Analysis of daily rainfall data, and subsequent modelling of some derived variables concerning rainfall, is fundamental in different areas such as agricultural, ecological, and engineering disciplines. A way of studying the alternance of consecutive rainy days (wet spells) and no-rainy days (dry spells) is through the interarrival time (IT), which is the time elapsed between two consecutives rainy days. If we suppose that IT observations are independent and identically distributed (i.i.d.), ITs are usually modelled through a renewal processes. The simplest renewal process is the Bernoulli process with ITs geometrically distributed. The need to suppose a non-constant probability of rain brings to more sophisticated models. Previous works [Agnese et al. (2014), Baiamonte et al. (2019)] have successfully proposed the three-parameter family of the Hurwitz-Lerch-Zeta distribution (HLZD), which represents a forward step with respect to other commonly used IT distributions. In [Agnese et al. (2022)], a second successfully reached goal was to show that the HLZD is also suitable to model the rainfall depth, h. In literature, rainfall depths are more frequently treated as continuous, despite sometimes these models fail to account for the time discreteness of the sampled process. Indeed, daily rainfall depth measurements are usually carried out by automatic-counting how many times a small bucket corresponding to 0.2 mm is filled. Due to the abundance of ties in the data, the variable depth h is well suited to be considered discrete. We present results involving data never considered in literature and consisting of measures sampled along 60-70 years at 7 different stations. These stations represent different climates from the rainfall characteristics point of view and let us to infer about the great handiness of the HLZD within rainfall modelling. Current research is addressed to modelling further rainfall variables related to IT and h, such as wet and dry spells, and the cumulative rainfall depth in a wet spell. Furthermore, given the remarkable performance of the HLZD family of distributions in the univariate modelling, we aim at modelling the dependence structure between IT and h, exploiting possibly new methodological advances in the subject, such as discrete copulas.

References

• Agnese, G. Baiamonte, E. Di Nardo, S. Ferraris, and T. Martini (2022). Modelling the frequency of interarrival times and rainfall depths with the Poisson Hurwitz-Lerch zeta distribution. Fractal and Fractional, 6(9).
• Agnese, G. Baiamonte, and C. Cammalleri (2014). Modelling the occurrence of rainy days
  under a typical Mediterranean climate. Advances in Water Resources, 64:62–76.
• G. Baiamonte, L. Mercalli, D. C. Berro, C. Agnese, and S. Ferraris (2019). Modelling the frequency distribution of inter-arrival times from daily precipitation time-series in north-west Italy. Hydrology Research, 50(1):339–357.

How to cite: Ferraris, S., Agnese, C., Martini, T., Di Nardo, E., and Baiamonte, G.: Modelling rainfall interarrival times and rainfall depths at daily scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1314, https://doi.org/10.5194/egusphere-egu23-1314, 2023.

We perform a large-scale assessment of the probabilistic behaviour of rainfall extremes over the Greek territory aiming to construct a national model for design rainfall. To this aim, we employ multiple sources of rainfall data: from long-term daily records to samples of multi-scale annual maxima, reanalysis rainfall products and satellite information. We identify suitable probability distributions for the multi-scale rainfall extremes useful for design rainfall estimation and regionalize their parameters over Greece using two-dimensional multivariate smoothing techniques. Unique insights are derived regarding the spatio-temporal variability of extreme rainfall over the Greek area, notable for its highly variable topography and climate.

How to cite: Iliopoulou, T., Koutsoyiannis, D., Koukouvinos, A., Malamos, N., Tepetidis, N., Markantonis, D., Dimitriadis, P., and Mamassis, N.: Regionalized design rainfall curves for Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8740, https://doi.org/10.5194/egusphere-egu23-8740, 2023.

Rain-on-Grid (RoG) modelling offers an attractive alternative to more traditional routing methods. Currently, few publications are addressing the suitability of this approach to modelling a storm event, and fewer benchmark findings present its possible limitations. In the present study, it is verified whether RoG is able to replicate the 2007 flash flood event that occurred in the Selška Sora watershed, located in western Slovenia. The results are validated against a high-resolution benchmark run, and the flood footprint extracted from the field by the Slovenian Environment Agency. Results display a satisfactory description of the flood event using uniform station rainfall data as an input. The flood extent slightly exceeds the confines of the runup measured in the field. RoG offers a more realistic description of the downstream hydrograph, with a sharper initial peak, when antecedent soil moisture is lower.

Keywords: Rain-on-Grid, Flash flood, Slovenia

How to cite: Alexopoulos, M. J., Iliopoulou, T., Dimitriadis, P., Bezak, N., Kobold, M., and Koutsoyiannis, D.: Application of Rain-on-Grid for flash flood modeling: A case study in the Selška Sora watershed in Slovenia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16120, https://doi.org/10.5194/egusphere-egu23-16120, 2023.

Deep-learning methods are receiving great scientific attention and increasingly gaining popularity in a variety of water-resources tasks as well. Yet till now they are less employed for the simulation of hydroclimatic timeseries, the stochastic properties of which are usually challenging and dealt by the application of stochastic methods. The latter are well-established for the analysis and simulation of hydroclimatic processes and are particularly successful in capturing their long-term dependence behavior, so-called Hurst-Kolmogorov (HK) dynamics. In this work, we aim to assess the suitability of a state-of-the-art deep learning algorithm, called Transformer Neural Network (TNN) for hydroclimatic processes, as it is claimed to have a good performance in time series data. The Transformer Neural Networks is a novel architecture that aims to track relationships in sequential data while it is suggested that it can handle long-range dependence. We apply the TNN for the simulation and prediction of timeseries from various hydroclimatic processes (such as rainfall, runoff,  temperature) and evaluate its performance in relation to the application of the HK algorithms.

How to cite: Tepetidis, N., Iliopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Comparison of Stochastic versus Deep Learning methods for simulation and prediction of hydroclimatic time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16222, https://doi.org/10.5194/egusphere-egu23-16222, 2023.

North Euboea is a place with high topographic relief, covered mostly by wild forests, with a lot of small rivers receiving high amounts of rainfall. After 2017 a severe disease started to eliminate plane trees (Platanus orientalis), which were growing on the riverbanks stabilizing the flow of water. One more dramatic event which severely impacted North Euboea was the wildfire that occurred in August 2021 and burnt 52,900 ha. Both events drastically changed the land terrain, causing various impacts on the area’s watersheds. In this vein, we try to investigate the changes in the water flow and inspect the combined effects of these landscape alterations on water management. 

How to cite: Dimitriadis, P., Kougia, M., Sargentis, G.-F., Iliopoulou, T., Mamasis, N., and Koutsoyiannis, D.: Violent land terrain alterations and their impacts on water management; Case study: North Euboea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13318, https://doi.org/10.5194/egusphere-egu23-13318, 2023.

Demographic expansion along with shifts in precipitation trends and temperature rise considerably impact the availability of surface water resources, causing serious consequences for human and the environment. The identification of the human and climatic dynamics contributing to the expansion and reduction of the extent of surface water bodies is key to guarantee the preservation of freshwater ecosystems and water scarcity.

In this work, we evaluated the variation of surface water extent and five potential drivers that occurred between 1984 and 2020 within the river basins of the contiguous United States (CONUS). We selected built-up area, population, and irrigated land as anthropogenic drivers, while precipitation and temperature represent the hydroclimatic drivers. The analysis of the interaction between changes in surface water extent and its drivers revealed that there has been an expansion of surface water extent over the majority of the CONUS, which was mainly induced by an increase in the mean annual precipitation, mostly in river basins with a continental and temperate climate. The reduction of the extent of surface water observed in the river basins in the arid southwestern region of the CONUS resulted to be influenced by all the anthropogenic and hydroclimatic factors, especially by temperature rise and population growth. We also noticed that river basins sharing the same climatic condition present similar trends of change in surface water extent and its drivers. In particular, arid river basins show distinct pattern of variations with respect to other climatic regions of the CONUS. This result highlights the need to further analyze these vulnerable areas where water availability is greatly affected on anthropogenic activities and climate change.

How to cite: Montanari, A., Palazzoli, I., and Ceola, S.: Contribution of Anthropogenic and Climatic drivers to the Surface Water Extent Change in the Contiguous United States, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16278, https://doi.org/10.5194/egusphere-egu23-16278, 2023.

The Marathon Dam is the oldest one in modern Greece located close to Athens and serving its water supply. Its reservoir has a capacity of 41 hm³. Several residential areas exist downstream of the dam, the nearest of which is just one kilometer away. Therefore, in the event of high reservoir spill (let alone dam failure), downstream local communities and properties are at considerable risk. In this work, we aim to assess the risk due to spill employing stochastic simulation of the reservoir water balance based on existing data. In addition, we attempt to derive operational rules to mitigate the risk of its downstream failures due to spill.

How to cite: Bessas, N., Partida, K., Illiopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Risk assessment of Marathon reservoir spillway based on water level, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7675, https://doi.org/10.5194/egusphere-egu23-7675, 2023.

Simulation of daily rainfall for the region of Bologna produced by 13 up-to-date climate models for the period 1850–2100 are considered. In particular, model simulations are compared with the historical series of daily rainfall observed in Bologna in terms of annual, seasonal, extreme precipitation and meteorological drought for the period 1850–2014. Future changes of both precipitation and meteorological drought are analysed to assess future scenarios up to 2100 to derive information on the future development of critical events for water resources availability and flood risk. The results prove that rainfall statistics, including seasonal patterns and extremes, are fairly well simulated by models. For future projections, extreme rainfall shows a more significant change compared to the mean annual rainfall. In terms of meteorological droughts, we conclude that historical data analysis under the assumption of stationarity may depict a more critical future with respect to climate model simulations, therefore outlining important technical indications.

How to cite: Guo, R. and Montanari, A.: Future changes of extreme precipitation and meteorological drought in Northern Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-355, https://doi.org/10.5194/egusphere-egu23-355, 2023.

The threat that climate change poses to water resource systems has led to a significant and growing number of impact studies. These studies tend to follow two general methodological approaches: (1) top-down, process-based studies driven by projections of future climate change supplied by downscaled general circulation models (GCMs), and (2) bottom-up, vulnerability-based studies driven by exploratory scenarios. Top-down studies generate realistic climate scenarios, but computational burdens limit the ensemble size. As a result, critical vulnerabilities may be left unexplored. Bottom-up approaches make it possible to assess a wide range of scenarios, but usually without connection to physically plausible climate processes, limiting their utility in adaptive planning. This study develops process-informed exploratory scenarios that bridge the gap between top-down and bottom-up methods. This hybrid approach yields several advantages. First, emerging vulnerabilities associated with non-linear hydrologic changes are linked to thermodynamic and dynamic climate drivers modeled in the GCMs with differential likelihoods and plausible ranges of change. This provides a transparent link between stakeholder defined vulnerabilities and climate processes that is often missing in bottom-up assessments. Second, non-linear shifts in vulnerability are directly linked to specific climate drivers, through the systematic perturbation of process informed climate variables. Making this connection in top-down assessments is difficult since the climate response to an emissions scenario is modeled as part of an endogenous process. The hybrid approach developed by this study is presented with a case study in the Tuolumne River watershed; through which thermodynamic and dyanamically guided climate scenarios were created by a process-informed stochastic weather generator to evaluate flood and drought related performance vulnerabilities at the New Don Pedro Dam near the watershed’s outlet. This case study finds that flood and drought performance at the dam is more sensitive to process-informed climate drivers than less theoretically grounded delta shifts precipitation, and non-linear system responses to climate drivers are revealed through the systematic perturbation process-informed climate variables.

How to cite: Kucharski, J., Steinschneider, S., Olszewski, J., Herman, J., Rahat, S., Ray, P., Arnold, W., and Maendly, R.: Linking exploratory scenarios to process-informed insights in climate vulnerability assessments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10272, https://doi.org/10.5194/egusphere-egu23-10272, 2023.