HS7.1

The quantification of future flood risk, as well as the assessment of impacts attributed to the evolution of extreme rainfall events under rapidly changing climatic conditions, require multi-year information at adequately high spatiotemporal scales. The spatial and temporal evolution of regional extreme rainfall patterns, however, is quite challenging to describe due to natural climate variability and local topography. Hence, the use of conventional climate model outputs to evaluate the frequency of extreme events may not be conclusive due to significant epistemic uncertainties.  To date, there is limited knowledge on how extreme precipitation patterns will evolve under the influence of climate change, at spatiotemporal resolutions suitable for hydrological modeling, and considering the non-stationarity of rainfall as a process. In this study, we evaluate future trends related to extreme rainfall using hourly estimates acquired through the North American (NA) CORDEX Program (see Mearns et al., 2017), spanning from 1979 to 2100, over a 25-km CONUS-wide grid. In view of the practical importance of high spatial and temporal resolutions in hydrological modeling, we first simultaneously bias-correct and statistically downscale the NA-CORDEX model outputs, by using the two-component theoretical distribution framework described in Emmanouil et al. (2021), as well as the Stage IV weather radar-based gridded precipitation data (4-km spatial resolution) as a high-resolution reference. To investigate the validity of the yielded rainfall intensity quantiles, we use as benchmark the hourly rainfall measurements offered by NOAA’s rain gauge network (National Centers for Environmental Information, 2017). Finally, to evaluate the effects of climate change on the spatial and temporal evolution of rare precipitation events while taking into consideration the nonstationary nature of rainfall, we apply a robust (Emmanouil et al., 2020) parametric approach founded on multifractal scaling arguments (Langousis et al., 2009) to sequential 10-year segments of the data, where conditions can be fairly assumed stationary. In view of revealing future infrastructure vulnerabilities over a wide range of characteristic temporal scales and exceedance probability levels, our analysis is founded on Intensity-Duration-Frequency (IDF) curves, which are derived using the previously acquired CORDEX-based, gridded (4-km), hourly precipitation estimates, and cover the entire CONUS for a period of 120 years.

References

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2020). Quantitative assessment of annual maxima, peaks-over-threshold and multifractal parametric approaches in estimating intensity-duration-frequency curves from short rainfall records. Journal of Hydrology, 589, 125151. https://doi.org/10.1016/j.jhydrol.2020.125151

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2021). An ERA-5 Derived CONUS-Wide High-Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework. Water Resources Research, 57(6), 1–17. https://doi.org/10.1029/2020WR029548

Langousis, A., Veneziano, D., Furcolo, P., & Lepore, C. (2009). Multifractal rainfall extremes: Theoretical analysis and practical estimation. Chaos, Solitons and Fractals, 39(3), 1182–1194. https://doi.org/10.1016/j.chaos.2007.06.004

Mearns, L. O., McGinnis, S., Korytina, D., Arritt, R., Biner, S., Bukovsky, M., et al. (2017). The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway. Boulder (CO): The North American CORDEX Program, 10.

National Centers for Environmental Information. (2017). Cooperative Observers Program Hourly Precipitation Dataset (C-HPD), Version 2.0 Beta. NOAA National Centers for Environmental Information, [accessed July 17, 2020].

How to cite: Emmanouil, S., Langousis, A., Nikolopoulos, E. I., and Anagnostou, E. N.: Assessing future extreme rainfall trends through multifractal scaling arguments: A CONUS-wide analysis based on NA-CORDEX model outputs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10931, https://doi.org/10.5194/egusphere-egu22-10931, 2022.

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and, especially in regions where rainfall variability is high, such as the eastern Mediterranean, they are critical to the hydrological budget. Reliable projections of future HPEs are needed, but global climate models are too coarse to explicitly represent rainfall processes during HPEs. In this study we used pseudo global warming high-resolution (1 km²) weather research and forecasting (WRF) model simulations to provide rainfall patterns projections based on simulations of 41 pairs of historic and “future” (end of 21^st century) HPEs under global warming conditions (RCP8.5 scenario). Changes in rainfall patterns were analyzed through different properties: storm mean conditional rain rate, storm duration, and rain area. A major decrease in rainfall accumulation occurs in future HPEs (−30% averaged across events). This decrease results from a substantial reduction of the storms rain area (−40%) and duration (−9%), and occurs despite an increase in the mean conditional rain intensity (+15%). The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms. (For hydrological results see: abstract #EGU22-4777)

• Armon, M., Marra, F., Enzel, Y., Rostkier‐Edelstein, D., Garfinkel, C. I., Adam, O., et al. (2022). Reduced Rainfall in Future Heavy Precipitation Events Related to Contracted Rain Area Despite Increased Rain Rate. Earth’s Future, 10(1), 1–19. https://doi.org/10.1029/2021ef002397

How to cite: Armon, M., Marra, F., Garfinkel, C., Rostkier-Edelstein, D., Adam, O., Dayan, U., Enzel, Y., and Morin, E.: Reduced rainfall in future heavy precipitation events tied to decreased rain area and takes place despite increased rain rate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1996, https://doi.org/10.5194/egusphere-egu22-1996, 2022.

Precipitation reanalysis products (PRP) are a promising data source for ungauged regions. Since observed time series are often i) too short, ii) their temporal resolution is not sufficient or iii) the network density is too low, they cannot be used as e.g. input for rainfall-runoff (r-r) modelling and derived flood frequency analysis. Reanalysis products as global simulation of the atmosphere over the last decades solve the aforementioned issues.

From the latest PRP three are most promising due to their spatial and temporal resolution for r-r modelling of small to mesoscale catchments: ERA5-Land (raster with approx. 9 km width), REA6 (6 km) and CFSv2 (22 km). These three PRP are able to cope with the dynamics of the r-r process due to their hourly resolution. The PRP are evaluated for Slovenia (Europe) with both, precipitation characteristics in space and time, and runoff characteristics. For areal precipitation, continuous and event-based characteristics are evaluated as well as precipitation extreme values. Simple correction methods for identified biases are suggested and applied. It can be seen that although the PRP clearly differ from each other, there is no clear ‘favourite’ to use as input for the r-r modelling.

To conclude about the suitability of the PRP for r-r modelling, continuous simulations are carried out with GR4H for 20 catchments in Slovenia (55 km²-480 km²). Models are re-calibrated for each PRP input based on KGE. Simulation results of calibration and validation period are evaluated by runoff extreme values, KGE, flow duration curve and intra-annual cycle. Interestingly, first results show that the deviations of some rainfall characteristics do not necessarily transfer to deviations in runoff characteristics, which can be explained by the high nonlinearity of the r-r process. PRP lead to better, at least similar results for runoff characteristics for catchments without rain gauges in their centre.

How to cite: Müller-Thomy, H., Nistahl, P., Bezak, N., and Alexopoulos, M.: Evaluation of precipitation reanalysis products in space and time for ungauged sites in Slovenia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4405, https://doi.org/10.5194/egusphere-egu22-4405, 2022.

Global climate change has become one of the major environmental issues today. Climate change impacts rainfall (and other hydroclimatic processes) in many ways, including its temporal and spatial variability. Hence, understanding the impact of climate change on rainfall is important to devise and undertake more effective and efficient adaptation and management strategies. The present study attempts to determine the temporal dynamic complexity of monthly historical and future rainfall in India at a spatial resolution of 1º × 1º. The historical and future rainfall data are simulated from 27 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The historical rainfall consists of the rainfall data simulated by GCMs for the period 1961–2014, and the rainfall simulated by the GCMs under shared socio-economic pathway scenarios (SSPs) constitutes the future rainfall. Four scenarios (SSP126, SSP245, SSP370, and SSP585) and two different timeframes (near future (2015–2060) and far future (2061–2099)) are considered to determine how the rainfall and its dynamic complexity vary across the scenarios and timescales. The false nearest neighbor (FNN) algorithm is employed to determine the dimensionality and, hence, the complexity of the rainfall dynamics. The algorithm involves two major steps: (i) reconstruction of the single-variable rainfall time series in a multi-dimensional phase space; and (ii) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the rainfall time series. The results suggest that the FNN dimensions of both the historical rainfall and future rainfall simulated by the 27 GCMs across India under all scenarios range from 3 to 20, indicating low to high-level complexity of the rainfall dynamics. However, only less than 1% of the study area shows high-level complexity in historical and future rainfall dynamics. Moreover, around 20 GCMs exhibit low to medium-level complexity of rainfall dynamics in 80% of the study area, with the dimensionality in the range from 3 to 10. Therefore, considering both the historical rainfall and future rainfall under all the four scenarios and the two timeframes considered in this study, the number of GCMs simulating rainfall that exhibits dimensionality in the range 11 to 20 are few. This may be an indication that the complexity of rainfall dynamics in India in the future will be low-to-medium dimensional.

How to cite: Deepthi, B. and Sivakumar, B.: Complexity of rainfall dynamics in India in the context of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4453, https://doi.org/10.5194/egusphere-egu22-4453, 2022.

Users of climate data must often confront the problem that information is not available at the precise spatial locations of interest; or the related problem that multiple sources of information provide data at different collections of locations. An example of the first situation is the use of weather station data to calibrate a hydrological or land surface model requiring inputs on a regular grid; an example of the second is the use of information from an ensemble of climate models to sample structural uncertainty, but where each model produces output on its own grid. Dealing with this spatial misalignment is a common first step in any analysis, and is usually done by some form of interpolation. In this poster, we use standard approaches to convert regional climate model (RCM) outputs from the EuroCORDEX ensemble to the common grid used in the UK national Climate Projections (UKCP). We find that although these standard approaches perform acceptably in some situations, in others they can induce surprisingly large biases and inconsistencies in the statistical properties of the resulting fields – particularly those relating to variability and extremes. For example, although the resolutions of the UKCP grid and the EuroCORDEX RCMs are all similar, it is not hard to find locations where the maximum daily precipitation within a month is systematically underestimated by 5-10% in the regridded data; and where the maximum daily precipitation over a 20-year period is systematically underestimated by 25%. These effects could have major implications for impacts studies carried out using interpolated or regridded data, if they are not recognised and dealt with appropriately. We offer some suggestions, varying in ease of implementation, for dealing with the problem.

How to cite: Chandler, R., Barnes, C., Brierley, C., and Alegre, R.: Regridding and interpolation of climate data for impacts modelling – some cautionary notes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4004, https://doi.org/10.5194/egusphere-egu22-4004, 2022.

Extreme rainfall events can cause flash floods and are responsible for socioeconomic damage worldwide. In Campinas, southeastern Brazil, countless events take place throughout the year. In order to monitor and predict these events, with the support of Fapesp's SOS-Chuva project, a mobile rainfall radar was installed in the region. With the purpose to identify the accuracy of this data, the radar data were compared with rain gauge data. Through this study, it is noted that, at some points, the difference between the rain gauges measurements and the radar data is significant, which may hinder the calibration and performance of a rainfall-runoff hydrological model. To improve the rainfall measurement considering both data source, this study proposes to combine both information and generate rainfall probabilistic maps, derived from geostatistical methods, thus making possible to quantify the uncertainty of these data.

How to cite: Caseri, A. and Angelis, C. F.: Uncertainty Quantification of Precipitation Measurement with Weather Radar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10437, https://doi.org/10.5194/egusphere-egu22-10437, 2022.

The investigation of the influence of terrain morphology on rainfall extremes has never been conducted over the entire Italy, where some studies have been carried out over limited areas. We then present the first systematic investigation of the role of elevation and other morphological attributes on rainfall extremes over Italy, that is made possible by using the Improved Italian – Rainfall Extreme Dataset (I²-RED). I²-RED is a database of short duration (1 to 24 hours) annual maximum rainfall depths collected from 1916 until 2019 by more than 5200 rain gauges.

The analyses involved the relations between morphology and the mean annual rainfall extremes (index rainfall) using univariate and multivariate regressions. These relations, built countrywide, demonstrated that the elevation alone can explain only a part of the spatial variance. The inclusion of regression covariates as longitude, latitude, distance from the coastline, indexes of obstructions and the mean annual rainfall depth demonstrated to be significant in relations built at the national scale.

However, high local bias with notable spatial correlation derives from the national-scale analysis. This led us to focus on smaller areas. We started dividing Italy into 4 main regions: the Alps, the Apennines, and the two main islands (i.e. Sicily and Sardinia). A dedicated multiple linear regression analysis was conducted over each of these areas. Evident improvements were obtained through this approach; nevertheless, clusters of high residuals persisted, especially in orographically-complex areas. A different approach was then undertaken, based on a preemptive subdivision of Italy in morphologically similar regions, to both reduce the clustering of errors and better define the role of elevation. Using four morphological classifications of Italy from the literature, we applied simple regression models to the rain gauges available inside each region. Among all, the classification that embeds hydrological information turned out to produce the best results in terms of local bias, MAE and RMSE, outperforming the multivariate relations obtained at the national scale. This approach proved to better reproduce the effects of geography and morphology on the spatial variability of rainfall extremes.

Our analysis confirmed a general increase of 24-hour rainfall depths with elevation, as already pointed out by studies conducted over smaller areas. For 1-hour rainfall depths, in flat or in pre-hill zones a modest increase with elevation is visible, while over the Alps and in most of the Apennines a reverse orographic effect (i.e., a reduction of rainfall depth with increasing elevation) is clearly detected, confirming previous outcomes in those areas.

How to cite: Mazzoglio, P., Butera, I., Alvioli, M., and Claps, P.: Influence of morphology on the spatial variability of rainstorms over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6342, https://doi.org/10.5194/egusphere-egu22-6342, 2022.

Rainfall is highly variable in space and time. The knowledge of precipitation variability is very important for design or for uncertainty assessment of models. In this contribution two different aspects of variability are investigated – the treatment of zero observations for spatial interpolation and the problem of high order dependence. The finer the temporal resolution of precipitation observations the more zeros have to be considered. Should one include all zeros for the description of the spatial variability (for example variograms)? Examples corresponding to different time aggregations are show that zeros need a specific treatment. High order dependence is investigated using time series observed at multiple sites. Results are compared to a meta-Gaussian approach. A large high-resolution dataset from South-West Germany is used to demonstrate the problems and the different approaches.

How to cite: Bárdossy, A.: Spatial and temporal variability of rainfall on different time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10253, https://doi.org/10.5194/egusphere-egu22-10253, 2022.

We present an analysis of uncertainty in model-based Probable Maximum Precipitation (PMP) estimates. The focus of the study is on “model-based” PMP derived from WRF (Weather Research and Forecasting) model reconstructions of severe historical storms and amplified by the addition of moisture in the boundary conditions (so-called Relative Humidity Maximization technique). Model-based PMP offers numerous advantages over the currently-used approach that is described in NOAA Hydrometeorological Reports. By scaling moisture and producing the resulting precipitation according to model formulation, the model-based approach circumvents the need for linearly scaling precipitation. Despite the significant improvement this represents, model-based PMP retains some degree of uncertainty that precludes its use in operational settings until the uncertainty is rigorously evaluated. This paper presents an ensemble of PMP simulations that samples recognized sources of uncertainty: (1) initial/boundary condition error, (2) choice of physics parametrizations and (3) model error due to unresolved subgrid processes. To our knowledge, this is the first uncertainty analysis conducted for model-based PMP. We applied this ensemble approach to the Feather River watershed (Oroville dam) in California. We first carried out in-depth evaluation of model reconstructions and found that the performance of some storm reconstructions that underlie the PMP estimate is not ideal, though the lack of uncertainty information about observations currently prevents us from identifying “well-reconstructed” storms or performing bias correction. That being said, our ensemble indicates that the 72-hour maximized precipitation totals used for PMP estimation do not differ greatly (110% at most) from the single-value estimate when model uncertainty is considered. We emphasize that model-based PMP estimates should always be presented as a range of values that reflects the uncertainties that exist, but concerns about model uncertainty should not hinder the development of model-based PMP.

How to cite: Tarouilly, E., Cannon, F., and Lettenmaier, D.: Improving confidence in model-based Probable Maximum Precipitation : Assessing sources of model uncertainty in storm reconstruction and maximization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6217, https://doi.org/10.5194/egusphere-egu22-6217, 2022.

Simulating storms, or hydro-environmental fluxes in general, in space and time is challenging and crucial to inform environmental risk analysis and decision making under variability and uncertainty. Here, we advance space-time modelling by enabling simulation of random fields (RF) described by general velocity fields and anisotropy. This advances the skills of the Complete Stochastic Modeling Solution (CoSMoS) framework in space and time and enables RF's simulations that reproduce desired: (a) non-Gaussian marginal distribution, (b) spatiotemporal correlation structure (STCS), (c) velocity fields with locally varying speed and direction that describe advection, and (d) locally varying anisotropy. We demonstrate applications of CoSMoS by simulating storms at fine spatiotemporal scales that move across an area, spiraling fields such weather cyclones, air masses converging to (or diverging from) a point and more. The methods are implemented in the CoSMoS R package freely available in CRAN.

Reference: 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

How to cite: Papalexiou, S. M., Serinaldi, F., and Porcu, E.: Space-time simulation of storms and beyond!, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8792, https://doi.org/10.5194/egusphere-egu22-8792, 2022.

Mass scaling of atmospheric precipitation has been successfully characterized by multifractal frameworks in the literature dedicated to this subject. However, the dependence of the statistics of interarrival times and run lengths on the employed detection threshold, as theoretically predicted by multiplicative cascade models with different degrees of multifractality, is yet another aspect of interest when such models are being used for the purpose of rainfall modelling. It must be noted that interarrival times and run lengths are complementary variables, by representing uninterrupted time intervals above and below the detection threshold, respectively. The present communication deals with the intricacies of parametrizing and validating those aspects of multifractal rainfall models.

How to cite: Carsteanu, A.-A., Langousis, A., and Deidda, R.: Intensity-dependence of interarrival times and run lengths in multifractal rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11055, https://doi.org/10.5194/egusphere-egu22-11055, 2022.