The recurrent and destructive nature of floods causes enormous economic damage and loss of human lives, leaving people in flood-prone areas fearful and insecure. It is essential to have a thorough knowledge of the factors that contribute to it. However, most catchment characterization studies are limited to case studies or regional domains. A detailed global characterization is currently unavailable due to the limitation in the holistic dataset that it demands. This study aims to fill this gap by utilizing multiple global datasets describing physiographic explanatory variables to characterize streamflow extremes. The role of catchment features such as landcover, geomorphology, climatology, lithology, etc., on spatial patterns and temporal changes of high streamflow extremes, was investigated in detail. Moreover, the multidimensional correlations between streamflow extremes and catchment features were modeled using a Random Forest approach and integrated with an interpretable machine learning framework to find the most dominating elements in different climate classes. The interpretation reveals that climatological variables are the most influential across all climates. However, the variables and their influences fluctuate between climates. Furthermore, distinct geomorphological variables dominate throughout climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights of this study would play a vital role in estimating the unit peak discharge at ungauged stations based on known watershed features. In addition, these findings can also help assess the nature of extremes in future climate scenarios, consequently implicating risk management methods.

How to cite: Kuntla, S. K. and Saharia, M.: Embracing Large-sample Data to Characterize Streamflow Extremes at a Global-scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-90, https://doi.org/10.5194/egusphere-egu23-90, 2023.

The prevalence of heavy tailed (HT) populations in hydrology is becoming increasingly commonplace due in part to the increasing need and use of high frequency and high-resolution data.   In addition to the impact of HT on extremes, HT populations can have a profound impact on a wide range of other hydrologic statistics and methods associated with planning,  management and design for  extremes.   We review the known impacts of HT populations on the instability and bias in a wide range of commonly used hydrologic statistics. Experiments reveal that HT distributions result in the degradation of many commonly used statistical methods including the bootstrap, probability plots, the central limit theorem, and the law of large numbers.     We document the gross instability of perhaps the best-behaved statistic of all, the sample mean (SM) when computed from HT distributions.  The SM is ubiquitous because it is a component of and related to a myriad of statistical methods, thus its unstable behavior provides a window into future challenges faced by the hydrologic community.  We outline many challenges associated with HT data, for example, upper product moments are often infinite for HT populations, yet upper L-moment always exist, so that the theory of L-moments is uniquely suited to HT distributions and data.  We introduce a magnification factor for evaluating the impact of HT distributions on the behavior of extreme quantiles

How to cite: Vogel, R., Lamontagne, J., and Dolan, F.: The Prevalence and Impact of Heavy Tails on Hydrologic Extremes and Other Statistics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2129, https://doi.org/10.5194/egusphere-egu23-2129, 2023.

Drought is one of the world's major natural disasters. In order to monitor drought and reduce drought damage through preemptive response, it is important to understand the spatiotemporal evolutionary characteristics of drought. Droughts have a three-dimensional (3-D) space-time structure, typically spanning hundreds of kilometers and lasting months to years. In this study, a high-resolution(5 km) SPEI-HR(Standardized Precipitation Evaporation Index) dataset was used, considering climatic (typical temperate continental climate) and various geographic characteristics (mountainous terrain, lowland basin, desert, grassland, etc.). In addition, all large- and small-scale drought events that evolve spatiotemporally were extracted using the dynamic drought detection technique (DDDT) algorithm. These 3D-drought properties are important information to explain the spatiotemporal evolution of drought and are characterized by drought patches in dynamic drought maps. As a result, most of the trajectories of droughts in Central Asia during the period 1981 to 2018 tended to move laterally to the east and west (ENE, E, ESE, WNW, W, WSW). In addition, droughts in Central Asia are characterized by very strong correlations between indicators of duration, severity, area, and trajectory movement distance. These Central Asian drought characteristics are interpreted as meaning that there is consistency among various drought information in determining the most severe drought event. In addition, the dynamic drought map, which includes all 3D-drought properties, has the advantage of producing high-level drought information (temporal continuity of drought events and dynamic evolution characteristics, etc.) that are not found in general drought maps through various conditional drought monitoring.

Acknowledgements: This work was supported by the National Research Foundation of Korea (No. NRF-2020R1C1C1014636) and Korea Environment Industry & Technology Institute (KEITI) (No.2022003610001) funded by the Korean government (MSIT and MOE).

How to cite: Yoo, J., Kim, J., Kwon, H.-H., and Kim, T.-W.: Spatial and Temporal Evolution of Drought Events Using High-Resolution SPEI and Dynamic Drought Detection Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3709, https://doi.org/10.5194/egusphere-egu23-3709, 2023.

Heavy precipitation events are strongly affected by climate change and there is a high confidence that these extremes will become more frequent and more severe in the future. Moreover, potential changes in the seasonality of these events are important in terms of planning and preparation against these events. While efforts have been focused on changes in the magnitude and seasonality of extreme precipitation events, these studies have treated these two quantities separately.

In order to overcome to this limit, a different perspective is here used by modeling the seasonality and magnitude of extreme precipitation events together through circular-linear copulae. We perform analyses at the global scale and develop bivariate models for an historical dataset. The outputs provided from several global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Shared Socioeconomic Pathways (SSPs) from 1-2.6 to 5-8.5 are then used to examine the joint projected changes in the seasonality and magnitude of extreme precipitation at the global scale.

How to cite: Treppiedi, D., Villarini, G., Bender, J., and Noto, L.: On the Projected Changes in the Seasonality and Magnitude of Precipitation Extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3851, https://doi.org/10.5194/egusphere-egu23-3851, 2023.

Storm surges and accompanying extreme water levels pose a major threat to coastal structures, urban and industrial areas and human life in general. In order to develop effective risk mitigation strategies, it is crucial to improve the understanding of these extreme events as well as their occurrence probabilities and quantiles, respectively.

The standard procedure to estimate extreme quantiles (return-levels) is to fit a suitable distribution to the observed extreme values on a site-by-site basis. However, this approach exhibits some disadvantages: 1) Estimates of extreme quantiles are only available at gauged locations. 2) The small amount of extreme events in tide gauge records makes these estimates highly uncertain.

We tackle both issues by pooling all available tide gauge records together through a covariates model that allows for smoothly varying distribution parameters in space. Using this approach, the model is not only able to reduce the uncertainty in quantile estimates, but also enables the interpolation of the distribution parameters at arbitrary ungauged locations, e.g. in between tide gauge locations.

Deploying our model for the German North Sea coast, we generate a probabilistic reanalysis of extreme water levels as well as associated probabilities for the period 2000 – 2019.

How to cite: Ditzinger, G., Rust, H., Möller, J., Kruschke, T., Schaffer, L., and Hinrichs, C.: A spatial covariates model for storm surge extremes in the German Bight, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5298, https://doi.org/10.5194/egusphere-egu23-5298, 2023.

Using the German weather radar data for the last 20 years with a high spatial and temporal resolution, the occurrence of rainfall extremes was analysed. By extracting and examining connected rainfall areas, several research questions were investigated: (1) How many extremes occur in a given area independent of their location? (2) To what extent is their occurrence in space a random and to what extent a structured process? (3) How are the connected volumes behaving in space and time? (4) How does the areal extent relate to event duration, rainfall volume, and discharge volume? The first two research questions were investigated for all of Germany, the last two by analysing rainfall and run-off data in several small and medium size headwater catchments in southern and western Germany.

The results show that the occurrence of events in space is related to their areal extent; there are regions where the frequency of occurrence of large spatially distributed events is greater than that of smaller ones. Moreover, there are interesting relationships between the spatial extent of an event, the event duration, and the event rainfall volumes. For high discharge values, not only does the rainfall intensity matter but also the event duration and spatial distribution of rainfall within a catchment. Many discharge peaks are not necessarily caused by high-intensity events (hourly or daily maxima) but by the accumulation of rainfall cells in space and time.

How to cite: El Hachem, A., Seidel, J., and Bárdossy, A.: Areal extremes from a different perspective: rainfall as 2D and 3D connected objects., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7564, https://doi.org/10.5194/egusphere-egu23-7564, 2023.

Quantifying design rainfall events at varying durations is crucial for assessing flood risk and mitigating losses and damages. Yet, in a changing climate, they are fundamental tool for a reliable design of water related infrastructures, such as flood retention reservoirs, spillways, and urban drainage systems. Usually, design rainfall is quantified where rain gauges are located, and regionalization methods are used to provide estimates in ungauged locations. During the last years, convection-permitting climate models (CPM) are receiving increasing attention because, thanks to their high spatial resolution (~3km) and ability of explicitly resolving atmospheric convection, they allow for better estimating precipitation spatial patterns and extreme rainfall at multiple durations compared to coarser models.

In this work, we combine at-site rain gauge measurements with CPM simulations, within a non-asymptotic statistical framework for the analysis of extreme rainfall. We aim at quantifying the added value of the physics-based information provided by CPM simulations for the estimation of high quantiles of rainfall in ungauged locations.     

The performance of the new regionalization approach is compared with traditional interpolation methods (i.e. interpolation of distribution function parameters) using leave- one-out cross-validation as well as considering different rain gauge densities.

Preliminary results show that the proposed methodology based on CPM simulation provides: i) similar performances compared to traditional gauge-based regionalization methods for high station density scenarios and ii) improved performances for low station density scenarios.

How to cite: Formetta, G., Marra, F., Dallan, E., and Borga, M.: Interpolation of design rainfall at ungauged locations exploiting the potential of convection-permitting climate models., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11332, https://doi.org/10.5194/egusphere-egu23-11332, 2023.

The multivariate return period is a measure of the frequency with which simultaneous sets of variables are expected to occur in a given area. So far, most approaches to calculate the multivariate return period of various hydrological variables have used copulas in two and three dimensions. (Salvadori et al., 2011) proposed a methodology for calculating the return period based on Archimedean copulas and the Kendall measure in 2 and 3 dimensions. (Gräler et al., 2013) proposed the calculation of the trivariate return period based on Vine copulas and Kendall distribution functions to describe the characteristics of the design hydrogram, considering the annual maximum peak discharge, its volume and duration. (Tosunoglu et al., 2020) applied three-dimensional Archimedean, Elliptical and Vine copulas to study the characteristics of floods. These studies have shown that the use of copulas can improve the accuracy of the risk measure of extreme events compared to univariate approaches, that only consider one variable at a time.

One of the limitations in describing the occurrence of multivariate extreme involving more than three simultaneous variables is the complex mathematical model to be solved (highest probability density point of a hypersurface) and the high computational cost that this imposes. However, in some areas of hydrology, developing more robust analyses that consider more than three variables can further improve risk assessments. For example, considering multiple rainfall stations in a watershed may help to properly capture the spatial structure of extremes -instead of relying on other spatial distribution procedures-. This improvement can provide a more accurate measure of the return period in the design of critical infrastructure, flood prediction, risk plans, etc.

In this context, we present an application where an extreme characterization of 5 rain gauges is considered simultaneously, using vine copulas based on Kendall distribution functions. More specifically, we analyze which measures are suitable for explaining the spatial and temporal correlation of rain events in different locations within a network of stations; which families and structures of vine copulas can optimally capture the spatial dependence structure within a region; how to solve the complex mathematics that is imposed when expanding the dimensionality; what is a computationally reasonable alternative to improve the computational cost involved.; and how multivariate analysis can improve the precision of the extreme event risk measure compared to univariate approaches.

These questions are answered by applying the proposed methods to a pilot case, which is developed in a basin located in northern Spain. Multivariate modeling is becoming increasingly relevant in the field of hydrology due to its ability to model extreme stochastic events, which are key to mitigating the risk and damages caused by floods.

References

Gräler, B., Berg, M. J. van den, Vandenberghe, S., Petroselli, A., Grimaldi, S., De Baets, B. & Verhoest, N. E. C., 2013. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrol. Earth Syst. Sci., 17(4), 1281–1296.

Salvadori, G., De Michele, C. & Durante, F., 2011. On the return period and design in a multivariate framework. Hydrol. Earth Syst. Sci., 15(11), 3293–3305.

How to cite: Urrea Méndez, D. A., Gómez Rave, D. V., and Del Jesus Peñil, M.: The Future of Extreme Event Risk Assessment: A Look at Multivariate Return Periods in More than Three Dimensions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11828, https://doi.org/10.5194/egusphere-egu23-11828, 2023.

Coastal cities are increasingly prone to compound flooding events. Particularly in estuaries, interactions between both freshwater fluxes (rainfall or discharge) and coastal water levels (tide, surge, waves, or combinations thereof) can strongly modulate flood hazard. These separate but physically connected processes can often occur simultaneously (but not necessarily in extreme conditions), resulting in compound events that may eventually have significant economic, environmental and social impacts. Conventional risk assessment mainly considers univariate-flooding drivers and does not include multivariate approaches; nevertheless, ignoring compound analysis may lead to a significant misestimation of flood risk.

In this respect, the complex interactions between coastal flooding drivers imply multidimensionality, nonlinearity and nonstationarity issues, and consequently, more relevant uncertainties. Copula-based frameworks are flexible alternatives to overcome limitations of traditional univariate approaches, and can incorporate the joint boundary conditions in riverine and coastal interactions in a statistically sound way (Harrison et al., 2021; Bevacqua et al., 2019; Couasnon et al., 2018, Moftakhari et al., 2017).  However, incorporations are often limited to the bivariate joint case. Trivariate (or higher dimensional) joint distribution are scarce, due to the convoluted and computationally expensive composition (Latif & Sinonovic, 2022). Notably, a need for robust and efficient approaches that help to characterize the nature of compound hazard remains (Moftakhari et al., 2021).

This study aims to improve copula-based methodologies that can adequately estimate the compound flood probability in estuarine regions, considering more than two variables, including more sources of uncertainty into the stochastic dependence analysis, raising the degree of accuracy to risk inference. This work develops a vine copula framework for the analysis of estuarine compound flooding risk, considering interactions and dependency structures between several oceanographic, hydrological, and meteorological processes and variables (rainfall, river discharge, waves, and storm tides). We show the potential of the framework in Santoña, a strategic estuarine ecosystem in Northern Spain. In order to yield proper design events, we focus here on estimating the multivariate joint and conditional joint return periods statistics, using the best-fitted model in the assessment of the extreme regime, based on Archimedean and Elliptical copula families. We also present the complexities of determining the ensemble of compound events corresponding to a given return period and compare these ensembles to the results of univariate extreme value analysis, to remark the importance of multivariate characterization of extremes.

References

Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., & Widmann, M. (2019). Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Science advances, 5(9), eaaw5531.

Couasnon, A., Sebastian, A., & Morales-Nápoles, O. (2018). A copula-based Bayesian network for modeling compound flood hazard from riverine and coastal interactions at the catchment scale: An application to the Houston Ship Channel, Texas. Water, 10(9), 1190.

How to cite: Gomez Rave, D. V., Urrea Méndez, D. A., and Del Jesus Peñil, M.: Multivariate Probability Analysis of Compound Flooding Dynamics., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12736, https://doi.org/10.5194/egusphere-egu23-12736, 2023.

Daily precipitation extremes are crucial in the hydrological design of major water control structures. The extremes are usually present in the upper part of the probability distribution of daily precipitation data, known as the tail. The distributions are bifurcated into heavy or light-tailed distributions depending on the tail. Heavy tails signify a higher frequency of occurrences of extreme precipitation events. Prediction of extreme precipitation depends on reliable modelling of the tail. Tail behaviour can be studied by graphical as well as threshold-based fitting approaches; however, each approach has associated shortcomings. In this work, we utilize a versatile and simple empirical index known as the “Obesity Index” (OB) to assess the tail of probability distributions of daily gridded precipitation data for India. This comprehensive regional analysis has been undertaken to quantify the tail heaviness of 4801 daily precipitation records over India for historical (1970–2019) and future (2020–2100) time periods. Future projections of daily precipitation are downscaled from the latest generation of climate models knowns as Coupled Model Intercomparison Project Phase 6 (CMIP6) under different emission scenarios. Finally, the application of the OB-based approach is extended to characterize daily precipitation in Indian Meteorological Subdivisions. Results indicate the applicability of heavy-tailed distributions in representing daily precipitation over India and establish the utility of the OB-based approach in diagnosing tail behaviour. Also, the spatial patterns of the tail heaviness are found to be matching with the Köppen–Geiger climate classification of India. The findings from this can be an input for the policymakers to develop adaptation strategies in response to the projected climate change impact.

Keywords: Extreme precipitation, Climate Change, India, Obesity index, Tail heaviness

How to cite: Gupta, N. and Chavan, S.: Assessing daily precipitation tails over India under changing climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13328, https://doi.org/10.5194/egusphere-egu23-13328, 2023.

Walls of water (WoW) are a subset of flash floods characterised by an extremely fast increase in the discharge rate of rivers. In the UK, WoWs, events where an almost instantaneous increase in river flow happens, are responsible for several deaths, even when the maximum peak flow of the said event is not as noticeable. Using a national 15-minute continuous dataset, this study identified WoWs for catchments in the UK. Next, the antecedent atmospheric conditions for these WoWs were extracted from gridded datasets. Furthermore, catchment descriptors such as catchment area, elevation, slope, land use, and permeability of every catchment were downloaded from the National River Flow Archive. Finally, with the use of machine learning algorithms, that is, tree regressions and neural networks, this study identified vulnerable catchments and key conditions for WoWs to occur. Early results indicate that WoWs are not solely driven by rainfall intensity and that larger catchments (>500km) with low permeability are the most vulnerable to these hazards. Further studies using additional atmospheric conditions, i.e., temperature and windspeed will allow a better understanding of the drivers of these events.

How to cite: Fileni, F., Archer, D., Fowler, H., McLay, F., Lewis, E., and Yang, L.: Use of high temporal resolution data to identify the key drivers and locations where walls of water occur in the UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13386, https://doi.org/10.5194/egusphere-egu23-13386, 2023.

South-western France has witnessed some of the most devastating extreme precipitation events that eventually lead to record-breaking severe flash flooding in the region and cause losses to human lives, urban transportation, agriculture, and infrastructure. In this study, two cases of deadly flash floods that occurred/reported in the Aude watershed in south-western France during 12-13 November 1999 and 14-15 October 2018 are studied using the WRF4.3.1 model simulations, with a particular emphasis on the model ability to capture these heavy precipitation events. We performed two simulations one with parameterized convection and one without the use of convection parameterizations for each case at gray-zone resolution (~9 km horizontal grid spacing) using the ERA5 reanalysis as the lateral boundary condition. In addition, attempts have been made to investigate the role of large-scale atmospheric circulation and atmospheric rivers in the production of these heavy precipitation events. The results from model simulations are compared quantitatively with available observations and reanalysis and found that the simulations at ~9 km gray-zone resolution capture the observed spatio-temporal distribution of precipitation characteristics during both extreme cases. The added value of gray-zone resolution simulations over driving coarse-scale ERA5 reanalysis datasets is observed in the representation of the precipitation characteristics. It has also been observed that the model simulation without the use of convection parameterization yields a reasonable and realistic representation of the precipitation characteristics during both extreme cases, and this suggests that at this “gray-zone” resolution the organized mesoscale convective systems/processes can be resolved explicitly by the model dynamics. The contribution of the large-scale atmospheric circulation and the atmospheric river (i.e., moisture transport) in the production of these flood events has also been observed.

How to cite: Shahi, N. K., Zolina, O., Gulev, S. K., Gavrikov, A., and Jomaa, F.: Simulation of extreme precipitation events over south-west France: the role of large-scale atmospheric circulation and atmospheric rivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13732, https://doi.org/10.5194/egusphere-egu23-13732, 2023.

For many flood risk assessments at large spatial scales, long-term meteorological data (e.g. precipitation, temperature) with spatially coherent representation are needed. This is where a regional weather generator comes into play. Meteorological fields for a specific region are strongly dependent on weather circulation patterns (CP) at larger scales. Additionally, there is evidence that these fields covariate with the average regional surface temperature (ART). With future climate change, such changes in both CP and ART should be included in weather generators.

This study presents the development of such a non-stationary gridded weather generator conditioned on large-scale weather circulation patterns for Central Europe. The reanalysis dataset ERA5 (1^o x 1^o) is used for weather type classification. The E-OBS gridded observational dataset (0.25^ox 0.25^o) is used to parameterize the meteorological fields, such as precipitation and temperature (minimum, maximum, average). The spatial and temporal dependence is represented by the multivariate auto-regressive model. Daily precipitation amount is modelled by the extended generalized Pareto distribution and daily temperature is modelled by the transformed normal distribution. Both fields are conditioned on CP and allow to covariate with ART. In this way, the regional weather generator is capable of capturing “between-type” and “within-type” climate variability and can be used to generate long synthetic data for flood risk assessment in present and future periods.

How to cite: Nguyen, V. D., Vorogushyn, S., Nissen, K., and Merz, B.: A non-stationary gridded weather generator conditioned on large-scale weather circulation patterns for Central Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15475, https://doi.org/10.5194/egusphere-egu23-15475, 2023.

The recent AR6 report of the Intergovernmental Panel on Climate Change (IPCC) explicitly shows that the observed change in hot extremes (including heatwaves) with high confidence in human contribution to the observed changes has highly increased in the South Asian (SAS) domain which comprises the Indian subcontinent. Extreme heat events are more frequent and intense across the globe since the 1950s and have adverse societal and economic impacts. Considering current warming trends and projections, heatwaves are becoming a serious problem in India. Exposure to extreme heat in the population is increasing due to climate change. Also, observed temperatures are increasing globally as well as regionally as an effect of global warming. As heat stress occurs when the human body cannot get rid of the excess heat, it can be considered a good proxy for the heatwave hazard. Heat stress results in heat stroke, exhaustion, cramps, or rashes. Exposure to extreme heat can result in occupational illnesses and injuries. An agrarian country like India will have large economic damage when climate-related heat stress increases the occurrence of droughts and exacerbate water scarcity for irrigation. Hence the impact of the heat stress hazard is spotted and largely discussed both in the academic and political domains. In this study, Universal Thermal Climate Index (UTCI) based hazard map is developed for India with a non-parametric multivariate approach. The prominent heat stress hazard areas are identified and mapped with reference to the UTCI assessment scale which is categorized based on thermal stress. The probability of occurrence is also mapped using the exceedance probability with the UTCI reference. Heat stress hazard map provides the basis for a wide range of applications in public and individual precautionary planning such as heatwave action plans, urban and regional planning, the tourism industry, and climate research. Hence a country-level extreme temperature hazard map is of dire necessity.

Keywords: Exceedance probability, hazard map, heat stress, multivariate approach, non-parametric method

How to cite: Shilin, A., Sudharsan, N., Mondal, A., Kalbar, P., and Karmakar, S.: Mapping Hazard to Extreme Temperature Events Over the Indian Subcontinent, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16623, https://doi.org/10.5194/egusphere-egu23-16623, 2023.

Alaba Boluwade*

School of Climate Change & Adaptation, University of Prince Edward Island, Charlottetown, Canada; aboluwade@upei.ca; abolu2013@gmail.com

*Correspondence: aboluwade@upei.ca

Abstract

Hydrological risk assessment, such as flood protection, requires estimates of variables (e.g., precipitation) measured from several weather stations. The spatial modeling of average rainfall estimates differs from extreme precipitation analysis. This is because extremes are focused on the tail of the probability distribution and the assumption of Gaussianity may not be suitable. Extreme Value Theory (EVT) application to univariate weather variables measured at weather stations has been well documented; however, extreme precipitation at closer stations tend to show trends and dependencies (similar values). It is, therefore, crucial to quantify the dependency structure and trend surface of weather stations in space. The max-stable process has been well documented to model spatial extremes. The objective of this study is to quantify the spatial dependency and trend of an annual maxima precipitation (annual highest daily precipitation, from 1970-2020) across selected weather stations in the Northern Great Plains (i.e., Nelson Churchill River Basin (NCRB)) of North America. The annual maxima data were extracted from the Global Historical Climatology Network Daily (GHCNd) and Environment and Climate Change Canada (ECCC). NCRB covers four states and four provinces in the United States and Canada. A heterogenous rainfall pattern characterizes NCRB. This is due to enormous quantities of orographic rainfall in the west and the convective precipitation in the Prairies (which is dominated by short-duration, sporadic, extreme rain), causing millions of dollars in damages. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation.

The results show that topography, time, and geographical coordinates were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). Takeuchi’s information criterion (TIC) shows that the SPEV model with all the covariates above superseded the one without the covariates.   The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted Extremal-t max-stable model captured the spatial dependency and equally predicted the 50-year return period levels. Furthermore, ten realizations (equal probable) were simulated from the max-stable model. The study is relevant in quantifying the spatial trend and dependency of extreme precipitation in the Northern Great Plains. The result will help as a decision-support system in climate adaptation strategies in the United States and Canada.

Keywords: extreme events; Max-Stable processes; flood protection; maxima annual rainfall; flash flood protection; Canada, United States

How to cite: Boluwade, A.: Application of Max-Stable Process Model in Estimating the Spatial Trend & Dependency of Extremes in the Northern Great Plains, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9758, https://doi.org/10.5194/egusphere-egu23-9758, 2023.