year return levels and the Standardised Precipitation Index (SPI) was used to identify anomalous wet and dry events. A log-term correlation analysis was also performed to characterise the relationship between maximum rainfall and its duration. The results indicate that the 10-year return level is approximately between 500 mm and 850 mm and the 20-year return level is between 600 mm and 1000 mm. Results also show that the extreme maximum rainfall events occur mostly during austral summer (November to April) and could be related to the effects of tropical cyclones and La Niña events, while anomalous dry events were found to be significantly persistent with very long periods of drought. Moreover, there was a strong correlation between maximum rainfall and its duration. The methodology used in this work could be very useful in similar studies for other Small Island Developing States.

Risk Analysis of Gold Prices in Pakistan Using Extreme Value Theory

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution.

Chinese Sunspot Drawings and Their Digitizations-(VI) Extreme Value Theory Applied to the Sunspot Number Series from the Purple Mountain Observatory

Based on the daily sunspot number (SN) data (1954–2011) from the Purple Mountain Observatory, the extreme value theory (EVT) is employed for the research of the long-term solar activity. It is the first time that the EVT is applied on the Chinese SN. Two methods are used for the research of the extreme events with EVT. One method is the block maxima (BM) approach, which picks the maximum SN value of each block. Another one is the peaks-over-threshold (POT) approach. After a declustering process, a threshold value (here it is 300) is set to pick the extreme values. The negative shape parameters are obtained by the two methods, respectively, indicating that there is an upper bound for the extreme SN value. Only one value of the N-year return level (RL) is estimated: N = 19 years. For N = 19 years, the RL values of SN obtained by two methods are similar with each other. The RL values are found to be 420 for the POT method and the BM method. Here, the trend of 25th solar cycle is predicted to be stronger, indicating that the length of meridional forms of atmospheric circulation will be increased.

Design Wave Height Parameter Estimation Model Reflecting the Influence of Typhoon Time and Space

Typhoon storm surge disasters are one of the main restrictive factors of sustainable development in coastal areas. They are one of several important tasks in disaster prevention and reduction in coastal areas and require reasonable and accurate calculations of wave height in typhoon-affected sea areas to predict and resist typhoon storm surge disasters. In this paper, the design wave height estimation method based on the stochastic process and the principle of maximum entropy are theoretically advanced, and it can provide a new idea as well as a new method for the estimation of the return level for marine environmental elements under the influence of extreme weather. The model uses a family of random variables to reflect the influence of a typhoon on wave height at different times and then displays the statistical characteristics of wave height in time and space. At the same time, under the constraints of the given observations, the maximum uncertainty of the unobtainable data is maintained. The new model covers the compound extreme value distribution model that has been widely used and overcomes the subjective interference of the artificially selected distribution function—to a certain extent. Taking the typhoon wave height data of Naozhou Observatory as an example, this paper analyzes the probability of typhoon occurrence frequency at different times and the characteristics of typhoon intensity in different time periods. We then calculate the wave height return level and compare it with traditional calculation models. The calculation results show that the new model takes into account the time factor and the interaction between adjacent time periods. Furthermore, it reduces the subjective human interference, so the calculated results of the typhoon’s influence on wave height return level are more stable and accurate.

A Comparison of Tropical Cyclone Projections in a High-resolution Global Climate Model and from Downscaling by Statistical and Statistical-deterministic Methods

In this study, we investigate the response of tropical cyclones (TCs) to climate change by using the Princeton environment-dependent probabilistic tropical cyclone (PepC) model and a statistical-deterministic method to downscale TCs using environmental conditions obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Forecast-oriented Low Ocean Resolution (HiFLOR) model, under the Representative Concentration Pathway 4.5 (RCP4.5) emissions scenario for the North Atlantic basin. The downscaled TCs for the historical climate (1986-2005) are compared with those in the mid- (2016-35) and late-twenty-first century (2081-2100). The downscaled TCs are also compared with TCs explicitly simulated in HiFLOR. We show that while significantly more storms are detected in HiFLOR towards the end of the twenty-first century, the statistical-deterministic model projects a moderate increase in TC frequency, and PepC projects almost no increase in TC frequency. The changes in storm frequency in all three datasets are not significant in the mid-twenty-first century. All three project that storms will become more intense and the fraction of major hurricanes and Category 5 storms will significantly increase in the future climates. However, HiFLOR projects the largest increase in intensity while PepC projects the least. The results indicate that HiFLOR’s TC projection is more sensitive to climate change effects and statistical models are less sensitive. Nevertheless, in all three datasets, storm intensification and frequency increase lead to relatively small changes in TC threat as measured by the return level of landfall intensity.

Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.