Many water-related systems and defensive structures require the design of rainfall amounts at various durations and frequencies, commonly referred to as Intensity Duration-Frequency (IDF) curves. Usually, these curves are derived from observed data, but there is a chance that the risk has been underestimated because of various uncertainty sources. As a result, measuring the uncertainty ranges of these curves becomes essential. To do this, the regionalization of the IDF curves for BW is inspected for the propagation of possible sources of uncertainty. For each site, annual extremes are obtained for varying durations (from 5 min to 16 days), and local extreme value analysis is performed in compliance with Koutsoyiannis et al. (2021).  Following this investigation, Kriging with External Drift (KED) is used to interpolate all seven parameters of theoretically consistent IDF models for each station; this implies that no parameter remains constant across the region.  Quantiles are then retrieved for every station, duration, and given recurrence interval. The uncertainty is estimated for each of the three components of the regionalization—local parameter estimation, variogram estimation, and spatial parameter estimation—in terms of accuracy (expected error) and precision (95% confidence interval width) using bootstrapping (non-parametric) and geostatistical spatial simulations. The reason for selecting Conditional Sequential Gaussian (CSG) simulations was their capability to produce a large number of equiprobable spatial simulations. Many recent studies also demonstrated its accuracy, which is why CSG was chosen to evaluate the uncertainty from spatial simulations. Subsequently, one hundred realizations were carried out at every regionalization component to examine their ultimate impact on the regionalization of parameters and IDF curves. Afterward, combined simulations were executed for the propagation of the uncertainty from the key components to the final IDF curves.

It turned out that the primary source of uncertainty in the selected regionalization process is spatial estimation, which is followed by local estimation of rainfall extremes. More specifically, the total estimation of IDF curves was mostly insensitive to variogram uncertainty. The integration of spatial simulations with local resampling yielded accurate estimates of the overall uncertainty at sampled sites, whereas at unsampled sites, the accuracy decreased based on the density and proximity of the surrounding observations. This combination was used to simulate the total uncertainty in BW via 100 runs. The results showed that, depending on the site and duration interval, tolerance ranges should be expected to be between ± 0.9-4.2 mm/h for low-recurrence intervals (less than 5 years) and ± 2.2-5.5 mm/h for high-recurrence intervals (more than 50 years), but very short durations (5 min) are relatively more uncertain than longer durations.

How to cite: Amin, B., Bárdossy, A., and Haberlandt, U.: Uncertainty Quantification of Theoretical Consistent Intensity Duration Frequency (IDF) Curves of Rainfall Intensity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15926, https://doi.org/10.5194/egusphere-egu24-15926, 2024.

From the perspective of a team of engineers that studies an area in order to estimate flood risk, a highly studied area presents both benefits and challenges. On the one hand, stands the benefit of accessibility to data and existing knowledge, while on the other the challenge of optimal analysis and utilization of such material.

The region of Attica, Greece, fits exactly the description of a highly studied area. It hosts the capital of the country, Athens, and close to half of the national population. For this reason, flood risk has been studied throughout the 20^th and 21^st century in various spatial scales, and using different methods and tools. In a contemporary study of flood risk with modern digital computational tools, we undertook the tasks of compiling, analyzing and mapping important information from those past studies.

In this work, we present the relevant challenges faced during the project “Earthquake, fire and flood risk assessment in the Region of Attica” as well as inferences for future studies of similar type. Following the previous step of our comprehensive methodology for the estimation and mapping of flood risk in Attica Region, we received as an input a set of existing studies for our study area. In the process of abstracting the spatial data from those studies, we grouped such data in three categories: spatial information in text, in CAD files and in GIS files. Each type of information presented its own type of challenges. Information in text was time-consuming to process, both because it necessitated reading the whole studies to be pinpointed, but also due to the requirement for manual extraction and conversion to GIS format. Spatial information found in CAD and GIS files presented mostly software-related challenges in managing to achieve a common representation scale and geospatial reference of the plans. Lastly, a challenge that was common for all types of data was the identification of whether the hydraulic works or terrain conditions that were presented in each study were actually present as such today. This required cross-methodological communication including both with the previous methodological steps of contact with the institutions that provided the studies, and with the next methodological steps of site visits for the confirmation of the recorded information.

Overall, we identify the following as crucial priorities for efficient abstracting and synthesizing the spatial data on flood risk in a highly studied area. Firstly, clarity in defining the types of data being searched for, secondly, the utilization of CAD and GIS software with interoperability functions and easy scaling and georeference functionalities, thirdly, the direct communication with the institutions that created or utilize the studies, and lastly, the realization of site visits to verify the recorded information.

How to cite: Ioannidis, R., Dimitrakopoulou, D., Sigourou, S., Pagana, V., Alexopoulos, M. J., Dimitriadis, P., Sargentis, G.-F., Tsoutsos, M.-C., Chardavellas, E., Tsouni, A., Mamassis, N., Koutsoyiannis, D., and Kontoes, C.: Abstracting past studies and synthesizing their spatial data in GIS for utilization in a study of flood risk in Attica region, Greece , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11912, https://doi.org/10.5194/egusphere-egu24-11912, 2024.

Ιn the framework of the Programming Agreement between the Prefecture of Attica and the Operational Unit BEYOND Centre of EO Research and Satellite Remote Sensing of the Institute of Astronomy, Astrophysics, Space Applications Remote Sensing (IAASARS) of the National Observatory of Athens (NOA), in cooperation with the Research Group ITIA of the Department of Water Resources and Environmental Engineering of the School of Civil Engineering of the National Technical University of Athens (NTUA), a rather innovative approach is applied for the purpose of flood risk assessment, regarding the contribution of citizens to the identification of the areas which are vulnerable to flood. It is highlighted how the experience of residents can lead to the identification of areas prone to flood, which could not be easily located otherwise, especially through large-scale flood risk maps. Moreover, it is demonstrated how the knowledge of residents can be used as a validation tool for the flood risk assessment results. Consequently, it is argued that the residents must play an active role in the conception, design and implementation of flood protection works in any infrastructure project within their area of interest. Such implementations of any mitigation measures should have as a prerequisite their acceptance by the residents. Their understanding is also important, on the one hand, to deal with possible reactions, appeals, and conflicts throughout the execution of the project, and, on the other hand, to ensure that residents are properly informed about the utility of such works and projects.

How to cite: Dimitrakopoulou, D., Ioannidis, R., Dimitriadis, P., Sargentis, G.-F., Chardavellas, E., Alexopoulos, M. J., Pagana, V., Tsouni, A., Sigourou, S., Kontoes, C. (., Mamassis, N., and Koutsoyiannis, D.: The importance of citizens’ engagement in the implementation of civil works for the mitigation of natural disasters with focus on flood risk in Attica Region (Greece)., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12686, https://doi.org/10.5194/egusphere-egu24-12686, 2024.

According to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), there is a low agreement on the type of change in heavy precipitation for the Mediterranean area. The challenge lies in comparing studies that employ different time scales. While most of the research works are conducted on a daily scale due to the abundance of data at this resolution, only a limited number of studies delve into shorter (sub-daily) durations because of the scarcity of historical data in digital format at high temporal resolution.

A breakthrough in this challenge comes from the Improved Italian-Rainfall Extreme Dataset (I²-RED), a systematic collection of short-duration (1 to 24 hours) annual maximum rainfall depths recorded by more than 5000 rain gauges located all over Italy from 1916 up to the present.

This dataset has enabled a comprehensive analysis of temporal trends in extreme precipitation using spatial scales that range from the national to the regional to the local ones. The Mann-Kendall test and the Sen’s slope estimator were first applied to each individual station to investigate at-site statistically significant trends. Regional- and national-scale variations were instead investigated with the record-breaking analysis and the Regional Kendall test.

The results confirm that rainfall extremes of different durations are not increasing uniformly over Italy and that separate tendencies emerge in different sectors, even at close distances.

The tendencies obtained in this work are used, within the framework of the Italian National Recovery and Resilience Plan RETURN (multi-Risk sciEnce for resilienT commUnities undeR a changiNg climate) project, to identify critical infrastructures that will be likely affected by more severe rainfall extremes in the near future. These results have the potential to be used in revising hydrological design approaches to enable adaptation of the infrastructures to future precipitation conditions.

How to cite: Mazzoglio, P., Viglione, A., Ganora, D., and Claps, P.: Investigation of changes in precipitation extremes and implications for hydrological design: the Italian case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-553, https://doi.org/10.5194/egusphere-egu24-553, 2024.

How to cite: Chauhan, T. A. and Kleidon, A.: Estimating the sensitivity of continental aridity to global warming using Budyko’s framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14442, https://doi.org/10.5194/egusphere-egu24-14442, 2024.

The causes of recent hydrological droughts and their future evolution under a changing climate are still poorly understood. By analysing a a 216-year river flow time series of the Po River at the closure section, we show that the 2022 hydrological drought is the worst event (30% lower than the second worst, with a six-century return period) ever observed. We prove that the 2022 drought is part of an increasing trend in severe drought occurrence. The decline in summer river flows (−4.14 cubic meters per second per year), which is more relevant than the precipitation decline, is attributed to a combination of changes in the precipitation regime, resulting in a decline of snow fraction (−0.6% per year) and snowmelt (−0.18 millimeters per day per year), and to increasing evaporation rate (+0.013 cubic kilometers per year) and irrigated areas (100% increment from 1900). Our study presents a compelling case where the hydrological impact of climate change is exacerbated by local changes in hydrologic seasonality and water use.

How to cite: Montanari, A., Nguyen, H., Rubinetti, S., Ceola, S., Galelli, S., Rubino, A., and Zanchettin, D.: Why the 2022 Po River drought is the worst ever observed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8968, https://doi.org/10.5194/egusphere-egu24-8968, 2024.

River discharge forecasting plays a pivotal role in water resource management and environmental planning. Understanding the long-term dependence or changes in these processes is crucial for accurate predictions. Deep-learning methodologies have garnered significant scientific interest and are progressively becoming more prevalent across water-resources-related endeavors. Transformer models, a novel architecture that aims to track relationships in sequential data through attention mechanism, have increasing popularity last years. Through comprehensive experiments and analysis on real-world river discharge datasets, we aim to elucidate the impact of long-term dependence detection, as facilitated by the climacogram and Hurst coefficient, on the predictive capabilities of a transformer-based model. Insights from this investigation are anticipated to contribute to the advancement of river discharge forecasting methodologies, enhancing our understanding of long-term dependencies in these environmental processes.

How to cite: Tepetidis, N., Iliopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Investigating the Impact of Time Series Structure in Performance of Transformer-Based Model for River Streamflow Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19687, https://doi.org/10.5194/egusphere-egu24-19687, 2024.

Originating in the high mountains of the western Tien Shan and Pamir, the two transboundary rivers (the Syr Darya and the Amu Darya) are the only sources of streamflow into the Aral Sea Basin, and constitute a crucial freshwater source for central Asia. Climate change is one of the giant global issues which adversely affects the water resources. Although the current water crisis in the Aral Sea Basin is largely due to human activity, the region is also strongly impacted by climate change. Upstream streamflow has important influence on downstream ecological security, environmental stability, and sustainable development. Therefore, conducting a comprehensive, long-term analysis of the impact of climate change on the hydroclimate of the Upper Aral Sea Basin is crucial in confronting freshwater challenges and solutions. However, this task still poses a significant challenge. To fill this research gap, the present study employs tree-ring based streamflow reconstruction and hydrological modeling forced by past and future hydroclimate variables to comprehensively analyze the shifts in the hydrological regime within the Upper Aral Sea Basin. We utilize data from CMIP6 and PMIP4, integrating them into hydrological models to generate detailed monthly and yearly runoff time series for the Upper Amu Darya and Upper Syr Darya, spanning from 850 to 2100. By comparing the performance of hydrological simulation and reconstruction, we aim to identify the unique strengths and weaknesses inherent in each method or dataset. This approach will significantly contribute to advancing our understanding of the hydrological dynamics in the region.

How to cite: Li, Y., Guo, R., Tian, F., and Montanari, A.: Historical and Future Climate Impacts on Hydrological Regimes: A case Study in the Upper Aral Sea Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12405, https://doi.org/10.5194/egusphere-egu24-12405, 2024.

Floods and their subsequent socioeconomic exposures are increasing in most parts of the world due to global warming. However, less attention is given in the arid Central Asia (CA), in which floods usually occur in data-scarce high-mountainous regions with complex cryospheric hydrological processes (CHP). In this study, an improved hydrologic-hydrodynamic model coupled with a glacier mass balance module was developed to enhance flood simulations in CA. The effects of the CHP on future flood inundation and the subsequent socioeconomic exposures were also investigated. We found that the simulations of daily streamflow and flood magnitudes improved significantly over the selected hydrological stations after considering the glacier mass balance. Our estimations indicated that the flood inundation and its dynamic evolution generally agreed with satellite observations. Moreover, CHP-induced (rainfall-induced) flood inundation plays a significant role in China’s Xinjiang and Tajikistan (other regions of CA). The CHP would amplify the effects of future flood on socioeconomics in CA, with population (Gross Domestic Productivity, GDP) exposure up to 2.25 million persons/year (150 billion $ PPP/year) for 2071 – 2100. These findings could provide scientific evidence to improve the understanding of CHP effects on future floods and the subsequent exposures, informing the prioritization and design of flood mitigation strategies in CA.

How to cite: Wang, N., Sun, F., Wang, H., and Liu, W.: Effects of cryospheric hydrological processes on future flood inundation and the subsequent socioeconomic exposures in Central Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2840, https://doi.org/10.5194/egusphere-egu24-2840, 2024.

Short instrumental streamflow records in the European Alps limit our understanding of the full range and long-term variability in river discharge, which could greatly impact the management of freshwater resources for the densely populated area downstream. Enhancing the understanding of past climatological and hydrological information is also essential to improve the accuracy of future scenarios for rare extreme events, such as multi-year droughts and unprecedented floods, which recently impacted severely important water resource systems and communities at the global level. Tree-ring data have proven to be a viable opportunity for reconstructing various climatic parameters, including streamflow. By using a novel climate-informed framework, the station-based streamflow records of several rivers originating from the European Alps are reconstructed dating back to the year 1100 AD. To further investigate the characteristics of streamflow and extreme events in both the past and future, this study also relies on state-of-the-art paleo simulations from the Paleoclimate Modeling Intercomparison Project phase 4 (PMIP4) and future projections from Coupled Model Intercomparison Project phase 6 (CMIP6). By integrating proxy-based reconstructions, climate model simulations and projections, and observation, the changes in streamflow and rare extreme events in the European Alps are put into a longer perspective covering both the past nine centuries and one century into the future, thus providing a unique opportunity to assess the risk of extreme events and to inform more effective water management strategies for climate change adaptation.

How to cite: Guo, R., Nguyen, H., Galelli, S., Ceola, S., and Montanari, A.: Past and future changes of streamflow in the European Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1050, https://doi.org/10.5194/egusphere-egu24-1050, 2024.

Under ongoing climate change, the projected increase in the magnitude of extreme precipitation is expected to intensify the magnitudes of future river floods. However, the disparate past changes in the latter, suggest that changing flood generation processes modulate the sensitivity of streamflow response to changing precipitation.

Here we examine how flood generation processes will change in Europe until the end of the 21^st century under high emission scenario (SSP585) using the climatic forcing (i.e., precipitation, temperature) from CMIP6 EC-EARTH3-Veg simulation (Döscher et al., 2022) dynamically downscaled using the atmosphere-ocean coupled regional climate system model COSMO-NEMO-TRIP (Primo et al., 2019) within the extended EURO-CORDEX domain at the spatial resolution of 0.11° and corresponding hydrological simulations (i.e., streamflow, soil moisture, snow water equivalent) using mesoscale Hydrological Model (mHM). Using this information, we classify the annual maximum floods into rainfall events that occurred on dry or wet soils, a mixture of rainfall and snowmelt, and pure snowmelt events. We evaluate the reliability of our modeling system by comparing the frequency of these flood generation processes and characteristics of annual floods for the historical period 1960-2010 using classified flood observations in 1353 European catchments (Tarasova et al., 2023).

We find that under exacerbating climate change the frequency of occurrence of flood generation processes in Europe will change considerably by the end of the century. Interestingly, the pace of change in the magnitude, runoff coefficients and time scales of floods differs considerably for floods generated by different processes, emphasizing an important role that these processes play in modulating climate change signal and shedding a light on the variable hazard that flood events generated by different processes pose in a warming climate.

Döscher et al. The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6. Geoscientific Model Development 15, 7 (2022). https://doi.org/10.5194/gmd-15-2973-2022

Primo et al. A regional atmosphere–ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance. Geoscientific Model Development 12, 12 (2019). https://doi.org/10.5194/gmd-12-5077-2019

Tarasova et al. Shifts in flood generation processes exacerbate regional flood anomalies in Europe. Commun Earth Environ 4, 49 (2023). https://doi.org/10.1038/s43247-023-00714-8

How to cite: Tarasova, L., Ahrens, B., Blöschl, G., Kumar, R., Hamouda, M., Rakovec, O., and Merz, R.: Warming climate will alter the characteristics and generation processes of European floods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6222, https://doi.org/10.5194/egusphere-egu24-6222, 2024.

This study investigates provincial-level extreme weather conditions over Zambia using the Coupled Model Intercomparison Project (CMIP6) climate projections for various emission scenarios from 25 Global Climate Models (GCMs). Taylor diagram analysis is performed to identify the best-performing GCMs by evaluating precipitation and temperature variables with the observed datasets for the baseline period (1950-2014). Earlier studies have investigated the changes in precipitation and temperature variables alone. This study investigates the trends in Annual Precipitation, Annual temperature (mean, maximum and minimum) as well as Standardized Precipitaion Evapotranspiration Index (SPEI) for the near future (2021-2060) and far future (2061-2100) using Sen’s Slope Estimator. While all the projected climate scenarios depict an increasing trend in the mean temperatures for both near and far future periods, upto 4˚C increase is expected at the end the 21^st century under the worst-case scenario-SSP5-8.5. An overall decrease (upto -65 mm) in precipitation is expected in the near future and far future periods across the country, expect the North-eastern provinces. Corroborating with such a spike in climate conditions, the SPEI decreases by -1.16, -0.95, -0.86, -0.83 (near future, 2021-2060) and -1.36, -1.75, -1.98 and -1.99 (far future, 2061-2100) for SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5 respectively. Larger changes in SPEI is observed in Western, Southern, Northwestern and Lusaka provinces in both near and far future indicating worst drought conditions. The outcome from the present study provides a basis for undertaking provincial-level adaptation and mitigation measures under the evolving climate and framing policy interventions to combat climate change.

How to cite: Venkataswamy, S., Panjwani, S., and Amarnath, G.: CMIP6 climate scenarios for climate adaptation studies in Zambia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-851, https://doi.org/10.5194/egusphere-egu24-851, 2024.

A deep understanding of the dynamics of green and blue water resources is crucial for accurately estimating future water availability. Although projections of precipitation trends are robust in many regions, changes in precipitation partitioning into green and blue water fluxes present a significant source of uncertainty for water management. To quantify water partitioning dynamics, we introduce the Blue-Green Water Share (BGWS) metric. This metric utilizes monthly precipitation data, while monthly runoff and transpiration data are used as proxies of blue and green water fluxes. We investigate the output of fourteen CMIP6 models for the historical period and three Shared Socioeconomic Pathways to assess the scenario dependency of the BGWS changes. Most importantly, we examine how and why the BGWS varies across different regions. Additionally, primary drivers of the BGWS changes are identified by applying a multivariable regression analysis and computing the permutation importance of selected ecohydrological variables.

The results illustrate a strong regional dependency and interplay of the driving variables. However, clustering the variable importance demonstrates that BGWS changes in higher latitudes tend to be more dependent on temperature, while precipitation patterns dominate partitioning changes in the tropics. Several regions, including the Mediterranean, Northern South America, and Eastern Australia, show a substantial influence of vegetation alterations on the BGWS change, shifting the partitioning imbalance towards more green water flux. We discuss the varying variable importance based on the counteracting mechanisms of increased CO₂ concentrations, altered growing seasons, and changed precipitation patterns. Our results highlight the importance of comprehensively understanding green and blue water dynamics in the context of water resources availability under a changing climate.

How to cite: Heselschwerdt, S. P. and Greve, P.: Projected shifts and dynamics in blue and green water resources availability , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9302, https://doi.org/10.5194/egusphere-egu24-9302, 2024.