The peak-over-threshold (POT) model is the most extensively used for regional precipitation frequency analysis (RPFA) for estimating extreme precipitation events (EPEs). Yet, choosing proper threshold values is critical and challenging while estimating rainfall quantiles for the Indian subcontinent due to the diverse climatic conditions and physical barriers. This study investigates and compares various threshold methodologies, including graphical, analytical, and multiple threshold methods (MTM) for identifying EPEs. These extracted extreme events with high thresholds followed the Generalized Pareto distribution (GPD), whose shape and scale parameters remain constant and increase linearly with increased threshold values. Therefore, the POT-GPD model was employed in the current work, and the parameters were estimated using L-moments to explore and quantify the heavy tail behavior. In addition, the uncertainty associated with the quantiles was also evaluated using nonparametric bootstrapping techniques and later understanding the spatial variability of the GPD parameters from various methods. The effectiveness of the models is assessed on daily gridded precipitation datasets for the Indian region and validated using synthetic datasets generated through Monte Carlo simulations. Results reveal the importance of combining the MTM and analytical threshold methods for identifying a range of critical thresholds to overcome the subjectivity of graphical methods and quantify the uncertainty. These findings contribute to developing region-specific thresholds, highlighting the importance of modifying thresholds to the regional characteristics rather than relying on a fixed percentile for characterizing the EPEs. The proposed approach is essential for assessing the increasing intensity and frequency of precipitation extremes associated with climate change while allowing for more focused mitigation actions and disaster risk reduction.

How to cite: Ganapathiraju, S. A. and Rathinasamy, M.: Intercomparison of Different Automatic Threshold Selection Methods in Modelling Precipitation Extremes via Peak Over Threshold Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1126, https://doi.org/10.5194/egusphere-egu24-1126, 2024.

The lack of reliable data on daily snow depth (SD) is a significant challenge for studying water systems, ecology, and resources. Climate models present a potential solution for generating daily SD data, but the literature has not thoroughly explored how accurately they simulate this data. This study investigates the capabilities of CMIP6 climate models to replicate daily SD characteristics in eleven major Canadian catchments. The results depict that CMIP6 simulations overestimate the average SD values by a median of 57.7% (6.9 cm). In the Arctic and Pacific regions, this overestimation becomes particularly pronounced. However, the simulations align more closely with observations in smaller catchments with homogenous land characteristics. This finding suggests a shortcoming in how these models simulate different land types within the grid. Additionally, the models appear to overestimate the snow cover duration, with a median underestimation of 30.5 days. This overestimation could be due to the models failing to accurately account for the rates at which snow accumulates and melts away. However, the models perform relatively well when predicting extreme SD conditions. This study carries valuable implications for refining the outputs of climate models and effectively utilizing them in impact studies.

How to cite: Abdelmoaty, H., Papalexiou, S., Gaur, A., and Markonis, Y.: Examining Daily Snow Depths at the Catchment Scale in Canada Using CMIP6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2195, https://doi.org/10.5194/egusphere-egu24-2195, 2024.

Drought is a major hazard with significant impacts on agriculture, water resource availability, and terrestrial ecosystems. Under climate change drought events are expected to increase in frequency, severity, duration, and propagation with consequent impacts on crop yields. Given these circumstances, a thorough understanding of drought is needed to increase societal preparedness to drought effects on food production particularly in regions where agriculture is dominantly rainfed. Unfortunately, drought events remain very unpredictable suggesting the need to enhance the understanding of drought effects on rainfed crops. Hence, this study aims to examine the relationships between drought characteristics and rainfed crop yields. Particularly, the study uses probabilistic and machine learning (i.e., random forest) approaches to investigate the influence of standardized precipitation and evapotranspiration index (SPEI) severity and duration on the yield of corn, cotton, peanuts, and soybeans in the southeast region of the United State (US). County wise analyses were conducted for three contiguous southeastern States including North Carolina, South Carolina, and Georgia. Preliminary results outlined different performances depending on the approach, the counties, and the crops. Highly performing approaches could be considered for modeling drought effect on crops at county, State, or regional levels.

How to cite: Sohoulande, C. and Khedun, P.: Modeling drought effects on rainfed crop yields using probabilistic and machine learning approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2908, https://doi.org/10.5194/egusphere-egu24-2908, 2024.

Flood frequency analysis is a challenging but essential hydrologic problem for design of control structures and water resources management. The design flood estimates based on traditional stationary assumption may lead to inaccurate estimation of flood risk because of non-stationarity and the compounding impacts of several drivers in a dynamic environment. Copulas are a useful and adaptable technique for determining the multivariate joint dependency amongst flood variables. This study employed time-varying copula models to investigate the nonstationary dependence structures between two highly correlated flood variables, such as flood peak and flood volume, in order to determine the joint and conditional return periods of the flood events revealed by the 2018 Great Kerala floods. The proposed approach is executed for two potential locations of high flood risk namely, Periyar river basin and Greater Pamba river basin of Kerala, India. The Archimedean copula (Clayton, Frank and Gumbel) parameters were estimated using Maximum likelihood estimation and the optimal copula selection was made using Akaike Information Criterion. The non-stationary joint return time was found to be shorter than the stationary joint return period, suggesting that the extreme flood occurrences happened more frequently in the non-stationary bivariate study. Thus, it can be demonstrated that the extreme flood episodes are underestimated by stationary bivariate flood frequency analysis. The validation of results by comparing the flood magnitude of Neeleswaram station for 2018 flood quantile ascertained the necessity of non-stationary flood risk estimation. The study advocates the conduct of multivariate frequency analysis over the univariate analysis for the risk assessment of hydrological extremes. The results demonstrate that the long-term decision-making methods need to be updated to account for the oddities of the nonstationary climate. This study rendered flood risk assessment indicators as well as a risk-based design approach for hydraulic infrastructures in a non-stationary environment, which is crucial for climate change adaption and water security management.

How to cite: Sankaran, A., Mohan, M. G., Raj, A., and Shaji, A.: Time Varying Copula based formulations for Flood Risk Assessment of two Tropical basins of Kerala, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5024, https://doi.org/10.5194/egusphere-egu24-5024, 2024.

Since the start of the 21st century, greater focus has been put on drought and its wide range of environmental and socioeconomic effects, particularly in the context of climate change. This is especially true for the North-western region of Italy, comprising the Piedmont and Aosta valley, which have been affected in recent years by droughts that have had acute effects on water resources and water security in all sectors, including agriculture, energy and domestic use. The region also belongs to the Mediterranean hot-spot, characterized by faster than global average warming rates and higher vulnerability to their effects. Therefore, characterizing the observed changes and trends in drought conditions is of particular significance. To this end, 60 years of precipitation and temperature data from the North West Italy Optimum Interpolation data set are used to calculate the drought indices SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) at a shorter (3-month) and at a longer (12-month) time scale. First, trend analysis on precipitation and temperature is performed, finding limited areas with significant precipitation decrease and, conversely, a general temperature increase over the region, with higher values found in the higher elevation areas. Changes in meteorological drought are then evaluated, both in terms of drought indices trends and in terms of changes in the characteristics of drought periods, on both a local and regional scale. A relation between the altitude of the area and the observed changes is highlighted, with significant differences between the plain and mountainous portion of the region. The differences are mainly related to the observed trends, with the low-altitude part of the region displaying a tendency towards dryer conditions not in common with the mountainous area. Significantly, no trend is found at a region-wide level but is instead found when considering homogeneous areas defined by terrain ruggedness. Furthermore, changes in the number of drought episodes and in their severity, duration and intensity are found to be correlated with terrain ruggedness at all time scales.

How to cite: Mombrini, E., Tamea, S., Viglione, A., and Revelli, R.: Metereological Drought in the western Po river basin: trends and characteristics from 1958 to 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5234, https://doi.org/10.5194/egusphere-egu24-5234, 2024.

The assessment of multivariate return periods determines how frequently different variables co-occur within a specific region. Recent studies have used two- and three-dimensional copulas for this assessment. G. Salvadori et al., (2011) introduced an approach based on Archimedean copulas and the Kendall measure. Gräler et al., (2013) calculated the trivariate return period using Vine copulas and Kendall distribution functions, incorporating annual maximum peak discharge, volume, and duration. Tosunoglu et al., (2020) applied three-dimensional Archimedean, Elliptical, and Vine copulas to study flood characteristics. These methodologies enhance the accuracy of extreme events risk measurement, emphasizing the importance of understanding tail dependence and the appropriate selection of copulas.

In multivariate analysis of compound extreme events, addressing the dependence structure in the tails of the variables of interest becomes essential. If the selected copula fails to accurately capture this extreme dependence, the estimation of extreme values may be significantly affected by uncertainty (Hangshing & Dabral, 2018). Therefore, conducting a comprehensive assessment of the copula model fit to the data is crucial, with a particular focus on tail dependence (Serinaldi, 2015). This process guides the choice of the most suitable copula family to model these compound extreme events.

We propose a two-part methodology: (I) In this phase, we focus on comparing various multivariate models that address the entirety of uncertainty. This involves analyzing different models and copula structures. The main objective is to evaluate how goodness of fit and tail dependence impact the calculation of design events, where, in some cases, underestimation may occur.

(II) In a subsequent stage, we formulate a more robust approach that encompasses the study, evaluation, and implementation of various statistical and machine learning techniques. The focus is on using the results obtained in the previous stage to develop flood models. These models enable us to compare multivariate approaches in terms of their performance in flood prediction and other associated impacts.

The study results highlight the importance of diversifying approaches in the hydrological analysis of precipitation-conditioned design events. It was found that the use of a multivariate approach provides more accurate estimations of precipitation compared to the univariate method. The careful choice of the multivariate model is crucial, as Gaussian models underestimate extreme events, while extreme vine copula models yield more tightly fitted results. This advancement benefits engineering by reducing uncertainty in design processes and providing a more precise approximation of climate impacts, with the potential to enhance territorial management.

References

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

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.

Hangshing, L. & Dabral, P. P., 2018. Multivariate Frequency Analysis of Meteorological Drought Using Copula. Water Resour Manage, 32(5), 1741–1758.

Serinaldi, F., 2015. Dismissing return periods! Stoch Environ Res Risk Assess, 29(4), 1179–1189.

How to cite: Urrea Méndez, D. A., V. Gómez, D., and Del Jesus Peñil, M.: Exploring Multivariate Return Periods: Enhancing Accuracy in Hydrological Analysis for Flood Prediction , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5685, https://doi.org/10.5194/egusphere-egu24-5685, 2024.

This task focuses on the understanding of the spatial connections among 91 NATO installations subject to hydroclimatological extremes, including precipitation, surface temperature, and wet bulb temperature, under both historical and future conditions. It allows a system-level view of the vulnerabilities of NATO installations to climate change and the associated extremes. We first perform statistical bias correction and evaluate how well global climate models (GCMs) part of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) are able to reproduce the historical trends. Based on these analyses, we select a subset of well-performing models, which we use to examine how the spatial dependence in climate extremes is projected to change. In particular, we consider two future periods (Mid-of-Century: 2015-2048; End-of-Century: 2067-2100) with respect to the 1981-2014 period, under four shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5).

We show that temperature-based extremes are correlated in space and have a large footprint, impacting more than one base at once. When we focus on precipitation extremes, we find that their spatial correlation is much weaker, with a much smaller chance of impacting more than one installation. Moreover, GCMs can reproduce these observed behaviors. In analyzing the future projections of these hydroclimatic extremes, we show that the spatial correlation in temperature-based extremes across NATO installations is projected to increase, especially toward the end of the 21^st century and for higher emission scenarios. These results highlight the current and future susceptibility of the NATO installations to climate extremes in light of climate change when viewed through a system-level perspective.

How to cite: Villarini, G., Carniel, S., Kim, T., Kim, H., Russo, A., Yang, W., Vecchi, G., and Wahl, T.: On the Vulnerability of NATO Installations to Climate Variability and Change: A System-Level Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6170, https://doi.org/10.5194/egusphere-egu24-6170, 2024.

Extreme precipitation events have a significant impact on hydraulic infrastructure design and risk management. The World Climate Research Programme (WCRP) Grand Challenges highlights the need for further investigation in analyzing and modeling weather and climate extremes due to their substantial effects. These rare meteorological occurrences represent the upper tail of the probability distribution, which can be effectively defined as heavy or light. The Obesity Index (OB) represents a user-friendly, non-parametric, empirical method capable of quantitatively assessing the heaviness of probability distribution tails directly from the original dataset, without the need to extract only extreme values. Our assessment of OB involves two distinct gridded datasets: one specific to Italy (SCIA) and another covering the entire Europe (E-OBS). The analysis shows a robust correlation between OB and L-moment ratios (L-variation, L-skewness, L-kurtosis), along with the Coefficient of Variation (CV). It is interesting to note that the tail heaviness in a specific region may vary depending on the dataset employed. For instance, OB indicates a prevalence of heavy tails across Italy or lighter tails in specific areas of the peninsula when employing SCIA or E-OBS dataset, respectively. This divergence could be attributed to an excessive smoothing of rainfall observations during the interpolation procedures used in generating E-OBS dataset. Thus, our findings reinforce the thesis of using light-tail probability distributions with caution when addressing rainfall extremes.

How to cite: Marconi, F., Moccia, B., Ridolfi, E., Russo, F., and Napolitano, F.: Detecting the heaviness of daily rainfall probability distributions in Europe through an expeditious method, the Obesity Index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6701, https://doi.org/10.5194/egusphere-egu24-6701, 2024.

Extreme precipitation is projected to intensify and occur more frequently under climate change. However, the effect of global warming on the spatial and temporal structure of extreme rainfall events at the local scale is uncertain. In the UK, the current method for estimating changes in flood hazard under climate change involves applying a simple multiplicative uplift to spatially uniform catchment rainfall. This approach neglects spatio-temporal characteristics of rainfall, which are known to be important for flood hazards. The UCKP Local Convection Permitting Model (CPM) has for the first time provided the capacity to assess these characteristics of rainfall at the local scale. Here, we use an ensemble of 2.2km hourly convection-permitting transient projections from UKCP Local to identify changes in the spatial and temporal characteristics of precipitation extremes over 100-years (1981-2080) across the UK. The analysis uses an ‘event-based’ approach, exploring seasonal changes in the peak intensity, total rainfall, and duration of events, but also changes in the spatial extent and temporal clustering of events through time. We identify ~13000 extreme rainfall events across the UK over the 100-year period. Event peaks are identified using a seasonal and time-varying threshold (99^th percentile) on hourly rainfall rates, and event start and stop times are extracted using a lower threshold (20^th percentile). We identify seasonal differences in how spatial extents of rainfall extremes will change, with winter and spring events growing, but summer and autumn events reducing in areal coverage. We also identify changes in the sub-seasonal timing of rainfall extremes, with events becoming more clustered, particularly during the winter months. Understanding changes in the spatial and temporal characteristics of rainfall events is critical as they may compound with increases in rainfall intensity, exacerbating the impacts of flooding.

How to cite: Devitt, L., Coxon, G., Neal, J., Archer, L., Bates, P., and Kendon, E.: Changing spatio-temporal characteristics of extreme rainfall events under climate change using high resolution climate projections , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10268, https://doi.org/10.5194/egusphere-egu24-10268, 2024.

Mountains cover approximately one-quarter of the total land surface on the planet, and a significant fraction of the world’s population lives in their vicinity. Orography critically affects weather processes at all scales and, in connection with factors such as land-cover heterogeneity and mesoscale atmospheric process, is responsible for high spatial variability in mountain weather, such as the Tropical Andes. Due to this high complexity, monitoring the atmosphere in the Ecuadorian Andes has remained a challenge due to the lack of high spatio-temporal resolution operational observing systems. We studied heavy rainfall associated with floods to identify the main rain types and their sources of moisture based on non-stationary rainfall-similarity indices and Lagrangian approaches. We analyzed five years of data collected from a high space-time resolution (5 min and 500 m) X-band weather radar that was located at 4450 m a.s.l in the Tropical Andes of southern Ecuador. To identify the origin and trajectories of water vapor masses, we used the NOAA meteorological database (GDAS, global data assimilation system, at 0.5° resolution). Our analysis shows that the heavy rainfall in the region can be divided into five rainfall types: two spatially-clustered rain types (convective) and three spatially-homogenous rain types (stratiform). We found that air masses typing as convective reach the study area preferentially from the eastern flank of the Andes through the Amazon basin (~ 70% of all events). We also compared discharge data with rain types and discussed the type and source of rainfall potentially responsible for triggering flash floods in the Andes of southern Ecuador.

How to cite: Urdiales-Flores, D., Mariethoz, G., Célleri, R., and Peleg, N.: Climatology and moisture sources of heavy rainfall in the Andes of southern Ecuador, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11587, https://doi.org/10.5194/egusphere-egu24-11587, 2024.

Hydroclimatic extremes, such as floods, present complex challenges in risk assessment due to their spatial and temporal compounding nature. This research aims to improve our understanding and modelling capabilities by investigating the complex interactions among record length, flow regime, and upper tail of floods. Our study resolves conflicting results in prior studies by utilizing a quasi-global peak-over-threshold (POT) analysis of flood with the Generalized Pareto (GP) distribution. Based on an analysis of 4,482 streamflow series over six different regime types with record lengths ranging from 30 to 213 years, our results show a strong relationship between the GP shape parameter and record length. The results show that the variance of the shape parameter of GP distribution diminishes with record length, and it eventually converges to a single value depending on the flow regime. We show that the shape parameter of snow-dominated streams is the lowest, whereas intermittent streams have the highest. Our research reveals regime-specific patterns in the impact of hydroclimatic and catchment controls on flood tails, underscoring the necessity of regime-specific strategies for flood risk management. Identifying catchments that are more likely to experience extreme flooding provides useful information for determining which mitigation measures to prioritize.

How to cite: Zaerpour, M., Papalexiou, S. M., and Pietroniro, A.: A Global Analysis of Daily Streamflow Data to Unravel the Heaviness of Flood Distribution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13301, https://doi.org/10.5194/egusphere-egu24-13301, 2024.

Snowmelt is a vital source of freshwater for a large proportion of North America’s population. Sudden snowmelt can also lead to various extreme events and environmental hazards, such as floods in the cold season and droughts in the upcoming warmer months. However, this natural water resource is at risk due to climate change and variability. Temperature and precipitation are significant climatic controllers that regulate snowmelt dynamics. Warmer temperatures can affect snowmelt extremes, persistence, and distribution, while changing precipitation alters the available snow budget and, consequently, the snowmelt amount. Yet, the precise role of the compound changes in temperature and precipitation under changing climate on future snowmelt dynamics is unknown. To address this knowledge gap, we use observation-driven data and future projections to quantify the response of winter snowmelt to changes in temperature and precipitation across North America (United States and Canada). Our analysis of far-future (2091-2100) changes reveals a significant increase (> 60%) in winter (November-March) snowmelt in northern latitudes, while it declined (by up to< 38%) in southern latitudes. Higher temperatures proved to be the primary driver of the increased snowmelt, whereas decreased snowfall modulated the declines in snowmelt, with variability seen across the study domain. Our findings suggest that the probability of an increase in winter snowmelt is high under the warmer and wetter climatic conditions prevailing in northern regions. In contrast, winter snowmelt across southern latitudes is likely to decline. These findings have significant implications for freshwater availability in the future in the affected areas. 

How to cite: Hatami, S., Zaerpour, M., Adamowski, J., and Papalexiou, S. M.: Climate Change both Increases and Decreases Winter Snowmelt across North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14054, https://doi.org/10.5194/egusphere-egu24-14054, 2024.

Flood risk assessment is generally carried out at a basin scale by developing hydrologic and hydraulic models with an objective to arrive at hazard/inundation maps for the river segments/reaches. A hydrologic model is used to derive a flood hydrograph corresponding to a specific return period and this is routed through the flood plain of the study area with the aid of a hydraulic model to obtain water surface elevations and inundation extents. This approach is best suited to represent a flood event at a river segment accurately, but the risks associated with floods need to be analyzed on a regional scale for effective flood risk management. As the size of the region increases beyond one basin, this approach fails to realistically represent the flood events across different river segments and basins. The simultaneous occurrences of different return periods on different river segments cannot be captured by this approach. The traditional approach to model these simultaneous occurrences is by using the streamflow records to arrive at spatially correlated stochastic simulations of streamflow. One issue that compounds this problem is the data scarcity in certain regions to accurately estimate the return periods of floods at distinct locations.

To address these issues, Impact Forecasting, Aon’s catastrophe model development team, has undertaken a study to simulate stochastic flood events in the Southeast Asian region by considering Malaysia as a case study. The approach involves (i) downscaling of precipitation and temperature data from an ensemble of Global Circulation Models (GCMs), (ii) calibration of a grid-based Rainfall-Runoff (RR) model using available historical data of meteorological variables and streamflow, (iii) providing the downscaled precipitation and temperature data as input to the calibrated RR model to simulate streamflow across the study area and (iv) identifying flood events from the simulated streamflow to extract an exhaustive set of realistic flood events in the region. The approach involves the use of meteorological data of 15,000 years from 7 different GCMs, downscaled to 10 km grids from 100 km resolution. This enables capturing a broader spectrum of potential climate conditions and therefore generating a wide range of possible flood events without relying significantly on in-situ data. The proposed methodology considers the uncertainty inherent in climate models, providing a robust framework for assessing flood risk and results in a more reliable and realistic representation of stochastic flood events over a region. The approach presents a physically based alternative to the commonly used statistical approaches based on extreme value theory and could be a valuable tool for policymakers, researchers, and practitioners in making informed decisions in the face of evolving climate conditions.

How to cite: Kezhkepurath Gangadhara, K., Muraleedharan, S., Bharath, R., and Kadlec, M.: Regional Flood Risk Assessment Using Ensemble of GCMs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14570, https://doi.org/10.5194/egusphere-egu24-14570, 2024.

This study aims to identify key characteristics of rainfall events, such as critical duration, extent, and severity (i.e., return period), to unveil potential dependencies with the resulting impact scenarios. Severity diagrams, introduced by Ramos et al. (2005) serve here as a straightforward tool, providing a synthetic visualization of storm severity while accounting for the complexity associated with rainfall spatial variability and duration. The method emphasizes the coexistence of extreme and ordinary (non-extreme) rainfall intensities. In contrast, the conventional approach of assigning a single return period to an event obscures a significant portion of storm complexity by overlooking spatial variability. Maximum mean areal precipitations observed over different areas during the storm event are evaluated. Subsequently, maximum equivalent point rainfalls are derived using ARF (Areal Reduction Factor) estimation, and their return period values deduced from the IDF (Intensity Duration Frequency) curves. The return periods are eventually mapped as a function of area and duration of rainfall accumulation. Several damaging storm events observed in the Calabria region (south Italy) over the last 20 years have been selected as illustrative examples for the analysis.

How to cite: Biondi, D. and Bloise, S.: Characterization of Extreme Rainfall Events Severity in Calabria: Exploring Spatial-Temporal Variability through Severity Diagrams, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20687, https://doi.org/10.5194/egusphere-egu24-20687, 2024.

Nature depends on the inherent unpredictability of randomness, a significant force influencing hydrometeorological processes. While physics provides sophisticated models, understanding the variability within randomness is crucial for evaluating environmental risks. Despite the availability of numerous stochastic models tailored to specific statistical properties, identifying essential features for accurate simulations across time, space, and scales remains a challenge. This presentation outlines the progress in CoSMoS, a user-friendly stochastic modeling framework that advances from basic scenarios to complex multisite and space-time simulations. The underlying philosophy of this framework is to faithfully replicate the probabilities describing the occurrences of magnitudes and correlations in space and time. CoSMoS excels in generating time series for various hydroclimatic variables and simulating intricate space-time phenomena, as demonstrated by its effectiveness in replicating storms, cyclones, and air mass collisions. This showcases its versatility in capturing complex behaviors across different scales.

References

• Papalexiou, S. M., Serinaldi, F., & Clark, M. P. (2023). Large-Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites. Water Resources Research, 59(3), e2022WR034094. https://doi.org/10.1029/2022WR034094
• Papalexiou, S. M. (2022). Rainfall Generation Revisited: Introducing CoSMoS-2s and Advancing Copula-Based Intermittent Time Series Modeling. Water Resources Research, 58(6), e2021WR031641. https://doi.org/10.1029/2021WR031641
• Papalexiou, S. M., Serinaldi, F., & Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466. https://doi.org/10.1029/2020WR029466
• Papalexiou, S. M., & Serinaldi, F. (2020). Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity. Water Resources Research, 56(2), e2019WR026331. https://doi.org/10.1029/2019WR026331
• Papalexiou, S. M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources, 115, 234–252. https://doi.org/10.1016/j.advwatres.2018.02.013
• Papalexiou, S. M., Markonis, Y., Lombardo, F., AghaKouchak, A., & Foufoula‐Georgiou, E. (2018). Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435–7458. https://doi.org/10.1029/2018WR022726

How to cite: Papalexiou, S. M.: Simulating Nature’s randomness with CoSMoS - A Versatile Stochastic Modeling Framework for Hydrometeorological Phenomena, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21666, https://doi.org/10.5194/egusphere-egu24-21666, 2024.