Water distribution networks (WDNs) are essential systems that supply water for the most basic normal activities of our society. Their design is a complex problem because WDNs must function properly, permanently responding to consumer needs and taking into account many different technical and social issues.

In recent decades, they have been extensively studied considering only a single demand setting. Therefore, the pipe sizes obtained using this approach are unreliable to address a wide spectrum of situations that WDNs face during their service life. Water demand is one of the most important sources of uncertainty for the design of WDNs.

In fact, water demand is affected by different types of uncertainty (Walker et al., 2003). It can be the result of users' behavioural variability on a daily and seasonal scale (which can be classified as 'statistical uncertainty’) and can also be related to "social variability" due to the unpredictable nature of social, economic, and cultural dynamics (a more challenging level of uncertainty, 'scenario uncertainty').  'Statistical uncertainty’ can be addressed by extracting information from available data and making assumptions about the statistical parameters of water demand distribution (Magini et al., 2019). 'Scenario uncertainty’ may be handled through various approaches for creating plausible future demand changes i.e., likely assumptions about the future hypotheses on demand change that can concern the number of users, socio-economic situation, changes in technology, tariffs, cultural dynamics and users' behaviour (Cunha. 2023).

Robust optimization models may embrace scenarios with different levels of demand uncertainty. It is essential to generate demand scenarios to develop robust WDNs, in multi-objective environments, including issues such as their reliability and the level of service they provide (Cunha et al., 2023). Thorough comparisons of the robustness of WDNs sized either using deterministic approaches or considering different levels of uncertainty are vital to understand the benefits of these last ones.

This presentation will provide an overview of the key issues and challenges in defining robust WDNs to cope with multiple states of the world.

REFERENCES

Cunha, M. C. (2023). Water and Environmental Systems Management Under Uncertainty: From Scenario Construction to Robust Solutions and Adaptation. Water Resources Management, 1–15

Cunha, M. C., Magini, R., & Marques, J. (2023).  Multi-objective optimization models for the design of water distribution networks by exploring scenario-based approaches. Water Resources Research, 59, e2023WR034867. https://doi. org/10.1029/2023WR034867

Magini, R., Boniforti, M. A., & Guercio, R. (2019). Generating scenarios of cross-correlated demands for modelling water distribution networks. Water (Switzerland), 11(3), 493.

Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support. Integrated Assessment, 4(1), 5–17.

Acknowledgments: The author thanks the Portuguese public agency “Fundação para a Ciência e a Tecnologia” (FCT) the support of national funds under the project UIDB/ 00285/2020. 

How to cite: Cunha, M. C.: Key issues and challenges for  managing water distribution networks under uncertainty , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21604, https://doi.org/10.5194/egusphere-egu24-21604, 2024.

Hydrological models are crucial in various engineering applications, including streamflow forecasting and flood risk estimation. Tools like the Stormwater Management Model (SWMM) are indispensable for efficacious water resource management. Calibrating these models is a necessary step to minimize parameter uncertainties and ensure accurate representation of a catchment area's hydrological response. However, the calibration process often faces challenges due to the need for extensive parameter adjustments. Sensitivity analysis (SA) is employed to mitigate these challenges by identifying and focusing on the most influential parameters, thereby streamlining the calibration process. In this work, the Morris method was applied to identify the sensitive parameters in the SWMM model, which were subsequently considered in the optimization process using Genetic Algorithms (GA). The results of the sensitivity analysis highly depend on the model output targets, such as total runoff volume and peak flow rate.

The traditional approach of dividing data into calibration and evaluation subsets is a fundamental practice in model development. Nevertheless, the impact of data allocation on model evaluation performance has not received sufficient attention in the literature. This study investigates the influence of calibration data selection on model performance, utilizing high-resolution experimental rainfall-runoff data from the urban catchment of Cascina Scala in Pavia, Italy. Four criteria—rainfall depth, mean intensity, hydrograph's center of mass, and maximum rainfall depth over five minutes—were employed to select the calibration set. From a total of 24 events, the four criteria were employed to select 8 events from 16 for calibration, while the remaining 8 events were designated for validation. The findings underscore that the selection of the calibration dataset substantially influences the optimally calibrated parameters, subsequently altering model performance.

How to cite: Assaf, M. N., Salis, N., Creaco, E., Tamellini, L., Sauro, M., and Todeschini, S.: Optimizing hydrological modeling on real urban catchment: impact of calibration data selection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12604, https://doi.org/10.5194/egusphere-egu24-12604, 2024.

Environmental mining and exploration present a challenge for spatial analysis due to small sample sizes and data clustering near mining sites. Additionally, the properties of the covariance function may vary across different mines within the same region, necessitating adaptable geostatistical techniques. A novel approach to analyzing mining data spatial dependence introduces the use of Gaussian Anamorphosis, employing the recently proposed Kernel Cumulative Density (KCDE) method. This technique is particularly effective for data sets that exhibit non-Gaussian distributions, such as the typically asymmetrically distributed natural resources data. Gaussian Anamorphosis through KCDE enables the transformation of skewed probability density functions (PDFs) into the normal distribution. KCDE converts the original data distribution into a continuous cumulative density function (CDF), smoothing out the discontinuities inherent in the traditional staircase CDF estimation approach.
We extend our analysis by conducting Kriging interpolation on the transformed data. Since the transformed data distribution closely approximates the normal distribution, it is possible to use the Kriging variance to reliably estimate prediction intervals before the results are inversely transformed back to their original scale for practical interpretation.
To explore the variability of our results, we implemented Monte Carlo simulations based on the transformed data. The simulations provide insights into the potential outcomes and their variabilities, which were then inversely transformed back to their original scale for practical interpretation.
The findings of this study underscore the effectiveness of Gaussian Anamorphosis using KCDE transformation in dealing with non-Gaussian data distributions in geostatistical analyses. The approach enhances the reliability of spatial predictions and offers robust confidence intervals. Our research demonstrates the potential of combining advanced transformation techniques with geostatistical models to address complex spatial dependencies of natural resources data.

The research project is implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 16537).

How to cite: Varouchakis, E. A., Pavlides, A., and Hristopulos, D. T.: A Gaussian anamorphosis model for asymmetrically distributed data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10654, https://doi.org/10.5194/egusphere-egu24-10654, 2024.

In recent years, frequent climate extreme events have significantly impacted various sectors, especially critical ones like hydropower generation. In Latin America and the Caribbean, hydropower constitutes a pivotal element, contributing 45% to the electricity supply. Among the countries in the region, Ecuador heavily relies on hydropower generation (80%). However, since October 2023, Ecuador has faced unprecedented challenges marked by significant deficits in energy production, not witnessed in the last few decades. This crisis, attributed to severe drought events in the Amazon region, directly impacts one of Ecuador's most crucial hydropower systems—the Paute system. In addition to the crisis, suboptimal reservoir management practices exacerbate these impacts due to the lack of provision for extreme events. The resultant energy deficits are currently causing extensive power outages throughout the country, highlighting the urgency of addressing the issues in reservoir management.

In this research, we introduce an innovative approach to enhance reservoir management efficiency. This approach involves integrating hydrometeorological in-situ and satellite-based data to develop forecasting models for reservoir water levels. We use Ecuador’s largest reservoir, the Mazar reservoir belonging to the Paute system, as a case study. The modeling will employ advanced machine learning (ML) techniques, such as the proven-effective Long-Short Term Memory (LSTM), with the aim of identifying key influencers that significantly impact reservoir level forecasting. Furthermore, we will complement the modeling with the Shapley Additive Explanation method to enhance interpretability, providing insights into hydrological processes. This is intended not only to deepen our understanding of the relationship between hydrometeorological variables and reservoir water levels but also to enrich the input space for our reservoir level forecasting models, contributing to a more accurate and comprehensive predictive framework.

The results of the innovative approach will be then used to develop a methodological framework named ML-Driven Reservoir Management with Integrated Extreme Events Forecasting for the Mazar reservoir, aimed at enhancing reservoir management efficiency during extreme events. The expected results include the identification of crucial hydrometeorological variables for Mazar level forecasting, with models capable of predicting reservoir levels at 15-day to monthly intervals based on dominant variables. This will provide a tangible demonstration of its application to improve management in future extreme event scenarios. Beyond optimizing reservoir management for enhanced hydropower generation efficiency, this approach aims to mitigate adverse impacts on Ecuador's developing sectors, fostering sustainability. By addressing inefficiencies in reservoir management, our study contributes to a more resilient and sustainable hydropower sector in Ecuador.

How to cite: Merizalde, M. J., Corzo, G., Muñoz, P., Guzmán, P., Samaniego, E., and Célleri, R.: Enhancing Reservoir Management for Sustainable Hydropower Generation: A Machine Learning-Driven Approach in Response to Increasing Extreme Events in Ecuador, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13490, https://doi.org/10.5194/egusphere-egu24-13490, 2024.

Hydroclimatic extremes, such as droughts, floods, and extreme rainfall have been increasing worldwide leading to severe impacts on society and ecosystems. For that reason, hydrological modelling research has advanced to improve flood and rainfall prediction and control.  This estimation has been traditionally carried out using physical and process-based hydroclimatic models, however, they have limitations due to their physical-based nature. They often require a large amount of different hydro-geomorphological monitoring datasets, as well as in-depth knowledge and expertise regarding hydrological parameters, which must be correctly selected, calibrated, and further interpreted to ensure the reliability of the model. In recent years, data-driven hydrological modelling, such as Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) methods have demonstrated a great deal of promise for enhancing the forecasting of hydroclimatic extremes. In data-driven modelling, the models use a generalized relationship between input and output disregarding the physical mechanism behind the process, built based on historical data. ML methods have some advantages over physical-based models, such as not requiring an understanding of internal specific mechanisms, which can be highly complex to reproduce, as well as having a higher calculation efficiency which may provide a quicker response to extreme events of high-intensity and short duration such as urban flash floods. Although there have been significant advances from the scientific community toward understanding and testing different ML and AI models for various hydrological applications, there are still limitations in their applications. A huge challenge that remains in ML modelling for future extreme floods, is its ‘black-box’ nature where the interactions among various components are unknown, which hinders its further use in supporting important decision-making. Along with that, other challenges in the current hydroclimatic modelling approaches presented by the hydrological community are data availability and assimilation, uncertainty analysis, and model generalisation. Some studies have addressed these issues, showing satisfactory results, especially for hybrid models between ML and traditional process-based approaches and ensembles of multiple methods. However, in light of so many new methodologies and algorithms, we must address their benefits and drawbacks, through an interdisciplinary effort. Understanding the best way to select appropriate methodologies for different settings of data availability, climate variability, and uncertainty, generating rapid and interpretable responses to urgent hydrologic hazards.

How to cite: Antunes Meira, M. and Xuan, Y.: Machine Learning Modelling for Future Hydroclimatic Extremes Under Climate Change: A Review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20800, https://doi.org/10.5194/egusphere-egu24-20800, 2024.

In the realm of scientific water supply and the vigilant monitoring of advancing marine geohazards, the importance of sustainable aquifer management in coastal cities cannot be overstated. Investigating aquifer heterogeneity and the characteristics of coastal groundwater fluctuations serves as an effective approach to unveil the dynamic nature of aquifers. In this study, we simulated discrete geological variables using SISIM and T-PROGS, leveraging data from 8629 sample points across 111 boreholes in Beihai city, southern China. The results demonstrate heightened accuracy in depicting both lateral sediment distribution driven by river dynamics and vertical processes governing hydraulic conductivity coefficients within the aquifer. Our analysis underscores effectiveness of SISIM in reducing initial data requirements without necessitating Gaussian transformation, ensuring broad applicability, while T-PROGS proves suitable in environments characterized by prevalent lateral accumulation. This study employed the fractal method and wavelet analysis to investigate coastal zone groundwater fluctuations. Daily groundwater fluctuations displayed a distinct periodic variation, indicating a biased stochastic traveling pattern and potential short-term predictability. Notably, time-frequency characteristics exhibited a strong correlation with tidal fluctuations at smaller scales (12-24 hours). Additionally, the study provides initial modeling insights into the impact of heterogeneity on groundwater fluctuations.

How to cite: Gong, K., Wen, Z., Li, Q., and Zhu, Q.: Geostatistical Stochastic Simulation of Hydraulic Conductivity and  Groundwater Dynamics Interpretation in an Alluvial-Marine Sedimentary System: A Case Study in Beihai City, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14513, https://doi.org/10.5194/egusphere-egu24-14513, 2024.

Groundwater, constituting approximately 65 percent of Europe's drinking water sources, plays a crucial role in sustaining both urban and agricultural needs. Particularly during periods of drought, groundwater abstraction becomes a key resource, alleviating adverse impacts on people's livelihoods. Recent European drought events, for example in 2003 and 2015, exhibited spatial coherence in surface water deficits across European regions, hinting at potential impacts on groundwater levels. However, the unique hydrogeological settings and recharge patterns of groundwater systems, coupled with diverse meteorological influences, can also lead to distinct spatial coherence in groundwater droughts. Despite these complexities, no comprehensive, decadal pan-European analysis of historic groundwater level data has been conducted until now.

To bridge this gap, we conducted a continent-wide assessment of groundwater drought responses, based on over 3000 groundwater level timeseries spanning from 1986 to 2015, and providing the first extensive overview of historic groundwater droughts across Europe. Utilizing the Standardised Groundwater Index (SGI), the spatio-temporal analysis allowed for consistent comparisons of sites across disparate regions. Impulse response functions were used to identify differences in response times of the aquifers and cluster analysis of the standardized hydrographs allowed for the identification of spatially coherent 'type' groundwater hydrographs, characterized by differences in autocorrelation and reflective of continental-scale variations.

Initial findings highlighted variations in groundwater system responses to meteorological drivers, distinctions between fast and slow responding sites and their spatial coherence. For example, differences in response times of the aquifers in Northern Germany produced local differences in the effects of the 2015 drought in this region and droughts in the late 90s showed good spatial coherence across large areas of Europe, but with distinctly smaller impact on groundwater levels in Balkan region.

This analysis, coupled with an examination of driving factors, promises to enhance our understanding of how catchment and local characteristics influence groundwater responses. Additionally, areas particularly vulnerable to groundwater droughts will be identified, thus allowing for improved groundwater management.  

How to cite: Brauns, B., Bloomfield, J., Hannah, D., Marchant, B., and van Loon, A.: Spatio-temporal analysis of drought: A multidecadal study of European groundwater systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17276, https://doi.org/10.5194/egusphere-egu24-17276, 2024.

The quality of water supplied through water distribution systems is traditionally assured through sampling of different physical, chemical, and biological parameters at the exit of water treatment works and at different locations within the network. However, sampling is periodic and does not capture the complex processes that take place within the network over time and space. The time of residence of the resource within the network (also called water age) has been used in the past as surrogate indicator for water quality (Machell and Boxall, 2012). As water age increases, water quality parameters tend to worsen (Machell and Boxall, 2013). This is more likely in terminal and meshed areas with low renovation rates (high water age). This work presents a detailed analysis of residence times at a terminal branch. This benchwork branch is inspired in the real topology of water connections at several dead ends in a water supply network in Spain. Stochastic demands are simulated per water connection thanks to a demand model inspired in SIMDEUM (Blokker et al., 2010). Demands are then used to run a hydraulic and water quality model with high resolution. Different metrics are then computed to probabilistically assess water age within the branch. These metrics are useful to identify topologies that are especially problematic.

Acknowledgements:

The authors would like to thank the financial support provided by the Spanish Ministry of Science and Innovation - State Research Agency (Grant PID2019-111506RB-I00 funded by MCIN/AEI/10.13039/ 501100011033; Grant TED2021-131136B-100 funded by MCIN/AEI/10.13039/501100011033) and Junta de Comunidades de Castilla-La Mancha (Grant No. SBPLY/19/180501/000162, funded by Junta de Comunidades de Castilla-La Mancha and ERDF A way of making Europe).

References:

Machell, J. and Boxall, J. (2012) Field studies and modeling exploring mean and maximum water age association to water quality in a drinking water distribution network. Journal of Water Resources Planning and Management, 138(6), 624-638, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000220

Machell, J. and Boxall, J. (2013) Modeling and field work to investigate the relationship between age and quality of tap water. Journal of Water Resources Planning and Management, 140(9), 04014020, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000383

Blokker, E.J.M., Vreeburg, J.H.G. and van Dijk, J.C. (2010) Simulating residential water demand with a stochastic end-use model. Journal of Water Resources Planning and Management, 136(1), 19-26, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000002

How to cite: Díaz García, S. and González Pérez, J.: Probabilistic topology-based analysis of water age at terminal areas in water distribution networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14731, https://doi.org/10.5194/egusphere-egu24-14731, 2024.