Displays

The 2018 and 2019 heatwaves set all-time temperature records around the world and were associated with adverse effects on human health, agriculture, natural ecosystems and infrastructure in the affected regions. Often, severe impacts relate to the joint spatial and temporal extent of the heatwaves, but most research generally focuses either on spatial or temporal attributes of heatwaves. In addition, possible effects of adaptation are generally ignored, i.e. the extent to which society or ecosystems might be able to adapt to on-going changes in mean climate or associated extremes.
Here, we analyze the largest spatiotemporally contiguous heatwaves -- i.e. three-dimensional (space-time) clusters of hot days -- in simulations of global state-of-the-art Earth System models.  To assess the role of different levels of adaptation, we use three different thresholds to define a hot day: no adaptation (time-invariant climatological threshold), seasonal adaptation to the new summer means, full adaptation (hot days defined relative to the future climatology). 
We find a strong increase of spatiotemporally contiguous heatwaves with global warming for the no adaptation case whereas changes for the other two adaptation thresholds are much less pronounced. In particular, no or very little changes in the overall magnitude, spatial extent and duration are detected when heatwaves are defined relative to the future climatology using a temporally moving threshold (full adaptation). This suggests a dominant contribution of thermodynamic compared to dynamic effects.
Given the implied time scale, full adaptation is a rather unrealistic assumption. Hence, both strong mitigation and adaptation are necessary to limit impacts of heatwaves in the future.

How to cite: Vogel, M. M., Zscheischler, J., Fischer, E. M., and Seneviratne, S. I.: Development of future heatwaves under different adaptation levels, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1521, https://doi.org/10.5194/egusphere-egu2020-1521, 2020.

How to cite: Casey, A. and Papastathopoulos, I.: Spatial conditional extremes via the Gibbs sampler., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9570, https://doi.org/10.5194/egusphere-egu2020-9570, 2020.

Analysis of extreme precipitation events has been the cornerstone of statistical hydrology and plays a crucial role in planning and designing hydraulic structures. Extreme value theory offers a solid theoretical basis for using the Generalized Extreme Value (GEV) distribution as a probabilistic model to describe precipitation annual maxima. Several large-scale studies investigate the properties of the GEV distribution in point measurements offering insights on its spatial variability. Yet the sparse station network in most regions, as anticipated, leads to sparse point estimates that may distort the actual spatial patterns of the GEV’s parameters. Here, we use fine-resolution satellite-based gridded product, that is, the CHIRPS v2.0, to investigate the spatial variation of the GEV distribution over Italy. Our results show that the GEV shape parameter forms clear spatial patterns. We use these results to offer robust estimates and create maps for different return periods all over Italy.

How to cite: Moccia, B., Papalexiou, S. M., Russo, F., and Napolitano, F.: Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11948, https://doi.org/10.5194/egusphere-egu2020-11948, 2020.

A combination of high-resolution models in space and time was used to evaluate the impacts of climate change on streamflow statistics and their uncertainties throughout three mountainous catchments in Switzerland (Thur, K. Emme and Maggia). The two-dimensional AWE-GEN-2d model was used to simulate ensembles of gridded climate variables at an hourly and 2-km resolution based on ground and remote-sensing observations. The model was re-parametrized using the “factors of change” approach, calculated from regional climate models, and it was used to simulate ensembles of climate data until the end of the 21st century. These ensembles were subsequently used as inputs into the fully distributed hydrological model Topkapi-ETH, which is suitable for simulating streamflow over complex terrain, and considers all the relevant hydrological processes. Based on large ensembles of simulated hydrological variables, the changes of the hydrological components in space and time were evaluated along with their uncertainty due to the internal variability of the climate and the climate model selection. Results indicate a rather uniform increase in temperature for all catchments, characterized by high uncertainty toward the end of the century (with strongest increases of over 5°C). On the other hand, the magnitude and spatial patterns (namely, mountain vs valley) of change in precipitation differ between catchments, and the uncertainty of changes in extreme events is of larger magnitude than the climate change signal. The changes in climate are foreseen to affect the hydrological components in the catchments: evapotranspiration is projected to increase, while snowmelt contribution to the streamflow is expected to decrease by 50% at the end of the century. Model results indicate a decrease in streamflow at the outlet during the summer months and an increase in winter as early as the 2020-2049 period. Conversely, changes in extreme discharge show an uncertainty greater than the change signal for most climate models. Spatially heterogeneous changes in temperature and precipitation lead to elevation-dependent hydrological responses: e.g., streamflow annual means would decrease 20% in the upper reaches of the Thur catchment, while decreasing a similar amount in the downstream reaches. Correspondingly, hourly extremes are expected to decrease 20% in the upper reaches and increase up to 50% in the lowest part of the catchment. However, the signals of the change for extreme streamflow, compared to their uncertainty, are stronger for the upper parts of the river network. These results illustrate the benefit of using stochastic downscaling of climate variables to capture climate variability and assess uncertainty, and emphasize the importance of investigating the distributed impacts of climate change in mountainous areas, which may differ between high and low elevation reaches. 

How to cite: Moraga, J. S., Peleg, N., Fatichi, S., Molnar, P., and Burlando, P.: Distributed climate change impacts and uncertainty throughout mountainous catchments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13364, https://doi.org/10.5194/egusphere-egu2020-13364, 2020.

Among the hydrological impacts of global warming one of the most debated is the possible increase in extreme rainfall and floods. In sensitive environment, like the mountains, global warming directly affects the alternance of snow deposition and melting, with highly likely changes foreseen in the runoff regimes. As regards floods, recent results related to the trends in Europe have highlighted non-uniform evidence for an increase of peaks in cold regions, including high-elevation and high-latitude regions. Reasons for possible reduction of peaks against climate change are that anticipated melting can reduce the rain-on-snow phenomena in some areas. In the Alpine region, however, a closer look to the possible trends of flood peak is in order, as current knowledge indicates that the a dominant positive trend exists. 
Considering all discharge stations with historical flood peak data in Italy, a group of 140 mountain basins in the whole Alpine chain has been analysed according to the selection criteria of: i) average elevation of at least 1000 m a.s.l.; ii) absence of significant natural or man-made lakes within the basin; iii) at least 10 years of observation available in the last century. Areas of the selected basins range from 10 to about 10000 km2 and the average elevations reaches 3000 m a.s.l. The full range of observations available encompasses one century, as the oldest values dates 1911 and the most recent ones are recorded in the 2013. Half of the series available have less than 25 observations.
Among the possible techniques for trend analysis, the heterogeneous nature of this unprecedent database led us to initially consider only the quantile regression, due to the its robustness against the patchiness and the insufficient length of the time series. The same weaknesses in the data consistency suggest to complementing empirical statistical results with a subsequent attribution framework.  
Quantile regression application to all the flood peaks of a given year versus time provide marked indications of positive trends. Results were positive for quantiles 0.5, 0.75 and 0.95 even reducing the analyzed time span to 1951-2007, where at least 60 contemporaneous active stations can be considered. A specific role in the results of the elevation and of the area of the active station group over time was also investigated, by means of a multivariate quantile regression. Indeed, both the elevation and the area demonstrated to be significant covariates in the trend which, nevertheless, remained clearly positive for the same quantiles. 
A bundle application of the geomorphoclimatic attribution model of Allamano et al. (2009) on the 140 basins allowed to start the attribution exercise. Reconstruction of the dependence of the first moment of the time series on elevation provided a first confirmation to the empirical findings. 

How to cite: Claps, P., Ganora, D., Viglione, A., and Apostolo, A.: Trends in flood quantiles of the Italian alpine basins: statistical testing and directions for attribution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11652, https://doi.org/10.5194/egusphere-egu2020-11652, 2020.

Flash floods impose extensive damage and disruption to societies, and they are among the deadliest natural hazards worldwide. Flooding is an on-going global-scale socio-economic risk that is likely to increase in the future under climate change and human development. This risk has led to a variety of studies on the natural and anthropogenic causes of floods. Also, the massive socioeconomic impacts engendered by extreme floods is clear motivation for improved understanding of flood drivers. This presentation is two-fold: first, I demonstrate a machine learning approach to perform clustering of reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the Continental United States (CONUS). We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. Focusing on atmospheric circulation patterns leading to extreme rainfall, which is a major factor in nearly all except snowmelt-driven extreme floods, can be especially used to inform continental-scale modeling and forecasting effort. Considering that flash flood is mainly initiated by intense rainfall, and due to its rapid onset, taking action for effective response is challenging. Therefore, building resilience to flash floods require understanding of the socio-economic characteristics of the societies and their vulnerability to these extreme events. The second part of this presentation provides a comprehensive assessment of socio-economic vulnerability (SEV) to flash floods, investigates the main characteristics of flash flood hazard and accordingly a SEV index is developed at the county level across the CONUS. The coincidence of SEV and flash flood hazard are investigated to identify the critical and non-critical regions. The results indicate the resemblance and heterogeneity of flash flood spatial clustering and vulnerability of the regions over the CONUS. We show how identifying these spatial patterns will assist policy makers reach informed and effective decisions for planning and allocating resources.

How to cite: Moradkhani, H., Khajehei, S., Ahmadalipour, A., Moftakhari, H., and Shao, W.: From Causative Mechanisms of Extreme Events to a Place-based Assessment of Flash Flood Hazard and Vulnerability in the Continental United States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20980, https://doi.org/10.5194/egusphere-egu2020-20980, 2020.

How to cite: Richards, J. and Tawn, J.: Aggregation of Spatial Extremes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2625, https://doi.org/10.5194/egusphere-egu2020-2625, 2020.

Warming climate has significantly influenced the environment on the earth, which attracts wide attention in society. Previous studies show that precipitation extremes increase with warmer temperature. This phenomenon has been observed in the regions with various climates, with the theoretical support of the Clausius-Clapeyron relation. However, the effect of temperature on the spatio-temporal characteristics of precipitation extremes are less studied. In this study, we propose a new index to represent the temporal and spatial concentration of rainfall events, and study how temperature affect the rainfall concentration. It is found that precipitation events tend to have higher temporal and spatial concentration at higher temperatures, and rain events with shorter duration is more likely to be concentrated than those with longer duration in both time and space. The results indicate that rain events would be concentrated over smaller regions and during shorter periods under warming climate in the future, which leads to flood and drought occurring simultaneously.

How to cite: Wang, D. and Long, K.: Higher temperatures enhance spatio-temporal rainfall concentration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4350, https://doi.org/10.5194/egusphere-egu2020-4350, 2020.

Complex Time Methods and Chameleon Scalar Fields in the Dynamics of Spatial Extremes

Dionysia Panagoulia1 and Kalomoira Zisopoulou 2

1 School of Civil Engineering, Department of Water Resources and Environmental Engineering, National Technical University of Athens, Zografou, Greece. E-mail dpanag@hydro.ntua.gr

 2 Travaux Publics, Becket House, London, United Kingdom

It is shown that complex time in classical physics may transform the action functional Lagrangian and Lagrangian density processes to, among others, energy descriptive functionals. By imposing restrictions in the problem coordinate space as per need, such as Sobolev or Hardy spaces, or to the complex time plane such as the two variable Hilbert Space dependent Bergman. Decomposition new results are obtained which facilitate a better understanding of the mechanism governing spatial extremes in terms of flows.
The introduction of Khoury-Weltman type chameleon scalar fields will, by the recognition of existing oscillatory patterns, pave a connective chain of momenta between smaller and larger objects which will uncover the causal relationships between them which will allow for variable reduction in multivariate methods.

How to cite: Panagoulia, D. and Zisopoulou, K.: Complex Time Methods and Chameleon Scalar Fields in the Dynamics of Spatial Extremes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5946, https://doi.org/10.5194/egusphere-egu2020-5946, 2020.

Extreme precipitation can lead to great floods and landslides and cause severe damage and economical losses. It is therefore of great importance that we manage to assess the risk of future extremes. Furthermore, natural hazards are spatiotemporal phenomena that require extensive modelling in both space and time. Extreme value theory (EVT) can be used for statistical modelling of spatial extremes, such as extreme precipitation over a catchment. An important concept when modelling a natural hazard is the degree of extremal dependence for the given phenomenon. Extremal dependence describes the possibility of multiple extremes occurring at the same time. For the stochastic variables X and Y, with distribution functions F_X and F_Y, the measure

χ = lim_u→1 P(F_X(X) > u Ι F_Y(Y) > u)

describes the pairwise extremal dependence between X and Y. If χ = 0, then the variables are asymptotically independent. If χ > 0, they are
asymptotically dependent. Thus, extremes tend to occur simultaneously in space for processes that are asymptotically dependent, while this seldom occurs for asymptotically independent processes. It is a general belief that extreme precipitation tends to be asymptotically independent. However, to our knowledge, not much work has been put into analysing the extremal dependence structure of precipitation. Different statistical models have been developed, which can be applied for modelling spatial extremes. The most popular model is the max-stable process. Unfortunately, this model does not provide a good fit to asymptotically independent processes. Other models have been developed for better incorporating asymptotic independence, but most have not been extensively applied yet. We aim to examine the extremal dependence structure of precipitation in Norway, with the ultimate goal of modelling and simulating extreme precipitation. This is achieved by examining multiple popular statistics for extremal dependence, as well as comparing different spatial EVT models. This analysis is performed on hourly, gridded precipitation data from the MetCoOp Ensemble Prediction System (MEPS), which is publicly available from the internet: http://thredds.met.no/thredds/catalog/meps25epsarchive/catalog.html.

How to cite: Vandeskog, S. M. and Martino, S.: Examining the extremal dependence structure of precipitation in Norway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6998, https://doi.org/10.5194/egusphere-egu2020-6998, 2020.

The climatic condition for the dry or wet situations from 15 meteorological stations in the Danube basin has been evaluated using four indices: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z-index (ZIND).

The overall temporal characteristic of the four indices has been analysed by means of the principal component of the Multivariate Empirical Orthogonal Functions decomposition (PC1-MEOF). Also, a simple drought index (TPPI) calculated as the difference between PC1 of the standardized temperature and precipitation, was considered.

To find the simultaneous influence of both solar and geomagnetic activities on drought indices in the Danube basin, the difference between synergistic and redundant components for each season was estimated, using the mutual information between the analyzed variables. The greater this difference is, the greater the simultaneous signature of the two variables in the drought indices is more significant, than by taking each of the two variables separately.

The solar activity was highlighted by Wolf numbers for the period 1901-2000 and for 1948-2000 by solar radio flux. For both periods the geomagnetic activity was quantified by the aa index.

The most significant results for the 100-year period were obtained for the autumn season for which the two predictors representing solar and geomagnetic activities, if considered simultaneously could be one of the causes that produce extreme hydroclimatic events. The analysis from 1948-2000 revealed that the simultaneous consideration of the two external factors is more significant in the summer and autumn time.

How to cite: Mares, I., Dobrica, V., Mares, C., and Demetrescu, C.: Mutual information applications to estimate the solar/ geomagnetic signatures in the drought indices in the Danube basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7319, https://doi.org/10.5194/egusphere-egu2020-7319, 2020.

The Mediterranean region is an area with half a billion population, about 10 percent contribution to the world’s GDP, and locations of global natural, historical and cultural significance. In this context, natural hazards in the area have the potential for severe negative impacts on society, economy, and environment. 

Some of the most frequent and devastating natural hazards that affect the Mediterranean relate to extreme precipitation events causing flash floods and landslides. Thus, given their adverse consequences, it is of immense importance to better understand their statistical characteristics and connection to large-scale atmospheric patterns. Such advances can substantially support the accurate and early identification of these extreme events, improve early warning systems, and contribute to mitigating related risks. 

This work focuses on the characteristics and spatiotemporal variability of extreme precipitation events of large spatial coverage across the Mediterranean region. The study uses the ERA5 dataset, the latest, state of the art, reanalysis dataset from Copernicus/ECMWF. Initially, exploratory analysis is performed to assess the different characteristics at various subdomains within the study area. Furthermore, composite analysis is used to understand the connection of extreme events with large-scale atmospheric patterns. Finally, the Empirical Orthogonal Function (EOF) analysis is implemented to quantify the importance of weather regimes with respect to the frequency of extreme precipitation events. 

Preliminary results indicate that there is a spatial division in the occurrence of identified events. Winter and autumn are the seasons of the highest frequency of extreme precipitation for the east and west Mediterranean respectively. Troughs and cut-off lows in the lower and middle-level troposphere have a strong association with such extreme events, and the effect is modulated by other parameters, such as local orography. Results of this work are in accordance with previous studies in the region and provide information that can be utilized by future research for improving the predictability of such events in the medium- and extended-range forecasts. 

This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.

How to cite: Mastrantonas, N., Magnusson, L., Pappenberger, F., and Matschullat, J.: Extreme precipitation events in the Mediterranean region: their characteristics and connection to large-scale atmospheric patterns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8593, https://doi.org/10.5194/egusphere-egu2020-8593, 2020.

Understanding flood generating mechanisms is critical for model development and evaluation. While several studies analyse how catchment attributes influence flood magnitude and duration, very few studies examine how they influence flood generating processes. Based on prior knowledge about runoff behaviour and flood generation, we assume that flood processes depend not only on climate, but also on catchment characteristics such as topography, vegetation and geology. Specifically, we hypothesize that the influence of catchment attributes on flood processes will vary between different climate types. We tested our hypothesis on the CAMELS dataset, a large sample (671) of catchments in the United States. We classified 61,828 flood events into flood process types using a previously published location-independent classification methodology. Then we quantified the importance of both individual attributes (comparing probability distributions of different flood types) and interacting attributes (using random forests). Accumulated local effects allow interpretability of random forest with correlated attributes. Results show that climate attributes most strongly influence the distribution of flood generating processes within a catchment. However, other catchment attributes can be influential, depending on climate type. Based on the subset of influential catchment attributes, a random forest model can predict flood generating processes with high accuracy for most processes and climates, demonstrating capabilities to predict flood processes in ungauged catchments. Some attributes proved less influential than common hydrologic knowledge would suggest and are not informative in predicting flood process distribution.

How to cite: Stein, L., Clark, M., Pianosi, F., Knoben, W., and Woods, R.: Understanding catchment influences on flood generating processes - accounting for correlated attributes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9918, https://doi.org/10.5194/egusphere-egu2020-9918, 2020.

Global warming is expected to enhance El Niño Southern Oscillation (ENSO), with potential impacts on frequency and severity of floods and droughts in numerous countries of the Asia-Pacific region. However, the limited time coverage of historical records and the large uncertainties underlying climate model projections impair our ability to identify trends in extreme rainfall and dry spells. Here, we generate and analyze a long-term stochastic precipitation dataset for New Zealand that accounts for the potential effects of climate change. For this purpose, we draw on a 60 year-dataset of daily precipitation maps to identify the rainfall principal components and quantify their temporal correlations with the ENSO signal. We then generate a long-term stochastic set of daily rainfall maps correlated with ENSO projections, corresponding to different climate change scenarios. Our results indicate that climate change may lead to more intense precipitation in the Southern Alps during positive ENSO phases. Conversely, extreme precipitation is likely to increase in the North Island during negative ENSO phases. Our analyses also suggest that the duration of extreme dry spells may significantly increase along the east side of the North and South Islands during positive ENSO phases. These results may guide the implementation of effective adaptation and mitigation strategies against the increasing risk of natural catastrophes.

How to cite: Nicotina, L., Comola, F., Satyam, S., Scudeler, C., and Prakash, M.: Impacts of climate change on extreme precipitation and dry spells in New Zealand, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10382, https://doi.org/10.5194/egusphere-egu2020-10382, 2020.

Identifying hidden spatial patterns that define sub-regions characterized by a similar behaviour is a central topic in statistical climatology. This task, often called regionalization in hydrology, is helpful for recognizing areas in which the variables under consideration have a similar stochastic distribution and thus, potentially, in reducing the dimensionality of the data. Many examples are available in this context, spanning from hydrology to weather and climate science. However, the majority of regionalization techniques focuses on the spatial clustering of a single variable of interest. Given the often severe impacts of climate extremes at the regional scale, here we develop an algorithm that identifies homogeneous spatial sub-regions that are characterized by a common bivariate dependence structure in the tails of a bivariate distribution.  In particular, we use a novel nonparametric distance able to capture the similarities and differences in the tail behaviour of bivariate distributions as the core of our clustering procedure. We apply the approach to identify homogeneous regions with varying coherence in the co-occurrence of sea level pressure and precipitation extremes in Great Britain and Ireland.

How to cite: Vignotto, E., Engelke, S., and Zscheischler, J.: Clustering dependence structures of environmental extremes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11008, https://doi.org/10.5194/egusphere-egu2020-11008, 2020.

Rapid urban development, along with human modifications in river discharge (both frequency and magnitude) increase the need to design safe and resilient infrastructure. In addition, continental-domain studies show that there are significant changes in the intensity and frequency of the extreme rainfall events. Importantly, Earth System Models predict that these changes will continue to grow in the future. Consequently, flood frequency from heavy precipitation events is expected to increase, thereby threatening human society and the environment. Therefore, the stationary climate assumption — the idea that the future variability of the system will remain within the limits observed in the past record — may not be valid and should be carefully examined. Despite the existing awareness of potential non-stationarity, there has been a limited research on analysis of non-stationary of extreme precipitation at the global scale. This motivated us to conduct a comprehensive global study to compare the performance of non-stationary and stationary models in describing precipitation extremes.

How to cite: Sheikholeslami, R., Papalexiou, S. M., and Clark, M.: A Global Assessment of Non-Stationarity in Extreme Precipitation , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11943, https://doi.org/10.5194/egusphere-egu2020-11943, 2020.

Extreme precipitation event, along with its secondary disasters, is one of the largest natural hazards leading to massive loss in human society. In the coastal areas of southeast china, tropical cyclones (TC) frequently visit the region with intensive precipitation in summer and autumn. Besides TC induced extreme precipitation, convectional precipitation is an alternative reason of extreme precipitation. This study investigated the spatial effects of the extreme precipitation during the raining season for both TC induced and non-TC induced extreme precipitation. The seasonal maximum daily precipitation data through 94 stations in southeast coastal areas of China from 1964 to 2013 were used. We developed a hierarchical Bayesian model with generalized extreme value distribution (GEV) to quantitatively assess the effects of spatial factors on the extreme precipitation. TC induced and non-TC induced extreme precipitation are modelled separately. It was found that the spatial factors that affect the TC induced and non-TC induced extreme precipitation are clearly different. For the TC induced extreme precipitation, the distance to the coastline has been found to be a significant spatial covariate that affects both the location and scale parameter of GEV across the whole areas, while spatial factors are diverse in different locations for non-TC induced extreme precipitation.

How to cite: Qian, W. and Sun, X.: Spatial effects on extreme precipitation in the coastal areas of southeast China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12697, https://doi.org/10.5194/egusphere-egu2020-12697, 2020.

Streamflow extremes, especially, summer seasonal streamflow in monsoon climate makes a significant contribution to the reliability of water resources and the health of ecology. The summer extreme precipitation and streamflow also cause severe floods resulting in loss of life and property. Large scale climate drivers impart strong spatial and temporal variability in the flow extremes, which needs to be modeled for use in efficient management of resources. To this end, we developed a space-time model to capture the variability of –summer season 3-day maximum streamflow. In this, the extremes at each station are assumed to be distributed as Generalized Extreme Value (GEV) distribution with non-stationary parameters. Thus, the parameters are modeled as a linear function of suitable covariates – typically, large scale climate variables and regional mean precipitation. In addition, the spatial dependence of the extremes is modeled via a Gaussian copula. The parameters of the nonstationary GEV at each location are estimated via maximum likelihood, whereas those of the Copula are estimated via the Inversion of Kendall’s tau estimator method. Ensembles of streamflow in time are based on the temporal varying covariates and from the Copula are generated, consequently, capturing the spatial and temporal variability and the attendant uncertainty. Furthermore, various return level can also be obtained from these simulations. The model is demonstrated by application to 3-day maximum summer streamflow in a representative basin from two different monsoonal climate – India and Southwest U.S. In addition to comparing the performance of the median of the simulations with the historic observations, we also compare the number of stations that exceed a specific level- say, 75th percentile which indicates the spatial performance. The model validation indicates that the model is able to capture the space-time variability, furthermore, it captures the variability in wet and dry years, consistent with observations. This framework can be applied to generate ensembles of at several lead times – week to seasonal, to provide risks of various levels of streamflow. This will be of immense use in water resources, agriculture and flood management and planning.

How to cite: Ossandon, A., Rajagopalan, B., and Kleiber, W.: A Space-Time Modeling Framework for Streamflow Extremes , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13012, https://doi.org/10.5194/egusphere-egu2020-13012, 2020.

Spatial and frequency distributions of precipitation should be considered in determining design water demand of irrigation for a large region. In Guangdong province, South China, as a study case, an eight-dimensional joint distribution of precipitation for agricultural sub-regions was developed. A design procedure for water demand of irrigation for a given frequency of precipitation of the entire region was proposed. Water demands of irrigation in the entire region and its sub-regions using three design methods, i.e. equalized frequency (EF), typical year (TY) and most-likely weight function (MLW), were compared. Results demonstrated that the Gaussian copula efficiently fitted the high-dimensional joint distribution of eight sub-regional precipitation values. The Kendall frequency was better than the conventional joint frequency to analyze the linkage between the frequency of precipitation of the entire region and individual sub-regions. For given frequencies of precipitation of the entire region, design water demands of irrigation of the entire region among the MLW, EF and TY methods slightly differed, but those of individual sub-regions of the MLW and TY methods fluctuated around the demand lines of the EF method. The alterations of design water demand in sub-regions were more complicated than those in the entire region. The design procedure using the MLW method in association with a high-dimensional copula, which simulated individual univariate distributions, captured their dependences for multi-variables, and built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended for design water demand of irrigation for a large region.

How to cite: Tu, X.: Design water demand of irrigation for a large region using a high-dimensional Gaussian copula, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13634, https://doi.org/10.5194/egusphere-egu2020-13634, 2020.

Compound flooding in deltas and estuaries can be defined as the combination of various flood drivers leading to a significant flood impact (Zscheischler et al., 2018). For example, elevated sea-levels can impede flood drainage and create backwater effects that worsen flood damages. This was observed recently in March 2019 during cyclone Idai, where devastating floods from a high storm surge and discharge destroyed the port city of Beira. Even though the importance of accounting for compound flooding in flood risk assessments has been heavily underlined in recent literature, little research has been done on the impacts of compound flood events globally.

In this study, we investigate how compound flood hazard in estuaries is influenced by their various geophysical characteristics and the nature of their upstream river basins. The influence of riverine and coastal flood drivers on the water level varies along the estuary.  The water level at the river mouth is dependent on sea-levels, whereas one can expect this influence to reduce moving upstream in the river system and to become negligible completely upstream in large river systems. The location within a river system where both riverine and coastal flood drivers significantly contribute to the water level is referred to as the transition zone (Bilskie and Hagen, 2018).

We set up a model experiment to compare maximum water levels across realistic estuary types and boundary conditions. We use the 1-D unsteady hydrodynamic model LISFLOOD-FP to simulate water level time series for average and anomalous compound flood events of sea-levels and discharge. For each estuary type, resulting water level time series are analyzed to quantify the contribution of each flood driver in the maximum water level obtained along the complete coastal river profile and on the extent of the transition zone. We find that the interaction between the extreme sea level and extreme discharge is highly nonlinear and that this effect strongly varies depending on the estuary shape and length. We foresee this extensive overview of estuarine compound flood behavior to globally identify areas particularly vulnerable for interactions between extreme discharge and sea levels.

How to cite: Couasnon, A., Eilander, D., Bates, P., Winsemius, H. C., and Ward, P. J.: Hydrodynamic modelling of compound flood drivers in estuaries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14885, https://doi.org/10.5194/egusphere-egu2020-14885, 2020.

The contribution deals with spatial extremes of intense precipitation at the global scale, with the help of data-driven modelling. We ask whether the inter-annual and inter-decadal climate variability track plays a dominant role in the interpretation of the variability of heavy precipitation, globally. The study aims at discovering spatially and temporally organized links between climate oscillation indices, such as El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, Atlantic Multidecadal Oscillation and heavy precipitation. To this aim, we induce a range of machine-learning models, primarily recurrent neural networks, from multiple sources of global observations, including E-OBS data set from the UERRA project, GPCC Full Data Daily, and climate variability indices. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We expect to identify climate-oscillation drivers for spatial dependence of heavy precipitation.

How to cite: Kałczyński, M., Krawiec, K., and Kundzewicz, Z.: From climate variability to heavy precipitation – Learning transfer functions from data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18705, https://doi.org/10.5194/egusphere-egu2020-18705, 2020.

Heavy precipitation rates of more than 15 mm per hour are captured only about once a year at each rain gauge within Germany. More extreme events are even less frequent. Point by point verifications show that forecasts of heavy precipitation of the ensemble system COSMO-D2-EPS of DWD can be improved by statistical postprocessing. This is done in a MOS approach using long time series of synoptic observations and numerical forecasts that are required in or­der to gather a significant number of heavy precipitation events for reliable statistical model­ling.

Highest precipitation rates of convective events usually realise more likely in the surrounding of rain gauges rather than exactly above their small funnels. Statistical forecasts modelling these point observations usually underestimate maximal rain rates and result in low probabili­ties for the occurrence of heavy precipitation at a given location.

Point processes of stochastic geometry can be used to model area probabilities that provide the probability that precipitation occurs anywhere (at least at one point) within that area. Verifications with gauge adjusted radar data reveal that point probabilities are representative for very small areas, but area probabilities are significantly larger already for areas of 20*20 km².

The use of radar data as area observation system allows to statistically generate calibrated precipitation forecasts for arbitrary areas. However, the question remains, which size of area is most relevant for the public and most suitable for weather warnings.

How to cite: Hess, R., Schaumann, P., and Schmidt, V.: Statistical postprocessing of heavy precipitation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19436, https://doi.org/10.5194/egusphere-egu2020-19436, 2020.

A recent development in the modeling of intensity-duration-frequency (IDF) curves involves the use of a spatial max-stable process to explicitly account for asymptotic dependence between durations. To accomplish this, we use a duration-space instead of a geographic-space, following Tyralis and Langousis (2018). The resulting IDF curves can then be used to estimate extreme rainfall for any arbitrary rainfall duration. We aim to determine whether the use of a model that explicitly accounts for the dependence between durations could improve the estimates of extreme rainfall. The performance of the max-stable process is compared to the duration dependent GEV (d-GEV) approach for IDF-curve estimation proposed by Koutsoyiannis et al. (1998). The max-stable approach explicitly models the dependence via a parametric model, while the d-GEV approach assumes that the durations are independent. The performance of both approaches is assessed for two scenarios, in a controlled simulation experiment, and for observations from a rain gauge. A Brown-Resnick max-stable process and a duration-dependent GEV was fitted to the data in both scenarios. The performance is measured using the Quantile Skill Score (QSS) with the d-GEV as the reference model. The resulting skill scores show that correctly specifying the dependence structure leads to the max-stable model perfomring similarly to the d-GEV. This pattern was observed also for low and high levels of dependence.

How to cite: Jurado, O. E., Ulrich, J., and Rust, H. W.: Evaluating the performance of a max-stable process for estimating intensity-duration-frequency curves, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19707, https://doi.org/10.5194/egusphere-egu2020-19707, 2020.