Annual Maximum Precipitation Research Articles

Probability Distribution Analysis of Annual Maximum Precipitation in the Niger Delta: A Critical Step towards Effective Flood Risk Management

Aims: The study aimed to analyze historical maximum precipitation data from seven cities in the Niger Delta to assess vulnerability to extreme rainfall events, identify optimal probability distribution models for future predictions, and provide insights for flood risk management strategies. Study Design: This is a retrospective analytical study based on historical precipitation data. Place and Duration of Study: The study was conducted across seven major cities in the Niger Delta region of Nigeria, utilizing data collected over several decades. Methodology: Historical maximum precipitation data were statistically analyzed to determine the best-fit probability distribution models. The Kolmogorov-Smirnov (KS) test and Mean Squared Prediction Error (MSPE) were used to validate the suitability of distributions, including Log-Normal, Normal, Log-Pearson III, and Generalized Extreme Value (GEV) for each city. Return periods of 10, 25, 50, and 100 years were calculated to predict future precipitation trends and assess flood risks. Results: Significant variations in annual maximum precipitation were observed across the cities, with Calabar recording the highest peak at 4062.7mm. The Log-Normal distribution was the best fit for Akure and Calabar, while the Normal distribution best described rainfall in Benin City. Log-Pearson III was optimal for Owerri, Umuahia, and Uyo, and GEV best fitted Port Harcourt’s data. High P-values (>0.87) indicated good model fits across the cities. Return period analysis suggested greater risks of extreme precipitation events in coastal cities like Calabar and Port Harcourt. Conclusion: The study underscores the importance of tailored, city-specific flood management strategies in the Niger Delta to mitigate the impact of extreme rainfall events, particularly in the face of climate change.

Frequency Analysis of Annual One Day Maximum Rainfall at Faisalabad, Lahore and Multan (Punjab)

Effective management of water systems and seepage projects depends on accurate precipitation data, particularly annual daily maximum precipitation, which is essential for designing hydraulic structures. This study conducts a frequency analysis of annual daily maximum precipitation for Faisalabad, Lahore, and Multan in Punjab, Pakistan, using over 50 years of historical data from these three stations. The analysis, performed with RAINBOW software, evaluates five probability distributions—normal, log-normal, weibull, gamma, and exponential. The fit of these distributions was assessed using Chi-square, Kolmogorov-Smirnov, and Anderson-Darling tests at significance levels of 5%, 10%, and 20%. Results indicate that the Weibull and Log-normal distributions most accurately represent the precipitation data. Based on this analysis, the study estimates the magnitude of maximum annual precipitation for return periods of 2, 5, 10, 25, 50, and 100 years. The findings aim to improve water management strategies, enhance flood and drought prevention measures, and optimize the hydraulic design of seepage structures, contributing to more effective planning and resilience in the face of extreme weather events.

The Role of Atmospheric Rivers Moisture Origin in the Seasonality of Extreme Precipitation in the Eastern United States

Abstract Regional patterns of the seasonal weather and atmospheric moisture origins can impact the seasonal activities of extreme precipitation in the eastern United States (eUS). At many locations, tracks of atmospheric moisture can have different influence on timing of extreme precipitation based on the moisture origin source. In this study we evaluate the contribution of Atmospheric Rivers (ARs), and their moisture origin sources to the distribution and seasonal effectivity of annual maximum precipitation (AMP) across the eUS during 1950–2021. Our results suggest that AR is a dominant mechanism of AMP in the eUS as they contribute to 75% (31,438 out of 41,976) of total AMP events recorded between 1950–2021. The seasonal analysis based on circular density approach shows that spring, summer and fall seasons display strong signals of seasonality of AMP-AR events. The spatial patterns of AMP associated with the four major moisture sources (the Pacific Ocean, the Atlantic Ocean, the combined source of Caribbean Sea and Gulf of Mexico, and the local source of moisture) reinforce the key role ARs play in transporting water vapor to eUS from both oceanic and inland originated moisture. The results additionally highlight the importance of moisture sub-sources (major sources sub-regions) in modulating the seasonality of extreme precipitation in the eUS.

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

AbstractProjecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Analysis of the Relationship between Extreme Precipitation Pattern and Maximum Runoff in Hongze County From the Perspective of Urban Underlying Surface Production and Confluence

Based on the hourly precipitation data and evaporation data of the meteorological in Hongze County from 1959 to 2020, we studied the relationship between extreme precipitation and annual runoff control rate from the perspective of the underlying surface of the Hongze County watershed. We conducted a literature review and statistical analysis using SPSS in this study. From 1959 to 2020, precipitation in the Hongze region varied greatly annually, with the maximum and minimum annual precipitation of 1473.8 and 349.6 mm. The precipitation fluctuated but increased, and the extreme precipitation was concentrated in July to August. The annual average evapotranspiration from 1959 to 2020 was 1165.8 mm with an inter-annual fluctuation and decrease due to the urbanization process. The process affected the local evapotranspiration condition. More evaporation was observed from May to August than from December to January with a difference of 50 mm. The evaporation data coincided with seasonal variation in Hongze County. In determining the control rate of annual total runoff, we considered the inter-annual and annual differences in precipitation. The natural runoff condition and economic cost before urbanization were explored, too. The greater the rain intensity, the earlier the runoff generation time, the shorter the confluence time, and the greater the possibility of floods. In short-duration extreme precipitation, it is necessary to pay attention to its middle period, while for long-duration extreme precipitation, it is necessary to focus on its late period.

Expanding information for flood frequency analysis using a weather generator: Application in a Spanish Mediterranean catchment

Study regionThe proposed methodology has been applied in the Segura River basin (south-eastern Spain) whose hydrological regime has a high anthropic alteration and catastrophic floods have occurred at different times for centuries. The climate is generally semi-arid, with frequent droughts but also floods caused mainly by rainfall associated with mesoscale convective systems. Study focusWe present a methodology that exploits all available information to obtain reliable low-frequency flood quantiles through the integration of different methods. First, a Weather Generator (WG) was implemented with the results from a regional study of annual maximum precipitation. Second, a rainfall temporal disaggregation procedure was carried out to capture the sub-daily behavior of flood generating storms. Third, a fully-distributed Hydrological Model (HM) was implemented including the role of sediments in extreme events. Finally, the estimation of flood quantiles using plotting positions was validated with systematic and non-systematic information. New Hydrological Insights for the regionThe use of this process-based approach allows to reproduce the main hydrometeorological mechanisms associated with floods in the region studied. Accurate flood quantiles up to 200 years have been possible to obtain, which represents an important advance in the knowledge of the basin since reliable flood quantiles of only 20 years were adequately captured with the current observations. Finally, sediment yield has been proven to be an important factor for the region hydrographs’ reconstruction.

Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

Nonstationarity in Extreme Precipitation Return Values along the U.S. Gulf and Southeastern Coasts

Abstract This study estimates extreme rainfall trends across the Gulf Coast and southeastern coast of the United States while applying methods for extending the temporal record and aggregating across spatial trend variations. Nonstationary generalized extreme value (GEV) models are applied to historical annual daily maximum precipitation data (1890–2019) while using CMIP5 global mean surface temperature (GMST) as the covariate. County composites and multicounty regions are used for local data record extension and pooling. Unlike most previous studies, return periods as long as 100 years are analyzed. The local trend estimates themselves are found to be too noisy to be reliable as estimates of climate-driven trends. However, application of a Gaussian process model to the spatial distribution of observed trends yields overall trend detection at the 95% significance level. The overall historical increase due to nonstationarity across the study region, with associated 95% confidence intervals, is 9% (3%, 15%) for the 2-yr return period and 16% (4%, 26%) for the 100-yr return period. A trend is also detectable in the Gulf Coast subregion, but not in the smaller southeast subregion. Recent weather events and nonstationarity have caused the official return value estimates for parts of North and South Carolina to be much lower than the return values estimated here. Significance Statement Protection of people and infrastructure from flooding relies on accurate estimates of potential extreme rainfall intensity. Some official estimates of extreme rainfall near the Gulf Coast and southeastern coast of the United States are over 20 years old. We show that, across this region, there is a clear trend in daily rainfall so extreme that it only has a 1% chance of happening in any given year (the so-called 100-yr rainfall). This trend means that many existing estimates of extreme rainfall are too low, both now and in the future, so flooding risks based on those estimates would be underestimated as well.

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies.

Moisture Sources and Pathways of Annual Maximum Precipitation in the Lancang‐Mekong River Basin

AbstractRecent extremely heavy precipitation has led to substantial economic losses and affected millions of residences in the Lancang‐Mekong River Basin (LMRB). This study analyzed the spatial‐temporal characteristics of the annual maximum precipitation (R1X) of the LMRB and identified the moisture sources and pathways conducive to R1Xs using a Lagrangian back trajectory model. Results show that India Ocean and Bay of Bengal (IO/BOB), local evapotranspiration, and West Pacific Ocean and East China (WP/EC) are the three main moisture transport pathways of the R1Xs in LMRB, contributing 68.3%, 20.4% and 11.3% of the trajectories, respectively. R1Xs in the downstream eastern area are affected by tropical cyclones bringing large amounts of moisture from the WP/EC. As tropical cyclones shifted northward under climate change impact, more extreme precipitation occurred over the LMRB due to moisture coming from WP/EC, but those from the IO/BOB had decreased because of the slowdown of flows across the Equator.

Analysis of Climate Variable and Fisherman Perception on Climate Change Divers in Negombo Lagoon Area


 
 
 Climate change has undoubtedly become one of the most contentious issues in the international environmental debates and has escalated the impacts on fishery industries and fishing communities globally. This study was conducted to quantify the variations in climate variables, and coastal vegetation that may affect the fisheries industry due to climate change over past three decades in the Negombo lagoon area. Further, the knowledge and awareness of fisheries and associated communities on climate change was investigate. To understand the dynamics of the precipitation and temperature, monthly data for 30 years (1989-2022) were collected from the Department of Meteorology and 19 Module Bio-climatic Variables (MBV) were calculated to determine the climatologycal variation. The change in the coastal vegetation was analyzed using the Landsat 8 satellite using ArcGIS 10.8. A questionnaire survey and a focus group discussion were carried out as assess the knowledge, awareness and impact of climate change in the fisheries community (n=80). The mean annual and minimum and maximum precipitation ranged from 118.6 mm-268.6 and 0.6 mm - 826.6 mm. The mean, maximum, and minimum temperatures varied from 28.8o C- 32.3o C, 33o C-34o C and 29o C-30o C. Out of 19 MBV variables, 9 variables had increasing trends while 3 variables had decreasing trend. The change percentage of coastal vegetation cover from 1992-2002, 2002-2012, 2012-2022 was observed as 35.71%, 4.99% and 6.13%. As per the peoples‘ knowledge of climate change, it was highlighted that around 50% of respondents does not have a proper idea on what is climate change (40% of respondents stated that the variation of precipitation and wind is the climate change drivers; Further, 40% of respondents attested that land use change with time has accelerated climate change while 30% of respondents mentioned that lack of coastal awareness and restoration programs is the second main reason for accelerated climate change that affect the fishery industry). The outcomes of the focus group discussion revealed that fishing activities are heavily affected by climate change drivers, and some engage in alternative jobs in extreme climatic conditions to feed their families. Finally, fishery communities highlighted the need for the government and other responsible authorities to take necessary actions to adapt and strengthen the resilience to climate change. This study corroborates the timely and topical obligation for sustainable fisheries management action planning for the Negombo area.
 Keywords: Climate change, Coastal mangrove vegetation-precipitation-temperature change, Fisheries communities, Knowledge, Awareness, Negombo
 
 

Determination of Suitable Probability Distribution of Rainfall in Pakistan Considering Multiplicity

Extreme rainfall events are occasional, and understanding their intensity and frequency is important for long-term planning and for public safety. The current study aims to investigate the stability of extreme precipitation events in different regions of Pakistan, their independence and the uniformity of their occurrence. A rainfall frequency analysis (RFA) was conducted based on information from 8 weather stations namely, the distribution and moments L of the annual maximum precipitation in Pakistan were investigated; Using goodness-of-fit criteria, the study determined the possible distribution of precipitation. Relatively Absolute Error (RAE) results are based on the most appropriate GUM distribution, revealed that the Sialkot, Multan, Faisalabad and DI Khan stations produce very low errors (0.266, 0.847, 0.075, 0.856, 1.671, 2.522, 3.659, 4.524). It was found that the most suitable distribution is LN distribution for Peshawar, Sibbi and Karachi Stations. The GEV distribution also performs well with small errors for various return periods (0.266, 0.847, 0.075, 0.856, 1.671, 2.522, 3.659, 4.524), corresponding to the return periods 2, 5, 25, 50, 100, 200, 500 and 1000 years. In contrast, P3 has the advantage of a return period of 10 years for all stations, as they all produce similar results for this particular return period. The current research provides valuable insights into estimating extreme rainfall at stations where rainfall data are available

Spatio‐temporal analysis of trends in annual maximum rainfall in the North‐West of Algeria: Comparative analysis of recent and old non‐parametric methods

AbstractThis article examines the spatial variability of extreme precipitation trends in northwestern Algeria (Macta) and compares the results obtained from the four recent and old non‐parametric methods. A dataset of annual maximum precipitation consisting of 41 observation years (1970–2010) and 41 rain gauges was used. The results of the four old and new methods used to detect trends, Mann–Kendall (MK), Bravais–Pearson (BP), Spearman (SR) and innovative trend analysis (ITA), show good agreement. They revealed that a decrease in the trend of annual maximum precipitation was detected during the first period (1970–1992) with −44% (MK), −61% (BP), −68% (SR) and −76% (ITA). On the other hand, in the second period (1993–2010), a total shift occurred in which a significant increase in annual maximum precipitation trends was observed with +63% (BP), +34% (MK and SR) and +93% (ITA). These results show the ability of ITA to detect partial trends that the other three tests do not allow. Our results allow decision‐makers to properly design adaptation strategies in the face of the intensification of these extreme events.

The Variability of Maximum Daily Precipitation and the Underlying Circulation Conditions in Kraków, Southern Poland

This article studies the intra-annual and long-term variability in the maximum daily precipitation totals and their association with atmospheric circulation in Kraków. It investigates daily precipitation maxima by year and by month. The research is based on daily precipitation totals in the years 1863–2021 and draws on the calendar of atmospheric circulation types by Niedźwiedź. It examines the frequency of precipitation maxima in individual months and their variation from one year to another. No statistically significant trend of change in precipitation over the study period has been found. All annual maximum daily precipitation totals in Kraków fall into the category of heavy precipitation (>10 mm), and almost 99% qualify as very heavy (>20 mm). In the summer months, these are about 3–4 times higher than in winter. The share of the daily precipitation maximum in the monthly total exceeds 30% in all months. The maximum daily precipitation occurring on 5 August 2021 was the highest in the period that extends from the start of instrumental measurements. The study period saw 12 cases of maximum precipitation that belong to ‘flood-inducing’ categories (over 70 mm/day). Such cases of the very heaviest precipitation occurred in cyclonic situations: Cc, Bc, Nc, NEc, Ec and SEc. Most spring and summer maxima were seen on days with a cyclonic circulation. The instances of high daily precipitation in the Kraków area led to the flooding of residential and historic buildings, as well as of municipal infrastructure.

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Derivation of nonstationary rainfall intensity-duration-frequency curves considering the impacts of climate change and urbanization

Urban infrastructure traditionally relies on stationary rainfall intensity-duration-frequency (IDF) curves. However, this assumption is challenged by climate change and urbanization. Many studies tried to update IDF using time covariate which lacks physical significance. More importantly, the stationary (ST) design method is not applicable for nonstationary (NS) design where the distributions of extreme precipitation change over time. For the annual maximum precipitation (AMP) in Beijing, we utilized local factors (urbanization and temperature) and global factors (ENSO and EASM etc.) to develop NS models, with the average annual reliability method first employed to update the IDF curves. Short-duration (shorter than 6-h) AMP of most stations show upward trends, whereas the AMP with longer durations exhibits downward trends. The NS modeling reveals that the 18-h AMPs is mainly affected by global processes (ENSO and EASM). The predictive accuracy of the optimal NS model outperforms ST model by a remarkable 219% during the validation period. In addition, the ST design rainfall tends to overestimate rainfall for durations longer than 12-h. Interestingly, the gap between NS and ST design uncertainties diminishes as duration/return period expands. The above findings provide new insights about impacts of local and global physical processes on the variation of extreme rainfall.

Assessing the Return Periods and Hydroclimatic Parameters for Rainwater Drainage in the Coastal City of Cotonou in Benin under Climate Variability

Cotonou, the economic capital of Benin, is suffering from the impacts of climate change, particularly evident through recurrent floods. To effectively manage these floods and address this issue, it is crucial to have a deep understanding of return periods and hydroclimatic parameters (such as intensity-duration-frequency (IDF) curves and related coefficients), which are essential for designing stormwater drainage structures. Determining return periods and these parameters requires statistical analysis of extreme events, and this analysis needs to be regularly updated in response to climate change. The objective of this study was to determine the necessary return periods and hydroclimatic parameters to improve stormwater drainage systems in the city and its surroundings areas. This required annual maximum precipitation series of 1, 2, 3, 6, 12, and 24 h for 20 years length (1999–2018) as well as flood record data. The intensity series, derived by dividing the amount of rainfall by its duration, was adjusted using Gumbel’s law. IDF curves were constructed based on Montana and Talbot models, and their coefficients were determined according to the corresponding return periods. In 2010, which witnessed devastating floods in the country, the return period for the most intense rainfall events was 40 years, followed by 2013 with a return period of 13.4 years. Consequently, the commonly used 10-year return period for the design of stormwater drainage structures in Cotonou is insufficient. The Talbot model produced the lowest mean square errors for each quantile series and coefficients of determination closest to one, indicating that the parameters obtained from this model are well suited for designing hydraulic structures in Cotonou. The hydroclimatic parameters presented in this study will contribute to the improved design of hydraulic structures in the city of Cotonou.

Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes

Abstract The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation. The generalized extreme value (GEV) parameters are expressed as simple linear combinations of geographical coordinates (i.e., longitude and latitude) and topography. The results show that topography, geographical coordinates, and time (as a temporal covariate) were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted SPEV was used to predict the 25- and 50-year return period levels. The fitted Extremal-t max-stable process model captured the spatial dependency structure of the extreme precipitation in the NCRB. The study is relevant in quantifying the spatial dependency structure of extreme precipitation in the Northern Great Plains. The result will contribute as a decision-support system in climate adaptation strategies in the United States and Canada.

Deciphering the Trend and Interannual Variability of Temperature and Precipitation Extremes over Greenland during 1958–2019

Abstract Greenland experienced multiple extreme weather/climate events in recent decades that led to significant melting of the ice sheet. However, how the intensity of extreme climate events over Greenland varied under recent warming has not been fully examined. Here, we collect 176 in situ observations over Greenland and demonstrate that the observed extreme temperature/precipitation events over Greenland are well captured by the RACMO2.3p2 model, in terms of climatological distribution, interannual variability, and long-term trend. Thus, we then investigate the spatiotemporal features of extreme events over Greenland during 1958–2019, using the daily model outputs at 5.5-km resolution. The simulated annual maximum temperature exhibits a significant increasing trend (∼0.13°C decade−1) during 1958–2019, whereas there is a weakening trend (−0.24°C decade−1) in annual minimum temperature over Greenland, especially after the 1990s (−1.24°C decade−1). For the interannual variability, changes in temperature extremes between warm and cold temperature years share large similarities with the distributions of long-term trends. The extreme precipitation events measured by annual maximum daily precipitation amount show a profound increasing trend (0.52 mm day−1 decade−1) over northeastern Greenland during 1958–2019, with large interannual variability in the ice-free coastal region and southern Greenland. Additionally, the changes in extreme warm and cold events are generally linked with the variation of Greenland blocking in summer and Arctic polar vortex in winter, respectively, in terms of favorable circulation background, and the extreme precipitation events are often associated with the position of the polar jet stream.