Future Extreme Rainfall Research Articles

Projected future changes in food insecurity hotspots over the IGAD region of Eastern Africa

Food insecurity is a major issue in many parts of the world, driven by conflict, economic instability, environmental challenges, and poor governance processes. Understanding the impact of future rainfall extremes on areas already experiencing food insecurity is crucial. This study investigates how food insecurity hotspots (FIH), food crisis frequency, and duration will change in the near future (2021–2050) and far future (2071–2100) under Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study utilizes precipitation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and FIH data from the NASA Socioeconomic Data and Applications Center (SEDAC). To calculate future exposure and vulnerability to FIH, as well as food crisis frequency and duration, weighted sum models were used. The results indicate that arid and semi-arid areas in northeastern Kenya, most of Somalia, zones in southeastern Ethiopia, most of Djibouti, and central and northern Sudan are highly vulnerable to future extreme rainfall events, an increase in FIH cases, and longer food crisis frequency and duration in the near future (2021–2050) and far future (2071–2100) under all scenarios. On the other hand, most districts in Uganda, southern and southwestern South Sudan, counties in western Kenya, and the majority of zones in western Ethiopia are projected to have very few FIH cases, low food crisis frequency, and duration in both the near and far future under all scenarios. These findings are crucial for early warning systems, humanitarian responses, and food security interventions. We recommend harnessing projected increases in rainfall for water harvesting in Kenya, as well as promoting cash and food crop production in central and western Ethiopia, central and northern Uganda, and most of South Sudan.

Investigation the Impact of the Climate Change on Intensity Duration Frequency (IDF) Curve Development: Case Study at Hulu Terengganu, Malaysia

The intensity-duration-frequency (IDF) curves are the most common form of design rainfall data used for peak discharge estimation. Thus, the IDF curve needs to be improved with the expectation that rainfall intensity and frequency have increased as a result of climate change. The main purpose of this study was to investigate the changes of IDF curves considering the climate change impacts on Hulu Terengganu. The climate projection from MRI-ESM2-0, CMCC-CM2-SR5, and GFDL-ESM4 under 3 different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used to provide the climate changes pattern in the future year (∆2050). In order to downscale the climate projection, the statistical downscaling method (SD-LS) was employed to correct the biases of these three GCMs. The IDF curves for the return periods of 2, 5, 10, 20, 50, 100, and 200 year were then developed based on the maximum rainfall intensity that were projected by the SD-LS model. The results clearly indicated that there are possibilities for increasing patterns in the projected annual and monthly rainfall for both time periods compared to historical data. Thus, the future extreme rainfall events for various durations with different return periods are all likely to increase over time. The largest potential increase is predicted at Sg. Gawi (+2.0% to +86.0%) based on the different return periods and rainfall durations. It could change the pattern of IDF curve that been developed based on projected rainfall by various SSPs. The developed IDF curves shows higher rainfall intensities in a shorter duration under the same return periods. Therefore, comprehensive action must be taken immediately to regulate and manage the effects of climate change.

Modeling Temperature‐Dependent Sub‐Daily Extreme Rainfall With a Gridded Weather Generator

AbstractTemperature increases are associated with an intensification of heavy sub‐daily extreme rainfall by approximately 7% per °C, in accordance with the Clausius‐Clapeyron (CC) relation. As a result of this intensification, there are concerns regarding the increased frequency and magnitude of floods in small to medium‐sized catchments. The high‐resolution two‐dimensional weather generator (WG), AWE‐GEN‐2d, offers an ideal tool to simulate the climate variables required to assess the catchment‐scale hydrological response at high resolution. However, it lacked an explicit representation of the relationship between temperature and precipitation that can mimic the CC relationship. Therefore, we introduce a newly revised version of the model, named AWE‐GEN‐2d‐CC, designed to mirror the observed CC scaling by conditioning the simulation of precipitation properties (intensity and area) on temperature. We demonstrate the model's efficacy in representing future extreme rainfall by simulating their potential impact on the hydrological response of a mountainous catchment in the Swiss Alps. Based on observations and future climate model projections, AWE‐GEN‐2d‐CC was used to generate large ensembles of present and end‐of‐century climate data at hourly and 2‐km resolutions, used subsequently as input to Topkapi‐ETH, a physically‐based, distributed hydrological model. The new version of the WG successfully mimics the CC scaling of heavy rainfall, leading to an intensification of short‐duration heavy rainfall in future climates, in contrast with the results obtained using the original model. This allows for a more realistic assessment of future rainfall impact on hydrological response, which, in the demonstration application, shows a modulated effect even for short durations.

Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks

Abstract Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing climate changes in the future at different spatiotemporal scales. This study proposes a novel Statistical Downscaling (SD) model established on the Convolutional Long Short-Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over the Indian sub-continental region. The traditional multisite downscaling models typically perform downscaling on a single homogeneous rainfall zone, predicting rainfall at only one grid point in a single model run. The proposed model captures spatiotemporal dependencies in multisite local rainfall and predicts rainfall for the entire zone in a single model run. The study proposes a Shared ConvLSTM model providing a single end-to-end supervised model for predicting the future precipitation for entire India. The model captures the regional variability in rainfall better than a region-wise trained model. The projected future rainfall for different scenarios of climate change reveals an overall increase in the rainfall mean and spatially non-uniform changes in future rainfall extremes over India. The results highlight the importance of conducting in-depth hydrologic studies for different river basins of the country for future water availability assessment and making water resource policies.

Integrated assessment of future climate and land use changes on urban floods: A Markov chain and PCSWMM-based approach for Hyderabad case study.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1-10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

Artificial neural network-empowered projected future rainfall intensity-duration-frequency curves under changing climate

With the increasing trend of greenhouse gases in the atmosphere, by 2052 the temperature is expected to rise by 1.5 °C from the Pre-industrial Period, affecting future extreme rainfall events. This necessitates quantifying extreme hydrologic events to plan and design hydrologic and hydraulic structures using rainfall Intensity-Duration-Frequency (IDF) curves to adapt to future climate scenarios. This study developed future projected IDF curves for the Southeast United States using disaggregated sub-hourly (15-, 30-, and 45-min) monthly maximum rainfall from 2030 to 2059 using five climate models under the Representative Concentration Pathway 8.5 scenario. A computationally efficient feed-forward back-propagation Artificial Neural Network (ANN)-based approach was found to be significantly superior for disaggregating rainfall to a stochastic model with an average Nash–Sutcliffe efficiency (NSE) ranging from 0.67 to 0.84. The study found that there is an increasing rate of future projected annual maximum rainfall intensities in the range of 7% to 36% with reference to the historical period. The spatial variation in future projected extreme rainfall depths showed that the Gulf-Atlantic coast and the Appalachian Mountains are expected to receive more extreme rainfalls.

Evaluation of CMIP6 historical simulations over IGAD region of Eastern Africa

The Accuracy of model simulations is critical for climate change and its socio-economic impact. This study evaluated23 Global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). The main objective was to identify the top 10 best performance models in capturing patterns of rainfall for the 1981–2014 period over the Intergovernmental Authority on Development (IGAD) region of Eastern Africa. The total rainfall, annual cycle, continuous, categorical and Volumatic statistical metrics, scatter plots, Cumulative Distribution Function (CDF), and colored code portrait were used to assess the patterns of total rainfall. Results indicate that most CMIP6 models generally capture the characteristics of the observed climatology pattern of total rainfall, bimodal and unimodal rainfall regimes. The majority of models over Arid and Semi-Arid Lands (ASALs) in Kenya, Somalia, Ethiopia, and Sudan scored the lowest skills, highest bias, and over-estimated rainfall, and lower skills over June–September (JJAS) compared to March–May (MAM) and October-December (OND). Quantitatively, a high percent of bias exceeding 80% scored over ASALs, a high correlation coefficient ranging between 0.6 and 0.7 across Ethiopia’s highlands, and a 5–40 as the lowest Root Mean Squared Error scored over the majority of the region. In addition, 21 out of 23 CMIP6 over-estimated rainfall over most parts of the region. The ACCESS-ESM1-5 and MIROC6 are the most over-estimated models as opposed to CNRM-CM6-1HR as the most model under-estimated rainfall, highest bias, and RMSE values. The regional and sub-national analysis showed it is inconclusive to select best-performed models based on individual metrics and sub-national analysis. Out of 23 models, the INM-CM5-0, HadGEM3-GC31-MM, CMCC-CM2-HR4, IPSL-CM6A-LR, KACE-1-0-G, EC-Earth3, NorESM2-MM, GFDL-ESM4, TaiESM1, and KIOST-ESM are the best 10 performance models over IGAD region. These findings highlight the importance of selecting the best performance models for mapping present and future hotspots and extreme rainfall events over the IGAD region of Eastern Africa.

Assessment of the applicability of large‐scale climate ensemble simulations for analysing future extreme rainfall

AbstractExtreme rainfall events are a major cause of natural disasters and have been projected to increase in frequency and intensity under future climate change scenarios. Previous studies have used outputs of climate model simulations to evaluate changes in future extreme rainfall, but results have been uncertain due to the use of only a limited number of climate simulations. To address this issue, this study aims to evaluate the applicability of the d4PDF database (Database for Policy Decision‐Making for Future Climate Change), produced through large‐scale climate ensemble simulations, for analysing changes in extreme rainfall values between historical and future climatic conditions. A large‐scale sample size of the d4PDF allows the investigation of high‐magnitude rainfalls with long return periods. To utilize the d4PDF as a large‐scale sample for estimating extreme rainfall, the homogeneity between ensembles was assessed using the t test and Mann–Whitney U test, and the historical extreme rainfall with the d4PDF dataset was validated through comparison with observed data and using the Kolmogorov–Smirnov test. It was confirmed that the d4PDF ensembles could be used as a large‐scale sample, and the historical rainfall series of the d4PDF showed a similar distribution pattern to the observed rainfall series. Subsequent investigation of extreme rainfall for future ensembles during 2051–2110 indicated an increase by 20%–30% compared to historical climatic conditions in South Korea due to climate change. The results demonstrate the applicability of large‐scale climate ensemble simulations, such as d4PDF, for analysing changes in extreme rainfall and suggest that such information can be used to counter future extreme rainfall‐related disasters. Therefore, this study contributes to the knowledge gap in this area and highlights the need for further assessment and utilization of large‐scale climate ensemble simulations for future climate change research.

CMIP6 models simulate heavier future rainfall over the Highlands than across other agricultural areas of Yemen

The climate of Yemen remains largely unstudied, leading to major uncertainties and a lack of understanding of future extreme rainfall events and how they are likely to affect agriculture. Here, we examine future heavy rainfall events for the period 2025–2100 using the latest state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP 6). Based on differences in rainfall regimes, we divided the study area into two distinct parts: Highlands and the Red Sea Coast & Tihama Plain region. Analyzing annual total precipitation when daily precipitation exceeds the 95th percentile of wet day precipitation (R95p), we found that models predict 16–25% heavier future rainfall over the Highlands than across the Tihama Plain and other agricultural parts of Yemen. By mid-century, the Highlands will experience ~ 25% heavier rainfall than other agricultural areas under SSP2-4.5 and ~ 16% heavier under SSP5-8.5. In both cases, the risk of rejecting the null hypothesis (H0) when it is true is lower than 5%. These results suggest that whether the current status quo of emissions continues, or the radiative forcing level is stabilized at 4.5 W/m2, heavy rainfall events are projected to increase more over the Highlands than over other agricultural areas of Yemen. Considering the projected increase in heavy rainfall events, we argue that Yemen desperately needs investments and innovations that will foster readiness and overall climate disaster preparedness. Efforts and investments will need to be directed more toward bridging the gap between producers of climate information and end users. Ultimately, these efforts will contribute to shaping the future of farmer decision support across Yemen.

Estimation of future extreme rainfall in Barcelona (Spain) under monofractal hypothesis

AbstractClimate change effects on subdaily rainfall (from 5 min to a few hours) can hardly be measured in mid‐latitude climates due to the high natural variability of the precipitation patterns and their effects on local topography. The goal of this study was to obtain change projections of intensity–duration–frequency (IDF) curves, for up to 2‐h precipitation events, comparing two approaches that use the daily outputs of the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model projections: (a) direct scaling of the expected probable precipitation, from 2‐year to 500‐year return periods of daily rainfall and (b) a new semi‐stochastic approach, built by combining the physically forced outputs of climate models (on a daily scale) and stochastic simulation given by the probability distribution of a concentration index (n‐index) for individual rainfall events (on a subdaily scale). The approaches were applied to a set of 27 stations located around Barcelona, Spain, including a long reference series (with 5‐min rainfall records since 1927), representing the highly variable Mediterranean climate. The validation process showed a systematic error (bias) generally smaller than 10%, especially for rainfall extreme events with durations of less than 2 h. The concentration n‐index and IDF curves were projected by 10 downscaled CMIP5 climate models under 2 emission scenarios (RCP4.5 and RCP8.5), obtaining a consensual increase in both relative concentration and absolute intensities in Barcelona. Ensemble projection of rainfall concentration (n‐index) showed an increase up to 10% by 2071–2100 and about 20% (15%–30% range) for maximum intensities of 2‐year to 500‐year return periods. Results provide robustness in decision‐making regarding the design of stormwater management infrastructure at a local scale.

Changes in Extremes Rainfall Events in Present and Future Climate Scenarios over the Teesta River Basin, India

Globally, changes in hydroclimate extremes such as extreme precipitation events influence water resources, natural environments, and human health and safety. During recent decades, India has observed an enormous increase in rainfall extremes during the summer monsoon (June to September) seasons. However, future extreme rainfall events have significant uncertainty at the regional scale. Consequently, a comprehensive study is needed to evaluate the extreme rainfall events at a regional river basin level in order to understand the geomorphological characteristics and pattern of rainfall events. In the above purview, the current research focuses on changes in extreme rainfall events obtained through observed gridded datasets and future scenarios of climate models derived through the Coupled Model Intercomparison Project (CMIP). The results highlight a significant rise in the extremes of precipitation events during the first half of the 21st century. In addition, our study concludes that accumulated precipitation will increase by five days in the future, while the precipitation maxima will increase from 200 to 300 mm/day at the 2-year, 50-year, and 100-year return periods. Finally, it is found that during the middle of the 21st century the 23.37% number of events will increase over the TRB at the 90th percentile.

Two Quasi-Linear Convective Systems, Their Mesoscale Structure and Moisture Sources

Abstract Over half of the total rainfall and more than 70% of heavy and extreme rainfall in the Melbourne, Australia, region occurs on days with linearly organized precipitation. These systems are typically convective in nature and frequently associated with cold fronts. It is useful to understand the processes that support extreme rainfall in organized convection, for prediction of both near-term and future extreme rainfall, and the topography and climate of Melbourne are different from many of the regions where QLCSs have been studied more extensively (e.g., the U. S. Great Plains region). On both 7 and 8 December 2010, a QLCS passed through the Melbourne region. Both QLCSs resembled classic systems on radar, but heavy rainfall was much more widespread on the second day. The goals of this work are to 1) understand the processes that drive these seemingly similar QLCSs; 2) explore the relationship between the convective inflow layer and moisture sources; and 3) to better understand the characteristics of rain bearing systems in the Melbourne region, which have received little attention to date. A convection-permitting WRF-ARW simulation captures both events. The mesoscale structure is different in each case, but generally is explainable by the existing theory. The development of a mesoscale downdraft, along with more moisture (and CAPE) over a deeper layer, contributed to higher rainfall totals on the second day. Low-level moisture in the QLCS region comes from the east, and parcel trajectories become increasingly westerly with height. On the second day some parcels originate in the tropics; these tend to have the most moisture. Significance Statement A lot of the rain that falls in Melbourne, Australia, occurs in storms that are grouped or “organized” in the shape of a line. Many studies have looked at how lines of storms work in other places in the world. In southeast Australia, only one study in the 1980s looked at observations of a line of storms. Since then, our understanding of storms and our ability to use computer models to simulate them has improved considerably. In this study we simulate two lines of storms that happened two days in a row. We found that even though they looked similar on weather radar, they had many differences including how air flows through the storm, the role of rain-cooled air, and where moisture comes from.

Regional non-stationary future extreme rainfall under changing climate over Asian Monsoon Region

An increase in the Intensity-Duration-Frequency (IDF) of rainfall extremes questions the assumption of stationarity in the design and planning of water infrastructures under changing climate. To address this, non-stationary-based approaches are widely used for assessing the extremes over time. However, the trends associated with future extreme rainfall would differ from that of the historical dataset. Therefore, the main objective of this study is to develop a non-stationary modelling approach to generate future regional IDF curves by incorporating the trend in the rainfall over Asian Monsoon Region (AMR). Daily rainfall data from (i) APHRODITE and (ii) baseline period (1951–2005) and future projections (2020−2100) simulated from 27 GCMs are used in this study. The proposed framework includes (i) selection of GCM models, (ii) Stationary (S) and Non-Stationary (NS) Generalized Extreme Value Distribution (GEVD) Models, (iii) delineation of extreme precipitation zones, (iv) performance evaluation of zone-wise models, and (v) estimation of future regional IDF curves for near-future (2020–2060), and far-future (2061–2100). Results show that the eight models and their corresponding GCM covariates cover around 90% of grids across the AMR compared to S-GEVD models among the 27 GCMs. Six extreme precipitation zones were obtained, and fitted regional models could mimic the individual station models. The change in extreme rainfall intensity for the future climate is higher for zone-1 (low extreme rainfall region), with an increase of 87–98% across different emission scenarios using the non-stationary models, whereas for zone-6 (higher extreme rainfall region) has a 60–68% increase from its stationary counterparts. For high and low emission scenarios, intensity increases up to 98% and 87% respectively for a lower return period (5-year), and 65% and 58% for a higher return period (100-year) by the end of the century.

Assessment of Climate Change Impacts for Balancing Transboundary Water Resources Development in the Blue Nile Basin

An assessment of climate impacts in the hydrologic system of the Blue Nile basin is useful for enhancing water management planning and basin-wide policymaking. Climate change adaptation activities predominantly require an understanding of the range of impacts on the water resource. In this study, we assessed climate change impacts on the Blue Nile River using 30-year in situ climate data (1981–2010) and five bias-corrected General Circulation Models (GCMs) for future (2026–2045) climate projections of RCP8.5. Both historical and GCM precipitation projections show inter-annual and spatial variability, with the most significant increases in the rainy season and a significant decrease in the dry season. The results suggest the probability of an increase in total precipitation. The intensity and frequency of future extreme rainfall events will also increase. Moreover, the hydrological model simulation results show a likely increase in total river flow, peak discharges, flood inundation, and evapotranspiration that will lead to a higher risk of floods and droughts in the future. These results suggest that the operation of water storage systems (e.g., the Grand Ethiopian Renaissance Dam) should be optimized for Disaster Risk Reduction (DRR) and irrigation management in addition to their intended purposes in the Nile basin.

Projecting Changes in Rainfall Extremes for the Huai River Basin in the Context of 1.5 °C and 2 °C Global Warming

It is indisputable that global warming has triggered more frequent extreme weather and in turn led to severe flood disasters. To understand the trend of extreme rainfall under 1.5 °C and 2 °C warming, we investigated the historical variation and future trends in extreme rainfall for the Huai River basin, which has frequently been hit by floods, using recorded meteorological data and a projection of five General Circulation Models in the Coupled Model Intercomparison Project 6. We used the years 1995–2014 as the baseline period to study the temporal and spatial changes in extreme rainfall under 1.5 °C and 2.0 °C warming scenarios. The results indicated that (1) temperatures in the Huai River basin have risen significantly from 1995 to 2014, but there are insignificant variation trends in annual precipitation (AP), intensive precipitation (R95P), maximum daily precipitation (Rx1d) and heavy rain days (Rr50) during the same time span. (2) From 2015 to 2100, both temperature and extreme rainfall indices show increase trends, with a higher rate of increase under a higher emission scenario. (3) Under the warming scenario of 1.5 °C, AP, R95P, Rx1d and Rr50 in the basin will likely increase by 4.6%, 5.7%, 6.2% and 13.4%, respectively, compared with that in the baseline period. Under the warming scenario of 2.0 °C, AP, R95P, Rx1d and Rr50 will probably increase by 7.3%, 7.4%, 10.9% and 19.0%, respectively. (4) Spatially, the changes in extreme rainfall indices under the warming scenarios of 1.5 °C and 2.0 °C generally tend to increase from north to south. Higher intensity extreme rainfall will likely extend to the whole of the Huai River basin. It is therefore essential to study adaptive measures to cope with flooding in the Huai River basin induced by the increase in future rainfall extremes.

Rarest rainfall events will see the greatest relative increase in magnitude under future climate change

Future rainfall extremes are projected to increase with global warming according to theory and climate models, but common (annual) and rare (decennial or centennial) extremes could be affected differently. Here, using 25 models from the Coupled Model Intercomparison Project Phase 6 driven by a range of plausible scenarios of future greenhouse gas emissions, we show that the rarer the event, the more likely it is to increase in a future climate. By the end of this century, daily land rainfall extremes could increase in magnitude between 10.5% and 28.2% for annual events, and between 13.5% and 38.3% for centennial events, for low and high emission scenarios respectively. The results are consistent across models though with regional variation, but the underlying mechanisms remain to be determined.

Rainfall extremes under climate change in the Pasak River Basin, Thailand

Abstract Changes in extreme rainfall tend to be magnified into unpredictable fluctuations in runoff, leading to flooding and drought in the Pasak River Basin of Thailand. Moreover, it also affects the operation of the existing infrastructure. Therefore, it is important to monitor changes in the extreme rainfall events and integrate them into planning and operations with the additional challenges posed by climate change. In this study, rainfall data at the ten observed stations across the basin was used to assess the extreme rainfall indices over the baseline period 1985–2014. The five new CMIP6 global climate model datasets and two Shared Socioeconomic Pathways of SSP2-4.5 and SSP5-8.5 were selected to project the future climate scenarios from 2023 to 2100. The extreme rainfall indices trends are analysed using the Mann-Kendall test and Sen's slope, while the IDW technique is adopted to visualise the spatial trends. The results show that most of the rainfall indices in low-altitude areas are higher than in high-altitude areas, except for the duration-based indices CWD and CDD. The observed extreme rainfall shows a larger variation than that predicted by climate models. The very high greenhouse gas emissions exhibited by the SSP5-8.5 scenario contribute to greater uncertainty in future extreme rainfall for plain areas than in high-altitude areas. The Pasak River Basin is expected to experience wet rather than dry climates in the future. The spatial trends from past and future periods highlight the significant increasing trends in the area where the Pasak Jolasid reservoir is located. The results of this study will benefit policymakers in a position to reduce future climate vulnerabilities and can be used for building local adaptation strategies in response to long-term climate change.

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

This study examines the potential impacts of climate change on extreme precipitation. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend.

Addressing Climate Internal Variability on Future Intensity-Duration-Frequency Curves at Fine Scales across South Korea

Designing water infrastructure requires information about the magnitude and frequency of upcoming rainfall. A limited range of data offers just one of many realizations that occurred in the past or will occur in the future; thus, it cannot sufficiently explain climate internal variability (CIV). In this study, future relationships among rainfall intensity (RI), duration, and frequency (called the IDF curve) are established by addressing the CIV and tail characteristics with respect to frequency. Specifically, 100 ensembles of 30-year time series data were created to quantify that uncertainty. Then, the tail characteristics of future extreme rainfall events were investigated to determine whether they will remain similar to those in the present. From the RIs computed for control and future periods under two emission scenarios, following are the key results. Firstly, future RI will increase significantly for most locations, especially near the end of this century. Secondly, the spatial distributions and patterns indicate higher RI in coastal areas and lower RI for the central inland areas of South Korea, and those distributions are similar to those of the climatological mean (CM) and CIV. Thirdly, a straightforward way to reveal whether the tail characteristics of future extreme rainfall events are the same as those in the present is to inspect the slope value for the factor of change (FOC), mFOC. Fourthly, regionalizing with nearby values is very risky when investigating future changes in precipitation frequency estimates. Fifthly, the magnitude of uncertainty is large when the data length is short and gradually decreases as the data length increases for all return periods, but the uncertainty range becomes much greater as the return period becomes large. Lastly, inferring future changes in RI from the CM is feasible only for small return periods and at locations where mFOC is close to zero.

Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change

Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37%–75% for 2070–2100 (mean 55%, and 18%–30% for 2030–2060). This is considerably higher than the +0–60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.